Volume 44 No. 2 December 2012

Transcription

SABRAO
JOURNAL of
BREEDING
and GENETICS
ISSN 1029-7073
VOL. 44 NO. 2 DECEMBER 2012
CONTENTS
SABRAO
Journal,
Regional
Secretaries and Editorial Board.......i
Messages from the Editor-in-Chief...vi
Research Papers
Nair RM, Schafleitner R, Kenyon L,
Srinivasan R, Easdown W, Ebert AW,
Hanson P. Genetic improvement of
mungbean.......................................177
Shim J. Perennial rice: improving rice
productivity for a sustainable upland
ecosystem.......................................191
Dalamu, Bhardwaj V, Umamaheshwari
R, Sharma R, Kaushik SK, Joseph TA,
Singh BP, Gebhardt C. Potato cyst
nematode (PCN) resistance: genes,
genotypes and markers – an update...202
Singh VV, Singh M, Chauhan JS,
Kumar S, Meena ML, Singh BK, Singh
K, Singh UB. Development and
evaluation of half sib progenies for
morpho-physiological characters in
Indian mustard (Brassica juncea l.)
under rainfed conditions...................229
Girdthai T, Jogloy S, Vorasoot N,
Akkasaeng C, Wongkaew S, Patanothai
A, Holbrook CC. Inheritance of the
physiological
traits
for
drought
resistance under terminal drought
conditions and genotypic correlations
with agronomic traits in peanut.........240
Srivastava K, Kumar S, Kumar S,
Prakash P, Vaishampayan A. Screening
of tomato genotypes for reproductive
characters under high temperature stress
conditions………………..................263
Purnomo, Daryono BS, Rugayah,
Sumardi I, Shiwachi H. Phenetic
analysis and intra-spesific classification
of Indonesian water yam germplasm
(Dioscorea alata L.) based on
morphological characters.................277
Rao PVR, Anuradha G, Srividhya A,
Reddy VLN, Shankar VG, Prasuna K,
Reddy KR, Reddy NPE. Siddiq EA.
Genetics of important agro-botanic traits
in sesame........................................292
Shende VD, Seth T, Mukherjee S,
Chattopadhyay A. Breeding tomato
(Solanum lycopersicum L.) for higher
productivity and better processing
qualities.........................................302
Batool S, Khan NU. Diallel studies and
heritability estimates using Hayman’s
approach in upland cotton...............322
Singh S, Vidyasagar. Effect of common
salt (NaCl) sprays to overcome the selfincompatibility in the s-allele lines of
Brassica oleracea var. capitata L.....339
Pandey V, Chura A, Arya MC, Ahmed
Z. Estimation of iodine and antioxidant
activity coupled with yield attributing
traits in cabbage hybrids..................349
Singh V, Krishna R, Singh L, Singh S.
Analysis of yield traits regarding
variability, selection parameters and
their
implication
for
genetic
improvement in wheat (Triticum
aestivum L.)...................................370
Boonlertnirun K, Srinives P, Sarithniran
P, Jompuk C. Genetic distance and
heterotic pattern among single cross
hybrids within waxy maize (Zea mays
L.).................................................382
Tyagi W, Rai M, Dohling A. Haplotype
analysis for Pup1 locus in rice
genotypes of north eastern and Eastern
India to identify suitable donors tolerant
to low phosphorus...........................398
Bootprom N, Songsri1 P, Suriharn B,
Chareonsap P, Sanitchon J, Lertrat K.
Molecular diversity among selected
Momordica cochinchinensis (Lour.)
Spreng accessions using RAPD
markers..........................................406
SABRAO Board ………………........ix
Instructions for authors ……….......xi
Singh P, Pandey A, Mishra SB, Kumar
R. Genetic divergence study in aromatic
rice (Oryza sativa L.).......................356
SABRAO
THE SOCIETY FOR THE ADVANCEMENT OF BREEDING RESEARCH IN ASIA AND
OCEANIA
Visit our website at: http://www.sabrao.org/
PUBLISHED BY:
SABRAO
The Society for the Advancement of Breeding Research In Asia and Oceania
c/o Plant Breeding, Genetics and Biotechnology Division
International Rice Research Institute (IRRI)
Mail: DAPO Box 7777, Metro Manila, Philippines
Web: http://www.sabrao.org/
PRINTED BY:
Klang Nana Withaya Printing Co.Ltd
232/199 Srichand Rd. A. Muang,
Khon Kaen 40000
Thailand
Tel: 0-4332-8589-91/ Fax: 0-4332-8592
SABRAO JOURNAL OF BREEDING AND GENETICS
ISSN 1029-7073
The SABRAO Journal of Breeding and Genetics is the official publication of
the Society. Its objective is to promote the international exchange of research
information on plant breeding, by describing new findings, theories, and/or
achievements of a basic or practical nature. It also provides a medium for the
exchange of ideas, news of meetings, and notes on personal and organizational
achievements and developments among the members of the Society.
Research articles, short communications, methods, reviews, tutorials and
opinion articles will be accepted or invited for publication. Scientific
contributions will be refereed and edited to international standards.
The journal mainly publishes articles for SABRAO members and it is strongly
preferred that at least one author should be a current member of the
society. From January 2012, there is a US$50 publication fee for
SABRAO members FOR ALL ARTICLES, which must be paid before
publication (after acceptance of the article). This requirement is to cover
journal printing costs and to maintain the website. Non-members may also
publish in the journal for a publication fee of US$ 200 per article.
ADVERTISEMENTS
Advertising will be accepted from Universities offering courses of potential
interest to students from SABRAO countries and from book companies or
computer software suppliers whose products promote the aims of the Society.
Prices are available on application to the Editorial Board.
SABRAO WEBSITE
http://www.sabrao.org
This website will contain information about the society, information about
current officers and regional secretaries, upcoming congresses, and issues of
the SABRAO Journal of Breeding and Genetics. In order to improve access for
authors and researchers, reprints of journal articles will be posted as soon as
the journal issue is published.
i
REGIONAL SECRETARIES
Regional Secretaries are elected by the members in each Region. They play an
indispensable role in the operations of the Society by:
• notifying members of Society announcements, e.g. from the SecretaryGeneral;
• recruiting new members;
• collecting the annual subscriptions and transferring these to the Treasurer
after deducting expenses;
• distributing the Journal issues to paying members if these are delivered to a
region in bulk;
• organizing other activities, such as local chapter newsletters and meetings,
e.g., for the induction of new members;
• keeping books of account and sending an audited statement to the Treasurer
annually; and
• providing the Secretary-General with a list of financial members in their
region each year.
In 2012, the Regional Secretaries are as follows:
AUSTRALIA
Dr. Phillip Banks
Leslie Research Centre, 13 Holberton Street
PO Box 2282, Toowoomba, Queensland 4350, Australia
Email: [email protected]
BANGLADESH
Dr. Abul Kashem Chowdhury
Professor
DepartmentinGenetics and Plant Breeding
Patuakhali Science and Technology University
Patuakhali-8602, Bangladesh
CHINA (PEOPLES’ REPUBLIC OF)
Prof. Cheng Xuzhen
Institute of Crop Sciences
Chinese Academy of Agricultural Sciences
30 Bai Shi Qiao Road, Beijing 100081.
INDIA
Dr. Ramakrishnan M. Nair
AVRDC - The World Vegetable Center
Regional Center for South Asia
ICRISAT Campus, Patancheru 502 324
Hyderabad, Andhra Pradesh
ii
INDONESIA
Dr. Ismiyati Sutarto
Horticulture/Agriculture, CRDIRT-BATAN Jl
Cinere, Pasar Jumat, Jakarta 12440.
JAPAN
Prof. Kazutoshi Okuno
Laboratory of Plant Genetics and Breeding Science
Graduate School of Life and Environmental Sciences
University of Tsukuba, Tennodai 1-1-1, Tsukuba, 305-8572.
KOREA
Dr. Kyu-Seong Lee, Reclaimed Land Agriculture Research Division
NICS, RDA
570-080 #457 Pyeongdong-ro, IKSAN, Jeollabuk-do.
Email : [email protected]
MALAYSIA
Dr. Abdul Rahim Bin Harun
Malaysian Nuclear Agency
Bangi 43000, Kajang, Selangor.
PAKISTAN
Prof. Hidayatur Rahman
Department of Plant Breeding and Genetics
NWFP Agricultural University, Peshawar.
PHILIPPINES
Prof. Teresita Borromeo
Department of Agronomy, University of the Philippines
Los Baños, College, Laguna.
SRI LANKA
Dr. Tissa Rajapakshe, Central Rice Research Station, Batalagoda, Ibbagamuwa.
TAIWAN, REPUBLIC OF CHINA
Dr. Hsun Tu, Rural Development Foundation,
5F, 7, Section 1, 4 Roosevelt Road, Taipei 100.
THAILAND
Dr. Kamol Lertrat
Department of Plant Science and Agricultural Resources
Faculty of Agriculture,Khon Kaen University
Khon Kaen 40002, Thailand
iii
USA/CANADA.
Dr. Georgia Eizenga
USDA-ARS Dale Bumpers National Rice Research Center
2890 Hwy. 130 East, Stuttgart, AR 72160
VIETNAM
Dr. Bui Chi Buu
Institute of Agricultural Sciences for Southern Vietnam
121 Nguyen Binh Khiem, District I, Ho Chi Minh City.
SABRAO EDITORIAL BOARD
SABRAO is delighted to announce the formation of the Editorial Board in
2012. By establishing an editorial team co-ordinated by the Editor-in-chief, it is
hoped that the efficiency, content and quality of the journal will dramatically
improve. The main duty of associate editors will be processing manuscripts for
publication in the journal. This involves finding reviewers, communicating
with corresponding authors, following up completed evaluations of
manuscripts, checking revisions are thoroughly done, and editing/formatting.
Each Associate Editor will be acknowledged as the “communicating editor” for
the relevant article when it is published.
Other minor duties include being a contact point for SABRAO
members in their respective countries, providing new ideas for the journal (e.g.
topics for special issues, ideas for website etc.), and assisting in the preparation
and compilation of special issues and conference proceedings.
Associate Editors:
Dr. Sang-Nag Ahn
Professor
Department of Agronomy, College of Agriculture & Life Sciences
Chungnam National University, Daejeon 305-764
REPUBLIC OF KOREA
E-mail: [email protected]
Area of expertise: QTL mapping, molecular genetics and breeding of rice
Dr. CN Neeraja
Principal Scientist, Biotechnology Unit
Crop Improvement Section
Directorate of Rice Research,
Rajendra Nagar, Hyderabad – 500030
INDIA
Area of expertise: molecular genetics and breeding
iv
Dr. C. Ravindran
Assistant Professor
Krishi Vigyan Kendra
Agricultural College and Research Institute,
Tamil Nadu Agricultural Univeristy,
Madurai, Tamil Nadu, 625107
INDIA
E-mail: [email protected]
Area of expertise: breeding and genetics of horticultural and fruit species
Dr. Naqib Ullah Khan
Professor
Department of Plant Breeding and Genetics
Khyber Pakhtunkhwa Agricultural University
Peshawar 25130
PAKISTAN
Email: [email protected] OR [email protected]
Area of expertise: plant breeding and quantitative genetics
Dr. Sathiyamoorthy Meiyalaghan (Mei)
Scientist
Plant & Food Research
Private Bag 4704, Christchurch, 8140
NEW ZEALAND
Area of expertise: genomics and molecular breeding
Dr. Cheng Xuzhen
Professor
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS)
Beijing 100081, China
Area of expertise: plant breeding in pulses
Dr. Ramakrishnan M. Nair
Vegetable Breeder - Legumes
AVRDC - The World Vegetable Center
ICRISAT Campus, Patancheru 502 324
Hyderabad, Andhra Pradesh, India
Area of expertise: plant breeding and genetics research in pulses and pasture legumes
Dr. Sivananda V. Tirumalaraju
Research Associate II
Soybean Breeding, Genetics and Genomics Program
Department of Plant Science
South Dakota State University
Brookings, SD, USA- 57006
Area of expertise: plant breeding (peanut, soybean and canola), molecular breeding,
molecular marker technology
v
Deputy Editor-in-Chief
Dr. Sanun Jogloy
Department of Plant Science and Agricultural Resources
Faculty of Agriculture, Khon Kaen University
Khon Kaen 40002
THAILAND
Area of expertise: plant breeding, quantitative genetics, physiological traits
Editor-in-Chief
Dr. Bertrand (Bert) Collard
Scientist
International Rice Research Institute (IRRI)
Los Banos, Laguna 4031
PHILIPPINES
Alternative: [email protected]
Area of expertise: plant breeding and genetics, QTL analysis, molecular breeding
vi
MESSAGES FROM THE EDITOR-IN-CHIEF
MOVING TO AN ELECTRONIC JOURNAL SYSTEM
From 2013 onwards, the journal will completely move to an electronic format
with open access. All articles will be published as pdf files on the website. This
was a unamnimous decision made during the SABRAO General Meeting held
in Chiang Mai, Thailand (January 2012) based on providing greater access for
the journal and due to the financial status of the society. This issue will be the
last printed issue of the journal that will be posted to members. Hard
copies may still be requested by subscription for institutions or libraries.
NEW SCOPE
Plant breeding has changed considerably in the last 20 years. The Editorial
Board proposes that from 2013 onwards, the scope of SABRAO J. Breed.
Genet. will focus on more specific topics of breeding and genetics research that
are of direct practical relevance to plant breeders. Classical quantitative
genetics research will be considered in the context of how useful the research is
to breeders.
Authors conducting research in the following topics will be encouraged to
submit their articles to the journal:
•
•
•
•
•
•
•
•
Molecular breeding (e.g. marker assisted selection)
QTL mapping and validation
Genetic diversity analysis – primarily using DNA markers
Use of agronomic, morphological or physiological traits in selection
Multi-environment trial analysis
Germplasm evaluation
New methods (e.g. phenotyping methods) of broad interest to breeders
Classical quantitative genetics investigating genetic control of simple
or oligogenic, trait heritabilities, combining ability
Other topics may be submitted after consultation with the Editorial Board.
A survey of SABRAO members was conducted in November to December
2012, providing useful feedback. All respondents were generally satisfied with
the current scope of the journal. One exception was tissue culture and
transformation which several respondents considered beyond the scope of the
journal. Many members indicated a preference to see more articles involving
molecular breeding. Several excellent suggestions for review articles or special
issues were also made.
The Editor-in-Chief sincerely thanks members who completed the survey, and
welcomes any feedback or suggestions in the future.
vii
SPECIAL ISSUE OF SABRAO JOURNAL OF BREEDING AND GENETICS IN
2013.
To commemorate the 12th SABRAO Congress Plant breeding towards 2025:
Challenges in a rapidly changing world (An International Conference to Celebrate His
Majesty King Bhumibol’s 84th (7 Cycle) Birthday Anniversary), selected presentations
will be published in a special issue of the SABRAO Journal of Breeding and Genetics
early in 2013. All articles will be published online, from the Journal’s website
(http://www.sabrao.org/).
CALL FOR SOCIETY MEMBERS TO BE REVIEWERS FOR OUR JOURNAL
The SABRAO journal continues to receive a large number of articles. Reviewers play a
critical role for the journal by evaluating and editing manuscripts. Interested society
members - especially on topics involving quantitative genetics and genetic diversity are encouraged to register as a potential reviewer for manuscripts submitted to the
journal by emailing the Editor-in-Chief.
ACKNOWLEDGEMENTS
The Editor-in-Chief would sincerely like to thank the many reviewers in 2012 for their
time and effort. A list of all reviewers in 2012 will be indicated on the website.
Last but not least, the Editor-in-Chief would like to express his gratitude to the web
manager for the SABRAO webstie, Ms. Ella “Kaye” Domingo for ensuring the website
has run so smoothly, and to the Assistant Editors, Mrs. Marlyn Rala and Ms. Cheryl
Dalid (IRRI), for their hard work in 2012 preparing the December 2012 issue – one of
the biggest ever issues of the journal!
viii
SABRAO BOARD 2010-2013 (Executive positions in bold)
Prof. Sang-Nag Ahn, Vice President 2. Department of Crop Science, College of
Agricultural and Life Sciences, Chungnam National University, Daejeon 305-764,
Republic of Korea.
Dr. P. Banks, Leslie Research Centre, P.O. Box 2282, Toowoomba QLD 4350,
Australia.
Dr. D.S. Brar, Associate Secretary General, IRRI, DAPO Box 7777, Metro Manila,
Philippines.
Dr. Bui Chi Buu, Institute of Agricultural Sciences for Southern Vietnam, 121 Nguyen
Binh Khiem, District I, Ho Chi Minh City, Vietnam.
Dr. Bertrand C. Y. Collard, Editor-in-Chief, International Rice Research Institute,
DAPO Box 7777, Metro Manila, Philippines.
Dr. Georgia Eizenga, USDA-ARS Dale Bumpers National Rice Research Center, 2890
Hwy. 130 East, Stuttgart, AR 72160, USA.
Dr. Y. Fukuta, Associate Secretary-General, Japan International Research Center for
Agricultural Sciences, 1-1, Ohwashi, Tsukuba, 305-8686, Japan
Dr. Tianfu Han, Institute of Crop Science, Chinese Academy of Agricultural Sciences,
12 Zhongguanchun South Street, Beijing 100081, China.
Prof. H. Ikehashi. Laboratory of Plant Genetics and Breeding, Nihon University,
Kameino 1866, Fujisawa, Kanagawa 252-8510, Japan.
Dr. T. Imbe, National Institute of Crop Science, 2-1-18, Kannondai, Tsukuba, Ibaraki,
305-8518, Japan.
Dr. E. Javier, AVRDC – The World Vegetable Center, P.O. Box 42, Shanhua, Tainan
74151, Taiwan.
Dr. S. Jogloy, Associate Editor, Department of Plant Science and Agricultural
Resources, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002,
Thailand.
Dr. Kyu-Seong Lee, National Institute of Crop Science (NICS), 151, Seodun-dong
Gwonseon-gu, Suwon, Gyeonggi-do, Republic of Korea.
Dr. G.S. Khush, University of California, Davis, California 95616, USA.
Prof. F. Kikuchi, Past President, 1077-31 Yatabe, Tsukuba, Ibaraki 305-8572, Japan.
Prof. S. Lamseejan. Department of Applied Radiation and Isotopes Kasetsart
University, Bangkok 10900, Thailand.
ix
Dr. K. Lertrat, Treasurer, Department of Plant Science and Agricultural Resources,
Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand.
Dr. David J. Mackill, Vice President 1, IRRI, DAPO Box 7777, Metro Manila,
Philippines.
Dr. H.P. Moon, Past President, Hanjin-Hyundai A. 106-604, Hwaseo 2-dong, Paldalgu,
Suweon 440-152, Republic of Korea.
Dr. R. Ohsawa, Institute of Agriculture and Forestry, University of Tsukuba, 1-1-1,
Tennodai, Tsukuba, Ibaraki, 305-8572, Japan.
Dr. K. Okuno, Laboratory of Plant Genetics and Breeding Science, Graduate School of
Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan.
Dr. Mohamad bin Osman, School of Environmental and Natural Resource Sciences,
Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600
Bangi, Selangor, Malaysia.
Dr. E.D. Redoña, Secretary General, IRRI, DAPO Box 7777, Metro Manila,
Philippines.
Dr. L.S. Sebastian, Philippine Rice Research Institute, Maligaya, Munoz, Nueva Ecija,
Philippines.
Prof. Dr. Peerasak Srinives, President, Department of Agronomy, Kasetsart
University, Kamphaeng Saen, Nakhon Pathom 73140, Thailand.
Mr. T. Takatoshi, Faculty of Agriculture, Kyoto University, Oiwake, Kitashirakawa,
Sakyo-ku, Kyoto, 606-8502, Japan.
Dr. B.C. Viraktamath, Vice-President 3, Directorate of Rice Research, Rajendranagar,
Hyderabad-500 030, AP, India.
x
Instructions for authors
Articles submitted to the SABRAO Journal of Breeding and Genetics must be
original reports of merit dealing with any phase of plant breeding or genetics
not previously or simultaneously submitted to, or published in, any other
scientific or technical journal.
PUBLICATION FEES
The journal mainly publishes articles for SABRAO members and it is strongly
preferred that at least one author should be a current member of the society.
From January 2012, there is a US$50 publication fee FOR ALL
ARTICLES (including SABRAO members), which must be paid before
publication after acceptance of the article. This requirement is to cover journal
printing costs and to maintain the website. Non-members may also publish in
the journal for a publication fee of US$ 200 per article.
TYPES OF ARTICLES
The following types of articles are acceptable to the SABRAO Journal of
Breeding and Genetics:
• research articles (describing research that expands the existing
knowledge in a specific area)
• short communications (concise articles describing preliminary results)
• review papers (thorough review of literature with interpretations)
• opinions (personal reflections)
• tutorials (clear descriptions of topics to communicate specific research
topics to a broad audience)
Review articles should be discussed with a member of the Editorial Board prior
to submissions.
LANGUAGE
The official language of the Journal is English. It is expected that manuscripts
are clearly written with a high standard of English. n extra fee of US$25 per
article will be charged for articles requiring extensive editing for English
prior to publication.
FORMAT
Authors should follow the Journal format as closely as possible with respect to
headings, formatting and references. The corresponding author's email
addresses should be included on the manuscript. After acceptance of the article,
it is expected that the corresponding author will re-submit the manuscript
following the SABRAO J. Breed. Genet. template format, which is available
from the website.
xi
A research article or short communication manuscript will usually contain the
following parts:
1. TITLE - as concise and descriptive as possible, usually less than 20 words.
Include the scientific names of the species studied if one or two are involved.
2. AUTHOR'S NAMES - each followed by a superscript number referring to
the respective addresses of the author(s).
3. KEYWORDS - Six to eight keywords allowing the subject to be classified
in retrieval systems. The words may occur in the title, and may occur in pairs,
e.g., acid soils.
4. SUMMARY - should be concise and be completely self-explanatory and
should cover (under 300 words) the aim, methods, major findings and at least
one conclusion of the study.
5. INTRODUCTION - should briefly describe the subject area, with a
summary of previous reports, including citations of the most significant ones.
Point out the deficiencies in knowledge left by previous studies, then state
which experiments have been designed and conducted to add new knowledge.
6. MATERIALS AND METHODS - describe the origin and nature of the
materials used. Procedures used, experimental design, and methods of data
analysis should be presented. This section can be concise citing appropriate
references instead of lengthy descriptions of methods used. Statistical software
packages used for data analysis (indicating the version and software
distributor) should be indicated in this section.
7. RESULTS - present the key parts of the experimental data, referring to
figures and tables as necessary. Do not repeat information in the text if it is
shown in a table or figure. Use only the metric system of measurements. Place
figures and tables on separate pages at the end of the paper, giving captions for
figures and headings for tables which make them self-explanatory.
8. DISCUSSION - should be separate from the Results section, and should not
repeat information already presented elsewhere. It should start with a sentence
or two stating the main new findings of the research. This section should also
include comparisons made with the results and inferences of previous, related
studies. Criticisms of earlier studies are appropriate if they clarify the field.
The remaining gaps in knowledge may be briefly pointed out, with or without
an outline of future experiments which may provide some of the answers.
9. ACKNOWLEDGEMENTS - should be included if they are due to any
person or organization (especially for funding support).
10. REFERENCES - Examples of text citations are: (Yoshida, 1996); Smith
and Jones (1993); (Lucas et al., 1997). In the References section, the citations
xii
should be arranged alphabetically by first author, then by second and later
authors, and then by year. The references should be given in the format shown
below. Note that the author’s surname (or family name) should always be
indicated first, followed by initials with no full-stops or periods. Abbreviations
should follow abbreviations described by ISI Thomson Reuters
Journal articles:
Yoshida M, Smith KJ, Jones DB (1989). Title of article. SABRAO J. Breed.
Genet. 21: 105-122.
Finlay KW, Wilkinson GN (1963). The analysis of adaptation in a plant
breeding program. Aust. J. Agric. Res. 14: 742–754.
Book chapters:
Yoshida M, Smith KJ, Jones D B (1989). Title of chapter. In: A. Lucas. B.
Mason, and C. Baker, eds., Title of Book. Publisher, City, pp. 45-70.
Conference Proceedings (if widely available):
Yoshida M, Smith KJ, Jones DB (1989). Title of paper. In: S. Iyama, and G.
Takeda, eds., Proc. Sixth Inter. Cong. SABRAO, August 21-25, 1989,
Tsukuba, Japan. National Organizing Committee, Tsukuba. pp. 209-212.
Book:
Yoshida M, Smith KJ, Jones DB (1989). Title of Book. Publisher, City.
Theses:
Jones AB (1989). Title of thesis. Ph. D. Thesis. University, City.
Write out one-word journal titles in full. Use standard abbreviations for
multiple-word journal titles. Articles that have been accepted for publication
can be included and designated “in press”. Unpublished data, submitted
articles, and personal communications may be included in the text in
parentheses.
REPRINTS
No printed reprints are provided but a pdf file can be freely-obtained from the
website.
FIGURES
Colour figures and photographs can be included free of charge in the pdf file
but will incur additional costs in the printed issue. Authors should ensure that
colour figures and photos are compatible with black and white printing.
REVIEWERS
Papers will be refereed to international standards by two independent experts,
so content must be novel, well proven by careful examination, clearly
expressed, and concise as possible. In order to speed up the reviewing
process, the corresponding author may nominate up to 5 potential
xiii
reviewers by provide their email and institutional address. The senior and
corresponding author should not have published with these reviewers within
the last 3 years. The nomination of at least one overseas reviewer is encouraged
but not essential.
SUBMISSION
Manuscripts should be submitted by email to the Editor-in-Chief:
[email protected]
MANUSCRIPT REVIEWING/PROCESSING TIME
Generally it takes about 6 months for a decision on a manuscript to be made
from the date of submission which is comparable to many other journals in the
same area. The time required depends on many factors including the topic of
the article and promptness of reviewers. Generally articles are accepted in
revised form within 1 month after a decision is made and published in the next
issue of the journal. Authors should be aware of typical processing times
before submitting manuscripts in the journal.
For further information, please contact:
Dr. Bertrand (Bert) Collard
Editor-in-Chief
SABRAO Journal of Breeding and Genetics
Society for the Advancement of Breeding Research in Asia and Oceania
Website: http://www.sabrao.org/
Current address:
Plant Breeding, Genetics and Biotechnology Division (PBGB)
International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manilla,
Philippines
T: +63 2 580 5600 2478; E: [email protected]
xiv
REVIEW
SABRAO Journal
of Breeding and Genetics
44 (2) 177-190, 2013
GENETIC IMPROVEMENT OF MUNGBEAN♣
R. M. NAIR1*, R. SCHAFLEITNER2, L. KENYON2, R.
SRINIVASAN2, W. EASDOWN1, A. W. EBERT2 and P. HANSON2
1
AVRDC – The World Vegetable Center, South Asia, ICRISAT Campus, Patancheru 502 324,
Hyderabad,India
2
AVRDC – The World Vegetable Center, PO Box 42 Shanhua, Tainan 74199, Taiwan
*Corresponding author email: [email protected]
SUMMARY
Mungbean (Vigna radiata (L.) R. Wilczek) is cultivated on more than 6 million hectares
in the warmer regions of the world and is one of the most important pulse crops in South
and Southeast Asia. The productivity of mungbean is relatively low (average about 400
kg/ha) and similar to other pulse crops. Broadening the genetic base by selecting parents
from diverse and interspecific backgrounds is of paramount importance to achieve
productivity gains. Development of an open source online mungbean pedigree database
system will aid breeders in the choice of parents and avoid duplication in breeding efforts.
Development of varieties with resistance to major diseases such as mungbean yellow
mosaic disease and pests such as bruchids continues to be a breeding priority. Tolerance
to waterlogging and salinity would help to expand this crop further in South Asia.
Replacement of longer duration varieties with shorter duration types would contribute to
an expansion of the crop in sub-Saharan Africa. Adoption of quality seed and improved
agronomic practices are also important to enhance productivity.
Keywords: Mungbean, productivity, breeding
Manuscript received: June 13, 2012; Decision on manuscript: October 6, 2012; Manuscript
accepted in revised form: October 17, 2012.
Communicating Editor: Bertrand Collard
INTRODUCTION
Mungbean (Vigna radiata (L.) R.
Wilczek) is one of the most
important food legumes in South
and Southeast Asia. The grain is
consumed as dhal, in soup, as bean
sprouts, or processed into high value
noodles. Mungbean is a cheap
source of dietary protein for the
poor, with high levels of folate and
iron compared with many other
♣
legumes (Keatinge et al., 2011).
Mungbean in rotation with rice
improves the physical, chemical and
biological properties of the soil, and
helps to ameliorate degradation from
continuous cereal cropping. A
mungbean crop can increase the
yield of a subsequent rice crop by up
to 8% through the nitrogen it fixes
in the soil and by reducing pest and
disease problems (Weinberger,
2003).
Paper presented at 12th SABRAO Congress, Chiang Mai, Thailand in January 2012.
SABRAO J. Breed. Genet. 45 (1) 177-190, 2013
The
development
of
mungbean from a relatively
marginal crop to one of the most
important grain legume crops in
Asia was brought about by wellcoordinated, collaborative research
work led by Asian Vegetable
Research and Development Center
(AVRDC) – The World Vegetable
Center with national partners in
mungbean producing countries over
a period of about 40 years. This led
to the development of short-duration
pest- and disease-resistant varieties
that can fit into cereal based
cropping
systems
(Shanmugasundaram et al., 2009).
Current status of mungbean:
area, production and productivity
Mungbean is cultivated in Asia,
Africa and the Americas and covers
more than 6 million hectares per
annum worldwide. India, with
approximately 3 million ha, has the
largest mungbean production area
followed by China and Myanmar
(Figure 1). India has the greatest
mungbean production followed by
China and Myanmar (Figure 2),
while productivity in India is one of
the lowest (less than 400 kg/ha;
Figure 3) compared with other
countries. Indian states with very
low productivity (<400 kg/ha)
namely,
Karnataka,
Madhya
Pradesh, Maharashtra, Odisha and
Tamil Nadu account for about 40%
of the cultivated area under
mungbean. Very high yield gaps
have been observed between
experimental plots and farmers’
fields. For example, in yield trials in
India during the 2010 kharif season
(June-July sowing) some promising
varieties yielded around 1300 kg/ha
compared with the national average
of 400 kg/ha in Uttar Pradesh state
(IIPR, 2011a). Pest and diseases,
non-availability of seed of improved
varieties (Bains et al., 2006) and
poor crop management practices are
the major factors contributing to the
yield gap. In Vietnam, a lack of
suitable varieties with resistance to
pests and diseases was identified as
one of the major reasons for low
productivity (Singh et al., 2006).
Major
biotic
and
limitations to yield
abiotic
Varieties with resistance derived
from different sources will be
required to combat the emergence or
evolution of different strains,
species or biotypes of important
pests or pathogens. The most
important
diseases
include
mungbean yellow mosaic disease,
powdery mildew (Erysiphe polygoni
D.C.),
Cercospora
leaf
spot
(Cercospora canescens Illis &
Martin) and common bacterial blight
(Xanthomonas spp.). Important pests
include bruchids (Callosobruchus
spp.), pod borer (Maruca vitrata),
bean flies (Ophiomyia spp. and
Melanagromyza spp.) and thrips
(Megalurothrips spp.),
Mungbean yellow mosaic disease
Mungbean yellow mosaic disease,
caused by at least two different
species of begomoviruses (whiteflytransmitted geminiviruses) in South
Asia, is the most important viral
disease of mungbean in many areas.
In India, mungbean yellow mosaic
disease severely affects legumes
including black gram (V. mungo (L.)
Hepper) and mungbean. Improved
varieties with good resistance are
among the top Indian Council of
Agricultural
Research
(ICAR)
178
Nair et al. (2012)
research priorities for the Eastern
Region of India (IIPR, 2011b).
Annual yield losses in all legumes
due to begomoviruses in India are
estimated to be about USD 300
million (Kundagrami et al., 2009).
Among begomoviruses infecting
legumes in India, Mungbean yellow
mosaic
virus
(MYMV)
and
Mungbean yellow mosaic India
virus (MYMIV) are probably the
most important.
In Bangladesh, diseases
caused by MYMIV and Dolichos
yellow mosaic virus (DoYMV) have
been reported (Maruthi et al., 2006).
Most of the reported resistance
screening to date has been done in
north India and Pakistan and it is
likely that identified resistance
sources are effective against strains
of MYMIV (e.g. Akhtar et al.,
2011).
AVRDC lines NM 92 and
NM 94 are known for their
mungbean yellow mosaic disease
resistance. NM 94 shows resistance
to the disease during the summer
season, but is susceptible during the
kharif (June-July sowing) season.
All resistance found in mungbean to
date appears to be tolerance
(infection with mild symptoms)
rather than immunity (Akhtar et al.,
2011. Resistance to MYMIV also
has been detected in black gram in
Kanpur, Uttar Pradesh, but the value
of this resistance for variety
improvement is unclear (Anjum et
al., 2010).
New sources of resistance to
MYMV (including interspecific
sources) have been identified and
molecular markers linked to
resistance genes are becoming
available (Maiti et al., 2011, Chen et
al., 2012). These resistant genotypes
will be tested to determine whether
they are able to provide good
protection against the major strains
of both MYMV and MYMIV in
disease hot spots. Resistance genes
from confirmed sources will be
stacked or pyramided through
marker assisted selection to develop
varieties with expanded resistance to
the major strains of both MYMV
and MYMIV across a wide
geographic area and range of
seasons.
All begomoviruses are
transmitted by whiteflies of the
species Bemisia tabaci (Hemiptera:
Aleyrodidae). B. tabaci was
believed to be a complex of at least
24 cryptic species, some of which
have been referred to as ‘biotypes.’
However, it was recently reported
that B. tabaci is a complex of 11
well-defined high-level groups
containing
at
least
24
morphologically indistinguishable
species (de Barro et al., 2011). The
“B” and “Q” biotypes have proven
to be particularly invasive, and are
efficient virus vectors with a very
broad host range. The introduction
of more aggressive vector biotypes
into South Asia, perhaps as a result
of globalization and the increase in
international air freight shipment of
fresh produce and flowers may be a
factor contributing to the reemergence of mungbean yellow
mosaic as a major constraint to
mungbean production in recent
years.
179
3000
2500
Area
('000ha)
2000
1500
1000
500
0
Figure 1. Major countries where mungbean is cultivated (‘000 ha). Latest available year source: Bangladesh: Bangladesh Department of Agricultural Extension (2012); China:
Mogotsi (2006); India: 2007-08 - IIPR (2011); Indonesia: BPS-Statistics Indonesia, RIDS
(2011); Myanmar: Weinberger (2003); Pakistan: Ali et al (2010); Thailand: Chaitieng
(2002). Downloaded from www.riceindonesia.com, accessed 24April 2012.
1400
1200
1000
800
Production
'(000 tonnes)
600
400
200
0
Figure 2. Major mungbean producing countries (in ‘000 t). Latest available year - source:
Bangladesh: Bangladesh Department of Agricultural Extension (2012); China: Mogotsi
(2006); India: IIPR (2011); Indonesia: BPS-Statistics Indonesia, RIDS (2011); Myanmar:
Weinberger (2003); Pakistan: Ali et al (2010); Thailand: Chaitieng (2002). Downloaded
from www.riceindonesia.com, accessed 24April 2012.
180
Nair et al. (2012)
1400
1200
1000
800
Yield
(kg/ha) 600
400
200
0
Figure 3. Mungbean productivity in selected countries (kg ha-1). Latest available year source: Bangladesh: Bangladesh Department of Agricultural Extension (2012); China:
Mogotsi (2006); India: IIPR (2011); Indonesia: BPS-Statistics Indonesia, RIDS (2011);
Myanmar: Weinberger (2003); Pakistan: Ali et al (2010); Thailand: Chaitieng (2002);
Vietnam, Singh et al. (2006). Downloaded from www.riceindonesia.com (accessed 24
April 2012).
Climate uncertainty may be
another contributing factor by
increasing
the
number
of
generations or by extending the
geographic range where the vectors
and the viruses they carry can thrive.
For instance, “B” and “Q” biotypes
of B. tabaci completed one
generation within 17-18 days at 33
°C, whereas it took almost seven
weeks to complete its life-cycle at
17 °C (Muniz and Nombela, 2001).
In addition, the excessive and
inappropriate use of pesticides by
farmers attempting to control
whitefly-transmitted diseases may
exacerbate the situation by selecting
for pesticide-resistant strains of the
vector. For instance, B. tabaci has
developed
resistance
to
organochlorines, organophosphates,
neonicotinoids and insect growth
regulators in India (Jaglan, 2005;
Sethi and Dilawari, 2008). It should
be noted that the development of
resistance in B. tabaci to a particular
pesticide could lead to the
development of cross-resistance to
the entire chemical group to which it
belongs. For example, development
of resistance to imidacloprid led to
the development of high levels of
cross-resistance to three other
neonicotinoids (Wang et al., 2009).
Pesticide-induced resurgence of B.
tabaci is another concern. Use of
broad-spectrum
synthetic
pyrethroids leads to the development
of resurgence of B. tabaci
populations (Mohan and Katiyar,
2000). Synthetic pyrethroids reduce
the content of phenols of leaves;
phenols
are
the
compounds
responsible for resistance against B.
tabaci in plants. The decrease in
phenols leads to the resurgence of
the pest (Jeyakumar and Gupta,
2007). Pesticide misuse also
181
destroys natural enemies, and
adversely affects the environment
and the health of the farmers.
Bruchids
Postharvest damage to mungbean
seed by bean weevils or bruchids
(Callosobruchus chinensis L. and C.
maculatus F.) is common, causing
up to 100% loss (Zhang et al.,
2002). Bruchid infestation results in
bean weight loss, low germination,
and damage, making the seed unfit
for
human
consumption
or
agricultural or commercial uses
(Talekar, 1988). Bruchid infestation
starts in the field but most damage
occurs in storage, where several
cycles of egg laying and adult
emergence may lead to complete
destruction of the seed lot within 3–
4 months (Banto and Sanchez,
1972). The threat of mungbean
storage losses forces farmers to sell
the grain shortly after harvest when
prices are relatively low (Ali et al.,
2000). Mungbean varieties resistant
to bruchids would allow farmers to
store mungbean seed and sell when
prices are higher. Current popular
mungbean varieties lack bruchid
resistance, but AVRDC researchers
have identified two black gram
accessions (VM 2011, VM2164)
that are highly resistant to bruchids
(C. chinensis) as well as two
mungbean accessions (V2802, V
2709) that have been recently
confirmed to possess complete
resistance to C. chinensis and C.
maculatus (Somta et al., 2007). VM
2164 had significantly higher trypsin
inhibitor activities than susceptible
genotypes. The globulin of VM
2164 adversely affected the bruchid
egg deposition (Landerito et al.,
1993). The bruchid resistance of
V2709 is controlled by one
dominant gene, Br2 (Cheng et al.,
2008). Trombay wild black gram
(Vigna mungo var. silvestris)
collected from hilly areas around
Trombay (a northeastern suburb of
Mumbai) was shown to possess
resistance to bruchids, and the
resistance was found to be mediated
by antibiosis (Souframanien and
Gopalakrishna, 2007). 'Menaga' and
'Miyazaki,' two varieties of rice bean
(V. umbellata (Thunb.) Ohwi &
Ohashi), also exhibited high
resistance to bruchids through
antibiosis mechanism (Somta et al.,
2006). Hence, transfer of bruchid
resistance from resistant Vigna
genotypes into popular mungbean
lines
can
be
accomplished
efficiently through interspecific or
intraspecific crosses aided by use of
molecular markers linked to bruchid
resistance genes.
Abiotic stress
Mungbean can cope with drought
reasonably well, but it is much less
able to cope with waterlogging.
Waterlogging is a common problem
in locations such as southern
Bangladesh, where 60% of national
mungbean
production
is
concentrated,
and
results
in
significant yield loss (Islam et al.,
2007).
Development
of
waterlogging-tolerant varieties will
help to reduce damage caused by
unseasonal heavy rains during the
growing season and increase the
versatility of this otherwise hardy
crop. In some instances salt stress
affects the crop. Win and coworkers
(2011) found significant difference
in tolerance to salinity (0 to 225 mM
NaCl) among Vigna genotypes at
seedling stage. This calls for
screening of mungbean germplasm
182
Nair et al. (2012)
in the target environment to identify
lines suited for salt stress as well as
to look at the possibilities of
introgression of salt tolerance from
related species.
Major quality requirements of
mungbean
Grain colour, size and quality
Seed characteristics of mungbean
can have a large impact on prices
received by farmers, but these vary
depending on the market. Seed size
(large/medium/small), seed colour
(green/yellow) and seed luster
(shiny/dull) are traits that vary in
importance according to regional
preferences and depend on the form
in which mungbean is consumed.
Medium-sized seeds are preferred
for sprout production. Dull-seeded
types are preferred for soups,
whereas shiny-seeded varieties are
preferred for dhal. Cooking quality
is a trait that sometimes is not given
due consideration by breeders, but
can make a big impact amongst
consumers. For example, some of
the yellow-seeded types preferred by
consumers in countries such as Sri
Lanka and the Philippines require
longer cooking time as compared to
green-seeded mungbean.
Weather damage due to
dew, high humidity and rainfall
during the pre-harvest period can
lead to poor quality seeds that are
discoloured and unsuitable for use in
sprouting (Imrie et al., 1988). Preharvest sprouting is a serious
problem when the crop is subjected
to unseasonal rain prior to harvest.
Hence it is important to develop
varieties with significantly reduced
levels of pre-harvest sprouting for
regions where this is a common
problem.
Short duration types
Use of short-duration varieties to fit
into cropping systems and relatively
longer duration varieties for regions
where cropping duration is not a
constraint
will
help
expand
mungbean production. There is
ample scope for expansion of
mungbean cultivation in subSaharan Africa. For example, in
East African countries such as
Kenya and Tanzania, the varieties
currently grown by the farmers
mature in about 90 days.
Replacement of these varieties with
higher yielding, drought-tolerant
varieties that mature in about 60-65
days would be beneficial for farmers
already experiencing unreliable
rainfall. Recently Rizvi and others
(2012) reported that in Afghanistan,
improved mungbean varieties outyielded the local variety by 70% and
resulted in a net benefit of
US$575/ha additional income.
Enhanced protein quality
Protein malnutrition remains a major
nutrition problem in Asia and affects
children most severely (WHO 2000;
UNSCN 2010). About 150 million
children worldwide are underweight
and 182 million are stunted. At least
70% of these children are in Asia.
Meat is a good protein source, but is
either excluded from vegetarian
diets or unaffordable for poor
households where protein and
micronutrient deficiencies are most
prevalent.
Mungbean, with an average
protein content of about 26%,
provides a significant amount of
dietary protein for many South
Asians. However, the nutritional
value of mungbean protein is limited
by its low concentration of sulfur-
183
amino acids, including methionine
and cysteine. Black gram, a close
relative of mungbean, has higher
methionine content than mungbean.
Improving mungbean protein quality
through interspecific breeding with
black gram is feasible. Gammaglutamyl-methionine (γ-Glu-Met) is
the major dipeptide in blackgram
seeds, whereas gamma-glutamyl-Smethyl-cysteine (γ-Glu-S-metCys) is
the
dominant
dipeptide
in
mungbean. Both peptides were
found in F2 hybrids between
mungbean and black gram. Thus,
interspecific breeding has great
potential to improve nutritional
value of mungbean protein and to
help alleviate protein malnutrition in
developing countries.
Black
gram
accession
VM2164 possesses seed high
methionine content as well as
bruchid resistance and is therefore a
potential donor parent of both traits.
AVRDC has developed a nondestructive method to determine the
methionine content in seed, which
allows single seed selection for high
methionine content in mungbean
and black gram hybrids. AVRDC
already has made an interspecific
cross between VM2164 and NM94.
These interspecific crosses will be
used to develop a recombinant
inbred line (RIL) population for
mapping genes involved in high
methionine content,
molecular
marker development, nutritional
studies, and for improvement of
locally adapted preferred lines for
high methionine content through
backcrossing.
A narrow genetic base for a global
crop
Mungbean is native to tropical
regions of Asia such as the Indian
subcontinent,
Indo-China
and
Malesia (Indonesia and Papua New
Guinea) (USDA-ARS 2012). The
greatest genetic diversity of
mungbean can be expected in these
centres of origin. Mungbean is
naturalized in Australia and
elsewhere in the paleotropics. The
global mungbean crop has an
extremely narrow genetic base. The
pedigrees of the most popular
mungbean lines grown worldwide
are based on only a few dozen
parental sources (Yang, 1996).
Breeders and researchers need
access to a broad genetic base of
mungbean germplasm to tackle pest
and disease problems as well as
adapt the crop to new regions.
AVRDC is developing an open
source online database with pedigree
information of released varieties
worldwide. This will help breeders
in developing new varieties and
avoiding duplication of breeding
efforts.
Future
progress
in
mungbean breeding requires urgent
action to identify accessions with
favourable agronomic traits and to
provide tools to exploit the allelic
diversity of mungbean for crop
improvement. Worldwide, a total of
43,027 mungbean accessions are
held ex situ. Institutes with major
collections are: (1) The Institute of
Plant Breeding, University of the
Philippines, Los Baños, Philippines
(6889 accessions); AVRDC – The
World Vegetable Center, Taiwan
(6358 accessions (AVGRIS 2012a));
the University of Georgia, Griffin,
USDA-ARS,
USA
(3900
accessions); the National Bureau of
Plant Genetic Resources (NBPGR),
New Delhi, India (3147 accessions);
the Regional Station Jodhpur of
184
Nair et al. (2012)
NBPGR in Rajasthan, India (2466
accessions); the Field Crops
Research Institute of the Department
of Agriculture, Bangkhen, Thailand
(2250 accessions); and the National
Institute of Agrobiological Sciences,
Tsukuba-shi,
Japan
(1579
accessions) (WIEWS 2012). Many
of these accessions are still poorly
characterized with regard to their
genetic and phenotypic diversity,
and often characterization data are
not publicly available. AVRDC has
recently updated its Vegetable
Genetic
Resources
System
(AVGRIS) for mungbean and has
made characterization data for a
total of 9198 accessions available
online, covering mungbean, black
gram, and rice bean (AVGRIS
2012b).
Core
collections
representing a large part of
mungbean diversity have been
established and will be available for
mining of favourable alleles for
breeding.
Strategies
improvement
for
genetic
Conventional breeding methods
Pure line selection, recombination
breeding, and mutation breeding
have been successfully employed to
develop new varieties of mungbean
(Fernandez
and
Shanmugasundaram, 1988; Tickoo
et al., 2006). Although crosses with
related species (for example, crosses
with black gram) have been
employed, the number of varieties
developed has been relatively few.
The use of related species in
breeding programs will become
increasingly important so as to
broaden the genetic base.
Marker assisted selection
Marker-assisted
breeding
has
become a routine tool in crop
improvement. Several molecular
markers have been developed for
important agronomical traits of
mungbean, including resistance
against bruchids (Chen et al., 2007;
Cheng et al., 2008; Sarkar et al.,
2011), Cercospora (Chankaew et
al., 2010) and powdery mildew
(Kasettranan et al., 2010), and these
may be useful for marker assisted
selection. Linkage maps based on
restriction
fragment
length
polymorphism (RFLP), random
amplified
polymorphic
DNA
(RAPD), and simple sequence
repeat (SSR) markers are available
for
interspecific
crosses
of
mungbean and resolve 11 linkage
groups (Humphrey et al., 2002;
Zhao et al., 2010). However, the
polymorphism rates of current
marker systems for mungbean are
insufficient for molecular breeding n
due to the narrow genetic base of the
crop.
Single
nucleotide
polymorphism (SNP) markers are
highly abundant in the genome and
would provide an appropriate
marker resource for molecular
breeding. The small genome size of
mungbean (515 Mb/1C) would
make this species highly accessible
either for full genome sequencing or
a reduced representation library
sequencing effort, paving the path to
generate a large number of SNP
markers (Moe et al., 2011). A
marker resource densely covering
the mungbean genome would
facilitate marker-assisted selection
for simple and complex traits in this
crop. SNP markers associated with
important agronomic traits would
enable marker assisted backcrossing
and recurrent selection approaches
and thus provide a means for quick
185
progress in developing improved
mungbean lines.
Feasibility of hybrid production
Heterosis effects of up to 200% have
been reported in mungbean for grain
yield (Tickoo et al., 2006).
However, one of the major barriers
to hybrid seed production of
mungbean is its cleistogamy.
Recently Sorajjapinun and Srinives
(2011) reported a chasmogamous
flower mutant developed through
gamma irradiation, which showed
an increase of 9.6% in cross
pollination compared with the wild
type. Stable male sterile systems
will be required to test the feasibility
of hybrid mungbean.
CONCLUSION
To improve mungbean productivity
breeders must develop varieties with
multiple desirable traits. This
requires a long-term effort and a
multidisciplinary approach. Use of
related species such as black gram
and rice bean will become
increasingly important to tackle pest
and disease issues. Emphasis on
thermo
and
photo-period
insensitivity will be vital to improve
the adaptation of the crop. In
countries with established breeding
programs the adoption of improved
varieties can be significantly
improved by providing farmers with
better access to quality seed. An
increase in adoption rates has been
achieved in certain parts of India
and Bangladesh; expansion into
other areas of the region will benefit
farmers. Short-duration mungbean
(about 60 days) has the advantage of
fitting into more diverse cropping
systems. This should be promoted
widely in the region and in subSaharan Africa to expand mungbean
cultivation.
Intercropping
with
cereals such as maize, sorghum or
sugarcane is another option that
would attract smallholder farmers.
The adoption of improved varieties
and good agronomic practices
including timely weeding and
integrated pest management will
help to enhance crop productivity
and meet the ever-increasing global
demand.
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190
REVIEW
SABRAO Journal
44 (2) 191-201, 2012
PERENNIAL RICE: IMPROVING RICE PRODUCTIVITY FOR
A SUSTAINABLE UPLAND ECOSYSTEM
JUNGHYUN SHIM
International Rice Research Institute (IRRI), Philippines
Corresponding author email: [email protected]
SUMMARY
The world population will reach a staggering 9 billion by 2050. Recent statistics show
that an additional 40 million hectares of rice paddy is needed to increase rice
production to 118 million tons by 2035, a figure that is more than double the current
rice production. Every year, rice is planted in approximately 14 million hectares of
upland areas. Current benchmark yields of annual upland rice are lower than 1 t/ha. If
upland rice yield can be increased to 3-4 t/ha, a reasonable yield under barren and
infertile soil conditions, 40 Mt of rice can easily be secured. Traditionally, the uplands
suffer from drought, infertile soils, weed infestation and plant diseases. Compounding
these problems is the continuous erosion and degradation of upland soils due to
agricultural use. A practical solution to these problems would be to breed and cultivate
perennial upland rice that would not have to be planted annually. Not only would
perennial upland rice reduce soil erosion by providing permanent groundcover. It
would also improve the sustainability of the uplands for agricultural use and lower the
annual inputs related to field operations, thus increasing the income of farmers.
Key words: rice, perennial rice, sustainable agriculture, food security.
INTRODUCTION
Rice is the staple food source in
Asia. In Africa and Latin America,
rice is quickly becoming an
important crop. For over 40 years
after the 1960s Green Revolution,
the improved rice varieties and
cultural management practices
have kept rice production in pace
with the rice demand. In fact,
between mid-1960s and mid1980s, the annual rice output grew
by almost 3%. After the mid-1980s
however, a slower growth rate for
rice production was observed as
influenced by both supply and
demand factors. By the 1990s, the
191
technology that spurred the Green
Revolution that saved millions
from the threat of famine was
diminished and the yearly 3%
increase in rice production slipped
to 1.25%. This decline in
productivity was observed in an
increasing number of favorable rice
growing areas and may be
attributed
to
the
long-term
degradation of the paddy resource
base. Despite the declining growth
rate in rice production, the demand
for rice continuously increased
with the ever-growing human
population. Recent statistics show
that by 2035, rice production must
increase to 116 million tons to meet
the demands of the rice-consuming
population (GRiSP, 2010). This
increase will have to be achieved
using less land, less water and less
labour, in a more efficient and
environment-friendly production
systems.
Uplands as the stage for new
yield frontiers in rice
Based
on
general
surface
hydrology, rice ecologies can be
classified as irrigated, rainfed
lowland and rainfed upland. Some
80 million hectares of the world’s
rice land is irrigated, whereas 60
million hectares is rainfed lowland
(IRRI Rice facts, 2012). Modern
rice varieties grown in favorable
environments of in irrigated and
rainfed lowland regions produce
96% of the world’s rice. An
additional 14 million hectares of
land that produces 4% of the
world’s rice comprise rainfed
upland areas (Figure 1).
Nearly two-thirds of all
upland rice areas are in Asia.
Bangladesh, Cambodia, China,
India, Indonesia, Laos, Myanmar,
Thailand, and Vietnam are
important rice producing nations.
Unlike the irrigated rice areas in
these countries, most of the upland
rice fields are unfavorable due to
the slopes, high altitude, and
infertile and acidic soils. Only 15%
of upland rice grows in a favorable
upland sub-ecosystem that has
fertile soil and a long growing
season.
Yields in the uplands are
generally low and the prospect of
major
increases
are
lower
compared to irrigated and rainfed
lowland
rice
environments
(Swaminathan, 1989). The current
benchmark yields of annual upland
rice are actually lower than 1 t/ha.
Still, nearly 100 million people
depend on upland rice for their
daily staple food. Many upland rice
farmers plant local rice varieties
that do not respond well to
improved management practices.
But these cultivars are well adapted
to the variable constraints in the
ecosystem and have grain quality
characteristics that meet specific
local needs.
192
(a)
Yield Percentage
Irrigated
Rainfed lowland
Rainfed upland
(b)
Area (M/ha)
Irrigated
Rainfed lowland
Rainfed upland
4%
14Mha
9%
20%
76%
60Mha
39%
80Mha
52%
Figure 1. (a) World rice cultivation area. (b) Yield production percentage in rice
ecosystem (IRRI Rice facts, 2012).
In recent years, the
dramatic rise in population has
resulted in heavy pressure on the
fragile uplands of South and
Southeast Asia, as well as of Africa
where slash and burn is still the
most widely practiced cropping
system. The availability of this
system depends on a fallow period
that is long enough to allow the
vegetation to re-grow and the soil
to regenerate (Fujisaka 1993). Due
to land pressure however, fallow
periods became shorter, resulting in
gradual
soil
erosion
and
degradation
and,
eventually,
abandonment. This destruction of
watersheds adversely affects the
lowlands too, as sediment loads
resulting from erosion cause
siltation of reservoirs and drainage
canals, as well as increased
flooding
(Crosson,
1995).
Permanent and sustainable land use
systems are therefore critical in the
use of upland areas for agriculture,
particularly if rice production will
be intensified.
In the early 1990s at IRRI,
scientists embarked on a “new
frontier” project to develop a
perennial rice plant that would be
suitable for upland rice ecology. A
perennial rice won’t have to be
planted annually, thereby providing
permanent
groundcover
and
reducing soil erosion. Ultimately,
this cultural practice would
improve the sustainability of the
uplands for agricultural use and
lower the annual inputs related to
field operations, thus increasing the
income of farmers.
PERENNIAL UPLAND RICE
Considering the existing problems
in upland rice production, the
potential impact of perennial
upland rice is valuable. A rice plant
that would not have to be planted
annually could help reduce soil
erosion by providing permanent
groundcover. In this way, rice
cultivation
is
considerably
intensified while improving the
193
sustainability of the uplands for
agricultural use and lowering the
annual inputs related to field
operations because the soil does
not have to be prepared each year
(Schmit, 1996).
Perennial rice, like many
other perennial plants, can spread
by horizontal stems belowground
(i.e. rhizomes) (Fig. 2d) or just
aboveground
(i.e.
stolon).
Nevertheless, they can also
reproduce sexually by producing
flowers, pollen and seeds. The
wild ancestor of African rice,
Oryza longistaminata often lives
for many years and spreads
vegetatively. O. officinalis, O.
australiensis, and O. rhizomatis
also spread by underground stems,
called rhizomes (Khush, 1997).
The wild ancestor of Asian rice, O.
rufipogon
sometimes
spreads
vegetatively by above-ground
stems,
called
stolons.
O.
longistaminata has been reported
to have dominant, vigorous
rhizomes (Sacks et al. 2005). The
species is also characterized to
have high pollen fertility, strong
seed dormancy and a reproductive
barrier relative to other species of
the genus Oryza.
Breeding program for perennial
grains
For several decades, breeding for
perennial wheat, rye, triticale, oat,
rice, sorghum, Johnson grass, pearl
millet, maize, soybean and Illinois
bundle flower had been carried out
in a number of institutes and
countries (Cox et al., 2002). Wheat
for example, had been crossed with
a number of Triticum species and
perennial grasses. None of these
efforts, however, produced a
perennial wheat cultivar because of
the
complicated
chromosome
number of Triticum, as well as the
limited perennial Triticum source.
Efforts to develop perennial wheat
were hence diverted into producing
improved
annual
cultivars.
Another promising perennial grain
crop is rye. A perennial rye cultivar
Perenne had already been released
in Hungary for grain and forage
production (Hodosne-Kotvics et
al., 1999). Perenniality is also a
trait that can be observed in
sorghum.
In
a
tropical
environment, a new sorghum plant
can regrow from the basal nodes of
the main plant to produce a rattoon.
Rhizomatousness has also been
reported in the crop (Paterson et
al., 1995). For sorghum grown in
temperate regions, perenniality
would have to be combined with
winter hardiness for the crop to
survive the winter season. The real
challenge would be to exploit the
limited genetic resources of
sorghum that are mostly adapted to
tropical climate to find genes that
would allow the crop to
overwinter.
Unlike sorghum, rice is
genetically
diverse
and
is
distributed
worldwide.
This
diversity made available perennial
wild Oryza relatives that can be
used for crosses.
Early perennial rice research by
IRRI and partners
IRRI had worked on the Perennial
Upland Rice (PUR) project, which
was one of the several “New
Frontier” projects established at
IRRI. The PUR project was longterm and considered high-risk but it
has the potential to largely advance
194
Shim (2012)
not only rice research but also rice
production. Because the concept
was still in its infancy, the project
was given a 10-15 years timeline.
The PUR project ran for 6 years,
generating
valuable
new
information pertinent to developing
perennial rice.
A review of literature on
IRRI’s PUR generated new
information on the genetics of
drought and nematode resistance.
But more important was the
development of early stage
breeding lines that might require
further development to fit a
suitable agronomic type, but are
nevertheless able to perenniate.
In the years that followed,
interest in perennial rice but other
perennial crops did not waver and
studies showing the critical role of
perennial crops in sustainable
agriculture found its way in many
published works. The first QTLs
controlling rhizome formation was
identified in chromosomes 3
(between RM119 (2.2 cM) and
RM273 (7.4 cM)) and 4 (between
OSR16 (1.3 cM) and OSR13 (8.1
cM)) (Hu et al., 2003). In 2006,
Sacks et al. clearly demonstrated
the feasibility of perennial rice by
crossing interspecific genotypes
(IGs) from an intermated O. sativa/
O. longistaminata population with
male-fertile IG selections from the
intermated population, and with O.
sativa cultivars and found out that
the most important traits are
perenniality and survival by
rhizome. They reported that
rhizome presence and expression
were positively associated with
survival and vigor of the survivors.
Sacks et al. (2005) suggest that
backcross
progenies
of
RD23/O.longistaminata that have
moderate to long rhizomes would
be a useful source of genes for
developing perennial upland rice.
Because of the less expansive
rhizome
formation,
more
assimilated carbon would be
allocated to the grains and less will
be pumped to storage organs
(rhizome) thereby increasing the
size of the grains. In fact, the yield
of elite rhizomatous perennial
progenies was reported to range
from at least 5 to 10 g/plant
compared to the 11g/plant of O.
sativa, indicating the potential to
break the yield barrier for a
perennial variety. Earlier, Sacks et
al. (2003) also reported that the
most strongly perennial F4 and
BC1F4 families derived from
crosses between O. sativa and O.
rufipogon showed high yields
without any indication of a
negative correlation between yield
and survival (Sacks et al., 2003;
DeHaan et al., 2005). These results
indicate
that
backcrossing
perennial selections to cultivated
rice would be an efficient strategy
to improve the yield in perennial
rice.
Strategic approach to perennial
upland rice breeding
The major constraint in utilizing
wild rice species as a source of
perenniality is the existing
reproductive barriers that result in
sterile seeds or low seed setting in
the progenies. An approach to
overcome this specific constraint is
to construct chromosome segment
substitution lines (CSSLs) using a
wild rice with perennial traits such
as O. longistaminata as donor.
CSSLs are powerful tools to
identify genes or QTLs controlling
195
a trait because each line has a
specific chromosome segment
substituted from a donor in the
background of any elite rice variety
(Xu et al., 2010; Ebitani et al.,
2005; Doi et al., 1997). Using
CSSLs, conventional breeding
methods can be used to transfer
gene(s) for perenniality (i.e.
rhizome formation) in existing rice
cultivars without transferring the
unfavorable genes of the wild rice
parent.
With
collaborations
between IRRI and Nagoya Univ.,
Dr. Ashikari developed CSSLs of
O.
longistaminata
in
the
Nipponbare background. From the
interspecific progenies, 3 major
rhizome formations were observed:
(1) vigorous rhizome that grows
and propagate individually, (2)
intermediately vigorous rhizomes
that grow and produce tillers, (3)
short rhizomes that grow and give
rise to more than hundred rhizomes
that grow like tillers (Figure 2).
Identification
of
the
QTLs
controlling the different kinds of
rhizomes would be the major key
to perennial rice breeding.
The second approach to
perennial rice breeding is to
pyramid
genes/QTLs
for
perenniality into existing varities.
Once specific CSSLs carrying
fragments covering the QTLs for
rhizomes are ready, they will be
used for crossing with O. sativa to
pyramid the rhizome loci and other
genes controlling perenniality in
existing rice cultivars. The
candidate O. sativa cultivars are
selected for their fitness to specific
target regions as well as for other
traits including grain quality and
resistance to pathogens and
diseases.
Another approach for
perennial rice breeding is the use of
mutagenesis. O. rufipogon (Acc.
105491) has good agricultural traits
that are closer to O. sativa (D.
Brar, personal communication). It
often lives for many years, setting
seed each year and spreading
vegetatively although it does not
have rhizome. One strategy would
be to conduct mutagenesis (1 kg
seed) via chemical mutangenesis
system (CMS) treatment and
screen the mutants showing good
agricultural traits, alongside the
perennial trait. Penetrance for the
traits will be evaluated in the M2
population and the selected
mutants will be crossed with O.
sativa to recover other favorable
agricultural traits (D. Brar,
personal communication).
O.
longistaminata (Acc. 110404)
showing vigorous rhizomes will
also be mutagenized with CMS and
mutants at the M2 generation will
be observed and selected for
desirable agricultural traits. The
methodology for perennial upland
rice breeding is below (Figure 3).
196
Shim (2012)
(a)
(b)
(c)
(d)
(e)
(f)
Figure 2. Characteristics of O. longistaminata progenies. a) scattered rhizome growth
pattern b) localized rhizome growth pattern c) very short rhizome but high tiller
number d) 2-month-old O. longistaminata from one seedling e) high panicle numbers
f) progenies with rhizome derived from cross between O. longistaminata and O. sativa
(Picture taken in 2011 at IRRI screen house).
197
Shim (2012)
Year
1
2
3
4
5
6
7
8
9
10
11
12
O. sativa X O. longistaminata
F2 : Mapping
Finding rhizome
gene/QTLs
F3, F4, F5,,,
PerennialRILs
with various
rhizome
Crosses
between
intergenic
lines
Select
Perennial &
Intermediate
rhizome with
high yeild
Field trial
Multi-locational
yield trial
BC1F1
BC2F1
BC3F1
Selfing,,,
BC3F2,,, F5
BILs & CSSLs
Perennial
BILs &
CSSLs with
rhizome
CSSLs
X
Elite recurrent
varieties
O. rufipogon or O. nivara
20,000 seeds mutagenesis
Transferring
Genes &
Pyramiding
Intermediate
rhizome &
high yield lines
Field trial
Select
promising
Perennial
lines
M2: Good
agronomic
traits as well as
perenniality
Multi-locational
yield trial
Recovered
agronomic
favored lines
Field trial
Multi-locational
yield trial
Field trial
Multi-locational
yield trial
crossed toward
Breeding for higher yield and grain quality have to be followed
Developed
Figure 3. Perennial rice breeding scheme.
CHALLENGES
IN
PERENNIAL UPLAND RICE
CULTIVATION
Sustainability of intensified
upland rice production.
Sustainable agriculture is the
practice of farming following the
principles of ecology. This refers to
making the most efficient use of
non-renewable resources and onfarm resources and integrating,
where
appropriate,
natural
biological cycles and controls.
With the current agricultural
landscape, perennial crops will
have a critical role to play in
sustainable agriculture. Perennial
upland rice that will reduce costs to
farmers and the environment by
reducing the need for plowing
while obtaining similar yields as
for annual systems is a realistic
objective. However, even with
only a 2 tons/ha/season rice yield,
perennial upland rice would
represent a substantial benefit to
society as long as it’s cultivation
preserves the natural resource base.
Perennial crops have two
major sustainable agricultural
benefits, water management and
carbon storage.
Shifting from
annual to perennial food crops
would likely have important
consequences for how water is
managed
in
agricultural
landscapes, just as shifting from
198
Shim (2012)
perennial-dominated
native
vegetation to annual crops has had
dramatic, but generally detrimental,
impacts (Glover et al., 2010a,
2010b). The adoption of perennial
grain crops would likely be
advantageous in terms of climate
change. Greater soil carbon storage
and reduced input requirements
mean that perennials have the
potential to mitigate global
warming. Adding grains to the
inventory of available perennial
crops would give farmers more
choices in what they can grow and
where, while sustainably producing
high-value food crops for an
increasingly hungry planet (Bell et
al., 2010).
Disease and pest resistance
Because there would be no break
period in the cultivation of
perennial rice, there would be a
higher risk of a disease and/or pest
epidemic than if annual rice would
be cultivated. To address this
challenge, breeding for genetic
resistance to a wide range of pests
and diseases would be an important
aspect
of
perennial
rice
development. Newly developed
elite lines at IRRI that can be used
for perennial upland rice breeding
as sources of multi-resistance to
many diseases. Once perenniality
is fixed on the target rice variety,
genes for disease resistance can be
pyramided into the line.
Grain yield and quality
The yield of perennial rice is
expected to be lower than those of
existing cultivars because the
limited photosynthetic products
have to be distributed into the more
vigorous root system of the former.
On the other hand, a trade-off that
could possibly give better quality
grains might be expected from this
kind of system. In rice production,
yield increases are expected with
increased fertilizer application,
although a reduction in grain
quality is also observed. In a
perennial rice cultivation system,
ratooning will allow harvesting at
least six times in a year cultivation
period without a need for fertilizer
application. This means, an
augmented harvest within a year
cultivation of grains that meet
significant yield level.
Perennial rice was not
chosen
by
farmers
during
domestication due to its low yield
as well as characters that are
similar to those of wild species
including small grain size, grain
number shattering, and awn traits.
With the use of CSSLs, a perennial
rice that is more similar to existing
rice cultivars could be bred. CSSLs
having
small
chromosome
fragments from wild perennial
Oryza species could be used for
crossing followed by a series of
backcrossing to the recurrent parent
to
reconstitute
the
genetic
background of the preferred
cultivar to which perenniality is
being introduced. Grain quality
concerns could also be addressed
by backcrossing to the recurrent
parent which are good grain quality
varieties such as IR64 and NSIC
Rc222. once perennial rice lines are
developed.
Prospects
The yields of mega-varieties in
each country are widely grown and
yield production has almost
199
reached a plateau. The available
land area for rice cultivation is
continuously shrinking due to
industrialization and urbanization.
To break the existing ceiling in rice
yields,
the
utilization
of
unfavorable rice fields such as the
uplands represents a viable option.
However, the feasibility of turning
uplands to irrigated rice terraces is
very limited. The rice breeder
therefore, has to develop a variety
that is adaptable under upland rice
ecosystem. One realistic strategy is
to develop perennial rice. Current
benchmark yields for annual
upland rice are lower than 1 t/ha.
An increase of 3 tons/ha of upland
rice yield is a relatively feasible
target yield that could overcome
the current maximum in rice yields.
This increase in upland rice yield is
reasonable even in barren and
infertile soils, and could secure an
estimated 40 million tons of rice.
With perennial rice, farmers could
reduce the rice production cost
from field preparation, seeding,
and transplanting.
Upland
perennial can also prevent from
soil erosion. Increased yield will
give higher income and food
security for the world population.
Breeding for perennial upland rice
is
highly
innovative
and
challenging especially for the poor
farmers of the fragile upland
ecosystem.
International working groups on
perennial rice and collaborators
International Perennial Grain Crops
Workshop was held in Wagga
Wagga,
NSW
Australia
in
September 2010. From Yunnan
Academy of Agricultural Sciences,
Fengyi Hu presented the progress
in recent years in obtaining
perennial lines with reasonable
fertility and that grow well in
paddies. Initial testing of these
lines would be carried out in LAO
PDR as part of the ACIAR project
that Len Wade from Charles Sturt
University leads. The latest
movement on perennial rice is
quite active.
ACKNOWLEDGEMENTS
The authors are grateful to Dr. Rosalyn
A. Shim and Dr. Bertrand Collard for a
critical comment and support. The
content of this paper was submitted to
New Frontier Research proposal call of
GRiSP (2011).
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201
REVIEW
SABRAO Journal
44 (2) 202-228, 2012
POTATO CYST NEMATODE (PCN) RESISTANCE: GENES,
GENOTYPES AND MARKERS – AN UPDATE
DALAMU1, VINAY BHARDWAJ1, RAJAPPA
UMAMAHESHWARI2, REENA SHARMA1, SK KAUSHIK3, TA
JOSEPH2, BP SINGH1 and CHRISTIANE GEBHARDT4
1
Central Potato Research Institute, Shimla - 171 001, Himachal Pradesh, India
Central Potato Research Station, Muthorai, Udagamandalam - 643 003, Tamil Nadu, India
3
Central Potato Research Institute, Campus, Modipuram - 250 110, Uttar Pradesh, India
4
Max Planck Institute for Plant Breeding Research, Cologne – 50829, Germany
2
SUMMARY
Potato cyst nematode (PCN) species, Globodera pallida (white cyst nematode) and Globodera
rostochiensis (golden cyst nematode) cause serious yield losses to the potato crop
worldwide. Several cultivated Solanum (Group tuberosum) and wild potato species
possess R genes and QTL for resistance against PCN. R-genes like H1and Gpa5 confer
a high level of resistance to G. rostochiensis pathotypes Rol and Ro4 and G. pallida
pathotypes Pa2 and Pa3, respectively. Several PCN resistant potato cultivars that are
grown worldwide have been bred involving the H1 gene. With the development of
DNA marker technology linkage maps were constructed to map loci involved in
resistance to different pathotypes of PCN. Mapping these loci led to the development
of molecular markers to screen segregating populations for resistance to PCN in the
absence of the pathogen. Markers linked to R genes and resistance QTL have potential
for making tailor made varieties through marker-assisted selection (MAS). This article
presents an update on resistance sources available in major worldwide gene banks, R
genes, QTLs and molecular markers for PCN resistance.
Keywords: PCN, pathotypes/ races, linkage maps, R genes, QTL, molecular markers,
marker assisted selection.
Manuscript received: July 26, 2012; Decision on manuscript: October 6, 2012; Manuscript
accepted in revised form: November 14, 2012.
Communicating Editor: Sathiyamoorthy Meiyalaghan
INTRODUCTION
Potato (Solanum tuberosum L.;
2n=4x=48) belongs to the family
Solanaceae and globally is the
third most important food crop
after rice and wheat with
production reaching a record of
Dalamu et al. (2012)
324 million tonnes in 2010
(FAOSTAT, 2010). Today potato
is grown in about 149 countries
from latitudes 65º N to 50º S and at
altitudes from sea level to 4000
meter amsl (Spooner et al., 2007).
India holds the second rank in
production and area under potato
cultivation. Since the 1990s the
potato production in developing
nations exceeds that of the
developed world.
Potato is affected by
number of biotic and abiotic
stresses. Among these, infestation
by potato cyst nematodes is one of
the most important problems.
Recent data reveal that more than
10% yield loss occurs due to PCN
infestation (Finkers-Tomczak et
al., 2011). PCN was earlier
grouped in the genus Heterodera.
Skarbilovich (1959) erected the
sub-genus
Globodera
to
accommodate the PCN and related
species having round cysts. The
sub-genus Globodera was later
elevated to generic status (Behrens,
1975). Although, literature prior to
1973 refers to the potato cyst
nematode as a single species, G.
rostochiensis (Behrens, 1975),
today there are two recognized
species of potato cyst nematodes,
Globodera rostochiensis, having
golden females, and Globodera
pallida, having white or creamcoloured females (Stone, 1973).
The centre of origin of both species
is the Andes mountains in South
America (Evans and Stone, 1977).
A study revealed five distinct
clades (Picard et al., 2007) of a
Peruvian population of G. pallida
using two molecular marker
systems (partial sequence of the
mitochondrial gene cytochrome b
and microsatellite loci) with its
colonization from South to North.
When Peruvian populations from
these clades were exposed to
various potato resistance genes
they exhibited large differences in
virulence. The origin of Western
European populations of G. pallida
was derived from a single restricted
area located in extreme south of
Peru between the north shore of
lake Titicaca and Cusco (Plantard
et al., 2008).
The present
distribution of the pest extends
from sea level to higher altitudes in
different continents like Europe,
Africa, Asia, North America and
Oceania, where it spread with
exchange of seed potato and
breeding materials.
In India, Jones (1961) first
reported the entry of this pest from
the British Islands and its presence
in the Nilgiri hills of Tamil Nadu.
Subsequent surveys conducted in
Nilgiris by Seshadri and Sivakumar
(1962) revealed the widespread
prevalence of PCN in potato fields
around Ootacamund of Tamil
Nadu. Later on, their occurrence
was also reported from Kodaikanal
hills of Tamil Nadu (Thangaraju,
1983). In North India, an
exploratory survey was conducted
by Nirula and Bassi (1963) in the
hilly regions of Himachal Pradesh,
Punjab and Uttar Pradesh but did
not recover any cyst. Later,
Krishna Prasad and Singh (1986)
reported PCN from adjoining hills
of Karnataka, but the cysts were
observed to be non viable. Ramana
and Mohandas (1988) reported G.
pallida from Western Ghats of
Kerala bordering Tamil Nadu
indicating of the possibility of its
spread by infested seed materials
from nearby Kodaikanal hills.
Realizing the potential danger by
203
PCN to potato cultivation and its
spread to new areas through seed
potatoes, the ‘Golden Nematode
Scheme’ was launched by the
Government
of
India
in
Ootacamund in October 1963, to
impose domestic quarantine and
ensure strict checking of seed
potato for marketing from infested
field of Ootacamund (Krishna
Prasad, 1993).
In India a large scale
chemical control program under
the
Indo-German
Nilgiris
Development Project was carried
out during 1971-75 covering the
entire infested area of about 2000
ha in Nilgiri hills. The treatment
was made mandatory under Tamil
Nadu Pest Act and the pesticide,
Dasanit (10%) was supplied free of
cost (Seshadri, 1978). Although
good nematode control (75%) was
achieved, complete eradication was
not possible (Menon et al., 1974).
Recent surveys in the Nilgiris
revealed PCN presence in high
intensities (>51 cysts/100 cc soil)
in Ooty, Kundah, Kotagiri and
Coonoor taluks (Minhans et al.,,
2012). Owing to the intensive and
continuous cultivation of potato
throughout the year and favorable
climatic conditions, an increasing
trend in PCN population was
observed in the Nilgiris.
Potato
cyst
classification
nematode
Peruvian PCN populations were
classified into five clades Vm,
IVm, IIIm, IIn and Im (Picard et al.,
2007), while Western European
populations fell into two groups
only. One group corresponded to
clade Im and included all European
populations, the other comprised
the remaining Northern clades
(Plantard et al., 2008). There are
several pathotypes and races of
each PCN species (Kort et al.,
1974). These pathotypes are
characterized by their ability to
multiply on Solanum clones and
hybrids with different genes for
resistance (Brodie et al. 1988).
Two different schemes are reported
for identifying pathotypes or races
of PCN (Canto-Saenz and de
Scurrah, 1977; Kort et al., 1977).
Depending on the classification
scheme used, G. rostochiensis has
five pathotypes and four races,
whereas G. pallida has three
pathotypes and seven races (Table
1). G. pallida field populations are
however not uniform with respect
to pathotype composition and
populations containing mixtures of
Pa2 and Pa3 (Pa2/3) are generally
observed.
PCN species and pathotypes in
India
In India both G. rostochiensis and
G. pallida species are prevalent.
Mixed populations of both species
are present in all potato growing
areas of Nilgiris and Kodaikanal
hills (Krishna Prasad, 1993) in
Tamil Nadu. The cysts prevalent in
Karnataka were not viable, while
only G. pallida was found in
Kerala. Later on Krishna Prasad
(1996)
detected
different
pathotypes of Globodera spp.
(Table 2) using the host
differentials (Kort et al., 1977).
204
Table 1. Identification and classification of pathotypes and races of PCN.
Globodera rostochiensis
Pathotypes
Ro1
Ro4
Ro2
Ro3
Ro5
R1A
R1B
R2A
R3A
-
+
-
+
+
+
-
+
+
-
+
+
+
-
+
+
+
+
+
Pa1
P1A
P1B
P2A
P3A
Pa2
P4A
Pa3
P5A
+
+
+
-
+
+
+
+
+
+
+
-
+
+
+
-
+
+
+
+
-
+
+
+
+
+
-
a
Racesb
Differential host
S. tuberosum ssp. tuberosum
S. tuberosum ssp. andigena (H1)
S. kurtzianum KTT/60.21.19
S. vernei GLKS.58.1642.4
S. vernei (VTn)2 62.33.3
Globodera pallida
Pathotypes
Races
Differential host
S. tuberosum ssp. tuberosum
S. multidissectum (H2)
S. kurtzianum KTT/60.21.19
S. vernei GLKS.58.1642.4
S. vernei (VTn)2 62.33.3
CIP 280090.10d
“+” means susceptibility; “-” means resistance
a
Kort et al. (1977) ; b Canto-Sanz and de Scurrah (1977); * Franco and Gonzalez (1990)
Table 2. Characterization of Indian pathotypes of Globodera spp. at different
localities of Nilgiri hills, Tamil Nadu, India.
Locality
Adigaratty
Fern Hill
Kallahatty
Nanjanad
Thummanatty
Vijayanagram
G. pallida
Pa1, Pa2, Pa3
Pa1, Pa2, Pa3
Pa1, Pa2, Pa3
Pa1, Pa2, Pa3
Pa2
Symptoms and mechanism of
reistance by PCN tolerant
genotypes
PCN
specifically
infests
solanaceous
species
(potato,
tomato, brinjal and some weeds).
The symptoms are yellowing of
foliage, wilting and plants dying
G. rostochiensis
Ro1, Ro2
Ro1, Ro2, Ro5
Ro1
Ro1, Ro2, Ro5
Ro1
Ro1, Ro2, Ro5
pre-maturely.The larvae reside in
eggs encapsulated in small
(diameter <1 mm), spherical cysts.
The cysts are resistant to
unfavourable
environmental
conditions and nematicides and
persist more than 10 years in the
soil.
205
P6A
*
+
+
+
+
+
+
In spring and early summer
root exudates of Solanaceous crops
stimulate larvae hatching from
cysts. The 2nd stage larvae invade
the root system and, adjacent to the
xylem vessels induce multinucleate
transfer cells (Jones, 1972; 1981)
which serve as feeding sites
(syncytia).The males who do not
form syncytia, emerge from the
root and die after fertilization,
whereas the females develop into
spherical shaped adults and remain
attached to the roots. Resistant
plants stimulate the eggs of potato
cyst nematodes to hatch and are
invaded to a similar extent as
susceptible plants. But in the
incompatible combinations the
syncytia remain small and are often
accompanied by a necrotic reaction
(Hoopes, 1978) resulting in a too
limited amount of food for females
to develop. The few females that
still develop in resistant plants
have reduced fecundity due to
feeding from a poorly developed
syncytium. This results in marked
reduction
of
second
stage
juvenile’s infection (Mullin and
Brodie, 1988) in resistant plants.
Management of potato cyst
nematodes
Management of PCN is very
difficult because of its unique
survival strategies. PCN eggs are
protected inside cysts that without
host lie dormant in the soil for
many years. A new generation is
formed each time a host is grown.
Arrested development may be
further specialized as diapause
which synchronizes the life cycle
of the nematode with that of its
host plant.
Management practices like
crop rotation (with non-host crops
like corn, wheat, soybean etc.), soil
solarisation,
biofumigation,
growing trap crops, nematicide
application and planting certified
PCN free seed potatoes, can
prevent infection and reduce the
nematode population over time.
Chemical control is effective as
high yields are obtained but it is
toxic both to the person applying it
and the underground water.
Nematicides
stop
nematode
multiplication after the initial
protection
of
invasion
and
therefore, the best method to
control PCN is to plant tolerant/
resistant potato cultivars. Due to
complete dependency on their host,
PCN are very vulnerable to
introducing resistance in the host
plants.
This
makes
them
particularly
suitable
for
management with host resistance.
The objective of host resistance is
to interfere or disrupt the
nematode's life cycle sufficiently to
impair its ability to reproduce and
damage the host crop. Plant
resistance to nematodes is related
to the ability of the host to reduce
the development of nematode
juveniles into adult females. The
multiplication
rate
of
the
nematodes
depends
on
the
resistance genes present in the
potato host and on the virulence
genes present in the nematodes.
Host specific resistance follows a
gene-for-gene model (Flor, 1942)
if a race specific avirulence (Avr)
gene in the pathogen and a single
dominant gene (R) is present in the
host. In PCN resistance this model
also applies to H1 gene present in
Solanum tuberosum ssp. andigena
CPC1673 (Janssen et al., 1991).
206
Alternatively,
other
plant
components also interact with
avirulence proteins according to
the guard model (Dangl and Jones,
2001). When the same resistant
potato
variety
is
grown
successively, selection for virulent
nematodes
may
occur
that
overcomes the resistance source.
For
example,
continuous
cultivation of potato cultivars
carrying the H1 gene imparting
resistance to G. rostochiensis in
the United Kingdom led to increase
in the prevalence of G. pallida,
which was previously rarely
encountered (Minnis et al., 2002).
BREEDING FOR TOLERANT
GENOTYPES
Classical breeding methods for
PCN
resistance
are
time
consuming, expensive and marred
with the chances of susceptible
plants escaping attack. Either
simultaneous
or
sequential
screening of plants against several
different pathotypes is difficult and
more over, screening tests are
difficult to handle. Resistance to
PCN was not found originally
within Solanum tuberosum ssp.
tuberosum. Thus, breeding for
resistance to PCN has made
extensive use of other Solanum
species. The wild species mostly
exploited in PCN resistance
breeding are S. tuberosum ssp.
andigena, S. vernei, S. gourlayi, S.
sparsipilum and S. spegazzinii
(Dellaert et al., 1988). Breeding for
PCN resistance was initiated by
Ellenby (1948 and 1952) by
screening the Commonwealth
Potato Collection, which identified
resistance in Solanum vernei
(C105, 2413.1 and 2414.3) in a
triploid clone with affinities to
andigena (C 1647) and in five
andigena clones (C 1595, 1673,
1685, 1690 and 1692). Some
resistances are monogenic, others
are oligogenic or polygenic.
Solanum tuberosum ssp. andigena
CPC 1673 was found to have a
single gene for resistance against
G. rostochiensis (Ellenby, 1952).
This
resistance
has
been
incorporated in many potato
cultivars. Resistance breeding was
initially
directed
against
yellow/golden
PCN
G.
rostochiensis. Due to the extensive
use of cultivars resistant to G.
rostochiensis,
G.
pallida
populations got free of competition
and became the majority of the cyst
nematode pest population. The
breeding for resistance to G.
pallida was hampered by presence
of higher diversity of the G. pallida
populations and the polygenic
nature of the resistance. The
resistance to G. pallida derived
from S. vernei is polygenic in
nature (Dale & Phillips, 1982;
Dellaert el al., 1988). Thus, a few
genotypes with a high level of
resistance were found in the
progeny of crosses with susceptible
cultivars. Dunnett (1960) reported
single gene based resistance to G.
pallida in S. multidissectum.
However, this resistance was only
effective against a limited number
of G. pallida populations. Chavez
et al. (1988) detected resistance in
S. brevicaule, S. leptophyes, S.
canasense, S. multidissectum, S.
sparsipilum and S. microdontum
against pathotypes P4A and P5A
and reported good crossability
between cultivated and wild
diploid potatoes of series Tuberosa.
207
In hybrid progenies of resistant
wild diploid potato species of
series Tuberosa with cultivated
potatoes a high frequency of clones
resistant to P4A was obtained
(>50%) in progenies of S.
leptophyes, S. vernei, S. gourlayi
and S. capsicibaccatum, whereas
resistance to P5A was found in
progenies of S. leptophyes, S.
vernei,
S.
gourlayi,
S.
capsicibaccatum
and
S.
sparsipilum.
These genes identified in
the
wild
Solanum
species
conferring resistance to different
Globodera pathotypes have been
introgressed into Indian cultivars,
Kufri Swarna, Kufri Neelima
(Gaur et al., 1984). and Exotic
potato cultivars viz., Maris Piper,
Atlantic, Saturna, Lady Christl,
Nicola, Valor, White Lady etc.
(Ortega et al.,2012 ). The list of
wild
Solanum
species
and
accessions conferring resistance
against both the PCN species and
available in gene banks worldwide
are mentioned in Tables 3 and 4,
respectively.
MARKER
ASSISTED
SELECTION (MAS)
In contrast to the classical breeding
approach, DNA-based markers are
easy to score and cost effective.
Availability of genetic markers
tightly linked to resistance genes
facilitates the identification of
genotypes carrying these genes
without subjecting them to
infection tests in early generations.
Thus, MAS makes feasible to
conduct many rounds of selection
in a year without depending on the
natural occurrence of the pest.
Marker-assisted selection is very
effective for introgressing genes or
QTLs from landraces and related
wild species, because it reduces
both the time needed to produce
commercial cultivars and the risk
of undesirable linkage drag with
unwanted traits. High selection
efficiency at early generations and
characterization in later generations
are the tangible deliverables from
MAS. With the availability of
molecular markers linked to the
genes for PCN resistance rapid and
efficient positive assisted selection
can be performed. MAS also aids
the transfer of useful resistance
genes in inter-specific crosses
between wild and cultivated
genotypes through use of speciesspecific molecular markers which
allow the wild genomic content to
be reduced in few backcross
generations (negative assisted
selection).
MAS
facilitates
pyramiding of individual R-genes
with narrow spectrum for durable
resistance. Phillips and Blok
(2008) demonstrated that G.
pallida populations adapted to
resistance from S. tuberosum spp.
andigena do not acquire the
capacity to overcome the resistance
of S. vernei illustrating the
complementary effect of the two
resistance sources. Thus, joint
effect of multiple R-genes will
result in broad spectrum and high
level of resistance.
208
Table 3. Accessions of wild Solanum species with resistance to several PCN
pathotypes.
Species
S. gourlayi
S. multidissectum
S. oplocense
S. spegazzinii
S. sucrense
S. vernei
S. acaule
S. gourlayi
S. leptophyes
S. megistacrolobum
S. multidissectum
S. oplocense
S. spegazzinii
S. sucrense
S. vernei
S. acaule
S. berthaultii
S. brevicaule
S. goniocalyx
S. kurtzianum
S. megistacrolobum
S. microdontum
S. oplocense
S. phureja
S. raphanifolium
S. sanctae-rosae
S. sparsipilum
S. spegazzinii
S. stenotomum
S. stenotomum x S.
spegazzini
S. sucrense
Accessions
BGRC 7179, 7183
BGRC 18293
BGRC 27345
BGRC 18632
BGRC 10058
BGRC 15451
BGRC 18319
BGRC 7138
BGRC 7179, 7180, 18523,
18530, 18533, 27257
BGRC 71584
BGRC 7212
BGRC 18310
BGRC 27345, 27347
BGRC 15456, 18326
BGRC 27258
BGRC 15451
CPC 2456
CPC 3741
CPC 3923
CPC 3607
CPC 3713
CPC 4671
CPC 3783
CPC 5889
CPC 3704, 3706
CPC 2727
CPC 3777
CPC 4316
CPC 3533
CPC 3269, 3779
CPC 3488, 3562, 3563
CPC 544
CPC 3609
CPC 3618, 3622, 3625, 5703,
5704, 5874
CPC 3744, 3745, 3780, 3791,
4070
CPC 5926
CPC 2688
CPC 4691
CPC 4694
CPC 4699
CPC 4703
CPC 4710
CPC 4711
CPC 4072
Resistant to
G.rostochiensis
Ro 5
Ro 1,2,3,5
Ro 1,3,5
Ro 1,2,5
Ro 1,2,3,5
Ro 1,2,3,5
Ro 5
Ro 5
Ro 5
Ro 5
Ro 5
Ro 5
Ro 5
Ro 5
Ro 5
Ro 1,2,5
Ro 1,2,5
Ro 1
Ro 1,2,5
Ro 1
Ro 1,2,3,4,5
Ro 1,2,5
Ro 1,2,3,5
Ro 1,2,3
Ro 1,2,3,5
Ro 1,2,3,5
Ro 1,2,3,5
Ro 1,2,3,4,5
Ro 1,2
Ro 1,3,5
Ro 1,2,3,5
Reference
G.pallida
Pa 1,2,3
Pa 2,3
Pa 2,3
Pa 2,3
Pa 1,2,3
Pa 1,2,3
Pa 1, 2
Pa 2,3
Pa 2,3
Pa 2,3
Pa 2,3
Pa 2,3
Pa 2,3
Pa 2,3
Pa 2,3
Pa 2,3
Pa 1,2,3
Pa 1,3
Pa 3
Pa 3
Pa 3
Pa 3
Pa 1,2,3
Pa 1,2,3
Pa 1,3
Dellaert and
Hoekstra
(1987)
Turner
(1989)
Pa 1,3
Pa 1,3
Pa 1,3
Pa 1,3
Pa 1,2,3
Pa 1,2,3
Pa 1,3
Ro 1,2,3,5
Pa 1,3
Ro 1
Ro 1,2,3,4,5
Pa 1,2,3
Pa 1,2,3
Pa 3
Pa 1,2
Pa 3
Pa 1,2,3
Pa 1,3
Pa 1,2,3
Ro 3,4,5
Ro 1,2,3,4,5
Vansoest et
al., (1983)
Ro 1,2,3,5
209
Table 3. (cont’d)
Species
S. toralapanum
S. tuberosum
andigena
Accessions
ssp.
S. gourlayi
S. spegazzinii
S. vernei
S. brachistotrichum
S. brevicaule
S. chaucha
S. clarum
S. doddsii
S. gourlayi
S. incamayoense
S. leptophyes
S. marinasense
S. michoacanum
S. multidissectum
S. pascoense
S. tarijense
S. venturii
S. weberbaueri
S. yungasense
S. mochiquense
S. neocardenasii
S. trifidum
S. brevidens
S. semidemissum
S. schenkii
S. trifidum
S. brachycarpum
S. multinteruptum
S. semidemissum
S x doddsii
S. okadae
S. pampasense
S. alandiae
S. schenkii
CPC 3273
CPC 559A, 2735
CPC 591, 1645
CPC 1479C
CPC 3121
CPC 5244, 5460
INRA 88. 478.1, INRA 88.484.3,
INRA 88.484.7, INRA 88.484.8,
INRA 88.484.12
INRA 88.332.2, INRA 88.334.7,
INRA 88.334.12, INRA 88.334.17,
INRA 88.334.23, INRA 88.338.5,
INRA 88.511.7, INRA 88.511.8,
INRA 88.511.10, INRA 88.511.11,
INRA 88.511.15, INRA 88.511.24,
INRA 88.514.3, INRA 88.515.1,
INRA 88.515.4, INRA 88.515.5,
INRA 88.517.3, INRA 88.517.4,
INRA 88.515.5, INRA 88.517.12
INRA 88.341.12, INRA 88.342.5
7986
28023
116
2383
2880
7180
2853
27215
2278
8128
931
2877
24717
8239
2724
2173
7062
7208
7125, 7126
7134, 7135
7112
7164
7123, 7124
7029, 7032
7188
7103
7040, 7148
7129
7068
7212
7033
Resistant to
G.rosto G.pallida
chiensis
Ro 2,5
Pa 1,3
Ro 2
Pa 1,2,3
Pa 2,3
Pa 3
Pa 1,3
Pa 1
Ro 1
Pa 2/3
Ro 1
Pa 2/3
Ro 1
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Pa 2/3
Ro 1
Ro 1
Ro 1
Ro 1
Ro 1
Ro 1
Ro 1
Ro 1
Ro 1
Ro 1
Reference
Turner (1989)
RouselleBurgeois
and
Mugniery (1995)
Ruizdegalarreta
et al., (1998)
Castelli et al .,
(2003)
210
Table 4. Resistance to PCN pathotypes in Solanum spp. available in gene banks
worldwide.
Species
SCRI Commonwealth
Potato Collection
(CPC Number)1
NRSP-6 United States
Potato Genebank (PI
Number)2
Dutch-German Potato
Collection, The
Netherlands (CGN
Number)3
S.acaule
2113, 2456,
2522, 2523,
2525, 3741,
3758, 3760,
3775, 3923
230556
17674, 17675, 17676,
17677, 17678, 17843,
17844, 20562, 22359,
22766, 23023, 24129,
24285
S. agrimonifolium
S. alandiae
S.berthaultii
275177
3607
S. boliviense
S. brachycarpum
S.brevicaule
265857
210034, 265860, 265879
3713
205407, 208562, 210039,
233692, 246536, 265579,
275143, 275144, 320340
S. bukasovii
S. bulbocastanum
243508, 243512, 255518,
275187, 275189, 275191,
275194, 275195, 275196,
275197, 275198, 275199,
275200
S. canasense
S. candolleanum
S. capsicibaccatum
S. chacoense
S. chomatophilum
S. circaeifolium ssp.
quimense
S. clarum
S. coelestipetalum
S. demissum
S. etuberosum
S. gourlayi
S. hannemanii
S. hawkesianum
22349
17823, 18042, 18118,
18257, 18267, 20635,
20644
17680, 17681, 18261,
21316, 23488, 23506
18347
17682, 17841, 18030,
18034, 18071, 18223,
18231, 18232, 18247,
22321, 22322, 22717,
24423
17588, 17686, 17732,
17733, 17824, 18031,
20581, 24366
17690, 17692, 17693,
17869, 21306, 22367,
22698
17589, 17694, 17724,
22323, 23484
230506
18254, 18268, 22767
17704
3527, 3527A,
5918, 5919
18060
18127, 18133, 18158,
20643
283099
2480
17684
17802, 17817, 17818
17714
17591, 17592, 17864,
17873, 18038, 18039,
18040, 18066, 18102,
18176, 20585, 20594,
21320, 21341, 22340,
22342, 22343, 22380,
23022, 23486, 23497,
24135, 24139, 24372,
24395, 24601
17996
17717, 17891, 17892
211
S. hjertingii
S. hondelmanii
S. incamayoense
S. infundibuliforme
S. kurtzianum
251065, 251067
275146
3782, 3783,
4039,5889, 6056
S. leptophyes
S. lignicaule
S. marinasense
S. medians
S. megistacrolobum
3704, 3706
S. microdontum
2727
S. multidissectum
S. neorossii
S. okodae
S. oplocense
533
S.pachytrichum/lept
ophyes
S. pampasense
S. paucijugum
S. pinnatisectum
S. polyadenium
S. polytrichon
S. raphanifolium
S. sanctae-rosae
S. sandemanil
S. sokupii
S. sparsipilum
S. spegazzinii
S. stoloniferum
18032, 18104, 18117,
18126, 18138, 18163,
18167, 18168, 18172,
18173, 18174, 18177,
18178, 20608, 20611,
20619, 21312, 22346,
22381, 23060, 24380,
24402, 24406, 24414,
24587, 24598
17723
17594
21343
17725, 17727, 17828,
18023, 18081, 18184,
21309, 21317, 22364,
22700, 23016, 24293
17596, 17729, 21342,
22372
17599, 17763, 18051
18109, 22703
17736, 17870, 17871,
18085, 18086, 18087,
18088, 20638, 21352,
22324, 22713, 24376,
24410
20632, 24408
3777
275238
17751
283086
3269, 3779
2566, 3488,
3533, 3562,
3563, 3564,
3575, 3618,
3622, 3625,
5703, 5704, 5876
3744, 3745,
3780, 3791, 4070
2092
22369
18182
18077
17593, 24586
18117, 21399, 22699
17738
18050
17740, 17744
23013
17751
17598, 17753, 18033,
18320, 22363
17837, 18005, 18090,
18091, 20564, 22344
17600
17846
17734, 17756, 17758,
17838, 18052, 18061,
18094, 18131, 18142,
18220, 20556, 20602,
22315, 22702
17602, 17759, 17839,
17877, 18100, 20577,
20590, 21313, 21318,
21321, 21345, 22325,
22373, 22707, 23015
275246, 275248
212
S. sucrense
S. suaveolens
S. tarijense
S. tuberosum ssp.
tuberosum
4702
389, 559A, 591,
1479C, 1661,
1663, 1733,
1912, 2735,
2750, 2802,
2929, 3065A,
3121D, 3520,
4088, 4147,
4149, 4153,
4160, 4168,
4266, 4300,
4370, 4375,
4378, 4399,
4420, 4691,
4693, 4695,
4703, 4704,
4710, 4711,
4720, 4733,
4755, 4758,
4762, 4775,
4777, 4782,
4784, 4797,
5021, 5034,
5067, 5196B,
5215, 5274,
5281, 5307,
5317, 5349,
5458, 5508
S. tuberosum ssp.
andigena
S. unknown
S. venturii
S. vernei
S. verrucosum
17608, 17735, 17757,
18101, 18103, 18105,
18183, 18186, 18187,
18213, 20563, 20565,
20625, 20628, 20629,
20631, 20633, 21314,
22350, 22701, 23053,
24405, 24573, 24585
18123
18107, 22714, 22729
195191, 205624, 230457,
233987, 233993, 245320,
245321, 246516, 258877,
258886
CPC 2733, 4075
230468
17611, 23035
17587, 20580
17755, 17761
17762, 17836, 17995,
18111, 18113, 18114,
18277, 18278, 21315,
21350, 22345
17765, 17767, 17769,
17770, 17772
17775
20652
S. virgultorum
S. weberbaueri
1
http//germinate. scri.ac.uk/germinatecpc/app/index.potato/
2
http://www.ars-grin.gov./nr6/potato/
3
http://www.cgn.wur.nl/UK/CGN+Plant+Genetic+Resources/Collection/potato/
213
Table 5. Costs of applying each marker compared to artificial inoculation with
each pathogen.
Pathogen
Marker
Per sample cost (€)
PVY
RYSC3
STM0003
Gro 1-4
TG689
HC
SPUD
2.83
2.88
2.84
3.10
2.84
3.10
G. rostochiensis
G. pallida
Artificial inoculation cost
(€)
6-12 a
38.6-77.2 a
a
The lower amount corresponds to 1 year of inoculation (susceptible genotypes). The higher
amount corresponds to a second year of inoculation to confirm if the putative resistant genotypes
are really resistant.
Source: Ortega et al. (2012)
Worldwide many crop varieties
have been developed through
marker assisted breeding in crops
like rice, wheat, maize, tomato,
soybean, millet etc. (Greenpeace,
2009; Brumlop and Finckh, 2010).
In India MAS has been successfully
deployed in breeding for different
traits in many crops, for example
bacterial leaf blight resistance in
aromatic/basmati and Indica rice
(Gopalakrishnan et al., 2008;
Sundaram et al., 2008), improved
protein quality in maize (Gupta et
al., 2009) and Potato virus Y (PVY)
extreme resistant potato genetic
stock, ‘YY 6/3 C-11’ having the
Ryadg gene in Triplex (YYYy)
condition (Kaushik et al., 2009).
Application of MAS at early
breeding generation is critical for its
effectiveness and cost efficiency.
Incorporation of molecular markers
at the BC1F1 stage in wheat breeding
enhances genetic gain and reduces
the overall cost by 40% (Kuchel et
al., 2005). Detailed comparisons for
Soybean Cyst Nematode (SCN)
screening indicated that the cost and
time required for screening with
marker based selection was lower
than
phenotypic
selection
(Concibido et al., 2004) requiring 1
to 2 days at the cost of $0.25 to 1.00
per sample in contrast to SCN
greenhouse assay requiring 30 d at
the cost of $1.50 to 5.00 per sample.
On the basis of a hypothetical wheat
breeding program, Brennan and
Martin (2006) estimated the costs of
molecular markers analysis as US$
2.59
(genotyper
multipool,
multiplex), US$ 7.97 (genotyper
single marker) and US$ 16.28 (SDS
PAGE analysis). However for
phenotypic selection for Heterodera
spp. (cereal cyst nematode) and
Pratylenchus thornei (root-lesion
nematode) in the glass house the
cost per genotype was US $162.80
and US $74.00, respectively. Thus,
MAS is cost effective especially
when
applied
in
efficient
combinations using 96-well high
throughput
and
multiplexing
reactions to save cost and time. In
potato disease resistance breeding,
the cost of MAS was estimated
(Ortega et al., 2012) compared to
phenotyping
after
artificial
inoculations with Potato virus Y
(PVY) and PCN and was concluded
that markers are less expensive if
the resistance is determined with
214
one or two diagnostic markers
(Table 5).
Marker assisted breeding for
resistance against PCN has started
worldwide with the screening of
parental breeding lines. Skupinova
et al. (2002) obtained a high
correlation coefficient (r=0.96)
between occurrence of H1 gene and
resistance to G. rostochiensis.
Similarly Milczarek et al., (2011)
validated the suitability of marker
TG689 (linked to the H1 locus) for
identification of cultivars resistant to
G. rostochiensis. Usefulness of the
TG689
marker
was
also
demonstrated by Felcher et al.
(2012) and Galek et al. (2011) for
selection of resistant genotypes in a
conventional
potato
breeding
program
of
Michigan
State
University for golden nematode
resistance and Polish potato
genotypes, respectively. Schultz et
al., 2012 reported presence of the
57R marker linked to H1 locus is
predictive for PCN resistant
phenotypes in Australian and
Scottish
potato
breeding
programmes.
In order to economise the
MAS procedure, Milczarek (2012)
developed multiplex PCR method
for detection of H1 and Gro1-4
genes in single PCR reaction
conferring resistance of potato to G.
rostochiensis. Asano et al. (2012)
developed a multiplex PCR method
for simultaneous detection of PCN
resistance genes (H1, Gpa2 and
Gro1-4), while Mori et al., 2011
developed multiplex PCR method
for simultaneous selection of
resistance genes to cyst nematode
(H1), Potato virus X (Rx1) and late
blight (R1 and R2).
GENETIC MAPPING
The development of molecular
marker
techniques
such
as
restriction
fragment
length
polymorphism (RFLP) has provided
powerful tools for constructing
genetic maps for potato. Molecular
linkage
maps
provided
the
framework for the location of
resistance loci in the potato genome.
A number of resistance factors
introgressed from other wild and
cultivated species have been located
on the potato molecular linkage map
using DNA-based markers. They
were mapped either as major genes
(R genes) or as quantitative
resistance loci (QRL).
The knowledge of map position and
closely linked DNA-based markers
facilitates tracing and combining
resistance factors from different
sources. Nineteen PCN resistance
genes have been mapped in different
potato chromosomes (Gebhardt and
Valkonen, 2001; Caromel et al.,
2003, 2005), twelve of which impart
partial resistance (Gpa, Gpa4, Gpa5,
Gpa6, Grp1, Gro1.2, Gro1.3,
Gro1.4, GpaM1, GpaM2, GpaM3,
H3) while five of them (H1, H2,
Gro1-4, GroVI, Gpa2) and the
combination of GpaVsspl and
GpaXIsspl confer specific resistance
(Table 6).
Over the years, several
potato nematode resistance genes
have been mapped and cloned.
Different
genes
conferring
resistance against Globodera spp.
are as follows:
H1
Potato cultivars resistant to the
golden cyst nematode (pathotype
Ro1 and Ro4) were developed by
215
incorporating the H1 gene from S.
tuberosum ssp. andigena C 1673
(Ellenby 1952; Ross 1979; Gebhardt
et al., 1993). Maris Piper was the
first variety released in 1966 with
the H1 gene. H1 proved to be
durable and nowadays this gene is
present in almost all cultivars.
H2
The dominant gene H2 was found in
S. multidissectum accession PH
1366, which confers resistance
against G. pallida Pa1 (Dunnett,
1960). However, this gene is not
widely exploited in breeding as
pathotype Pa1 is less common.
H3
The H3 gene imparts quantitative
resistance to G. pallida Pa 2/3,
originated from S. tuberosum ssp.
andigena C2802 (Howard et al.,
1970) and was mapped in clone
12601ab1 (Bradshaw et al., 1998).
Evans (1978) concluded that the H3
locus is comprised of several genes
and is effective against European
and American populations.
Gpa_QTL
Gpa originated from S. spegazzinii
and confers quantitative resistance
to G. pallida pathotypes Pa2/3. This
locus explains 50% of the total
variance for resistance to both
pathotypes.
tuberosum ssp. andigena, CPC 2802
(Moloney et al., 2010).
Gpa5_QTL, Gpa6_QTL
Gpa5 and Gpa6 loci impart partial
resistance to G. pallida and were
derived from S. vernei (Rouppe van
der Voort et al., 2000). Gpa5
explains 61%, while Gpa6 explains
24 % of the resistance variation.
GpaM1_QTL,
GpaM2_QTL,
GpaM3_QTL
These loci were derived from S.
spegazzinii (Caromel et al., 2003).
GpaM1 explain more than 50% of
the total resistance variance against
G. pallida pathotype Pa2/3 while
the other two genes explain about
20% only.
GpaVs spl and GpaXIs spl
GpaVsspl and GpaXIsspl were derived
from S. sparsipilum (Caromel et al.,
2005) and have major and minor
effects, respectively, on the cysts of
G. pallida. These loci have an
additive effect on sex ratio of G.
pallida and restrict the nematode
development at a juvenile stage by
forming necrotic lesions in infected
roots.
GpaXIltar
GpaXIltar was derived from S.
tarijense (Tan et al., 2009) and
confers resistance against G pallida
pathotype Pa3.
Gpa4_QTL
This locus confers resistance to G.
pallida Pa2/3 and was introgressed
from S. tuberosum ssp. tuberosum
(Bradshaw et al., 1998).
GpaIVsadg
confers
quantitative
GpaIVsadg
resistance to G. pallida pathotype
Pa2/3 and was derived from S.
216
Table 6. Important potato R genes conferring resistance against PCN (Globodera spp.) and linked DNA markers.
Chromosome
Gene/ QTL
Resistance
III
IV
Gro 1.4
Gpa4_QTL
GpaIVsadg
V
H1
GroV1
Ro1
Pa2,3
Linked
markers
Ssp8
Stm3016
Type of
marker
RFLP
SSR
Pa2,3
Stm3016
SSR
Pa2,3
C237
(119)
CP113
SNP
Ro1,4
Ro1
Primer Sequence (5’- 3’)
References
TCAGAACACCGAATGGAAAAC
GCTCCAACTTACTGGTCAAATCC
TCAGAACACCGAATGGAAAAC
GCTCCAACTTACTGGTCAAATCC
-
Kreike et al., 1996
Bradshaw et al., 1998
SCAR
GCGTTACAGTCGCCGTAT
GTTGAAGAAATATGGAATCAAA
CD78
CT51
RFLP
CAPS/Alu1
239E4left
CAPS/Alu1
TG689
SCAR
57R
SCAR
110L
SCAR
TG69
CAPS/
BsaHI
GCAGGATTCCATTTGCTTGC
GTTATTGTCTAACCACCTCGG
GGCCCCACAAACAAGAAAAC
AGGTACCTCCATCTCCATTTTGTAAG
TAAAACTCTTGGTTATAGCCTAT
CAATAGAATGTGTTGTTTCACCAA
TGCCTGCCTCTCCGATTTCT
GGTTCAGCAAAAGCAAGGACGTG
GGCCCTCCCCGATGATAATTAGTTTC
GGCTGTTATGGGTTATTTGGTGGGC
TGCCATAACCCAGTTGAACA
TTGGAGTATGATTCCTTCAATGAG
Gebhardt et al., 1993; Skupinova
et al., 2002, Niewöhner et al.,
1995
Pineda et al.,1993
Bakker et al., 2004
U14
SCAR
X02
SCAR
GpaVvrn_QTL
Pa2/3
HC
ASA
Gpa_QTL
Grp1_QTL
Pa2,3
Ro5,
Pa2,3
Ssp72
GP21
RFLP
CAPS/
DraI
GGGCTTGTATAAGACCTCCGAGAGG
CCCTTCCTTGGGTAGTTTGAGCG
CCACCAAACCCATAAAGCTGC
TGTGAATTGGTATGAATCTGCAACC
ACACCACCTGTTTGATAAAAAACT
GCCTTACTTCCCTGCTGAAG
CTTTCATGTCTATGAGGTAATGGC
GTGTTAAATTTCTTATTAGTCTTTTGTATTCA
Moloney et al., 2010
Moloney et al., 2010
Bakker et al., 2004
Schultz et al., 2012
Jacobs et al., 1996
Jacobs et al., 1996
Jacobs et al., 1996
Sattarzadeh et al., 2006
Kreike et al., 1994
Rouppe van der Voort et al.,
1998
217
Table 6. (cont’d)
Chromosome
Gene/ QTL
Gpa5_QTL
GpaVs
spl_QTL
Resistance
Pa2,3
Pa2,3
Linked
markers
GP179
Type of
marker
CAPS/RsaI
TG432
CAPS/Rsa
GP21
CAPS/DraI
GP179
CAPS/RsaI
GP 179
CAPS/ EcoRV
GP21
SCAR
TG432
CAPS/DraI
Primer Sequence (5’- 3’)
References
GGTTTTAGTGATTGTGCTGC
AATTTCAGACGAGTAGGCACT
GGACAGTCATCAGATTGTGG
GTACTCCTGCTTGAGCCATT
GGACAGTCATCAGATTGTGG
GTACTCCTGCTTGAGCCATT
GTGCGCACAGGGTAAAACC
ACCTTAGCGGATGAAAGCC
-
Rouppe van der
Voort et al., 1998
Finkers-Tomczak
et al., 2009
Rouppe van der
Voort et al., 2000
Rouppe van der
Voort et al., 2000
Caromel et al.,
2005
Caromel et al.,
2005
Caromel et al.,
2005
Bryan et al., 2002
Gpa5_QTL,
Gpa6_QTL
Gro1
Pa2,3
SPUD1636
PCR marker
Ro1-5
CP56,
St3.3.2
RFLP
Gro1-4
Ro1
Gro1-4
ASA
TCTTTGGAGATACTGATTCTCA
CGACCTAAAATGAAAAGCATCT
IX
Gpa6_QTL
Pa2,3
CT220
CAPS/ MseI
X
Gro1.2_QTL
Ro1
CP105
RFLP
XI
Gro1.3_QTL
Ro1
TG36
RFLP
Pa3
GP163
XII
GpaXIltar
_QTL
Gpa2
Pa2,3
GP34
CAPS (allele
specific)
CAPS/TaqI
AAGCGAATTATCTGTCAAC
GTTCCTGACCATTACAAAAGTAC
TGTAAAGACTACAAATGCAAACTCCC
GCCTCTGTCTTTGCTGAAAAGAACA
TGGTAGTTACATGAATGAAAGTCG
ATGGAGGGTGAAGGCAAC
CTGCAGTTTTGAAATTACCATCT
CTGCAGCCAACTGATAACTCTCA
CGTTGCTAGGTAAGCATGAAGAAG
GTTATCGTTGATTTCTCGTTCCG
77R
CAPS/HaeIII
VII
CTCGAGGGATTGAATCCAAATTAT
GGAAGCAGAATACTCCTGACTACT
Barone et al.,
1990, Leister et
al., 1996
Gebhardt et al.,
2006, Paal et al.,
2004
Rouppe van der
Voort et al., 2000
Kreike et al., 1993
Kreike et al., 1993
Tan et al., 2009
Rouppe van der
Voort et al., 1997,
1999
Bendahmane et al.,
1997
218
Grp1_QTL
Grp1 is a compound locus
originated from S. vernei (Rouppe
van der Voort et al., 1998)
providing broad-spectrum resistance
to both cyst nematode species. It
confers major resistance to G.
rostochiensis pathotype Ro5 and G.
pallida pathotype Pa2 and partial
resistance to G. pallida pathotype
Pa3.
Gro1
Gro1 is dominant gene originated
from S. spegazzinii (Barone et al.,
1990, Paal et al., 2004) and confers
resistance to all G. rostochiensis
pathotypes.
Gro1.2, 1.3 and 1.4_QTLs
Gro1.2, Gro1.3 and Gro1.4 are
minor QTLs derived from S.
spegazzinii and impart partial
resistance to G. rostochiensis
pathotype Ro1 (Kreike et al., 1993,
Kreike et al., 1996).
GroV1 originated from S. vernei and
confers resistance against G.
rostochiensis. This locus is closely
linked to the H1 gene (Jacobs et al.,
1996).
BREEDING FOR PCN
RESISTANCE IN INDIA
In India, the breeding for PCN
resistance uses mostly sources from
S. vernei. The first cyst nematode
resistant cultivar was released in
year 1985, Kufri Swarna (Khan et
al.,1985). Recently, another S.
vernei derived resistant hybrid
‘OS/93-D-204’ has been selected
and released as a variety ‘Kufri
Neelima’ for Nilgiri hills (Joseph et
al., 2012).
The potato germplasm in India
reported as resistant to PCN (Gaur
et al., 1984, Kumar et al., 2005,
Kumar et al.,2008) is listed in Table
7.
GroV1
Table 7. Potato germplasm resistant to PCN (Globodera spp.) in India
Accessions
Solanum
tuberosum
ssp.
tuberosum
Resistant*
G. pallida
G. rostochiensis
CP No’s: 2937 (Maria Huanca, Peru), CP No’s: 1598 (B4845-4, USA), 1599
3182 (Santa Ana, Peru), 3247 (CIP (B4846-14, USA), 1668 (Ulster Glade,
381396.16, Peru), 3303 (CIP 283233.11, UK), 1669 (Antinema, GFR), 1670
Peru), 3305 (CIP 284301.4, Peru), 3306 (Apis, GFR), 1671 (Cobra, GFR), 1720
(CIP 284306.1, Peru), 3307 (CIP (Aquila, USA), 1781 (Nodoza, NET),
285262.21, Peru), 3308 (CIP 285307.21, 1805 (Aquila, GFR), 1806 (Tunika,
Peru), 3310 (CIP 285392.21, Peru), 3311 GFR), 1809 (HYB. No.20012, GFR),
(CIP 285411.22, Peru), 3312 (CIP 1815 (B 5281-1, USA), 1816 (B 5459285424.11, Peru), 3313 (CIP 285435.23, 1, USA), 1840 (Peconic, USA), 1857
Peru), 3314 (CIP 285472.4, Peru), 3315 (Wauseon, USA), 1869 (No-Nova,
(CIP 285513.30, Peru), 3340 (84.149.7, GFR), 1961 (2182ef(7), UK), 1971
Peru), 3493 (P-55.7, Peru), 3652 (CIP (Saturna, NET), 1978 (Amalfy, NET),
284028.6, Peru).
1979 (Proton, NET), 1981 (Cordia,
GFR), 1984 (61.303/34, GFR), 2044
(Maris Piper, UK), 2045 (P55/77, UK),
2288 (Kufri Swarna, IND), 2331
(65.346.19, Peru), 2348 (Stina, SWE),
219
Solanum
tuberosum
ssp.
andigena
Solanum
tuberosum
ssp.
tuberosum
Solanum
tuberosum
ssp.
andigena
2358 (Islander, USA), 2359 (Yankee
Chipper, USA), 2384 (AGG-69-1,
Peru), 2418 (Chiquita, Peru), 3087
(Hertha, NET), 3088 (Obelix, NET),
3090 (Van Gogh, NET), 3165 (Tarpan,
POL), 3170 (Ibis, POL), 3643
(Atlantic, Canada).
JEX/A Nos: 1, 4, 27, 65, 71, 79, 80, JEX/A Nos: 97, 119, 132, 137, 139,
100, 113, 177, 260, 309, 319, 328, 406, 160, 184, 220, 262, 308, 354, 405,
506, 712, 719, 720, 727, 750, 877, 879, 412, 413, 417, 430, 467, 503, 556,
891.
590, 592, 603, 622, 628, 637, 639,
649, 658, 669, 691, 732, 733, 734,
736, 746, 749, 751, 757, 780, 796,
797, 809, 815, 828, 844, 859, 873,
889, 890, 895.
Highly resistant**
G. pallid
JEX/A Nos: 128, 147, 164, 183, 210,
JEX/A Nos: 113, 141 145, 148, 149, 230, 235, 250, 265, 276, 286, 315, 323,
169, 203, 225, 226, 228, 266, 277, 283, 376, 442, 446, 471, 500, 524, 554, 762,
327, 335, 339, 365, 397, 418, 476, 526, 813.
566
Resistant to both species
CP No’s: 1664 (Royal Kidney, UK),
1729 (Ia 1106-5, USA), 1843 (Pontiac,
USA), 1879 ((VTn)2 62-33-3, NET),
2059 (CIP379389, Peru), 2134 (PI1230502, Peru), 2290 (G-1, Peru), 2329
(KTT60.21.19, Peru), 2339 (Garana,
Peru), 2417 (MEX 750838, Peru), 3011
(Garhuash Huayr, Peru), 3091 (Nicola,
NET), 3128 (G-5, NET), 3181 (G-2,
Peru), 3206 (Kufri Neela, NET), 3209
(GLKS-58-1642.4, Peru), 3534 (Raja-7,
NET).
JEX/A Nos: 251, 318, 433, 588, 609,
613, 651, 681, 704, 835, 846, 886.
Highly resistant to both species
Solanum
JEX/A Nos: 125, 171, 185, 216, 217,
tuberosum
225, 240, 252, 267, 281, 300, 310, 322,
ssp.
350, 428, 708.
andigena
* Resistant (1 to 5 females / root ball), ** Highly resistant (0 females / root ball), Krishna Prasad
(2006).
CP No’s: Indigenous and exotic S. tuberosum ssp. tuberosum accessions maintained in Central
Potato Research Institute, Shimla (India) repository. Each accession is being represented by its
indigenous/exotic name and country of origin.
JEX/A No.’s: Indigenous S. tuberosum ssp. andigena accessions maintained in Central Potato
Research Institute, Shimla (India) repository.
NET: Netherlands
GDR: German Democratic Republic
USA: United States of America
GFR: German Federal Republic
SWE: Sweden
POL: Poland
IND: India
220
CONCLUSION
The present breeding strategies for
PCN resistance rely on phenotyping
approaches. However, with the
availability
of
DNA
based
molecular
markers
and
the
development of MAS assays, a more
directed approach can be made
towards complete PCN resistance by
combining
genes
with
complementary effects. Genetic
mapping of loci involved in
resistance to different pathotypes of
G. rostochiensis and G. pallida is
the basic step in developing a
marker-based screening technique
that could be applied in the absence
of the pathogen.
The conventional PCN
resistance breeding strategies have
always been based on the results of
phenotypic screening and hence
required years of testing. The only
successful eradication programme in
case of PCN has been reported from
Australia recently. However, this
also took decades of concerted
efforts to eradicate this pest of
economic importance. However, a
slight mistake in declaring any area
PCN free can result in a catastrophe
in the areas where the seed potatoes
are being exported. In this
background, if the selection is based
on molecular markers tightly linked
with the genes for PCN resistance,
the breeder can diagnose the
presence of the gene in the host
background. Several genes for
resistance not only to PCN but also
to other diseases like late blight can
be combined in a single genetic host
background through MAS.
This paper reports the PCN
resistance genes spread across the
wild
and
cultivated
potato
germplasm. This will provide a
guide to breeders around the world
for planning breeding strategies and
deploy PCN resistant genes in
different regions.
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228
RESEARCH ARTICLE
SABRAO Journal
44 (2) 229-239, 2012
DEVELOPMENT AND EVALUATION OF HALF SIB PROGENIES
FOR MORPHO-PHYSIOLOGICAL CHARACTERS IN INDIAN
MUSTARD (Brassica juncea L.) UNDER RAINFED CONDITIONS
V.V. SINGH1*, MAHARAJ SINGH1, J.S. CHAUHAN1, SUNIL
KUMAR1, M.L. MEENA1, B.K. SINGH1, KHAJAN SINGH2 and U.B.
SINGH2
1
Directorate of Rapeseed-Mustard Research, Sewar, Bharatpur (Raj)-321303
Agricultural Research Sub-Station (SKRAU,Bikaner), Kumher, Bharatpur
2
SUMMARY
A diallel crossing programme using promising donors for drought tolerance was
planned during 2006-2007. Resultant F1s were crossed in chain crossing fashion and
equal amount of seed from each cross was mixed and base population was prepared.
Half sib progenies (104) were developed from this base population and evaluated for
morpho-physiological characters under rainfed conditions along with base population
and check varieties during 2009-10. Analysis of variance revealed significant
differences among half sib progenies for plant height, primary branches per plant,
siliquae per plant, 1000-seed weight, seed yield per plant, protein content, SPAD at seed
filling stage and transpiration at flowering and seed filling stages. Seed yield per plant
showed positive and significant correlation with plant height (0.252*) and primary
branches per plant (0.218*). 1000-seed weight showed positive and significant
association with SPAD values at seed filling stage (0.218*). Study revealed that plant
height, primary branches, 1000-seed weight, SPAD values and transpiration at
flowering stage should be considered during selection procedure. On the basis of these
criteria, high yielding half sib progenies were selected and characterized for different
morphological and physiological characters. These progenies are being used for
initiating second cycle of half sib progeny selection. The results indicated that half sib
progeny selection method is quite effective in generating genetic variability for some
important traits in self pollinated crops like Brassica juncea.
Keywords; half sib, population improvement, genetic variability, correlation, SPAD
value, transpiration
Manuscript received: January 7, 2012; Decision on manuscript: May 31, 2012; Manuscript accepted
in revised form: June 12, 2012.
INTRODUCTION
Rapeseed mustard group of crops
are grown in diverse agro-climatic
conditions in India ranging from
north eastern/ north-western hills
to
down
south
under
irrigated/rainfed, timely/late sown,
saline soils and mixed cropping.
During 2009-2010, these group of
crops contributed 25.9% and
22.0% to the total oilseeds
production
and
acreage
(Anonymous,
2010).
Indian
mustard (Brassica juncea L.)
accounts for about 75-80% of the
5.53 million ha under these crops
in the country during 2009-2010.
About 25% area of the total
rapeseed mustard is rainfed
(Chauhan et al., 2011) and suffers
from moisture stress at one or
more
phonological
stages.
Improvement in this crop for
drought conditions was initiated
long back by following pedigree,
bulk and backcross methods.
However, information on genetic
improvement of Indian mustard
using population improvement
methods is scanty. Out crossing in
Indian mustard (Brassica juncea
L) has been reported to vary
between 10 to 15% (Banga, 2008)
indicating
certain
level
of
heterozygosity prevailing in the
natural
populations.
In
populations, where the variability
is limited, it can be generated by
half-sib matings and improvement
can be brought about by following
half sib family selection. With this
background,
a
population
improvement programme has been
initiated
at
Directorate
of
Rapeseed
Mustard
Research
(DRMR), Bharatpur (Rajasthan)
(situated at 77.30o E of longitude
and 27.15o N of longitude and is
about 178.37 meters above mean
sea level) during 2006-2007 to
improve the tolerance level of
Indian mustard to moisture stress
through
development
and
evaluation of half sib progenies
under rainfed conditions.
MATERIALS AND METHODS
Half Sib Progeny Development
A diallel crossing programme
involving BPR 141 (derived from
the cross RH 781/Pusa Bold), BPR
148 (derived from the cross RH
819/Varuna), BPR 150 (derived
from the cross RH 819 x Pusa
Bold), BPR 582-36 (derived from
the cross Choupka/PAB 9511),
BPR 585-37 (derived from the
cross Choupka/Krishna 2-1), BPR
581-40 (derived from the cross
Choupka/Sej 2) and RH-819
(derived from the cross Prakash/
Bulk pollen) as donors for drought
tolerance was planned for half sib
progenies development (Figure 1).
All donors were crossed in diallel
fashion during December - January
of 2006-2007. Resultant F1s were
planted during subsequent season
i.e. winter 2007-2008 and again
crossed in chain crossing fashion.
Equal amount of seeds from each
cross was mixed and a base
population was constituted.
230
Singh et al. (2012)
Promising donors for drought tolerance
(BPR-141, BPR-148, BPR-150, BPR-582-36, BPR-583-37, BPR-581-40, RH-819)
Crossed in diallel fashion
F1
Crossing in chain crossing fashion
(1x2, 2x3, 3x4, 4x5……………… so on)
Equal amount of seed from each cross mixed
Base population
Grown in isolation under target environment
Individual plant selection under moisture stress condition
Figure 1. Schematic presentation of half sib progeny development in Indian mustard
During winter 2008-2009, this
population was grown in isolation
on conserved moisture at ARSS,
Kumher (Bharatpur) under rainfed
conditions. Experimental plot was
kept un-irrigated throughout the
season except one pre sowing
irrigation. Total rainfall received
during cropping season was 5.4
mm. A large number of individual
plants on the basis of competitive
growth, stay green trait under
rainfed condition, plant height,
main shoot length and vigour were
tagged. Finally, 104 individual
plants were retained and used as
half sibs.
Half Sib Progeny Evaluation
The base population and 104 half
sibs developed from the population
were evaluated during winter
2009-10 in an augmented block
design. The material was divided
into 5 blocks and 4 rainfed check
varieties namely RH-819, Geeta,
RB-50 and PBR-97 were repeated
in each block. In each block,
progenies and check varieties were
sown in a plot size of 4.0 x 0.60 m2
231
accommodating two rows spaced
30 cm apart with plant to plant
distance of 10 cm maintained by
thinning at 15 days after sowing.
The experiment was sown on
conserved moisture received from
monsoon rains during September
and October months of the season
under rainfed conditions. It is
worthwhile to mention that
experimental location received
44.1 mm rainfall during crop
growth period. Observations were
recorded on 10 randomly selected
plants for plant height, primary
branches per plant, total siliquae
per plant, seeds per siliqua, seed
yield per plant, 1000 seed weight,
oil and protein content. Data on
days to flower and days to
maturity were recorded on whole
plot basis. Data on physiological
characters namely SPAD (Minolta
company defined SPAD as Soil
Plant Analysis Development)
reading at flowering stage, SPAD
reading at seed formation stage,
transpiration at flowering stage
and transpiration at seed filling
stage were also recorded to
quantify physiological efficiency
of these progenies. The unit less
measurement obtained from the
SPAD chlorophyll meter (SPAD502, Minolta Corp., Ramsey, NJ)
is based on the differences
between light attenuation at 430
nm (the peak wavelength for
chlorophyll a and b) and that at
750 nm (near-infrared) with no
transmittance. Thus, the SPAD
chlorophyll meter reading (SCMR)
represent
the
chlorophyll
concentration in the leaf. The
SCMRs were made on full
flowering and seed filling stage in
the field trials. The leaves were
sampled from the nodal position
three and four below the apex on
the main axis of three randomly
selected plants from each progeny/
check variety. The transpiration
was recorded with LICOR-LI1600
steady state porometer on 3rd and
4th fully expanded leaf from the
top of three randomly selected
plants. The mean data were
subjected to analysis of variance
(Federer, 1956; Sharma, 1998)
using SPAD (Abhishek et al.,
2004)
software.
Genetic
parameters and simple correlations
in all possible combinations were
calculated
as
per
standard
procedure (Burton, 1952; Johnson
et al., 1955).
RESULTS AND DISCUSSION
Analysis of variance revealed
significant differences among half
sib progenies for plant height,
primary branches per plant, total
siliquae per plant, 1000-seed weight,
seed yield per plant, protein content,
SPAD at seed filling stage and
transpiration at flowering and seed
filling stages (Table 1), suggesting
that half sib progenies have adequate
variability for these traits and
response to selection may be
expected in the further cycles of
selection. The block effects were
non-significant for most of the traits
except
protein
content
and
transpiration at seed filling stage.
Check (control) varieties differed
significantly for days to 50%
flowering, days to maturity, plant
height, siliquae per plant, seeds per
siliqua, 1000-seed weight, oil
content (%) and SPAD at seed
filling stage. In general, magnitude
of the phenotypic coefficient of
variation (PCV) was higher as
232
Singh et al. (2012)
compared to genotypic coefficient of
variation (GCV) for all the
characters indicating a positive
effect of environment on the
character expression. Transpiration
at seed filling stage followed by
seed yield per plant and transpiration
at flowering stage exhibited
comparatively higher estimates of
GCV as well as PCV (Table 2).
High GCV in transpiration among
mustard genotypes was reported
earlier also (Singh et al., 2008,
Singh et al., 2011). It indicated that
simple selection for transpiration at
flowering and seed filling stage
might be effective.
Heritability estimates in
present investigation were moderate
to high (>50%) for all the characters
studied. The genetic advance was
highest for transpiration at seed
filling
stage
followed
by
transpiration at flowering stage, total
siliquae per plant and seed yield per
plant. High genetic advance was
observed for all these traits and also
for protein, oil content and SPAD at
flowering stage among a set of full
sib progenies evaluated under
rainfed environments (Singh et al.,
2011). These findings indicated that
there is good scope for development
of half sib populations/gene pools
having more number of siliquae per
plant which would perform better in
moisture stress conditions.
Simple
correlation
coefficients were calculated for
yield and its component characters
(Table 3). Seed yield per plant
showed positive and significant
correlation with plant height
(0.252*) and primary branches
(0.218*). Positive and significant
relationship of seed yield with plant
height under rainfed conditions was
also reported by Kardam and Singh
(2005). None of the physiological
characters were positively and
significantly associated with seed
yield per plant. However, positive
and significant relationship of SPAD
values with seed yield per plant was
also reported (Fanaei et al., 2009) in
mustard. 1000-seed weight showed
positive and significant association
with SPAD values at seed filling
stage (0.218*). The results indicated
that chlorophyll content has positive
impact on seed size during seed
development
stage.
Therefore,
genotypes having high chlorophyll
content and bold seed size should be
selected for maximizing the
yield/unit area under drought
conditions.
Oil
content
displayed
significant positive correlation with
transpiration at seed filling (0.226*)
stage. Protein content showed
positive and significant association
with days to flowering (0.268*) and
days to maturity (0.269*). SPAD
values and transpiration rate were
negatively correlated with each
other. Contrary to this observation
earlier (Singh et al., 2011) reported
significant and positive association
of these characters in full-sib
progenies. This might be due to
breaking of undesirable linkages
through full sib progeny selection
and making them available for
selection. Hence, these contradictory
results depend upon differences in
genetic material used for study. The
physiological
parameters
has
significant role in the drought
tolerance
mechanism.
The
transpiration represent the loss of
water, is essential for efficient
utilization and saving of water thus
it play an important role in drought
tolerance.
233
Table 1. Mean sum of squares for different morpho-physiological characters in half sib progenies of Indian mustard.
Source
d.f.
Days to
flowering
Days to
Maturity
Plant
height
(cm)
Primary
branches
Siliquae
per
plant
Seeds
per
siliqua
1000seed
weight
(g)
Seed
yield per
plant (g)
Oil
content
(%)
Protein
content
(%)
SPAD
reading
at
(FS)
SPAD
reading
at
(SFS)
Transpiration
at
(FS)
Transpiration at
(SFS)
Block
4
15.80
15.80
11.26
0.097
8.62
0.47
0.16
3.90
0.28
0.40*
20.41
6.29
4.81*
0.94*
108
12.47
12.38
193.97
**
0.40**
25.32*
0.70
0.29*
9.48*
1.26
0.50**
22.95
24.27*
2.44*
0.50*
Control
3
59.65**
59.65**
512.25
**
0.05
94.95**
2.08**
0.66**
2.86
3.79**
0.17
16.22
46.30*
0.20
0.36
Progeny
104
8.38
139.46
**
0.38**
23.29*
0.62
0.28 *
9.75*
0.76
0.51**
8.59
23.49*
2.51*
0.51*
Treatment
8.46
Progeny Vs
control
1
288.01**
286.68**
4908.0
**
4.20**
27.58*
4.14**
0.01
1.76
45.33**
0.00**
1536.86**
39.20
2.04
0.01*
Error
12
6.56
6.56
37.01
0.05
8.28
0.31
0. 09
3.25
0.56
0.10
10.04
9.63
1.00
0.20
FS= Flowering stage; SFS=Seed filling stage
234
Singh et al. (2012)
Table 2. Over all mean value of half sib progenies, their range, genotypic and phenotypic coefficient of variation, heritability in broad sense and genetic
advance expressed as per cent of mean (Expectations of mean squares used were σ2e + σ2g for progeny variance and σ2e for error variance)
Character
Days to flowering
Mean
50.07
Range
40.05.-60.3
GCV
NS
PCV
NS
h2
NS
GA
NS
Days to maturity
129.08
119.05-139.3
NS
NS
NS
NS
Plant height (cm)
160.00
121.9.-188.97
6.32
7.38
73.46
1.34
Primary branches per plant
3.86
2.44-5.69
14.88
15.96
86.84
12.48
Siliquae per plant
41.76
27.39-56.39
9.27
11.55
64.44
30.38
Seeds per siliqua
14.47
12.24-16.60
NS
NS
NS
NS
1000 seed weight (g)
4.44
3.07-6.68
9.81
11.91
67.85
6.73
Seed yield per plant (g)
7.62
1.83-17.52
33.45
40.97
66.66
19.13
Oil content
42.47
39.27-44.51
NS
NS
NS
NS
20.26
18.01-21.62
3.16
3.52
80.39
1.46
SPAD (FS)
45.05
34.02-55.9
NS
NS
NS
NS
SPAD (SFS)
40.17
24.26-50.86
9.26
12.06
59.00
17.41
Transpiration (FS)
4.99
1.03-10.19
24.62
31.74
60.15
46.4
Transpiration (SFS)
1.58
0.09-3.95
35.23
45.19
60.78
66.68
Protein content
(%)
(%)
NS - Mean sum of squares were non–significant
235
Table 3. Correlation coefficients between different characters in the half sib progenies of Indian mustard.
Characters
Day to
flowering
Plant
height
(cm)
Days to
maturity
Primary
branches
per plant
Total
siliquaep
er plant
Seeds per
Siliqua
1000 Seed
weight (g)
Seed yield
per plant(g)
Protein
Content
(%)
Oil Content
(%)
SPAD
Reading at
FS
SPAD
Reading at
SFS
Transpiration at
FS
Days to flowering
1
Day to maturity
0.999**
1
Plant height (cm)
0.244*
0.238*
1
Primary branches per plant
0.041
0.040
0.139
1
Siliquae per plant
0.162
0.163
0.255**
0.136
1
Seeds per siliqua
-0.098
-0.096
0.205*
0.133
0.166
1
1000 seed weight (g)
0.147
0.148
0.123
0.050
0.089
-0.083
1
Seed yield per plant (cm)
-0.055
-0.054
0.252*
0.218*
-0.014
0.102
0.024
1
Oil Content (%)
-0.195*
-0.196*
0.153
0.056
0.132
0.081
-0.076
0.021
1
Protein Content (%)
0.268**
0.269**
-0.070
-0.054
0.066
-0.295**
-0.110
-0.090
-0.214*
1
SPAD Reading at FS
-0.162
-0.163
-0.32**
-0.019
-0.112
-0.083
0.088
0.029
0.135
0.068
1
SPAD Reading at SFS
0.012
0.009
0.029
0.099
-0.112
0.022
0.218*
-0.130
-0.064
-0.059
0.482**
1
Transpiration at FS
-0.017
-0.020
0.182
0.036
0.040
-0.177
-0.035
0.028
-0.198*
0.049
-0.102
-0.077
1
Transpiration at SFS
0.063
0.066
0.025
-0.28**
-0.012
-0.051
-0.191
0.050
0.226*
0.0129
-0.065
-0.374**
0.042
Transpiration at
SFS
1
236
Singh et al. (2012)
Table 4. Characterization of selected half sib families for morph-physiological characters in Indian mustard.
Progeny
DHS-14
DHS-54
DHS-51
DHS-48
DHS-11
DHS-67
DHS-25
DHS-33
RB-50
(Best Check)
Base
population
Seed
yield
per
plant
(g)
17.52
16.72
16.55
15.44
15.37
15.16
14.39
13.87
50.55
50.05
49.05
45.05
51.55
54.30
46.05
45.05
129.55
129.05
128.05
124.05
130.55
133.30
125.05
124.05
167.50
172.27
182.67
159.47
150.90
170.37
163.47
142.87
5.64
5.69
3.69
4.69
3.24
4.79
3.24
4.24
42.49
46.86
38.86
43.66
40.09
40.19
35.64
39.84
15.32
15.41
13.69
13.77
14.48
14.73
13.84
13.88
1000seed
weight
(g)
4.00
3.98
5.31
4.26
4.12
4.68
3.93
3.80
8.69
54.00
133.00
171.88
3.40
36.40
14.686
4.88
40.644
20.274
38.08
43.04
5.00
1.38
4.45
49.05
128.05
174.20
3.44
37.42
14.448
4.21
42.02
20.351
45.6
42.29
3.238
0.85
Days to
flowering
Days to
maturity
Plant
height
(cm)
Primary
branches
per plant
Siliqua
per
plant
Seeds
per
siliqua
Oil
content
(%)
42.99
42.39
42.13
42.54
42.93
41.58
43.66
43.13
Protein
content
(%)
20.09
20.41
20.41
20.34
20.17
20.39
19.79
21.01
SPAD
reading
at
FS
45.80
45.62
46.42
44.92
50.20
47.97
46.70
51.80
SPAD
reading
at
SFS
30.37
35.96
36.56
38.16
41.37
48.21
27.66
42.06
Transpiration
at
FS
3.40
5.20
5.55
5.24
3.68
9.13
6.44
6.52
Transpi-ration
at
SFS
1.97
0.82
1.16
0.45
2.86
0.69
2.31
1.66
CD (5%) = 4.64 for seed yield/plant
237
In the similar way the SPAD,
relative chlorophyll content has
important role in the assimilation,
the higher assimilation rate under
drought helps the plants to survive
better. Days to flowering showed
highly significant and positive
association with plant height
(0.244*).
Plant height had
significant and positive association
with siliquae per plant (0.255*) and
seeds
per
siliqua
(0.205*).
Tolerance to drought of seed yield
was associated with plant height,
primary
branches,
1000-seed
weight,
SPAD
values
and
indicating consideration of these
traits
in
drought
breeding
programme.
The present study revealed
that plant height, primary branches,
1000-seed weight, SPAD values and
should be considered in selection
procedure as these traits have
significantly positive association
with seed yield per plant coupled
with high heritability. High yielding
half sib progenies were selected and
characterized for morphological and
physiological characters (Table 4).
Out of 104 half sib progenies
evaluated, 08 progenies significantly
out yielded best check and base
population in terms of seed yield per
plant. Most of the selected progenies
have early maturity, shorter plant
height, more primary branches,
more siliquae per plant and seeds
per siliqua. Selected progenies also
recorded higher oil content in
comparison to check varieties.
These progenies are being used for
initiating second cycle of half sib
progeny selection. The results
indicated that half sib progeny
development proved quite effective
in generating genetic variability for
some important traits in self
pollinated crops like Brassica
juncea.
ACKNOWLEDGEMENTS
We express our sincere thanks to
Director, DRMR, Bharatpur for
providing the necessary facilities.
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Singh M, Chauhan JS, Meena ML
(2008). Genotypic variation for
water use efficiency, gas
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239
RESEARCH ARTICLE
SABRAO Journal
44 (2) 240-262, 2012
INHERITANCE OF THE PHYSIOLOGICAL TRAITS FOR
DROUGHT RESISTANCE UNDER TERMINAL DROUGHT
CONDITIONS AND GENOTYPIC CORRELATIONS WITH
AGRONOMIC TRAITS IN PEANUT
T. GIRDTHAI1, S. JOGLOY1*, N. VORASOOT1, C. AKKASAENG1,
S. WONGKAEW2, A. PATANOTHAI1 and C.C. HOLBROOK3
1
Department of Plant Science and Agricultural Resources, Faculty of Agriculture, Khon Kaen
University, Khon Kaen 40002, Thailand
2
School of Crop Production Technology, Institute of Agricultural Technology, Suranaree
University of Technology, Nakhon Ratchasima 30000, Thailand
3
USDA-ARS, Crop Genetics and Breeding Research Unit, Coastal Plain Experimental Station,
Tifton, GA 31793, USA
*Corresponding author: [email protected]
SUMMARY
Breeding for terminal drought resistance in peanut can increase their productivity in
drought-prone environments and reduce aflatoxin contamination. To improve selection
efficiency for superior drought-resistant genotypes, a study of inheritance of traits is
worthy, and provides useful information for planning suitable breeding approaches. The
objectives of this study were to estimate the heritability of terminal drought resistance
traits, the genotypic and phenotypic correlations between drought resistance traits and
agronomic traits, and among physiological traits in peanut. The 140 peanut lines in the
F4:6 and F4:7 generations were generated from four crosses (ICGV 98348 × Tainan 9,
ICGV 98348 × KK60-3, ICGV 98353 × Tainan 9, and ICGV 98353 × KK60-3), and
tested under well-watered and terminal drought conditions. Field experiments were
conducted under the dry seasons 2006/2007 and 2007/2008 in the Northeast of Thailand.
Data were recorded for agronomic traits [biomass, pod yield, number of mature pods per
plant, seeds per pod, and seed size] and physiological traits [harvest index (HI), SPAD
chlorophyll meter reading (SCMR), and specific leaf area (SLA)]. The heritability
estimates for physiological traits were higher than for agronomic traits, and varied among
crosses. The heritability for HI, SCMR, and SLA ranged from 0.55 to 0.85, 0.72 to 0.91,
and 0.61 to 0.90, respectively. Positive correlation between HI and SCMR were
significant, and SLA was also found to be inversely associated with SCMR and HI.
Significant and positive correlations between HI and SCMR with most of agronomic
traits were found. SLA was also negatively correlated with agronomic traits. These results
indicated that HI, SLA, and SCMR are potentially useful as indirect selection traits for
terminal drought resistance because of high heritability and good correlation with pod
yield. Plant breeding approaches using these traits might be effective and valuable for
improving terminal drought tolerance in peanut.
Girdthai et al. (2012)
Keywords: Arachis hypogaea L., heritability, drought resistance, indirect
selection, relationship, end-of-season drought.
Manuscript received: January 8, 2012; Decision on manuscript: April 18, 2012; Manuscript
accepted in revised form: September 19, 2012.
INTRODUCTION
Improvement of drought resistance
in peanut (Arachis hypogaea L.), an
important oil and cash crop, would
be beneficial in rainfed regions
where drought is a major constraint
limiting productivity and quality.
Terminal drought occurring during
the seed filling phase of peanut
(Boote, 1982) has been observed to
decrease pod yield and increase
preharvest aflatoxin contamination
(Dorner et al., 1989; Nageswara Rao
et al., 1985; Ndunguru et al., 1995;
Ravindra et al., 1990; Wright et al.,
1991). Breeding peanut varieties
with drought resistance is seen as
providing
an
important
and
sustainable part of the solution. In
addition,
preharvest
aflatoxin
contamination induced by terminal
drought in peanut may be reduced
with improved resistance to drought
(Cole et al., 1993; Girdthai et al.,
2010; Holbrook et al., 2000; 2008;
2009). However, breeding progress
for drought resistance in peanut on
basis of using yield as a selection
criterion only has been slow due to
large and uncontrollable genotype ×
environment (G × E) interactions.
Breeding
approaches
using
physiological traits having high
heritability and low G × E
interactions can improve selection
efficiency for superior drought
resistant genotypes, and supplement
the selection on basis of yield
(Blum, 1988; Falconer and Mackay,
1996).
Putative selection criteria
that could be used as indirect
selection to increase drought
resistance in peanut have long been
identified (Craufurd et al., 1999;
Hubick et al., 1986; Nigam et al.,
2005; Wright et al., 1988, 1994;
Wright and Nageswara Rao, 1994).
Drought resistance might be
enhanced by improvement of soil
water extraction capability or
improvements
in
water
use
efficiency (WUE), or integration of
both (Wright and Nageswara Rao,
1994; Hebbar et al., 1994).
Improvement of WUE could
potentially lead to increased yield
under limited moisture availability
(Passioura, 1986). However, WUE
is not easy to measure and may not
be a feasible selection criterion in
large
segregating
breeding
populations. Wright et al. (1988,
1994) have found WUE to be
negatively correlated with carbon
isotope discrimination ∆)( and
specific leaf area (SLA) over wide
ranges
of
varieties
and
environments, but analysis of∆ is
expensive and not feasible without
well-equipped laboratory. SLA
which is negatively related to leaf
thickness
and
photosynthetic
capacity can be measured easily and
inexpensively. Although SLA is
affected by environment, the
relationship between SLA and∆ is
241
apparently
stable
across
environments in peanut (Nageswara
Rao and Wright, 1994). Nageswara
Rao et al. (2001) and Upadyaya
(2005) found a significant negative
correlation between SLA and the
SPAD chlorophyll meter reading
(SCMR), and suggested that this
chlorophyll meter could be used as a
rapid and reliable measure to
identify genotypes with low SLA
and hence high water use efficiency
in peanut. Harvest index (HI), an
important trait that provides a
measure of total biomass actually
partitioned into pod yield was also
found to be correlated with SLA and
SCMR under both well-watered and
long-term
drought
conditions
(Songsri et al., 2008).
Efficient utilization of the
physiological traits for improving
drought resistance in a breeding
program requires an understanding
of the inheritance and genetic
relationships of the trait that is
available for selection. Series of
experiment were conducted to
estimate
the
heritability
of
physiological traits for drought
resistance under different drought
conditions (Cruickshank et al.,
2004; Hubick et al., 1988; Ntare and
Williams, 1998a, 1998b; Nigam et
al., 2005; Puangbut et al., 2011;
Songsri et al., 2008). Hubick et al.
(1988) reported that heritability
estimates were high for transpiration
efficiency (TE) and especially for ∆,
and there was no significant G × E
interaction for ∆. Songsri et al.
(2008) found that heritability
estimates of physiological traits for
drought resistance in peanut were
high (h2 > 0.50) under drought and
well-watered
conditions,
and
physiological traits like SLA,
SCMR, HI, and drought resistance
index of pod yield and biomass were
associated well with agronomic
traits under long periods of drought.
Cruickshank et al. (2004) also found
that
broad-sense
heritability
estimates for HI were high under
rainfed conditions. Since the
mechanisms of drought resistance
are diverse under different timing
and periods of drought conditions
(Clavel et al., 2004; Subbarao et al.,
1995). Therefore, the inheritance of
terminal-drought resistance traits
might be different. Relatively few
studies to date have investigated the
heritability
and
genotypic
correlations of physiological traits
for drought resistance in peanut
especially on SLA and SCMR, and
none have been done under terminal
drought conditions.
The objectives of this study
were to estimate the heritability of
terminal-drought resistance traits,
genotypic
and
phenotypic
correlations
between
drought
resistance traits and agronomic
traits, and among physiological traits
in peanut under terminal drought
conditions in order to predict
indirect responses to selection for
drought resistance.
Plant material and experimental
design
Four peanut F1 hybrids (ICGV
98348 × Tainan 9, ICGV 98348 ×
KK60-3, ICGV 98353 × Tainan 9,
and ICGV 98353 × KK60-3) were
generated from the hybridization of
2 drought resistant lines [ICGV
98348 and ICGV 98353; medium
maturing (110 days to maturity) and
medium seeded type] selected for
242
low yield reduction and high pod
yield under well-watered and
terminal drought conditions with
KK60-3 [late maturing (120 days to
maturity) and large seeded type]
selected for high biomass and
Tainan 9 [early maturing (100 days
to maturity) and medium seeded
type] having low seed yield and
biomass under terminal drought
(Girdthai et al., 2010a). Two peanut
genotypes (ICGV 98348 and ICGV
98353) were obtained from the
International
Crops
Research
Institute for the Semi-Arid Tropics
(ICRISAT) having high SCMR and
low SLA under terminal drought
stress (Girdthai et al., 2010b). KK
60-3 having high SCMR and low
SLA, and Tainan 9 having low
SCMR with high SLA are released
cultivars and widely grown in
Thailand (Girdthai et al., 2010b;
Songsri et al., 2009). The F1 seeds
were planted and their seeds
harvested in bulk for each cross. In
F2 and F3 generations, one pod was
kept from each plant and bulked for
each cross. Line separation was
carried out in the F4 generation. A
total of 140 lines (35 lines for each
cross) were randomly selected and
multiplied in the F5 generation.
Parental lines and the 140
families from 4 crosses were
evaluated in the F4:6 and F4:7
generations (F4-derived lines in the
F6 and F7 generations, respectively)
under two soil moisture levels (field
capacity (FC) and 1/3 available soil
water (1/3 AW) at 80 days after
planting (DAP) to final harvest) for
two years in dry season 2006/2007
and repeated in dry season
2007/2008. A split plot design with
four replications was used for both
years at the Field Crops Research
Station, Faculty of Agriculture,
Khon Kaen University located in
Khon Kaen Province, Thailand
(latitude 16° 28´ N, longitude 102°
48´ E, 200 m above sea level). Soil
type is Yasothon Series (loamy
sand, Ocix Paleustults) with the soil
moisture of 10.2% at FC and
permanent wilting point of 3.1%.
Two soil moisture levels, FC
(10.2%) and 1/3 AW (5.5%) in 0-60
cm depth were assigned as main
plots, and peanut lines were laid out
in subplots. Each entry was planted
in five row plots with 3 m length.
Spacing was 40 cm between rows
and 20 cm between plants within the
row.
Crop management
Soil was prepared by ploughing the
field three times. Lime at the rate of
625 kg ha-1 was applied at first
ploughing. Nitrogen fertilizer as
urea at the rate of 31.1 kg N ha-1,
phosphorus fertilizer as triple
superphosphate at the rate of 24.7 kg
P ha-1 and potassium fertilizer as
potassium chloride at the rate of
31.1 kg K ha-1 were incorporated
into the soil by broadcasting during
soil preparation prior to planting.
Seeds were treated with captan
[3a,4,7,7a-tetrahydro-2[(trichloromethyl)thio]-1H
isoindole- 1,3(2H)-dione] at the rate
of 5 g kg-1 seeds before planting, and
seeds of the large seeded genotypes
were treated with ethrel [2chloroethylphospphonic acid] 48%
at the rate of 2 ml L-1 water to break
dormancy. The seeds were over
planted and later the seedlings were
thinned to obtain one plant per hill at
14 DAP. Weeds were controlled by
the application of alachlor [2-cholro2’, 6’-diethyl-N-(methoxymethyl)
acetanilide 48% (w v-1), emulsifiable
243
concentrate] at the rate of 3 L ha-1 at
planting and hand weeding during
the remainder of the season.
Gypsum (CaSO4) at the rate of 312
kg ha-1 was applied at 47 DAP.
Carbofuran
[2,3-dihydro-2,2dimethylbenzofuran-7ylmethylcarbamate 3% granular]
was applied at the pod setting stage.
Pests and diseases were controlled
by
weekly
applications
of
carbosulfan
[2-3-dihydro-2,
2dimethylbenzofuran-7-yl
(dibutylaminothio) methylcarbamate
20% (w v-1), water soluble
concentrate] at the rate of 2.5 L ha-1,
methomyl
[S-methylN[(methylcarbamoyl)oxy]
thioacetimidate
40%
soluble
powder] at the rate of 1.0 kg ha-1 and
carboxin [5, 6-dihydro- 2-methyl-1,
4-oxathine-3 carboxanilide 75%
wettable powder] at the rate of 1.68
kg ha-1.
Water management
A subsurface drip irrigation system
(Super typhoon®; Netafim Irrigation
Equipment & Drip Systems, Tel
Aviv, Israel) with a distance of 20
cm between emitters was installed
with a spacing of 40 cm between
drip lines at 10 cm below the soil
surface, to minimize water surface
evaporation,
midway
between
peanut rows to supply water to the
crop. Drip lines were fitted with a
pressure valve and a water meter at
the main pipe of each replication to
ensure a uniform supply of the
required amounts of water. Soil
water level was maintained at FC at
0-60 cm depth. This soil depth
should reasonably cover the
majority of the rooting zone. In
stress treatments, water was
withheld at 60 DAP for 20 days
according to 20 years historical pan
evaporation data to allow soil
moisture to gradually decline until
reaching the predetermined levels of
1/3 AW at 80 DAP, and then the soil
moistures were held fairly constant
until harvest. Irrigation was applied
regularly to prevent soil moisture
from increasing or decreasing by
more than 1% in each plot. In
maintaining the specified soil
moisture levels, water was added
every 2 or 3 days to the respective
plots by subsurface drip irrigation
based on crop water requirement and
surface evaporation, which were
calculated following the methods
described by Doorenbos and Pruitt
(1992) and Singh and Russell
(1981), respectively.
Data collection
Weather parameters
Relative humidity, pan evaporation,
rainfall, maximum and minimum air
temperature, and solar radiation
during two cropping seasons were
recorded daily from sowing until
final harvest by a meteorological
station located 600 m away from the
experimental field. 40 mm of the
total amount of rainfall was recorded
during 80-100 DAP in 2006/2007,
and 22.7 mm was recorded during
this period in 2007/2008 (Figure 1).
Air temperature, relative humidity
and evaporation in 2006/2007 were
higher than in the 2007/2008,
especially during the water stress
period. During stress period (80
DAP to final harvest), mean
evaporation was 6.0 and 5.0 mm in
2006/2007
and
2007/2008,
respectively. The minimum and
maximum air temperature ranged
from 11.8 to 38.5 oC in 2006/2007
and 14.5 to 35.2 oC in 2007/2008,
244
being lower during 80-110 DAP in
2007/2008.
Relative
humidity
ranged from 54 to 93% in 2006/07
and from 57 to 92% in 2007/2008.
The seasonal mean solar radiation
was 0.13 and 0.11 Cal cm-2 in
2006/2007
and
2007/2008,
respectively.
Soil moisture status
Soil moisture in each main plot was
monitored using the gravimetric
method before planting, at planting,
and three times after planting (60
DAP, 80 DAP, and at final
harvesting) at the depth of 0-5, 2530, and 55-60 cm. Readings were
taken at two positions in each main
plot. The measurement before
planting was used for calculating the
correct amount of water to be
applied for the crop. Soil moisture
volume fraction was also monitored
at 10 day intervals from planting to
final harvest using a neutron
moisture meter (Type I.H. II SER,
no.
N0152,
Ambe
Didcot
Instruments Co. Ltd., Abingdon,
UK). Five aluminium access tubes
were installed in each main plot.
Readings were taken in access tubes
from the depth of 30-90 cm at 30 cm
intervals.
SPAD chlorophyll meter reading
and specific leaf area
Data were recorded for SCMR and
SLA at 80, 90, and 100 DAP when
crop exposed to terminal drought
stress following by Girdthai et al.
(2010b). Five plants were randomly
selected in each plot to record
SCMR and SLA following the
procedure described by Nageswara
Rao et al. (2001). The second fully
expanded leaves were detached from
the chosen plants at 9-10 a.m. and
brought to the laboratory in zipped
polythene bags for recording
observations. SCMR was recorded
using a Minolta SPAD-502 meter
(Minolta
SPAD-meter,
Tokyo,
Japan) on the four leaflets from each
leaf. An average SCMR for each
plot was derived from 20 single
observations (four leaflets × 5 plants
plot-1). In recording the SCMR, care
was taken to ensure that the SPAD
meter sensor fully covered the leaf
lamina and that interference from
veins and midribs was avoided.
After recording SCMR, the
leaf area of all five sampled plants
was measured with a leaf area meter
(LI 3100C Area Meter, LI-COR
Inc., USA) after which the leaves
were dried in an oven at 80 oC for at
least 48 hours to determine leaf dry
weight. Immediately after drying,
the leaves were weighed and the
SLA was derived as leaf area per
unit leaf dry weight (cm2 g-1).
Agronomic traits
For each plot excluding border
plants, three rows with 2.6 m in
length (3.12 m2) were harvested at
maturity (R8) (Boote, 1982), and
their pods and roots were removed
before taking fresh shoot weight in
the field. Five plants were randomly
selected for measuring shoot fresh
weight and then oven dried at 80 °C
for at least 48 hours and dry weight
was measured. Shoot dry matter was
then calculated and used in
determining shoot dry weight for a
plot. Pod yields were weighed after
air drying to approximately 7-8%
moisture content. The number of
mature pods per plant (mature pods
were separated from immature pods,
which were identified by dark
internal pericarp color), number of
seeds per pod, and 100 seed weight
were also recorded at final harvest.
245
HI was computed by the following
formula:
HI = pod weight/total biomass
(1)
Statistical analysis
Analysis of variance was performed
for each trait in each year following
a split plot design (Gomez and
Gomez, 1984). Because water
regime × genotype interaction was
significant, each water regime was
analyzed separately according to a
randomized complete block design
(RCBD). Homogeneity of variance
was tested for all characters and
combined analysis of variance of
two years data was performed where
appropriate. Calculation procedures
were conducted using Statistix 8
(Analytical Software, Tallahassee,
FL, USA).
Estimates of broad-sense
heritability for the four crosses were
calculated by partitioning variance
components of family mean squares
to pooled environmental variance
(δ2E) and genotypic variance (δ2G),
and then broad-sense heritability
estimates (h2b) were calculated as
follows (Holland et al., 2003):
h2b = δ2G/δ2P
(2)
δ2P = δ2G + δ2GE/e + δ2E/re
(3)
where, h2b = broad-sense heritability,
δ2G = genotypic variation, δ2P =
phenotypic variation, r = number of
replications, and e = number of
environments.
The standard error (SE) of
heritability (Singh et al., 1993) for
each trait was calculated to give a
measure of the precision of the
estimate.
As the evaluation of
heritability was conducted in late
generations (F6 and F7) of
segregating materials when most
genes were nearly fixed in
individual genotypes, it would be
expected that additive genetic
variances for the traits under study
were fixed through generation
advance (Holland, 2001).
Phenotypic and genotypic
correlations
between
drought
resistance traits and agronomic
traits, and correlations among
physiological traits were calculated
following the methods of Falconer
and
Mackay
(1996),
more
descriptive information could also
be seen in Songsri et al. (2008).
Simple correlations were used to
determine the relationships between
biomass, pod yield, and drought
resistance traits under well-watered
and terminal drought conditions to
understand
whether
the
performances of peanut genotypes
were
consistent
across
environments.
Soil moisture data
Soil moisture data between different
water treatments were different in
both years. Soil moisture measured
by Neutron probe agreed well with
those measured by Gravimetric
method. Average soil moisture
under the drought conditions at 80
DAP (5.7% in both years) were less
than the non-stressed treatment
(11.5% in 2006/2007 and 10.2% in
2007/2008, respectively) (Figure 2).
Under drought treatment, means of
soil moisture during the growing
seasons were 8.48 and 8.1% in
246
2006/2007
and
2007/2008,
respectively. Soil moisture under
drought
conditions
slightly
decreased from 60 to 80 DAP. After
80 DAP, the soil moisture content of
both treatments was fairly constant
until harvest. These results indicated
that the degrees of terminal drought
were reasonably controlled at the
predetermined levels. Soil moisture
under the stressed treatment during
the end of the season (80-120 DAP)
were 5.7 to 6.9% and 5.7 to 5.2% in
2006/2007
and
2007/2008,
respectively. The increase in soil
moisture
under
the
stressed
treatment at 120 DAP in 2006/2007
was due to the presence of rainfall at
115 and 116 DAP.
Combine analysis
Combine analysis of variance
showed large and significant
differences
between
all
140
genotypes for all traits (P
≤ 0.01)
(Table 1 and 2). This reveals that the
tested progenies displayed high
variation. Hence, heritability of the
traits can be estimated in these
populations. Significant difference
in year for HI, SCMR, and SLA at
80, 90, and 100 DAP were also
found (P ≤ 0.05 to P ≤ 0.01) (Table
1 and 2), but were not found for pod
yield and biomass (Table 1).
Differences
among
interaction
effects of year × genotypes (Y × G)
for pod yield, biomass, HI under
stressed and non-stressed conditions,
and SLA under non-stressed
conditions at 100 DAP were also
significant (P ≤ 0.05 to P ≤ 0.01).
The Y × G interaction effect was not
significant for SCMR and SLA
under both water regimes, excepting
for SLA at 100 DAP under nonstressed conditions. The significant
G × E interaction indicates that
relative
performance
across
environments is inconsistent among
genotypes. For traits to be useful in
breeding programs, they must be
consistent from year to year. In this
study, SCMR, and SLA showed a
high degree of consistency in
comparison to yield and biomass
and thus it is appropriate to use them
for screening peanut with terminal
drought resistance.
Genetic variability of yield,
biomass, and physiological traits
Genetic variability of traits plays an
important role on plant survival,
adaptability, and can also be used to
predict the genetic gain form
selection in breeding programmes.
Wide ranges of yield, biomass, and
physiological traits of 4 peanut
crosses under different water
regimes were observed and reported
herein (Table 3). Pod yield, biomass,
and SLA decreased as affected by
terminal drought stress, whereas
SCMR increased as same as
reported in previous studies under
various environments (Craufurd et
al., 1999; Girdthai et al., 2010a;
Nigam et al., 2008; Nageswara Rao
and Wright 1994; Songsri et al.,
2009). Under both non-stressed and
stressed conditions, pod yield and
biomass of the crosses ICGV 98348
× KK60-3 and ICGV 98353 ×
KK60-3 were higher than ICGV
98348 × Tainan 9 and ICGV 98353
× Tainan 9 according to the different
between those traits of each parental
line selected form the experiment of
Girdthai et al. (2010a, 2010b) and
Songsri et al. (2009). Differences
among genotypes for pod yield and
total biomass were greater under
terminal drought than under well-
247
watered conditions (as indicated by
the wider ranges of means),
indicating that genetic variability of
those traits will larger under stress
conditions. Average pod yields
under
well-watered
conditions
ranged from 2,220 to 3,211 kg ha-1,
and higher than under terminal
drought conditions (1,733 to 2,649
kg ha-1). Under well-watered
conditions, means of total biomass
were from 7,420 to 10,367 kg ha-1,
and also higher than under terminal
drought conditions (6,079 to 8,549
kg ha-1). Means and ranges of
SCMR and SLA were also different
between crosses. SCMR at 80, 90,
and 100 DAP under well-watered
conditions ranged from 29 to 58, and
under terminal drought conditions
ranged from 32 to 60. SLA at 80, 90,
and 100 DAP under well-watered
conditions ranged from 94 to 245
cm2 g-1, and under terminal drought
conditions ranged from 99 to 216
cm2 g-1.
Heritability of traits
Heritability is a function of a
breeding population and the
conditions under which a study is
conducted (Falconer and Mackay,
1996). It provides an indication of
the expected response to selection in
a segregating population, and is
useful in designing an effective
breeding strategy. In this study,
heritability
estimates
for
physiological traits were higher than
for agronomic traits, and varied
among crosses under both wellwatered and terminal drought
conditions (Table 4). The heritability
estimates for pod yield (ranged from
0.25 to 0.79) and biomass (ranged
from 0.17 to 0.66) were moderate,
but high for HI (ranged from 0.58 to
0.85), SCMR at 80, 90, and 100
DAP (ranged from 0.72 to 0.91), and
SLA at 80, 90, and 100 DAP
(ranged from 0.61 to 0.90). The
estimates
of
heritability
for
physiological traits in the present
study were slightly lower than those
previously reported under long-term
drought conditions by Songsri et al.
(2008). This may be due to the
differences in genetic populations,
plant generations, planting seasons,
and drought stress periods and
durations. Moreover, ages of peanut
when physiological traits were
measured in this study were more
mature than that of Songsri et al.
(2008). Ntare and Williams (1998a)
also reported that heritability of pod
yield was lower than partitioning
coefficient but higher than other
physiological components (crop
growth rate and duration of
reproduction growth) from their
yield model. Cruickshank et al.
(2004) also found that heritability
estimates for HI were high (varied
from 0.58-0.85) and varied among
crosses depending on levels of
genetic variation in parents. In the
present study, the heritability
estimates for all three physiological
traits ranged from 0.61 to 0.91, and
the heritability estimates for pod
yield and biomass ranged from 0.17
to 0.79. Standard errors for
physiological traits were also lower
than those for pod yield and
biomass, especially under nonstressed conditions. Thus, the
expected genetic gain per cycle of
selection under terminal drought
conditions will be less for pod yield
and biomass compared with HI,
SCMR, and SLA.
248
Table 1. Mean squares from the combined analysis of variance for pod yield, biomass,
and harvest index (HI) at final harvest under field capacity (FC) and 1/3 available
water (AW) of 140 progenies.
Pod yield
SOV
Biomass
HI
Year (Y)
1
1293156
8936073
15150000
5852251
0.131*
1/3
AW
0.301*
Rep. within Y
6
5027895
9553543
46850000
31130000
0.020
0.050
Genotypes (G)
139
2981814**
2407581**
21990000**
16050000**
0.025**
0.022**
YxG
139
931405**
555736**
6699913**
4920515**
0.007**
0.005**
Pooled error
834
386108
313656
3166971
2143356
0.003
0.003
df
FC
1/3 AW
FC
1/3 AW
FC
* and ** significant at P ≤ 0.05 and significant at P ≤ 0.01, respectively.
Table 2. Mean squares from the combined analysis of variance for the physiological
traits [SPAD chlorophyll meter reading (SCMR) and specific leaf area (SLA)] at 80,
90, and 100 days after planting (DAP) under field capacity (FC)and 1/3 available
water (AW) of 140 progenies.
SCMR
SOV
df
80 DAP
90 DAP
FC
1/3 AW
1,112.6**
274.3
Rep. within Y 6
49.7
Genotypes (G) 139
Year (Y)
1
FC
100 DAP
1/3 AW
FC
1/3 AW
1.7
1,266.3**
14.2
60.0
67.9
56.2
60.9
193.4
123.1
47.6**
48.3**
38.7**
36.4**
44.4**
52.8**
Y×G
139
6.2
9.6
8.9
9.8
11.1
12.5
Pooled error
834
7.5
9.4
7.2
SLA
8.7
10.3
10.2
SOV
80 DAP
df
Year (Y)
FC
1
20,277
Rep. within Y 6
3,812
90 DAP
1/3 AW
FC
100 DAP
1/3 AW
FC
1/3 AW
529,847** 714,429** 214,650** 71,571** 109,706**
1,882
Genotypes (G) 139
776**
812**
Y×G
139
187
332
Pooled error
834
233
297
3,056
1,039**
2,736
1,755
2,165
543**
616**
443**
237
151
228*
118
228
124
185
106
249
Table 3. Range and mean of pod yield, total biomass, and physiological traits [specific leaf area (SLA) (cm2 g-1) and SPAD chlorophyll meter
reading (SCMR) at 80, 90, and 100 days after planting (DAP) of 4 peanut crosses under well-watered and terminal drought conditions.
ICGV 98348 × Tainan 9
ICGV 98348 × KK60-3
ICGV 98353 × KK60-3
Traits
Range
Mean
S.E.
Range
Mean
S.E.
1,218 - 5,396
3,211
922
5,088 - 17,093
10,367 2,441
Range
Mean
S.E.
1,077 - 4,823
2,220
761
3,347 - 14,813
7,420 1,911
Range
Mean
S.E.
1,219 - 4,750
2,688
801
1,117 - 5,470
2,528
792
Biomass (kg h )
4,193 - 15,320
8,289 2,027
4,644 - 15,542
9,617 2,421
SCMR 80 DAP
29 - 51
41
3.80
34 - 51
43
3.57
32 - 51
41
3.91
37 - 51
42
3.03
SCMR 90 DAP
33 - 52
42
3.39
36 - 51
43
2.96
32 - 54
42
4.03
34 - 50
43
2.83
SCMR 100 DAP
30 - 58
44
4.16
35 - 55
45
3.64
31 - 57
45
4.39
34 - 58
46
3.30
SLA 80 DAP
106 - 198
148
18.56
104 - 194
146
18.17
112 - 202
146
18.35
106 - 194
146
17.82
SLA 90 DAP
100 - 245
148
33.01
96 - 237
146
31.98
97 - 236
144
29.46
94 - 223
147
32.15
SLA 100 DAP
98 - 199
142
19.14
111 - 205
141
17.05
99 - 196
136
17.27
99 - 210
139
17.09
252 - 3,469
1,853
681
766 - 4,696
2,649
881
441 - 3,540
1,733
681
718 - 4,020
2,128
670
3,429 - 15,426
8,549
2,077
1,825 - 10,534
Well-watered conditions
Pod yield (kg h-1)
-1
Drought conditions
Pod yield (kg h-1)
-1
Biomass (kg h )
2,951 - 11,350
SCMR 80 DAP
34 - 53
43
3.88
37 - 54
44
3.54
35 - 55
44
4.30
37 - 53
44
3.37
SCMR 90 DAP
34 - 55
45
3.46
36 - 55
47
3.34
32 - 55
46
4.38
38 - 55
46
3.31
SCMR 100 DAP
38 - 57
47
4.01
37 - 60
49
3.84
35 - 60
47
4.45
40 - 57
48
3.59
109 - 206
142
29.46
106 - 195
142
26.55
108 - 201
146
28.88
105 - 216
147
30.69
139
138
18.88
16.56
103 - 195
99 - 175
135
133
19.99
14.39
103 - 197
101 - 181
137
132
18.81
17.52
101 - 194
100 - 175
139
135
21.28
15.07
SLA 80 DAP
SLA 90 DAP
104 - 198
SLA 100 DAP
101 - 181
S.E., Standard error for genotypes means.
6,498 1,678
6,079 1,532
3,273 - 14,030
8,003 1,968
250
Table 4. Broad-sense heritability estimates for pod yield, biomass, harvest index (HI), and physiological traits [SPAD chlorophyll meter reading
(SCMR), and specific leaf area (SLA) at 80, 90, and 100 days after planting (DAP)] under well-watered and terminal drought conditions of 4 peanut
crosses.
Broad-sense heritability
Peanut crosses
Pod yield
Biomass
HI
SCMR
80 DAP
90 DAP
SLA
100 DAP
80 DAP
90 DAP
100 DAP
0.43 ± 0.32‡
0.65 ± 0.24 0.67 ± 0.23 0.87 ± 0.13 0.79 ± 0.18 0.88 ± 0.12
0.84 ± 0.16 0.84 ± 0.14 0.89 ± 0.11
ICGV 98348 × KK60-3
0.73 ± 0.20
0.52 ± 0.29 0.77 ± 0.18 0.86 ± 0.14 0.81 ± 0.14 0.87 ± 0.13
0.75 ± 0.20 0.83 ± 0.15 0.74 ± 0.24
0.60 ± 0.26
0.49 ± 0.30 0.65 ± 0.25 0.91 ± 0.10 0.85 ± 0.13 0.85 ± 0.14
0.73 ± 0.21 0.75 ± 0.18 0.82 ± 0.15
ICGV 98353 × KK60-3
0.25 ± 0.37
0.17 ± 0.37 0.74 ± 0.20 0.77 ± 0.19 0.79 ± 0.20 0.76 ± 0.19
0.79 ± 0.18 0.79 ± 0.16 0.72 ± 0.21
0.57 ± 0.27
0.53 ± 0.29 0.58 ± 0.27 0.77 ± 0.18 0.79 ± 0.18 0.75 ± 0.19
0.61 ± 0.25 0.83 ± 0.25 0.76 ± 0.19
ICGV 98348 × KK60-3
0.75 ± 0.19
0.66 ± 0.23 0.85 ± 0.14 0.72 ± 0.20 0.77 ± 0.19 0.77 ± 0.15
0.90 ± 0.10 0.84 ± 0.25 0.68 ± 0.23
0.79 ± 0.17
0.32 ± 0.34 0.74 ± 0.20 0.90 ± 0.11 0.80 ± 0.15 0.82 ± 0.16
0.74 ± 0.20 0.78 ± 0.27 0.82 ± 0.15
ICGV 98353 × KK60-3
0.45 ± 0.31
0.36 ± 0.34 0.55 ± 0.29 0.75 ± 0.20 0.73 ± 0.21 0.76 ± 0.19
0.74 ± 0.20 0.74 ± 0.24 0.86 ± 0.14
Drought conditions
Correlation (r)†
0.43**
0.40**
0.42**
0.32**
0.26**
0.22**
0.30**
0.67**
0.48**
†
Correlations between well-watered conditions and drought conditions.
‡
Standard error.
251
Table 5. Genotypic (rG) correlations between drought resistance traits [harvest index
(HI), SPAD chlorophyll meter reading (SCMR), and specific leaf area (SLA) at 80,
90, and 100 days after planting (DAP)] and agronomic traits [pod yield, biomass,
number of pods plant-1(PPP), seed pod-1, and seed size at harvest] for 140 progenies of
peanut under well-watered conditions and terminal drought conditions (degrees of
freedom = 556).
Drought resistance traits
Agronomic traits
Pod yield Biomass
PPP
Seed pod-1 Seed size
HI
0.66**
-0.34**
0.69**
SCMR 80DAP
0.34**
0.23**
SCMR 90DAP
0.16**
0.27**
-0.22**
0.02
0.33**
SCMR 100DAP
0.13**
0.11**
-0.34**
-0.05
0.47**
0.00
0.52**
-0.27**
-0.05
0.32**
SLA 80DAP
-0.05
0.01
0.08*
-0.01
-0.18**
SLA 90DAP
-0.09*
0.06
0.10**
-0.15**
-0.03
0.20**
0.21**
-0.25**
-0.24**
0.78**
0.49**
SLA 100DAP
0.10**
Drought conditions
HI
0.74**
-0.03
SCMR 80DAP
0.37**
0.34**
SCMR 90DAP
0.34**
0.22**
SCMR 100DAP
0.42**
SLA 80DAP
SLA 90DAP
SLA 100DAP
-0.05
0.06
-0.12**
0.31**
0.10**
-0.13**
0.19**
0.37**
0.12**
-0.07*
0.25**
-0.56**
-0.31**
-0.24**
-0.02
-0.42**
-0.22**
-0.07*
-0.09*
-0.05
-0.04
-0.19**
-0.07*
0.09*
0.05
-0.13**
*, ** Significant at P ≤ 0.05 and P ≤ 0.01 probability levels, respectively.
252
Table 6. Genotypic (rG) correlation among drought resistance traits [harvest index
(HI), SPAD chlorophyll meter reading (SCMR), and specific leaf area (SLA) at 80,
90, and 100 days after planting (DAP)] for all 4 peanut cross of 140 progenies of
peanut under field capacity (FC) and 1/3 available water (1/3 AW) (degrees of
freedom = 556).
Drought resistance
Traits
SCMR
80 DAP 90 DAP
HI
0.16**
-0. 06
SLA
100
DAP
80 DAP 90 DAP 100 DAP
0.01
-0.05
SCMR 80DAP
-0.16**
-0.43**
SCMR 90DAP
-0.39**
SCMR 100DAP
-0.07*
-0.44**
Terminal drought conditions
Drought resistance
Traits
SCMR
80 DAP 90 DAP
HI
SCMR 80DAP
0.22**
SLA
100
DAP
0.29** 0.30**
80 DAP 90 DAP 100 DAP
-0.50**
-0.45**
SCMR 90DAP
SCMR 100DAP
-0.25**
-0.34**
-0.20**
-0.45**
253
The dry season 2006/07
40
Stress
Dec
Jan
Feb
Mar
T-max
T-min
Solar radiation
50
c
2
30
30
20
20
10
10
Nov
Dec
Jan
Feb
0
Mar
40
Stress
0
40
0
20
0
50
Stress
40
60
10
20
Nov
30
20
Nov
Dec
Jan
Feb
Mar
50
0
d
50
Stress
40
40
2
10
80
o
Air temperature ( C)
20
40
30
30
20
20
10
10
0
b
Relative humidity (%)
60
100
Solar radiation (MJ/m /day)
30
50
Rain and evaporation (mm)
80
Relative humidity (%)
40
0
a
100
Solar radiation (MJ/m /day)
Rain and evaporation (mm)
50
o
Air temperature ( C)
The dry season 2007/08
Rain fall
Evaporation
Relative humidity
Nov
Dec
Jan
Feb
Mar
Figure 1. Relative humidity (%) (a and b), pan evaporation (mm) (a and b), rainfall
(mm) (a and b), maximum and minimum air temperature (oC) (c and d), and solar
radiation (MJ m-2 day-1) (c and d) during the crop growth period in 2006/07 (a and c) and
in 2007/08 (c and d)
254
0
2006/07 (b)
14
0.20
12
Soil moisture (%)
Soil moisture volume fraction
2006/07 (a)
0.15
0.10
0.05
10
8
6
FC
4
1/3 AW
2
0.00
FC
1/3 AW
0
80
Harvest
80
Harvest
DAP
2007/08 (c)
Soil moisture volume fraction
60
H
DAP
90
10
0
ar
ve
st
80
70
60
50
40
30
20
0
10
0
2007/08 (d)
14
0.20
Soil moisture (%)
12
0.15
0.10
FC
0.05
10
8
6
4
1/3 AW
FC
1/3 AW
2
0.00
90
10
0
ar
ve
st
H
DAP
80
70
60
50
40
30
20
0
10
0
0
60
DAP
Figure 2. Soil moisture volume fraction (a and c) at planting, 10, 20, 30, 40, 50, 60, 70,
80, 90, 100 days after planting (DAP), and at final harvest and gravimetric soil moisture
content (b and d) at planting, 60, 80 DAP and at final harvest under different water
regimes [field capacity (FC) and 1/3 available water (1/3 AW)] average from 0-60 cm
depth in 2006/2007 (a and b) and 2007/2008 (c and d).
255
The
high
heritability
estimates for HI and for SCMR and
SLA indicates that selection for
these traits should be very effective.
Heritability estimates for these traits
were similar under different water
regimes and positive correlations
between traits under different water
regimes were significant (p = 0.220.67, P≤0.01) (Table 4), indicating
that these traits could be selected
under either well-watered or
terminal drought conditions.
Selection for HI, SCMR,
and SLA would allow improvement
of these traits and offer the potential
to transfer desirable benefits such as
increased WUE and drought
resistance to peanut. Evolutionary
response to selection requires
significant additive genetic variance
for a given trait (Falconer and
Mackay, 1996). Additive gene
action has been the main factor
responsible for variation in many
agronomic traits in peanut. Previous
studies reported that HI and SLA
were mainly under additive genetic
control and SCMR was found to be
under the influence of both additive
and non additive gene effects
(Dwivedi et al., 1998; Jayalakshmi
et al., 1999; Lal et al., 2006; Nigam
et al., 2001; Suriharn et al., 2005).
Hence, selection for these traits
should be effective. Nigam et al.
(2001) found that the selection for
SLA and HI was effective in early
generations. They also suggested
that the selection could be done in
late generation to exploit the effect
of additive × additive interaction.
Considerable genetic variation and
high heritability estimates of
physiological traits in this study
indicated
that
selection
for
increasing drought resistance in
peanut using HI, SCMR, and SLA
should be successful under terminal
drought conditions as well as longterm drought conditions (Songsri et
al.,
2008).
Although
all
physiological traits studied here
were found to be highly heritable,
genetic
correlations
between
physiological trait and economic
traits are needed in order to predict
the response of yield and other
agronomic traits from selection
based on the physiological traits.
Genotypic correlations between
drought resistance traits and
agronomic traits
Significant correlations between
drought resistance traits and
agronomic traits were observed
(Table 5). Genotypic (rG) and
phenotypic (rP) correlations were
similar, hence, only rG is reported
herein. Positive correlations were
found between HI and pod yield,
number of mature pods per plant,
and seeds per pod under nonstressed and terminal drought
conditions (rG = 0.49 to 0.78, ≤P
0.01). Positive correlations between
SCMR at 80, 90, and 100 DAP and
pod yield, biomass, and seed size
were also significant (rG = 0.11 to
0.47, P ≤ 0.01), and the correlations
were
higher
under
stressed
conditions except for seed size. The
results indicated that selection for
higher HI and SCMR would result
in higher pod yield in peanut. SLA
at 80, 90, and 100 DAP were
negatively
correlated
with
agronomic traits (rG = -0.07 to -0.56,
P ≤ 0.05 to P ≤ 0.01, respectively),
especially under terminal drought
conditions. Negative correlations
between SLA at 80, 90, and 100
256
DAP and pod yield under stressed
conditions were highly significant
(rG = -0.19 to -0.56, P ≤ 0.01). Weak
correlations between SLA and the
yield components, number of mature
pods per plant and seed size, were
also found (rG = -0.25 to 0.21, P ≤
0.05 to P ≤ 0.01, respectively). Thus,
genotypes with low SLA tended to
have high pod yield, biomass, and
large number mature pods per plant
and seed size. Associations between
SLA and agronomic traits were
stronger under terminal drought
conditions. These indicated that
SLA can be used as a selection
criterion for drought resistance
under both well-watered and
terminal drought conditions but
selection for SLA under terminal
drought would be more effective
than that under non-stressed
conditions.
Genotypic associations in
present study demonstrated that
lower SLA and higher HI and
SCMR were associated with
increased pod yield especially under
terminal drought stress. Hence, a
breeding approach using these traits
could be an effective tool to increase
pod yield in peanut. Genotypic
correlations between SCMR and
SLA and agronomic traits were
weak and lower than those between
HI and most agronomic traits (pod
yield, biomass, number of pods
plant-1, seed pod-1). However,
SCMR and SLA are less expensive.
SLA and SCMR were found to be
associated
with
photosynthetic
capacity and HI in peanut
(Nageswara Rao et al., 2001;
Songsri et al., 2008; Upadhyaya,
2005). Peanut genotype with low
SLA expressed as thicker leaves
usually has a greater photosynthetic
capacity than those having high SLA
(Nageswara Rao et al., 1995;
Nageswara Rao and Wright, 1994;
Wright et al., 1994). Significant
correlations between SCMR and
SLA and other physiological traits
i.e. ∆ and TE have also been
observed over a wide range of
environments (Arunyanark et al.,
2008; Nigam and Aruna, 2008;
Sheshshayee
et
al.,
2006).
Moreover, SPAD chlorophyll meter,
a portable hand held instrument,
could provides an easy opportunity
to integrate a surrogate measure of
WUE with pod yield in a drought
resistance breeding programmes
(Nigam et al., 2005).
Because of low rG between SCMR
and SLA and agronomic traits, the
use
of
a
combination
of
physiological traits as a selection
index may be advantageous to
increase the effectiveness of
drought-resistance
breeding
programs.
In
addition,
Bandyopadhyay et al. (1985) and
Subbarao et al. (1995) suggested
that breeding for drought resistance
using an integration of a selection
index based on physiological traits
such as leaf area, specific leaf
weight and leaf dry weight and
components of yield was more
efficient than an index based on
yield components alone, and is more
useful in crop improvement
programmes than single traits.
Genotypic correlations among
drought resistance traits under
well-watered
and
stressed
conditions
Correlations between traits of
interest can be used to determine if
selection for one trait will have an
effect on another trait. Genotypic
associations
among
drought
257
resistance traits of 140 progeny lines
under non-stressed and terminal
drought conditions were calculated
in this study (Table 6). Genotypic
correlations
among
drought
resistance traits were found under
both well-watered and terminal
drought conditions. Positive and
significant correlation between HI
and SCMR at 80 DAP was found
under non-stressed conditions (rG =
0.16, P ≤ 0.01), and the correlations
between HI and SCMR at 80, 90,
and 100 DAP were also found under
stressed conditions (rG = 0.22 to
0.30, P ≤ 0.01). The SLA was found
to be inversely associated with
SCMR and HI. The correlations
between SLA and HI were stronger
under water stress conditions. Under
terminal drought, SLA at 80, 90, and
100 DAP were negatively correlated
with HI (rG = -0.20 to -0.50, P≤
0.01) SCMR (rG = -0.34 to -0.45, P
≤ 0.01). Under non-stressed
conditions, negative correlation
between SLA and SCMR was also
observed (rG = -0.39 to -0.44, P ≤
0.01). The correlations among
physiological traits under terminal
drought found in present study are
agreed well with the earlier finding
report by Songsri et al. (2008) who
found the correlations among these
traits under long-term drought
conditions, and indicated that all
three physiological traits can be used
as indirect selection tools for each
other, especially under long-term
and terminal drought conditions.
CONCLUSIONS
Breeding for drought resistance in
peanut requires the information of
heritability and genetic associations
among traits to be used in
determining a proper selection
scheme. Our results suggest that HI,
SLA, and SCMR are potentially
useful as indirect selection index for
terminal drought resistance because
of their low G × E interactions, high
heritability
and
significant
correlations with pod yield and the
other agronomic traits under
terminal drought conditions. Hence,
plant breeding approaches using
these traits might be effective for
improving
terminal
drought
resistance in peanut. Heritability
estimates for HI, SLA, SCMR pod
yield, biomass, and harvest index
were similar under either wellwatered or terminal drought
conditions and positive correlations
between traits under different water
regimes were significant, indicating
that those traits could be selected
under both well-watered and
terminal drought conditions. This
study also found that selection for
HI is expected to have a greater
effect on yield and other agronomic
traits than selection for SCMR and
SLA. However, SCMR and SLA are
easier to measure and should be
more applicable in breeding
programs with large segregating
populations. To increase the
effectiveness of breeding program
for terminal drought resistance,
SCMR and SLA could be used as
early steps of screening program to
reduce breeding materials and then
HI and other agronomic traits could
be employed on more advance
materials. In addition, the use of an
integrated selection index based on
these physiological traits might be
more
profitable
in
breeding
programmes.
258
ACKNOWLEDGMENTS
The authors are grateful for the financial
support of the Royal Golden Jubilee
Ph.D.
program
(grant
no.
PHD/0208/2545).
Grateful
acknowledgement is made to the
Thailand
Research
Fund,
the
commission for High Education and
Khon Kaen University for providing
financial supports to this research
through the Distinguish Research
Professor Grant of Professor Dr. Aran
Patanothai
and
grateful
acknowledgment is also made to the
Peanut and Jerusalem Artichoke for
Functional Food Research Group , and
the Plant Breeding Research Center for
Sustainable Agriculture of Khon Kaen
University for providing financial
supports to the research.
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262
RESEARCH ARTICLE
SABRAO Journal
44 (2) 263-276, 2012
SCREENING OF TOMATO GENOTYPES FOR
REPRODUCTIVE CHARACTERS UNDER HIGH
TEMPERATURE STRESS CONDITIONS
KARTIKEYA SRIVASTAVA1*, SUNIL KUMAR1, SURENDER
KUMAR2, PRAVIN PRAKASH2 and A. VAISHAMPAYAN1
1
Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu
University, Varanasi-221005, India
2
Department of Plant Physiology, Institute of Agricultural Sciences, Banaras Hindu University,
Varanasi-221005, India
SUMMARY
A drastic decrease in tomato (Lycopersicon esculentum MIL L.) yield is recorded
during the summer season in India, due to high temperature. Fruit-set at high
temperature is strictly dependent on proper gametogenesis (pollen and ovule
development) and fertilization of this plant. In this investigation, thirty four tomato
genotypes were screened for tolerance to high temperature stress under spring-summer
season (February to May) at the day and night temperature range of 32-44 °C and 19.626.7 °C, respectively. At an average day/night temperatures of 38 0C/22.2 0C, a
significant increase in flower drop (22.55-42.56%), stigma exertion (7.82-50.35%) and
significantly decrease in pollen viability (18.8-86.49%), pollen germination (15.2967.59%) and fruit set truss-1 (21.7-56.15%); along with the relative cell injury ranging
between 18 and 52%, were noted among the genotypes, as compared to that recorded
in autumn-winter season under optimal temperature condition. On the basis of initial
screening with respect to fruit set at high temperature stress, three lines i.e. FLA-7171,
Pusa Sadabahar and NDTVR-60 emerged as heat tolerant genotypes. These lines,
along with two highly heat susceptible varieties i.e. Floradade and H-86, were critically
observed for the stress tolerance traits. The results demonstrated that: (1) the high
temperature condition adversely affects the vegetative and reproductive parts of the
plant; (2) membrane thermo-stability, pollen viability, pollen germination, stigma
exertion, percentage of flower drop, and percentage of fruit set ability could be used as
a selection criterion for heat tolerant genotypes with better fruit set, and (3) the three
tomato genotypes Pusa Sadabahar, FLA-7171 and NDTVR-60 could affirmatively be
useful as a source of heat tolerance genes for further breeding programs.
Keywords: Flower drop, fruit set, pollen viability, pollen germination, high
temperature, tomato.
Manuscript received: January 13, 2012; Decision on manuscript: June 22, 2012; Manuscript
accepted in revised form: August 4, 2012.
Communicating Editor: C. Ravindran
INTRODUCTION
Tomato (Lycopersicon esculentum
Mill.) is an important vegetable
crop all over the world. Due to rise
in average day/night temperature
(32/26 0C) a decrease in the yield
of
tomato
is
a
common
observation.
Different
global
circulation models predict that
greenhouse gases will gradually
lead to an increase in world
average ambient temperature.
According to a report of the Intergovernmental panel on Climatic
Change (IPCC), the global mean
temperature will rise by 0.3 °C
every decade (Jones et al., 1999),
reaching approximately 1 and 3 °C
above the present value by years
2025 and 2100, respectively,
adding to global warming. Rising
temperatures may lead to altered
geographical
distribution
and
growing season of agricultural
crops (Porter, 2005). In tropical
and sub-tropical regions heat stress
may become a major limiting
factor for crop production and thus
climate change can have dramatic
effects on agricultural production.
Effect of climate change is likely to
be greater in tropical and subtropical developing countries.
Ability to adjust to the effects of
climate change will be a key
adaptive measure in the agricultural
sector.
The reproductive phase is a
good candidate to be affected by
climate change on tomato crop. An
increase in the frequency of high
temperature stress during gamete
development
affects
plant
reproduction with immediate and
long-term effects. Some immediate
effects on the female gametes
mediated
through
flower
production and their viability have
been recorded (Warner et al.,
2005). However, probably in view
of the ease in handling, most of the
work has centered on the male
function. Thus, the quantity and
morphology of pollen, anther
dehiscence and pollen wall
architecture, as well as the
chemical
composition
and
metabolism of pollen have been
shown to be affected by high
temperatures (Aloni et al., 2001;
Prasad et al., 2002; Koti et al.,
2005). All these effects could alter
male fitness by reducing the
available amount of pollen.
Nevertheless,
if
the
pollen
limitation is severe it indirectly
limits female fitness by reducing
the number of fruit set. However, it
is known that small variations in
temperature, although inducing an
effect, are not expected to produce
very dramatic high temperature
stress, and this is one of the most
important causes of change in plant
morphology,
physiology
and
biochemical aspects, which reduces
plant growth and development in
this particular vegetable crop.
Effects of increasing temperature
on the sexual plant reproductive
phase
concern
about
the
reproductive output. More subtle,
but perhaps more important, is the
effect that the temperature during
pollen development seems to exert
on pollen performance. Mild
increases in temperature negatively
affect characteristics such as pollen
viability,
pollen
germination
ability, pollen tube growth rate and
fruit set. Thus, it seems that
temperature variation during pollen
development can alter pollen
performance in tomato. The
physiological parameters such as
264
Srivastava et al. (2012)
seedling and vegetative growth,
flowering, pollen viability, pollen
germination fruit set, and fruit
weight are adversely affected at the
temperature above 35 °C (Thomas
and Prasad, 2003, Wahid et al.,
2007). Reproductive development
has been rather demonstrated to be
affected more adversely at high
temperature stress than the
vegetative development (Sato et
al., 2002; Abdelmageed et al.,
2003).
High
temperatures
duringreproductive development
have been reported to limit the
flower
bud
initiation
with
significant increment in flower
drop (Hanna and Hernandez, 1982)
and significant decrease in fruit set
(Berry et al., 1988), leading to a
sharp decrease in tomato fruit
yield.
The objective of this study,
therefore, is to (1) investigate the
effect of heat stress on vegetative
and reproductive development of
heat sensitive and tolerant tomato
genotypes, (2) compare the growth
and development of different
genotypes under defined heat stress
conditions (intensity and duration),
as well as (3) evaluate cellular
membrane thermo-stability as an
indicator of heat tolerance in
different tomato genotypes and
determine its relationship with
yield.
Thirty four tomato genotypes were
used to evaluate their performance
under high temperature stress
condition (Table 1). The tomato
lines were naturally grown in the
field in two seasons. The average
day/night mean temperature in both
seasons, i.e. normal and summer
seasons were 27.12/15.15 oC and
39.24/24.42 oC, respectively. Seeds
were sown on nursery bed in the
first week of August, 2009 and
transplanted in first week of
September, 2009. For high
temperature, seeds were sown in
the third week of February, 2010
on nursery bed and transplanted in
the third week of March, 2010 in
Vegetable Research Farm, Institute
Agriculture
Sciences,
BHU,
Varanasi. The experiment was
carried out in complete randomized
block design with three replicates.
Each genotype was represented by
30 plants in each replication for
evaluation of heat stress tolerance.
All cultural practices, such as
application of fertilizer @120 kg
N, 80 kg P2O5 and 50 kg K2O per
hectare, irrigation, spraying of
micronutrients and pesticides, were
carried out as recommended
(ICAR) for commercial tomato
production. The experiments were
conducted under two different
growing periods to set two
temperature regimes, designated as
optimum
(non-stressed)
temperature and high temperature
regimes. The growing periods from
transplanting to harvesting were
September to December and April
to June. The optimum temperature
and high temperature regimes were
19°C-32 °C and 26°C-44 °C,
respectively. Flower drop and
stigma exertion was expressed in
percentage. Another experiment
were conducted under control
environment in which some heat
tolerance and heat susceptible
tomato genotypes were grown in a
growth chamber at 32/26 °C under
a 12/12 hours day/night cycle. The
pollen viability, pollen germination
265
and membrane thermo-stability
were assessed under controlled
environmental conditions.
Percentage
of
pollen
viability was tested a day before
anthesis at day /night temperature
39.5/26.4 °C and there was no rain,
cloudy weather or storm during the
sampling (Figure 1). Flower buds
were collected from 10 plants per
genotype by removing pollens
from the anthers using a needle.
The pollen grains were inoculated
on glass slide for determining the
number of viable pollen grains
through
triphenyltetrazolium
chloride (TTC) test as per Eti
(1991). Pollen germination was
tested following the Bedinger’s
liquid tomato pollen germination
media protocol (available online at
http://www.irbtomato.org/).
Flowers were collected
from 10 plants per genotype at
anthesis. Pollens, collected from
the anthers using a needle from 10
randomly selected flowers, were
put on a slide and mixed using a
nylon hairbrush. After mixing,
pollen grains were immediately
transferred within 30 minutes of
picking the flowers on to the
growth medium tube. Investigation
on pollen germination and pollen
tube growth was conducted by
placing the pollens on a slide with
the germinating media. A pollen
grain was considered to have
germinated when the length of the
germinated pollen tube was equal
to or longer than the diameter of
the pollen as suggested by Liza et
al. (1987). Counts on pollen
germination were made randomly
in three replications under a
microscope and expressed in
percentage. Fruit set was also
expressed in percentage by
counting the total number of
flowers as well as total number of
fruits per plant.
The cellular membrane
thermo-stability assay indirectly
measures integrity of cellular
membrane through quantifying
electrolyte leakage after heat
treatment. The cellular membrane
thermo-stability was determined
following the method proposed by
Sullivan
(1972).
Percentage
relative cell injury (RCI %), an
indicator of cellular membrane
thermo stability was calculated as
per Sullivan (1972), i.e. RCI % =
1-[1-(T1/T2)] / [1-(C1/C2)] x 100,
where,
T1 =
T2 =
C1 =
C2 =
EC of sap in 50°C before
autoclaving.
EC of sap in 50°C after
autoclaving.
EC of sap in 25°C before
autoclaving.
EC of sap in 25°C after
autoclaving
T and C refer to EC values of heattreated and controlled leaf tissues
test tubes.
RESULTS
The experiments were conducted in
which several tomato genotypes
were tested under two sets of
temperature
conditions.
At
optimum temperature condition
(15.15 oC-27.12 oC), all genotypes
produced the highest percentage of
pollen
viability,
pollen
germination, fruit set, along with
average fruit weight and minor cell
injury and low stigma exertion per
cent. In general, flower characters
were strongly associated with fruit
266
character and yield. At the high
temperature regime, significant
increase in the flower drop
percentage was observed in all
genotypes, which varied from
35.12 to 75.62%. Such effects were
more pronounced in varieties H-86
and
Floradade,
while
low
percentage of flower drops was
found in the varieties Pusa
Sadabahar,
FLA-7171
and
NDTVR-60. High temperature
regime caused a significant
decrease in the number of fruit set
in all genotypes related to 32/44 °C
regime. Variability was observed
among the lines with respect to per
cent fruit set, which significantly
varied from 12.1% to 52.73%
(Table 2). The highest percentage
of fruits (52.73%) was set by Pusa
Sadabahar, followed by FLA-7171
(49.6%) and NDTVR-60 (42.13%),
respectively, and the significant
losses of fruit set per cent were
found in varieties Floradade
(56.15%) and H-86 (54.4%) at high
temperature regime (Table 2), as
compared
to
the
normal
temperature regime (Table 1).
Average individual fruit weight of
the 34 tomato lines was noted to be
remarkably dissimilar (Table 1).
The extent of high temperature and
genetic variation is also related to
floral morphological display and
degree
of
stigma
exertion
(27.31%). The highest percentage
of exerted stigma was found in
varieties Floradade (46.86%) and
H-86 (31.81%) and lowest was
recorded in the genotypes, Pusa
Sadabahar (4.18%), FLA-7171
(4.82%) and NDTVR-60 (9.19%).
Significant variations were found
in individual fruit weight among
the lines. The highest losses in
average individual fruit weight
were observed in Co-3 (38.06%),
H-88-74 (33.83%), Floradade
(21.72%), H-86 (20.77%), Kashi
Amrit (19.87%), while the lowest
losses in average individual fruit
weight were noted in FLA-7171
(1.44%), NDTVR-60 (2.23%),
Selection-7 (6.29%) and Pusa
Sadabahar (6.29%). The viability
of released pollen grains from
plants
grown
at
optimum
temperature
ranged
between
66.36% and 95.48% in all
genotypes. Although, the viability
of pollen grains varied among
genotypes, the decreasing level of
viability at high temperature varied
from 66.59% to 8.99% among all
genotypes. Pusa Sadabahar had the
highest percentage (66.59 %) of
viable pollen.
267
Table 1. Performance of tomato genotypes at normal temperature.
Varieties
Flower
drop
(%)
Average
fruit weight
(g)
Stigma
excretions
(%)
Fruit set
(%)
Pollen
viability
(%)
Pollen
germination
(%)
15.23
Relative
cell
injury
(%)
21.55
Pusa Sadabahar
52.40
3.64
75.22
85.36
82.41
DVRT-1
H-88-74-1
Kashi Amrit
14.25
10.45
19.24
17.73
13.68
15.58
80.26
36.35
86.35
4.85
2.72
1.88
67.36
58.03
75.55
90.32
86.15
88.68
82.85
74.07
82.59
Floradade
6.25
22.71
83.19
3.49
68.25
95.48
78.42
KashiSharad
17.45
1.96
99.12
1.91
58.01
86.00
74.58
DT-2
Pant T-3
H-24
Co-3
26.13
10.25
12.54
5.14
7.36
21.85
8.14
12.24
68.07
70.94
61.62
73.00
3.86
1.55
1.33
2.43
73.86
68.10
68.00
58.52
82.53
85.95
72.70
80.31
81.84
75.78
70.32
68.93
Punjab Upama
6.21
9.34
70.95
4.70
72.44
86.33
66.16
H-86
7.12
8.41
80.15
9.21
71.60
88.87
78.88
N D T-3
12.21
18.79
64.26
5.24
70.81
84.31
67.97
Selection-18
21.12
7.62
61.25
6.41
67.91
85.78
87.85
VR-20
H-T-4
12.36
14.56
10.20
12.42
57.46
67.22
3.82
6.43
65.00
66.19
87.73
87.58
70.84
68.65
Azad T-5
27.48
13.53
69.48
3.64
72.52
88.72
75.93
Sworna Lalima
15.00
14.88
68.45
5.29
67.91
85.67
64.49
TLC-1
31.91
19.42
61.45
1.88
68.08
89.04
83.09
GT-20
Fla-7171
NDTVR-60
DT-10
Selection-7
35.68
14.51
31.71
11.56
23.83
10.85
14.41
22.45
20.01
9.89
72.51
69.25
94.26
85.25
65.14
2.73
1.97
3.04
2.69
1.85
64.31
71.30
68.28
73.93
76.16
91.90
87.04
83.15
66.36
88.74
63.73
81.80
71.96
83.45
81.25
Flawery
Feb-4
BT-120
NF-315
Grant
11.48
9.85
11.25
18.45
15.12
20.22
10.09
10.97
14.67
13.42
71.24
75.26
62.33
65.36
71.00
2.86
3.78
2.51
2.84
3.70
75.49
66.00
58.69
61.37
56.00
92.98
94.90
88.66
88.67
87.60
84.15
69.05
62.78
73.10
68.42
Ace
45.74
18.93
85.62
3.13
54.25
68.43
62.02
Kajela
PS-1
AngurLata
Columbia
9.00
8.74
38.69
35.17
16.34
11.42
15.41
14.74
52.10
47.15
53.89
73.78
2.14
3.45
3.57
1.64
64.60
72.38
61.31
64.83
75.63
85.68
88.43
92.30
68.22
69.72
71.65
72.33
C.V.
1.60
4.20
4.54
13.21
0.78
9.26
0.68
C.D. 5%
0.85
0.97
1.04
0.70
0.85
7.77
0.82
268
Table 2. Performance of tomato genotypes at high temperature
Varieties
Flower
drop
(%)
Relative
Cell injury
(%)
average
fruit
weight (g)
Stigma
excretions
(%)
Fruit
set (%)
Pollen
viability
(%)
Pollen
germination
(%)
Pusa
Sadabahar
DVRT-1
42.23
39.55
48.51
7.82
52.73
66.56
50.50
72.73
63.45
17.23
27.54
17.23
21.07
H-88-74-1
35.13
48.68
25.14
9.43
28.70
14.28
26.42
Kashi Amrit
53.14
50.58
69.19
21.95
21.80
17.72
19.51
Floradade
47.19
66.71
65.12
50.35
12.10
8.99
10.83
KashiSharad
53.89
48.96
68
19.44
22.99
16.91
18.97
DT-2
59.23
51.36
56.34
23.38
34.65
16.65
17.24
Pant T-3
41.54
73.85
62.14
18.08
20.05
26.16
21.44
H-24
51.62
54.14
56.12
18.11
30.33
19.54
16.30
Co-3
35.83
49.24
45.21
16.72
18.00
23.47
21.48
Punjab Upma
41.74
46.34
45.21
41.22
20.05
17.08
13.73
H-86
49.68
45.41
63.5
41.02
17.2
20.93
21.79
N D T-3
45.94
55.79
52.14
39.04
26.73
15.73
18.00
Selection-18
45.62
62.62
55.62
35.24
29.95
17.25
19.76
VR-20
61.03
82.2
56.21
26.36
33.29
15.19
14.94
H-T-4
57.22
44.42
51.45
23.94
29.99
16.25
16.25
Azad T-5
54.90
45.53
55.26
20.21
21.72
29.14
30.65
Sworn Lalima
54.57
46.88
51.24
41.19
25.05
27.83
29.44
22.87
67.12
TLC-1
59.90
51.42
52.34
32.84
16.35
18.56
GT-20
61.90
42.85
45.24
28.89
22.46
15.79
17.03
Fla-7171
38.45
34.41
68.25
6.79
49.60
54.32
56.65
48.71
NDTVR-60
54.26
42.45
92.25
12.23
42.13
39.48
DT-10
39.83
59.01
59.97
16.53
31.03
14.96
19.26
Selection-7
46.92
34.25
61.04
13.81
39.12
34.22
38.33
Flawery
50.46
52.22
55.21
43.72
28.30
23.71
28.94
Feb-4
42.55
42.09
55.32
33.08
16.16
20.17
21.82
BT-120
52.33
82.97
61.25
42.04
20.57
16.52
17.82
NF-315
55.36
72.67
38.78
25.55
24.12
14.58
17.97
Grant
61.00
46.42
63.76
28.43
27.23
15.84
17.50
ACE
75.62
51.93
69.9
34.14
21.09
21.42
26.16
Kajela
42.10
49.34
40.42
21.5
36.58
21.30
21.30
PS-1
37.15
44.42
35.42
22.75
30.59
15.44
16.05
AngurLata
43.89
64.41
52.56
47.91
38.43
15.52
15.52
Columbia
43.78
49.66
67.18
47.86
23.81
16.82
16.82
C.V.
3.90
9.80
5.71
4.91
12.40
10.00
6.44
C.D. 5%
4.80
9.80
5.81
2.20
5.80
3.60
2.49
269
80
70
60
50
40
30
20
10
0
March
April
May
June
Day temperature °C
Night temperature °C
Max.RH %
Min. RH %
Figure 1. Average temperature and relative humidity during crop season 2010.
80
70
60
50
40
pollen
pllen
viability %
pollen
germination
%
Relative cell
injury (%)
30
20
10
0
Figure2. Effect of high temperature (37/26 ºC) on pollen viability, pollen germination
and relative cell injury under control conditions.
270
In Pusa Sadabahar, FLA-7171 and
NDTVR-60 genotypes viability
rate of pollen was not affected
significantly at high temperature
conditions. High temperatures can
induce pollen sterility, which may
be due to disruption of sugar
metabolism in the ultimately
abolishing starch accumulation
(energy reserve) in the pollen
grains (Firon et. al., 2006).
Exposure to temperatures above 37
o
C, at the time of pollen-grain
germination, adversely affects the
germination percentage and rate of
pollen-tube development. Damage
is especially marked when the
source of pollen is flowers grown
under the high-temperature regime.
Result obtained from this study
indicated
that
the
highest
percentage of pollen germination
was found in cultivars Pusa
Sadabahar (67.12%), FLA-7171
(56.65%)
and
NDTVR-60
(48.71%). The drastically reduced
pollen germination was obtained in
tomato cultivars, i.e. Floradade
(67.89%), Kashi Amrit (63.08%)
and H-86 (57.05%). The relative
cell injury ranged between 39.55
and 82.97% among cultivars
(Table 2) at high temperature
regimes. The cultivars Pusa
Sadabahar
(18%),
FLA-7171
(20%) and NDTVR-60 (20%)
possessed the minor increase in
electrolyte
leakage
at
high
temperature. On the other hand,
among the 34 cultivars, Co-3,
Floradade and H-86 exhibited the
highest percentage of relative cell
injury, i.e., 52%, 37% and 37%,
respectively, at high temperature.
Similar trend of results (Figure 2)
were also found under the
controlled
environmental
conditions.
DISCUSSION
In the present study, the high
temperature regime decreased the
number of total flowers produced
in H-86 and Floradade genotypes.
However, small effect of high
temperature was observed in the
genotypes Pusa Sadabahar, FLA
7171 and NDTVR-60. High
temperatures induce drop of buds
and flowers in the tomato (Smith,
1935; Leopold and Scott, 1952;
Saito and Ito, 1967; Abdalla and
Verkerk, 1968). Our results, thus,
also confirmed the findings of
other investigations, affirming that
high temperature reduced the fruit
set in tomato (Iwahori et al., 1963;
Abdall and Verkerk, 1968, 1970;
Levy et al., 1978; Shelby et al.,
1978). In fact the failure of pollen
germination and pollen viability
has been shown to prevent fruit set
(Sato et al., 2000). Result indicated
in this experiment that the viability
rate of pollen was affected
significantly at high temperature
conditions. Pressman et al. (2002)
reported that the effect of heat
stress on pollen viability was
associated
with
carbohydrate
metabolism
during
anther
development.
Under
optimal
temperature,
soluble
sugar
concentration gradually increased
in pollen. Continuous high
temperature prevented the increase
in starch concentration and led to a
decrease in soluble sugar in mature
pollens. These possibly cause a
decrease in pollen viability.
Temperature stress can sometimes
have different effects on male and
female structures, thereby creating
asynchrony between male and
female reproductive developments
(Herrero, 2003; Hedhly et al.,
271
2008). More is known for the
effects of temperature stress on
male
reproductive
structures
(Barnabas et al., 2008; Thakur et
al., 2010). For example, in wheat,
heat stress during the period of
microspore meiosis can induce
tapetum degradation (Saini et al.,
1984; Sakata et al., 2000). This
degradation of the nutritive tissues
of the tapetum leads to pollen
sterility. High temperatures cause
poor
anther
dehiscence
characterized by tight closure of
the locules, which was shown to
reduce pollen dispersal in rice and
tomato (Matsui and Omasa, 2002;
Sato et al., 2002). Under high
temperatures, gametogenesis is
disturbed, gamete viability is
reduced (Iwahori and Takahashi,
1964; Iwahori, 1965, 1966) and
less pollen is produced in the
flower (Abdalla and Verkerk,
1968). Pollen maturation, viability,
germination ability, and pollen
tube growth can be negatively
affected by heat (Dupuis and
Dumas, 1990; Peet et al., 1998;
Prasad et al., 1999; Aloni et al.,
2001; Young et al., 2004).
However, contrary to these results,
Sato et al. (2000) did not find the
values significant, and they
concluded that pollen germination
and pollen viability may be the
most important factors affecting
fruit set at high temperature.
Significant cultivar differences for
pollen germination were observed
in the present study. High
temperatures can also affect the
germination and the elongation of
the pollen tube into the style and
thus inhibit fertilization (Smith,
1935; Smith and Cochran, 1935;
Iwahori and Takahashi, 1964;
Iwahori, 1967). The extent of high
temperature is also related to floral
morphological display and degree
of stigma excretions. In the present
investigation it has been found that
the susceptible genotype showed
more stigma excretions than
resistant genotypes. Levy et al.
(1978) also found that the amount
of flower abscission was strongly
correlated with the amount of style
excretions. No fruit set was ever
observed when the style protruded
more than 1 mm out of the
antheridial cone. Present result
sowed that average fruit weight
was affected by high temperature.
It is important to mention here that
in the range of temperature, many
authors reported no or negative
effects of increasing temperature
on final tomato size and attributed
this to compensating effects on the
rates of fruit growth and fruit
development (Ho, 1996; Adams et
al., 2001). Result observed in
current study at high temperature
regimes genotype shows the
highest relative cell injury. High
temperature modifies composition
and structure of cell membranes by
weakening the hydrogen bonds and
electrostatic interactions between
the polar groups of proteins within
the aqueous phase of the
membrane.
Thus,
integral
membrane proteins (which are
associated with both hydrophilic
and lipid regions of the membrane)
tend to associate more strongly
with the lipid phase. Disruption
and damage to membranes alters
their permeability, and results in
loss
of
solute
(electrolytes
leakage). The consensus is that
electrolyte leakage reflects damage
to cellular membranes (McDaniel,
1982) and is, therefore, an
important factor in heat tolerance
272
cellular
membrane
thermostability, that has been used as a
measure of heat tolerance in
several other crops, including
soybean (Martineau et al., 1979),
potato and tomato (Chen et al.,
1982), wheat (Saadallah et al.,
1990; Blum et al., 2001), cowpea
(Ismail and Hall, 1999).
On the basis of initial
selection, it was concluded that the
characteristics like membrane
thermo-stability, pollen viability,
pollen
germination,
stigma
exertion, percentage of flower
drop, and percentage of fruit set
ability could be used in selection of
heat tolerant genotypes for better
fruit set, and the three tomato
genotypes, i.e. Pusa Sadabahar,
FLA-7171 and NDTVR-60 can be
used as a source of heat tolerance
genes
for
further
breeding
programs.
ACKNOWLEDGEMENTS
Authors gratefully acknowledge the
receipt of financial assistance for this
work from the University Grants
Commission, New Delhi.
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276
RESEARCH ARTICLE
SABRAO Journal
44 (2) 277-291, 2012
PHENETIC ANALYSIS AND INTRA-SPESIFIC
CLASSIFICATION OF INDONESIAN WATER YAM
GERMPLASM (Dioscorea alata L.) BASED ON
MORPHOLOGICAL CHARACTERS
PURNOMO1, BUDI SETIADI DARYONO1, RUGAYAH2, ISSIREP
SUMARDI1 and HIRONOBU SHIWACHI3
1
Faculty of Biology, Gadjah Mada University (UGM). Jl. Teknika Selatan, Sekip Utara, Yogyakarta
55284, Indonesia.
2
Botany Division, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong,
Indonesia.
3
Laboratory of Crops Science, Graduate School of Agriculture, Tokyo University of Agriculture,
Tokyo, Japan.
Corresponding author email: [email protected],
SUMMARY
Research on morphological variability and intra-specific classification of Indonesian water
yam (Dioscorea alata L.) was conducted based on morphological characters (traits).
Sample collection of Dioscorea spp. was done in some provinces in Indonesia including
tubers, bulbils, stems, leafs, flowers, and fruits. Furthermore, tubers or bulbils were
planted as live collections. Morphological characterization was conducted based on field
and live collection data. Cluster analysis was carried out to identify key of intra-specific
groups. The results showed that Indonesian water yam germplasm is classified into green
and reddish-purple groups. Based on tuber shape and flesh color the green group was
classified into 6 sub-groups: (1) white rounded to oblong, (2) white sweetish ob-ovate, (3)
white ob-ovate to oblong, (4) yellow ob-ovate to oblong, (5) white to yellow long
cylindrical, and (6) white oblong Papua water yam. Based on tuber shape, tuber skin, and
color distribution in tuber flesh, the reddish-purple group is classified into 5 sub-groups:
(1) purple long cylindrical, (2) purple ring white flash ob-ovate, (3) purple to red ob-ovate
to oblong, (4) purple rounded to short cylindrical, and (5) yellow flesh purple skin water
yam. The similarity of height within germplasm was due to vegetative propagation (clone)
from tubers and bulbils.
Key words: Water yam (Dioscorea alata L.), intra-specific classification,
morphological characters.
Manuscript received: November 7, 2011; Decision on manuscript: August 31, 2012; Manuscript
INTRODUCTION
Water yam, greater yam, or uwi
(Dioscorea alata L.) belongs to the
Dioscoreaceae family, and is native
from South-East Asia with centre of
diversity in South-East Asia tropics
and sub-tropics (Balakhrisant et al.,
2007). Currently water yam is a
minor cultivated plant in Malaysia
(Hasan et al., 2006). Water yam
originated from South-Eastern Asia
or Asia (Burkill, 1935; Purselove,
1972). Morphologically, water yam is
a highly diverse species, with the
greatest variability is found in Papua
New Guinea and Philippines islands
(Martin and Rhodes, 1977; Malapa et
al., 2008). The centre of variation of
water yam is first Papua New Guinea
and second Indonesia (Martin and
Rhodes, 1977; Lebot et al., 2005).
Water yam can be grown in nonirrigation soil, critical soil, without
intensive nursing, as a cash crop
(Bimantoro 1981; Gsianturi, 2003).
Furthermore, water yam is used
rarely by Indonesians due to other
potential crops.
The quality of water yam
tuber starch is equal with sweet
potato, and the main composition of
tuber are carbohydrates, with protein,
vitamins, minerals and the starch is
composed of glucose, maltose, and
sorbitol (Bressan et al., 2007). Tuber
of water yam has a potential as a raw
material of food products like baby
food, snacks, jelly, and bread. Hence
it could be processed for soft drinks,
isotonic beverage, and alcohol or
ethanol (Lingga, 1992; Ariesta,
2004).
In the genus Dioscorea,
water yam is the most polymorphic
species, and it can be distinguished
from the other species by its
quadrangular winged stem (Heyne,
1950; Martin and Rhodes, 1977;
Hasan et al., 2008). Morphological
variation is observed in vegetative
parts include tubers and bulbils
(Backer and Bakhuizen v.d. Brink,
1968). Water yam is classified into
six forms: (1) ubium vulgare, (2)
ubium
digitatum,
(3)
ubium
anguinum (4) ubium draconum, (5)
ubium anniversarum, and (6) ubium
ovale (Heyne, 1950).
In a more comprehensive
attempt to study intra-specific
variation of D. alata, researchers
studied 235 cultivars and found that
up
to
fifteen
groups
were
differentiated (Martin and Rhodes,
1977; Lebot et al., 1998). Moreover,
Jui-Seng Lai et al. (2005) repoted
that D. alata can be further divided
into five sub-clusters: (1) clumpy
yam with branching tuber, (2) bottle
shape yam, (3) white flesh with red
skin yam (4) red flesh yam, and (5) a
long tuber length yam.
According to Shiwachi et al.
(2000) water yam strains from
Indonesia, Papua, Palau Island and
Japan were classified into 3 main
strain groups, based on stem nodes
color, leafs color, tuber shape and
color. The water yam germplasm
classification in Comoro island were
determine by the tuber shape and
color, shape and stem color, shape
and leaf color, and indumenta
(Wilkin et al., 2007). In Yogyakarta
special Province Purnomo and
Susandarini (2009) identified 11
strains or germplasmes from
Yogyakarta were classified based on
tuber shape, tuber color, leaf color
and stem color (i.e. uwi beras, legi,
kendil, ulo, ungu, bangkulit, butun,
kuning, luyung putih (jengking),
luyung senggani, and luyung
kuning).
278
Purnomo et al. (2012)
Hasan et al. (2006) showed
that greater yam or water yam in
Malaysia was grouped into 4 clusters
(groups): (1) white, (2) purple tuber
color, (3) as white and purple tuber
color, and (4) rounded and purple
tuber color, and more water yam
germplasm from Malaysia, classified
based on tuber morphology into
oblong, round, and irregular, and
based on tuber shape and color can
be differentiated six morphological
groups: round purple, irregular
purple, oblong purple, round white,
irregular white, and oblong white
(Hasan et al., 2008).
Onwueme and Ganga (1996)
identified five cultivar groups (cv.
Group) of water yam: (1) Purple
compact, with main region in
Philippines; (2) primitive purple,
with main region in New Guinea; (3)
primitive green, with main region in
New Guinea and Indonesia, (4)
compact, with main region in New
Guinea and Philippines; (5) poor
white, with main region in New
Guinea and India.
The data of morphological
variation, specific characters, and
relationship of each cultivar are
needed for water yam cultivation,
especially for seed screening and
selection (Hawkes, 1986). Zannou et
al. (2009) reported that genetic
diversity of yam changed according
to spatial gradient, most varieties
were found from North East and
North-West of Guinea than in Central
Guinea. In Kenya, Dioscorea spp.
hybrid show rich of morphological
variability and it is important for
screening cultivars (Mwirigi et al.,
2009), and were found to be coastal
and terrestrial morphological-type of
water yam (Muthamia et al., 2009).
Despite the research in other
countries, research on Indonesian
water yam germplasm is still lacking.
The objective of this research was to
study morphological variability,
phenetic relationships and intraspecific classification of Indonesian
water yam germplasm.
Plant material
A total of 44 accessions of water yam
(Dioscorea alata L.) was collected in
several islands in Indonesia i.e. Java,
Madura, Lampung (Sumatera), South
Kalimantan, Central Celebes, Ternate
(Maluku), Papua, and Lombok (Nusa
Tenggara) including tubers, bulbils,
stems, leafs, flowers, and fruits
(Table 1).
Data recording
Morphological characterization was
observed directly on living plants
under field conditions and living
collections. Data was collected based
on yam descriptors (IPGRI/IITA,
1997) using binary and multistate
scoring. The operational taxonomic
units (OTUs) in the research is
individual samples, and from 55
characters analyzed, 38 characters
were compared of each OTUs (Table
2), according to water yam descriptors
to create the present and absent
characters
between
OTUs
by
standardization
data.
Similarity
between OTUs was calculated using
Jaccard’s coefficient (Sokal and
Sneath, 1973).
279
Table 1. Indonesian germplasm of water yam (Dioscorea alata L.).
No.
Acc.
D. alata
germplasm
Origin
1
1
Uwi beras
2
5
Uwi elus
3
6
Uwi alas
4
9
Uwi alas
5
10
Uwi putih
6
12
Ubi putih
7
15
Ubi putih
8
20
Ubi putih
9
121
Uwi putih
10
22
Uwi legi
11
23
Uwi legi
12
26
Uwi butun
13
29
14
35
15
50
16
37
Uwi Luyung putih
or uwi jengking
Uwi Luyung
kuning
Uwi Luyung
senggani
Uwi ulo
17
39
Uwi ular
Sewon, Bantul,
Yogyakarta
Kulon Progo,
Yogyakarta
Sendangsari, Bantul,
Yogyakarta
Yogyakarta
Bawean, Madura
18
44
Uwi kuning
Sleman, Yogyakarta
19
45
Uwi kuning
20
47
Uwi bangkulit
21
115
Uwi bangkulit
22
118
Uwi bangkulit
23
53
Uwi senggani
24
55
Uwi ungu
25
120
Uwi ungu
26
136.a
Uwi merah
Pleihari, Banjarmasin,
South Kalimantan
Yogyakarta
Serongga, Batulicin,
South Kalimantan
Seikupang, Tanah
Laut, South
Kalimantan
Gunung Kidul,
Yogyakarta
Mondokerto, Demak,
Central Java
Sarimulya, Batulicin,
South Kalimantan
Mojopahit, Lampung,
Sendangsari, Bantul
Yogyakarta
Purwodadi, Central
Java
Rembang, Central
Java.
Gunung Kidul, Yg,
Central Java.
Purwodadi, Central
Java.
Banggai, Central
Celebes
Buon, Luwuk, Central
Celebes.
Kinton, Luwuk,
Central Celebes
Sariutama, South
Kalimantan
Yogyakarta
Gunung Kidul,
Yogyakarta
Sleman, Yogyakarta
Specific characters
(stem nodes, nerves, upper and low
end of petiole, auricle, color), tuber
shape, skin color, flesh color.
Green, irregular, dark brown, white
Green, oblong, dark brown, white
Green, ovate-round, dark brown, white
Green, round-short cylindrical, light
brown, white
brown, white
brown, white
Green, oblong, ligh brown, white.
Green, ovate-round, dark brown,
yellow
Green, long cylindrical, dark brown,
white
Green, long cylindrical, dark brown,
yellow
Reddish-purple, long cylindrical, dark
brown, purple
Green, long cylindrical, light brown,
white-yellow
white
Green, ovate-round, dark brown,
yellow
Reddish-purple, irregular, purple,
yellow
Reddish-purple, ovate-round, purple,
white with purple ring
purple
Reddish-purple, oblong, purple, purple
280
27
61
Uwi ungu
28
62
Obi item
Sumatera
Getas, Purwodadi,
Central Java
Bangkalan, Madura
29
63
Obi Violet
Pamekasan, Madura
30
67
Uwi hitam
purple
light purple
purple with blackish blotch
white light purple centre
Pelaihari, South
Kalimantan
31
68
Ubi ungu
Banggai, Central
Celebes
32
128
Uwi ungu
Ciherang, Cianjur,
West Java
33
131
Uwi ungu
Tanggamus, Lampung, Reddish-purple, oblong, purple, purple
Sumatera
34 136.b Uwi jingga
Mojopahit, Lampung,
Reddish-purple, oblong, purple, orange
Sumatera
35
108
Dioscorea sp
Kinton, Luwuk,
Central Celebes
yellowish-white
36
111
Dioscorea sp.
Buon, Luwuk, Central
Celebes
yellowish-white
37
127
Uwi kamayung
Banjar Patoman,
Green, irregular, light-brown, white
Ciamis, West Java
38
138
Uwi ungu
Lombok, Nusa
Tenggara.
39
140
Uwi ungu
Ternate, North
Maluku
40
142
Uwi ungu
Merauke, West Papua
41
141
Uwi putih
Lombok, Nusa
Green, oblong, light-brown, white
Tenggara
42
139
Uwi putih
Ternate, Maluku
Green, ovate-round, light-brown, white
43
143
Uwi putih
Merauke,West Papua
Green, oblong, light-brown, white
44
144
Uwi kuning
Merauke, West Papua
Green, oblong, light-brown, yellow
Note: Acc.: accession number. OTUs refer to accession number.
281
Table 2. Scoring or recording characters between OTUs of Indonesian water yam.
Number
1
2
3
4
5
6
7
Characters
Density of tuber roots
Tuber shape
Skin groove
Number of tuber per
plant
Tuber hardness level
Tuber texture
Tuber skin color
8
Tuber flesh color
9
10
11
12
13
14
15
16
17
18
19
Root fiber
Tuber size
Taste of cooked tuber
Tuber sap
Tuber growth
Wood particle of tuber
Young shoot color
Internodes length
Internodes diameter
Presence of wing stem
Color of old stem
node
Color of old
internodes
Color of wing stem
Color of leaf nerve
Color of leaf blade
Color of upper petiole
Color of petiole base
Leaf position
Lobe of leaf base
Distance of leaf base
lobe
Width of leaf blade
Length of leaf blade
Petal color
Anther color
Filament color
Stigma color
Style color
Capsule shape
Capsule color
Size of wing capsule
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
Binary and multistate scoring
0 = rare, 1 = dense
0 = round, 1 = ob-ovate, 2 = oblong, 3 = cylindrical
0 = absent, 1 = present
0 = 1 tuber, 1 = 2-3, and 2 = more than 3 tubers
0 = soft, 1 = hard
0 = smooth, 1 = rough
0 = yellowish-white, 1 = red, 2 = purplish-red, 3=
brown
0 = white, 1 = yellow, 2 = violet, 3 = purple 4=
blackish purple
0 = non fiber, 1 = fiber
0 = tiny, 1 = between tiny and large, 2 = large
0 = salty, 1 = sweet, 2 = bitter
0 = few, 1 = many
0 = lateral, 1 = vertical
0 = without wood, 1 = ½ part of harvest tuber
0 = green tinged with brown, 1 = purplish-red
0 = short category, 1 = long category
0 = short, 1 = length
0 = absent, 1 = present
0 = light green, 1 = purplish-red
0 = green, 1 = brownish-green, 2 = red
0 = light green, 1 = purplish-red, 2 = transparent
0 = spiral , 1 = one side series, 2 = opposite
0 = without lobe, 1 = shallow lobe, 2 = deep lobe
0 = without distance, 1 = short, 2 = length
0 = narrow, 1 = wide
0 = short, 1 = long
0 = green, 1 = reddish-green
0 = brown, 1 = reddish-brown
0 = light-brown, 1 = reddish-brown
0 = light-brown, 1 = reddish-brown
0 = whitish-green, 1 = reddish-green
0 = ball shape (sphaeris), 1 = oblong
0 = green, 1 = reddish-green, 2 = whitish-green
0 = narrow, 1 = wide
282
Data analysis
Cluster Analysis
Cluster analysis was carried out based
on the matrix to construct dendrogram
using Un-weighted Pair-Group Method
Using Arithmetic Average (UPGMA)
method with NTSYS pc version 2.1.
software (Applied Biostatistic inc.,
Microsoft, USA). A dendrogram was
constructed
based
on
the
morphological variability and intraspecific classification of each OTUs.
Furthermore, data was analyzed
descriptively
to
create
the
identification key of intra-specific
groups and sub-groups of water yam
germplasm.
A dendrogram (Figure 1) was
constructed based on the coefficient
similarity, illustrated the similarity
between OTUs of Indonesia water
yam (D. alata L.) germplasm (Table
1). The dendrogram was divided into
2 main clusters I and II. Both clusters
was differed for leaf, tuber shape and
color were 53% similar.
Based on dendrogram shown
in Figure 1, cluster I composed of 24
water
yam
accessions
are
characterized by green stem nodes,
lower and upper end of leaf petioles,
leaf nerves, and auricle at petiole
base. This cluster also has light to
dark brown tuber skin, and white,
yellowish white, or yellow tuber
color. Cluster I was determined as
green group. On the other hand,
cluster II composed of 20 water yam
accessions is characterized by
purplish-red of stem nodes, lower and
upper end of leaf petioles, leaf
nerves, and auricle at petiole base.
This cluster also has purple tuber
skin, and white, violet, purple,
purplish-red, and blackish purple
tuber color. Cluster II was
determined as purplish red group.
The green group (cluster I) is
classified into A and B groups, on
both groups has similarity coefficient
0.82. The A group is characterized by
rounded to ob-ovate tuber shape,
whereas the B group is characterized
by cylindrical tuber shape.
RESULTS
Morphological
observation
variability
Observations based on color of stem
nodes, lower and upper end of leaf
petioles, leaf nerves, and auricle at
the base of petiole differentiate two
main groups of Indonesia water yam
as
green
and
reddish-purple.
Observations
based
on
tuber
characteristic formed four groups
namely irregular, oblong, ovate or
round, and cylindrical tuber shape.
Most tubers developed branches or
lobes of various shapes and sizes,
although some tubers developed
without having any branch or lobe
and thus formed a single oblong,
ovate, or round tuber. The tuber flesh
color also varied, displaying white,
yellowish-white, yellow, purple,
reddish-purple, and mixed colors.
The green and reddish-purple group
supported by the color of tuber skin
such as; green group has light to dark
brown tuber skin, while reddishpurple group has purple tuber skin.
283
1
2
3
A
4
5
I
3
B
a
1
A
II
2
d
B
0.53
0.64
0.76
1
6
9
5
10
121
127
12
15
20
22
23
139
141
26
44
1 143
29
35
37
2 39
108
111
144
50
47
117
118
a
53
55
120
136.a
61
128
131
b 67
136.b
c 62
63
68
140
138
142
45
1.00
0.88
Coefficient
Figure 1. Dendrogram of 44 accession of Indonesian water yam based on
Morphological characters. Accession numbers are shown on the right hand side of the
dendrogram.
284
As shown in Figure 1, A group of
cluster I has 5 sub-groups. Sub-group
1 is characterized by rounded, to
oblong or bottle tuber shape with
white flesh and light brown tuber
skin, composed of uwi beras
accession from Bantul Yogyakarta,
uwi elus accession from Purwodadi
Central Java, uwi alas accession from
Rembang Central Java and Gunung
Kidul Yogyakarta, uwi putih
accession from Purwodadi, ubi putih
accession from Banggai, Buon,
Kinton Central Celebes, uwi putih
accession from Sarimulya, South
Kalimantan that were 91% similar, as
white rounded to oblong water yam.
Sub-group 2 is characterized
by ob-ovate tuber shape with
sweetish
taste
when
boiled,
composed of uwi legi from
Sendangsari and Sewon, Bantul,
Yogyakarta that were 91% similar, as
white sweetish irregular water yam.
Sub-group 3 is characterized by
oblong tuber shape with white tuber
flesh, composed of uwi putih
accession from Ternate, Moluccas
and Lombok that were 90% similar,
as white ovate to oblong water yam.
Sub-group 4 is characterized by
yellow tuber flesh, composed of uwi
butun and uwi kuning from
Yogyakarta that were 90% similar, as
yellow ovate to oblong water yam.
Sub-group 5 is characterized
by oblong tuber shape, with white
tuber flesh and light brown tuber skin
that were 86% similar, composed of
uwi putih accession from Merauke
West Papua as white oblong Papua
water yam.
On the other hand, as shown
in Figure 1, group B of cluster I has 3
sub-groups.
Sub-group
1
is
characterized by long cylindrical
tuber shape with white to yellow
flesh color, composed of uwi luyung
putih, uwi luyung kuning, uwi ulo
accession from Yogyakarta, and uwi
ular accession from Bawean Madura
Island that were 96% similar. Subgroup 2 is characterized by
cylindrical tuber shape with hard
tuber flesh, composed of Dioscorea
sp. accession from Kinton, Central of
Celebes. Sub-group 3 is characterized
by long cylindrical and yellow flesh
color and light brown tuber skin,
composed of uwi kuning accession
from Merauke, West Papua. All
group are identified as white to
yellow long cylindrical water yam.
Cluster I (intra-specific green group)
consisted of five sub-groups: (1)
white rounded to oblong; (2) white
sweetish irregular; (3) white ovate to
oblong; (4) yellow ovate to oblong;
and (5) white to yellow long
cylindrical water yam. All subgroups in group B in cluster I have
light to dark brown tuber skin.
Cluster II is classified into A and B
groups that were 67% similar. Group
A was characterized by yellow,
white, and purple flesh color with
purple tuber skin, group B was
characterized by yellow flesh with
purple tuber skin, and on both groups
had similar tuber skin color.
Group A of cluster II is
classified into two sub-groups: subgroup 1 and 2 that were 78% similar.
Sub-group 1 is characterized by long
cylindrical tuber with reddish-purple
flesh, and composed of uwi luyung
senggani accession from Yogyakarta
as purple long cylindrical water yam.
Sub-group 2 is classified into three
sub-sub groups (a,b,c, and d).
Sub-sub group a has two
variants including water yam with
white flesh and purple outer flesh
ring composed of uwi bangkulit
accession from Bantul Yogyakarta,
Serongga, and Sei Kupang. South
285
Kalimantan has purple ring white
flash ovate-round water yam. The
other variant of sub-sub group a is
characterized by purple flesh color
and ob-ovate to oblong tuber shape
composed of uwi senggani accession
from Sleman Yogyakarta, uwi ungu
accession
from
Demak
and
Purwodadi Cental Java, Sarimulya
South Kalimantan, Cianjur West
Java, and Tanggamus Lampung, uwi
merah accession from Lampung
Sumatera Island. All of them were
100% similar as purple irregular
water yam.
Sub-sub
group
b
is
characterized by oblong tuber shape
with orange flesh for uwi jingga
accession from Lampung and purple
with blackish blotches for uwi hitam
accession from Pelaihari, South
Kalimantan that were 94% similar as
Orange-blackish purple oblong water
yam.
Sub-sub
group
c
is
characterized by oblong to rounded
tuber with light purple for ubi ungu
accession from Banggai Central
Celebes, purple for obi violet
accession from Madura, and purple
with blackish blotches for obi item
accession from Madura that were
95% similar as purple round-ovate
water yam.
Sub-sub
group
d
is
characterized by oblong to cylindrical
tuber with purple flesh color
composed of uwi ungu accession
from Ternate Northern Moluccas,
Lombok Nusa Tenggara, and Papua
that were 93% similar as purple
oblong-cylindrical water yam.
Furthermore, group B of
cluster II is characterized by ob-ovate
tuber shape, yellow flesh color, and
purple tuber skin, composed only uwi
kuning accession from Pelaihari
South Kalimantan, this accession was
separate from group A and was 73%
similar as yellow flesh with purple
skin water yam.
Cluster II generally had five
intra-specific groups: (1) purple long
cylindrical; (2) purple ring white
flash ovate-round; (3) purple
irregular; (4) orange-blackish purple
oblong; (5) purple round-ovate; and
(6) yellow flesh purple skin water
yam. All groups in cluster II have
purplish-red tuber skin.
DISCUSSION
Intra-specific
classification
is
important to characterize germplasm,
including cultivated species, and for
comparison with weedy and wild types
(Hawkes,
1986).
Water
yam
(Dioscorea alata L.) germplasm in
Indonesia was classified into intraspecific green (I) and purplish-red (II)
clusters are based on the color of stem
nodes, upper and base of leaf petiole,
leaf nerves, auricle at petiole base,
tuber, and tuber shape. According to
Shiwachi et al. (2000) tuber shape and
presence of anthocyanin on the leaf
axils or petiole are indicators to
classify strains of water yam.
The sub-group white rounded
to oblong water yam (Cluster I, Group
A) was morphologically similar to D.
alata L. form. ubium vulgare or ubi
kelapa (Heyne, 1950), the white
cultivar group of Hasan et al. (2006),
to round or oblong white group of
Hasan et al. (2008), D. alata L. cv.
Group Poor White (Onwueme and
Ganga, 1996), or bottle shape yam
group of Jui-Seng Lai et al. (2005)
especially for ubi putih accession from
Banggai, Kinton, and Buon Central
Celebes.
The sub-group white sweetish
irregular water yam (Cluster I, Group
286
kelapa, especially uwi legi that is
similar to ubi manis, which were
consumed in Jakarta long ago (Heyne,
1950), clumpy yam group of Jui-Seng
Lai et al. (2005), white cultivar group
of Hasan et al. (2006), and irregular
white group of Hasan et al. (2008).
The sub-group white ovate to
oblong water yam (Cluster I, Group A)
was morphologically similar with D.
kelapa (Heyne, 1950), white cultivar
group of Hasan et al. (2006), round
white or oblong white group of Hasan
et al. (2008), or D. alata L. cv. Group
Poor White of Onwueme and Ganga
(1996). This sub-group is composed of
the accession from Lombok Nusa
Tenggara
Island
and
Ternate
Moluccas.
The sub-group yellow ovate to
oblong water yam (Cluster I, Group A)
is a specific group of Indonesian water
yam, because of no available
information of yellow tuber flesh color,
according to Heyne (1950), Onweume
and Ganga (1996), Jui-Seng Lai et al.
(2005), Hasan et al. (2006) and Hasan
et al. (2008).
The sub-group white to yellow
long cylindrical water yam (Cluster I,
Group A) was morphologically similar
to D. alata L. form. ubium anguinum
(ubi ular, uwi ulo) in Java, and had a
cylindrical tuber shape with a wide at
the middle section. In Yogyakarta was
found uwi luyung putih some time was
called uwi jengking similar to D. alata
L. form. as ubium anniversarum with
length and slender tuber, its very
difficult to put the upper part of tuber
(Heyne, 1950). Compared to Jui-Seng
Lai et al. (2005) it was similar to a
long tuber length yam, and D. alata L.
cv. Group Primitive Green from
Onwueme and Ganga (1996).
The sub-group purple long
cylindrical water yam (Cluster II,
Group A) was morphologically similar
to Dioscorea alata L. sub-form. ubium
vulgare as ubi mengari from Rumphius
(Heyne, 1950) with purple tuber flesh,
red flesh and a long tuber length yam
group from Jui-Seng Lai et al. (2005),
group purple tuber color (Hasan et al.
(2008), or D. alata L. cv. Group
Primitive Purple from Onwueme and
Ganga (1996).
The sub-group purple ring
white flesh ob-ovate water yam
(Cluster
II,
Group
A)
was
morphologically
similar
with
Dioscorea alata L. sub-form. ubi
senggani from Rumphius (Heyne,
1950), white flesh with red skin yam
group of Jui-Seng Lai et al. (2005), or
group white and purple tuber color of
Hasan et al.(2006).
The sub-group of purple
irregular water yam (Cluster II, Group
A) was morphologically similar to
Dioscorea alata L. form. ubium
vulgare sub-form. ubi mengari from
Rumphius (Heyne, 1950), group of red
flesh yam of Jui-Seng Lai et al. (2005),
group of rounded and purple tuber
color of Hasan et al. (2006), and
irregular purple group of Hasan et al.
(2008), and D. alata cv. group Purple
Compact, which is characterized by
large content of anthocyanin especially
in petiole, ruffled wings, short tubers
with colored flesh and skin (Onwueme
and Ganga, 1996).
The sub-group purple ovateoblong water yam (Cluster II, Group
alata L. form, ubium vulgare sub-form,
ubi mengari (pure purple) from
Rumphius (Heyne, 1950), group of
bottle shape yam of Jui-Seng Lai et al.
(2005), rounded and purple tuber color
yam of Hasan et al. (2006), round
purple or oblong purple group of
287
Hasan et al. (2008), or D. alata L. cv.
Group Compact. The latter is
characterized by foliage tinged with
anthocyanin, short tubers, and wide
and little skin and flesh coloration
(Onwueme and Ganga, 1996).
The sub-group orange-blackish
purple oblong and yellow flesh purple
skin water yam (Cluster II, Group B) is
a specific group of Indonesian water
yam. To date, scientific reports and
information of orange and blackish
blotches of color in water yam tuber
flesh is still lacking (Heyne, 1950;
Onwueme and Ganga, 1996; Jui-Seng
Lai et al., 2005; Hasan et al., 2006;
Hasan et al., 2008).
There are many accessions of
each sub-group that have high
similarity coefficients showing the
close relationship phenetically between
OTUs such as between OTU (3-4), (25), (9- 37), (10-11), (16-17), (20-2122), (23-24-25-26-27-32-33) and (2829) (Figure 1). They have 1.00
similarity coefficients that mean there
is no difference. According to Mwirigi
et al. (2009) the high similarity
between cultivars is due to vegetative
propagation. Purnomo (2010) reported
that the accession from Lampung
(Sumatera) and South Kalimantan most
of them ethno-botanically are the
clones of water yam from Java, with
anthropogenic dispersal more for ritual
activity in land opening than for food
resources.
Indonesia water yam has large
morphological variation, there are 11
sub-groups from 2 groups (in this
research), and to improve germplasm
management, it is important to reveal
the extent of genetic diversity present
in collections, using other means such
as molecular marker techniques.
Intra-Specific
Classification
of
Indonesian Water Yam (Dioscorea
alata L.)
Based on morphological characters and
the cluster analysis, the Indonesian
germplasm of water yam (D. alata) can
be classified as described in Table 3.
The results revealed that intra-specific
classification of Indonesian water yam
(Dioscorea alata L.) are green and
reddish-purple groups. The green
group is characterized by green of stem
nodes, lower and upper end of leaf
petioles, leaf nerves, and auricle at
petiole base with white, yellowish
white, or yellow tuber color, and light
and dark brown tuber skin. While the
reddish-purple group is characterized
by purplish-red of stem nodes, lower
and upper end of leaf petioles, leaf
nerves, and auricle at petiole base,
with white, violet, purple, reddishpurple, and blackish purple tuber color
with purple tuber skin. Furthermore,
the green group has five sub-groups:
(1) white rounded to oblong, (2) white
sweetish irregular, (3) white ovate to
oblong, (4) yellow ovate to oblong, and
(5) white to yellow long cylindrical
water yam. The five sub-groups were
identified based on tuber shape and
tuber flesh color. On the other hand,
the reddish-purple group has six subgroups: (1) purple long cylindrical, (2)
purple ring white flash ob-ovate, (3)
purple irregular, (4) Orange-blackish
purple oblong, (5) purple round-ovate,
(6) yellow flesh purple skin water yam.
The six sub-groups are identified based
on the tuber shape, tuber skin, and
color distribution in tuber flesh.
288
Table 3. Groups, sub-groups, sub-sub groups, and cultivar names of Indonesia water
yam.
Groups
Green
Sub group
Cultivars name and origin
White rounde Uwi beras, Bantul, Yogyakarta
oblong
Uwi elus, Purwodadi, Central Java
Uwi alas Rembang, Central Java, and Gunung
Kidul Yogyakarta
Uwi putih, Purwodadi, Central Java
Ubi putih, Banggai, Central Celebes
Uwi putih, Sarimulya, South Kalimantan
White
swUwi legi Bantul, Yogyakarta
irregular
White
ovat Uwi putih, Ternate, North Mollucas and Lombok, Nusa
oblong
Tenggara
Yellow ova Uwi butun Yogyakarta
oblong
Uwi kuning, Yogyakarta
White to yello Uwi luyung putih, Yogyakarta
cylindrical
Uwi luyung kuning, Yogyakarta
Uwi ulo, Yogyakarta
Uwi ular Bawean, Madura, East Java
Dioscorea sp., Kinton, Central Celebes
White oblong Ubi putih,Merauke, West Papua
Purplish
-red
Purple
Luyung senggani, Yogyakarta
cylindrical
Purple ring Uwi bangkulit, Yogyakarta
white flesh
ovate-round Uwi Bangkulit, Serongga, South Kalimantan
Uwi Bangkulit Sei Kupang, South Kalimantan
Purple irregul Uwi senggani, Yogyakarta
Uwi ungu, Demak and Purwodadi, Central Java), Sarimulya South
Kalimantan, Cianjur West Java, Tanggamus Lampung, Sumatera
Uwi merah, Lampung, Sumatera
Orange-blacki Uwi jingga, Lampung, Sumatera
purple
Oblong
Uwi hitam Pelaihari, South Kalimantan
Purple
o Uwi ungu Ternate, Northern Maluku and Lombok, Nusa Tenggara, and
cylindrical
Merauke, West Papua
Yellow flesh Uwi kuning, Pelaihari, South Kalimantan
purple skin
289
ACKNOWLEDGEMENTS
We would like to thank the Indonesian
Managing Higher Education for Relevancy
and Efficiency (I-MHERE) Project,
Faculty of Biology, Gadjah Mada
University Indonesia for supporting
financial through Research Grant No:
UGM/BI/1157/I/ 05/04 for 2010.
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291
RESEARCH ARTICLE
SABRAO Journal
44 (2) 292-301, 2012
GENETICS OF IMPORTANT AGRO-BOTANIC TRAITS IN
SESAME
P. VENKATA RAMANA RAO1, 2 & 3, G. ANURADHA2, A.
SRIVIDHYA2, V.L.N. REDDY2, V. GOURI SHANKAR3*, K.
PRASUNA3, K. RAJA REDDY2, N.P. ESWARA REDDY2 and E.A.
SIDDIQ2
1
Dept. of Genetics and Plant Breeding, S.V. Agricultural College, ANGRAU, Tirupati, AP, India
2
Institute of Biotechnology, ANGRAU, Rajendranagar, Hyderabad-30, AP, India
3
Regional Agricultural Research Station, Palem-509215, Mahabubnagar Dist., AP, India
SUMMARY
Two sesame genotypes Chandana (mixed leaf position, lobed basal leaf position,
horizontal leaf angle, profuse capsule hair density, medium capsule hair length, broad
oblong capsule shape, and sparse stem hairiness) and TAC-89-309 (opposite leaf
position, entire basal leaf position, acute leaf angle, sparse capsule hair density, short
capsule hair length, narrow oblong capsule shape and glabrous stem hairiness) that
differ in respect of the above mentioned agro-botanic traits were crossed to study the
inheritance of these traits. The F2 population along with the F1 and parents was
evaluated under field conditions and observations were made on seven agro-botanic
traits using International Board for Plant Genetic Resources (IBPGR) descriptor
grading. The traits, mixed leaf position, profuse capsule hair density, broad oblong
capsule shape and sparse stem hairiness are dominant over opposite leaf position,
sparse capsule hair density, narrow oblong capsule shape and glabrous stem hairiness,
respectively. Further these traits are independently governed by single dominant genes.
Inheritance of the traits, leaf angle and capsule hair length is governed by two
dominant genes wherein complementary gene action wherein horizontal leaf angle is
dominant over acute leaf angle and medium capsule hair length over short capsule hair
length. The F2 plants of entire and lobed leaf segregated in the ratio of 15:1 indicating
that leaf shape is controlled by two dominant genes.
Key words: sesame, agro-botanic traits, genetics
Manuscript received: February 2, 2012; Decision on manuscript: June 15, 2012; Manuscript
Rao et al. (2012)
INTRODUCTION
Sesame (Sesamum indicum L.) also
known as sesamum, til, gingelly,
simsin, gergelim is the most
ancient oilseed crop of the world. It
is being cultivated in Asia for last
5000 years (Joshi, 1961; Weiss,
1971). It is regarded as the ‘Queen
of Oilseeds’ as the quality of oil is
of high nutritional and therapeutic
value with distinct sweet flavor and
oil meal with rich protein make it
ideal
for
domestic
and
confectionery uses respectively.
The antioxidants ‘sesamin’
and ‘sesmolin’ enhance the
keeping quality of oil by making it
resistant to rancidity. Widely
naturalized
in
tropical
and
subtropical regions, sesame is the
sixth most important oilseed crop
of the world, occupying an area of
6.6 m. ha, with a production of
3.15 m. tonnes and its average
productivity being 460 kg/ha. India
and China together account for
over 70% of the global production.
India is the largest sesame growing
country in the world with an area
of 1.85 M ha, producing 0.64 M.
tonnes but productivity is among
the lowest with 345 kg/ha (CMIE,
2010). It is grown in marginal and
sub marginal lands as rainfed crop
mainly in the states of Gujarat,
West Bengal, Uttar Pradesh,
Rajasthan,
Madhya
Pradesh,
Andhra Pradesh, Maharashtra,
Tamil Nadu, Orissa and Karnataka,
which account for more than 96%
of the total area and production.
Despite its shorter life
cycle, suitability to different
cropping systems and land types,
adaptation to moisture stress and
low input management conditions,
sesame’s contribution to the
country’s oilseed production could
improve. The major reason for the
dismal state of production is very
low and inconsistent productivity
of the current varieties. All past
efforts to raise yield level by
conventional
recombination
breeding have hardly yielded
anything
tangible
due
to
dependence of breeders on narrow
cultivar gene pool for desired
variability and poor understanding
of the physiology and genetics of
yield related traits. Also, lack of
basic information on genetics and
inheritance of traits of economic
importance, especially complexly
inherited traits is causing hindrance
to the breeders in realization of
higher yields.
So far the knowledge of
genetics of traits of economically
important traits, very meager is
known. Most of the available
information (on branching habit,
multiple flowers and capsules/axil,
male sterility, indehiscent nature of
capsule, capsule length and
photoperiod response) is from the
pioneering studies of Mohammed
and Alam (1933), Pal (1934) and
Langham (1945, 1946, 1947a,
1947b)
which
have
been
extensively reviewed by Joshi
(1961), Weiss (1971) and Brar and
Ahuja (1979). There are no reports
on the linkage relationships of
these traits. Not much has been
done in identifying reliable
parental sources for investigations
related to many economic traits.
Keeping this in view the present
study was undertaken to study the
inheritance of important agrobotanic traits in sesame.
293
MATERIAL AND METHODS
Two sesame genotypes, Chandana
(a high yielding variety released
from
Regional
Agricultural
Research Station, Jagtial, Acharya
N.G.
Ranga
Agricultural
University,
India)
and
the
accession TAC-89-309 (made
available by Jawaharlal Nehru
Krishi Vishwa Vidyalay, Jabalpur)
with differential agro-botanic traits
were chosen as parents to study the
inheritance of these traits. At
flowering, plants were selected and
tagged for crossing. Emasculation
of female parents was done in the
previous evening with ease taking
advantage of the epipetalous nature
of sesame flower. The following
day, anthers along with petals
collected from the male parent in a
glass petriplate were pressed gently
with finger so that pollen gets
released. The crossed flowers were
properly labelled by tying a piece
of coloured thread. Matured
capsules were harvested before
dehiscence and F1 seeds collected
from them were dried and stored
safely. Crossing was done between
the parents to generate F1 during
kharif, 2009 at Seed Research and
Technology Centre, Rajendranagar.
The F1 was selfed to produce F2
during rabi, 2009-2010. The F2
population along with F1 and
parents was evaluated under field
conditions at the College Farm,
College
of
Agriculture,
Rajendranagar, Hyderabad during
kharif, 2010. Observations were
made on seven agro-botanic traits
(Table 1) using IBPGR descriptor
grading.
The segregating population (F2)
along with parents Chandana and
TAC-89-309 and F1 was evaluated
for seven agro-botanic traits and
the inheritance of these agrobotanic traits was studied. The
study revealed wide variation for
all the seven agro-botanic traits
which broadly include leaf, stem,
floral and capsule characters (Table
2). In leaf, traits related to mode of
inheritance of leaf position, leaf
type and shape were studied. Many
of the traits appear to be either
monogenic or digenic /dominant or
recessive.
The
female
parent
Chandana was characterized with
mixed leaf position (opposite +
alternate) while it was opposite in
TAC-89-309 and in F1 the leaf
position was mixed.
294
Rao et al. (2012)
Table 1. List of agro-botanic traits studied.
1
S. No.
Trait
Leaf position
2
Basal leaf shape
3
Leaf angle
4
Capsule hair density
5
Capsule hair length
6
Capsule shape
7
Stem hairness
Category
Opposite
Alternate
Mixed
Entire
Lobed
Acute
Horizontal
Drooping
Glabrous
Sparse
Profuse
Short
Medium
Long
Tapered
Narrow oblong
Broad oblong
Square
Glabrous
Sparse
Hairy
Very hairy
Grading
1
2
3
1
2
3
5
7
0
3
7
1
2
3
1
2
3
4
0
3
7
9
Table 2. Expression of Agro-botanic traits in parents and F1 (Chandana x TAC89- 309).
S.
No.
1
2
3
4
Trait
Chandana
TAC-89-309
F1
Mixed (3)
Lobed (2)
Horizontal (5)
Profuse (7)
Opposite (1)
Entire (1)
Acute (3)
Sparse (3)
Mixed (3)
Entire (1)
Horizontal (5)
Profuse (7)
5
6
Leaf position
Basal leaf shape
Leaf angle
Capsule hair
density
Capsule hair length
Capsule shape
7
Stem hairiness
Medium (2)
Broad oblong
(3)
Sparse (3)
Short (1)
Narrow oblong
(2)
Glabrous (0)
Medium (2)
Broad oblong
(3)
Sparse (3)
* Values in parenthesis indicate grading of each trait as per IBPGR descriptors
295
Table 3. Mode of inheritance of important agro-botanic traits.
Generations
Chandana
TAC-89309
F1
F2
Generations
Chandana
TAC-89309
F1
F2
Generations
Chandana
TAC-89309
F1
F2
Generations
Total
plants
Mixed
Mixed
Opposite
120
Total
plants
Leaf Position
Expected
frequencies
Observed
frequencies
Mixed
91
Opposite
29
Observed
frequencies
Entire
Lobed
Mixed
90
Total
plants
120
Total
plants
Entire
109
11
30
Basal Leaf Shape
Expected
frequencies
Entire Lobed
112.5
χ2
χ2
-
-
-
3:1
0.04
3.84
table
value
Opposite
Lobed
Entire
120
Ratio
M: O
7.5
Leaf Angle
Expected
frequencies
Horizontal Acute
Ratio
E:L
χ2
χ2
-
-
table
value
-
15:1
1.73
3.84
Ratio
H:A
χ2
χ2
Horizontal
Acute
-
-
table
value
-
Horizontal
66
9:7
0.07
3.84
χ2
χ2
Observed
frequencies
Horizontal Acute
54
Observed
frequencies
Profuse Sparse
67.5
52.5
Capsule Hair Density
Expected
Ratio
frequencies
P:S
Profuse Sparse
table
value
Chandana
Profuse
-
-
-
TAC-89-309
F1
F2
Sparse
Profuse
88
3:1
0.17
3.84
120
32
90
30
296
Rao et al. (2012)
Generations
Chandana
TAC-89309
F1
F2
Generations
Total
plants
120
Total
plants
Chandana
TAC-89309
F1
F2
120
Generations
Total
plants
Chandana
TAC-89309
F1
F2
Observed
frequencies
Medium Short
Capsule Hair Length
Expected
Ratio
frequencies
M:S
Medium Short
χ2
table
value
Medium
Short
-
-
-
Medium
64
9:7
0.41
3.84
56
Observed
frequencies
Broad
Narrow
oblong oblong
Broad
oblong
Narrow
oblong
Broad
oblong
96
24
Observed
frequencies
Sparse
Glabrous
67.5
52.5
Capsule Shape
Expected
frequencies
Broad Narrow
oblong oblong
90
30
Stem Hairiness
Expected
frequencies
Sparse Glabrous
Sparse
Glabrous
120
χ2
Sparse
87
33
90
30
Ratio
BO:NO
χ2
χ2
-
-
-
-
-
-
-
-
-
3:1
1.6
3.84
table
value
Ratio
S:G
χ2
χ2
-
-
-
3:1
0.40
3.84
table
value
297
Figure 1. Segregation for leaf position in F2.
Figure 2. Segregation for leaf shape in F2.
Figure 3. Capsule hair density and capsule shape in Chandana, TAC-89-309,
F1 and F2.
298
Rao et al. (2012)
In the F2 population, the ratio of
opposite to mixed leaf was 1:3
(Table 3) (Figure 1). So, mixed leaf
position is dominant over opposite
position and is controlled by a
single dominant gene. This finding
is in agreement with that of
Mohammad and Gupta (1941).The
fact that leaf arrangement could be
opposite, alternate or mixed as
reported by Hiltebrandt (1932),
however, warrants further study to
understand the relative dominancerecessive relationship of the alleles
governing
all
the
three
dispositions. In TAC-89-309 leaf
was entire with no lobes while in
Chandana it was lobed whereas,
the F1 had entire leaf shape. In the
F2, plants of entire and lobed leaf
segregated in the ratio of 15:1
(Table 3) (Figure 2). Therefore, it
can be concluded that leaf shape is
governed by two dominant genes.
Langham
(1945)
had
reported that entire leaf to be
dominant over wrinkled leaf.
Murthy and Oropeza (1989), based
on their study of the induced
mutant trait ‘narrow leaf’ in M4
generation, concluded that normal
leaf was dominant over narrow leaf
and was controlled by duplicate
dominant gene action. Sonali
Sengupta and Datta (2005)
reported that narrow leaf lamina
was governed by single recessive
gene. The findings are contrary to
Bayadar and Turgut (2000) who
reported that lobed leaf was
governed by single dominant gene.
Kashiram (1930) had reported that
leaf varied from ovate to coordate,
lanceolate to linear, tripartite to
variously lobed. Chandana and
TAC-89-309 were characterized by
horizontal and acute leaf angles
respectively. The leaf angle in F1 is
horizontal and F2 population of the
cross segregated in the ratio of 9
horizontal to 7 acute leaf angles
suggesting leaf angle to be
controlled by two dominant genes
showing complementary gene
action (Table 3) which is reported
for the first time.
Hairiness on various plants
is yet another trait that shows great
variation
depending
on
environmental influence. Hairiness
was profuse on capsules of
Chandana while, it was sparse in
TAC-89-309. The hairiness on
capsules of F1 was profuse as in
Chandana and in F2 it varied from
profuse to sparse in the ratio of 3:1
(Table 3) (Figure 3). Hence,
profuse hairiness was found to be
dominant over sparse hairiness and
controlled by single dominant
gene. The simple mode of
inheritance governing the trait is
consistent with Tan (1998) and
Falusi (2000) who reported
hairiness to be governed by a
single dominant gene. Rhind and
Thein (1932), while characterizing
Burmese
sesame
germplasm,
reported that capsule hair density
was variable and separable into
two groups i.e. “hairy” having
abundant long hairs and “slightly
hairy” having few shorter hairs. In
some cases hairiness is less
prominent and hence capsules look
somewhat smooth (Kashiram,
1930). The capsule hair was of
medium length in Chandana as
against short hairs in TAC-89-309.
In F1, length of hair was medium
and F2 segregated in the ratio of 9
medium to 7 short hairs (Table 3).
Hence, medium hair length is
dominant over short hair length
and controlled by two dominant
genes exhibiting complementary
299
gene action (9:7). The capsule
shape was broad oblong and
narrow oblong respectively in
Chandana and TAC-89-309. The
shape in F1 was broad oblong while
in F2 capsule shape segregated in
the ratio of 3 broad oblong to 1
narrow oblong capsules (Table 3)
(Figure 3). Capsule shape appears
to be simply inherited and
governed by a single dominant
gene with broad oblong capsule
being dominant over narrow
oblong.
The type and degree of
stem hairiness, although a varietal
characteristic, level of expression is
subject to environmental influence.
In Chandana, hairiness of stem was
sparse while no hairiness was
observed in TAC-89-309. The F1
had hairs on the stem while F2
segregated for hairiness in the ratio
of 3 hairy to 1 glabrous stem
(Table 3). In agreement with earlier
reports, stem hairiness in this study
has been found to be controlled by
a single dominant gene (Tan, 1998;
Falusi, 2000) whereas Falusi et al.
(2002) reported that stem hairiness
was
controlled
by
two
independently assorting genes.
Hiltebrandt (1932) recognized four
grades in stem pubescence viz.,
very short, velvety, short and
mixed. Mohammed and Alam
(1933) have classified the stem as
almost smooth, hairy and very
profusely hairy.
The study of genetics of
the agro-botanic traits is very
important as these traits could be
linked to complex inherited traits
like
yield
and
its
major
components and tolerance to
abiotic stresses. For example low
leaf area in the entire leaf type
facilitates better penetration of
sunlight to most of the lower
leaves. Recombining of the trait
‘entire’ leaf with otherwise
superior yielding genotypes might
help in improving harvest index
and thereby seed yield. Also,
ability to select genotypes with
desired degree of stem pubescence
could be of value in breeding for
tolerance to biotic/abiotic stresses
as strong association has been
reported between singly inherited
stem pubescence and complexly
inherited
drought
resistance
(Weiss, 1971). Therefore breeding
for improvement of such traits
would be rewarding which would
indirectly
help
in
yield
improvement.
REFERENCES
Bayadar H, Turgut I (2000). Studies on
genetics and breeding of
sesame (Sesamum indicum
L.) I. inheritance of the
characters determining the
plant type. Turkish J. Botany
24(3): 503-512.
Brar GS, Ahuja KL (1979). Sesame,
its culture, genetics, breeding
and biochemistry. Annual
Rev. Pl. Sci. 1: 245-313.
CMIE 2010. Centre for Monitoring
Indian Economy
Falusi OA (2000). Genetic studies in
the genes ‘Sesamum’ Ph.D
thesis, Federal University of
Technology, Minna.
Falusi OA, Salako EA, Funmi FM
(2002).
Inheritance of
hairiness of stem and petiole
in a selection from local
(Nigeria)
germplasm
of
sesame. Tropicultura 20(3):
156-158.
Hiltebrandt VM (1932).
Sesame
(Sesamum
indicum
L.).
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and plant breeding 9(2): pp.
1-114.
Joshi AB (1961).
Sesamum – a
monograph. Indian Central
Oilseed
Committee
Hyderabad, India.
Kashiram (1930). Studies in Indian
oilseeds.
The types of
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department agriculture. Ind.
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Langham DG (1945). Genetics of
sesame II. Inheritance of seed
pod number, aphid resistance,
yellow leaf and wrinkled
leaves. J. Hered. 36: 245-254.
Langham DG (1946). Genetics of
sesame III. Open sesame and
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149-152.
Langham DG (1947)a. Genetics of
sesame IV. Some genetic
variations in the colour of the
sesame flower. J. Hered. 38:
221-224.
Langham DG (1947)b. Genetics of
sesame
V.
Some
morphological differences of
the sesame flower. J. Hered.
38:347-352.
Mohammad A, Alam Z (1933) Types
of Sesamum indicum DC in
the Punjab. Ind. J. Agric. Sci.
3: pp. 897-911.
Mohammad A, Gupta ND (1941).
Inheritance of alternate and
opposite arrangement of
leaves in Sesamum indicum
L. Ind. J. Agric. Sci. 11: 659661.
Murthy BR, Oropeza F (1989). An
induced leaf differentiation
mutant in Sesamum indicum
L. Curr. Sci. 58(8): 464-466.
Rhind D, Thein UB (1932). The
classification of Burmese
sesamums
(Sesamum
orientale Linn.). Ind. J. Agric.
Sci. 3: 478-495.
Sonali Sengupta and Datta AK
(2005). Induced narrow leaf
mutant of sesame (Sesamum
indicum L.). Ind. J. Genet. Pl.
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Tan AS (1998). Genetics of hairiness
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301
RESEARCH ARTICLE
SABRAO Journal
44 (2) 302-321, 2012
BREEDING TOMATO (Solanum lycopersicum L.) FOR HIGHER
PRODUCTIVITY AND BETTER PROCESSING QUALITIES
VARUN DURWAS SHENDE1, TANIA SETH2, SUBHRA
MUKHERJEE1 and ARUP CHATTOPADHYAY3*
1
Department of Plant Breeding, Faculty of Agriculture, Bidhan Chandra Krishi Viswavidyalaya,
Mohanpur-741252, Nadia, West Bengal, India
2
Department of Vegetable Crops, Faculty of Horticulture, Bidhan Chandra Krishi
Viswavidyalaya, Mohanpur-741252, Nadia, West Bengal, India
3
All India Coordinated Research Project on Vegetable Crops, Directorate of Research,
Bidhan Chandra Krishi Viswavidyalaya, Kalyani-741235, Nadia, West Bengal, India
* Corresponding author’s email: [email protected]
SUMMARY
Tomato (Solanum lycopersicum L.) has the potential for improvement through
heterosis breeding which can further be utilized for development of desirable
recombinants. A 3 × 3, line × tester mating design was used to determine heterosis
over better parent, combining ability and gene action for eleven characters in tomato.
Preponderance of non-additive gene action was evident for control of all characters
studied except TSS content of fruit for which both additive and non-additive gene
actions were evident. Amongst the parental lines, ‘CLN2498-D’, ‘CLN2762-A’ and
‘BCT-110’ were the best general combiners for fruit yield and component characters
along with good processing traits and thus could be used in tomato hybridization
programs. Crosses showing high specific combining ability (SCA) for fruit yield
involved parents showing high general combining ability (GCA) for numbers of fruit
per flower cluster or numbers of fruit per plant or fruit weight or fruit diameter. The
promising hybrids of the CLN2498-D x DVRT-2 and CLN2777-C x BCT-53 were
selected on the basis of their performance per se; heterosis manifested in them and the
SCA effects. These two hybrids could be used commercially for high yield. However,
the cross CLN2498D x BCT-110 could be exploited for better processing qualities.
Keywords: combining ability, gene action, heterosis, processing quality, tomato
Manuscript received: February 2, 2012; Decision on manuscript: June 28, 2012; Manuscript
Communicating Editor: C. Ravindran
Shende et al. (2012)
used in developing ToLCV tolerant
breeding lines for development of
promising hybrids (Hazra et al.,
2009) but has also enabled the
acquisition of good, value-added
agronomic traits, such as high
fertility and fruit setting, earliness,
uniform
ripening,
adaptation,
firmness and long shelf-life
appropriate for shipping to distant
markets. The replacement of inbred
lines by hybrids has remarkably
increased yield, while the genetic
gain rate has been reduced due to
low genetic diversity within
processing tomatoes (Grandillo et
al., 1999). This suggests that the
introduction of hybrids, after the
first positive impact, made no
further contribution to a positive
genetic gain in yield.
Tomato breeders prefer
hybrid breeding to varietal
breeding, not only because it is
comparatively easier to incorporate
desirable characteristics in F1
hybrid but also the right of the bred
hybrid is protected in terms of
parental lines (Kumar and Singh,
2005). Moreover, despite high cost
of hybrid tomato seeds, there has
been added advantage to the
farmers in cultivation of hybrid
tomatoes. This is because under
optimum crop production and
protection
management,
crop
raised from the hybrid seeds has
several advantages like better yield
and adaptability, uniformity and
reactions to certain biotic and
abiotic stresses in comparison to
crop raised from the seeds of
improved pure line or open
pollinated varieties.
Genetic analysis provides a
guide line for the assessment of
relative breeding potential of the
parents or identify best combiners
INTRODUCTION
Tomato (Solanum lycopersicum) is
one of the important Solanaceous
vegetable crops of Peru-Ecuador
origin (Rick, 1969) and used as
fresh vegetable as well as raw
material for processed products
such as juice, ketchup, sauce,
canned fruits, puree, paste, etc.
Apart from contributing nutritive
elements, colour and flavour to the
diet, tomatoes are also a valuable
source of antioxidants, or chemoprotective compounds, and may
thus be termed as "functional food"
(Ranieri et al., 2004). The desirable
qualities for a tomato cultivar to be
used for processing includes high
total soluble solids (4-8° brix),
acidity not less than 0.4 %, pH less
than 4.5, uniform red color, smooth
surface, free from wrinkles, small
core, firm flesh and uniform
ripening (Adsule et al., 1980).
Throughout
the
last
century,
tomato
varietal
improvement program has been
based on various standard breeding
methods, including the pedigree
method of hybridization followed
by backcrossing of desired traits
from one parent into another,
which has resulted in generation of
improved tomato varieties and
hybrids with high quality and yield.
Tomato improvement occurred due
to increasing exploitation of exotic
resources and introgression of new
valuable genes into the tomato
gene-pool. Classical breeding has
not only developed cultivars with
monogenic
and
dominant
resistance for controlling certain
plant pathogens or F1 hybrids by
combination of resistances, as for
example, H-24 developed by
Kalloo and Banerjee (2000), was
303
in crops (Khattak et al., 2004;
Weerasingh et al., 2004; Sulodhani
Devi et al., 2005) which could be
utilized either to exploit heterosis
in F1 or the accumulation of fixable
genes to evolve variety. Such
studies not only provide necessary
information regarding the choice of
parents but also simultaneously
illustrate the nature and magnitude
of gene action involved in the
expression of desirable traits. The
impulse of progress
in crop
improvement
through
plant
breeding was propelled by a better
understanding and an appropriate
exploitation of
heterosis, the
classical term coined by Shull
(1914) implying the gain in
vigour on crossing two inbreds. A
considerable degree of heterosis
has been documented and utilized
in tomato for various characters
ever since the first official report
by Hedrick and Booth (1907).
Tomato is a self pollinated crop,
where degree of heterosis was
theoretically
considered
less
(Gallias, 1988). However, the
unusual high heterosis observed in
tomato crop has been attributed to
the fact that tomato was basically
a highly out crossing genus which
was later evolved into a self
pollinated crop (Rick, 1965).
Heterosis manifestation in tomato
is in the form of the greater vigour,
faster growth and development,
earliness in maturity, increased
productivity,
better
quality
attributes, and higher levels of
resistance to biotic stresses
(Yordanov, 1983; Mahendrakar et
al., 2005; Seeja et al., 2007;
Hannan et al., 2007; Gul et al.,
2010; Chattopadhyay et al., 2011).
Even though many studies have
been made on combining ability,
gene action and heterosis, the pace
of work on development of tomato
hybrid on commercial basis have
been limited due to lack of superior
combiners in India. Line × tester
technique (Kempthorne, 1957) is
one of the best techniques that
provide information about general
and specific combining ability of
the parents and at the same time, it
is helpful in estimating various
types of gene effects.
Keeping in view the
importance of the above studies,
the present research program has
been undertaken to determine the
nature and magnitude of heterosis
over mid- and better-parent for
yield component and processing
characters of tomato and to
determine the nature of gene action
for these traits with a view to
identify good general combiners, as
well as to frame the breeding
strategies
for
the
genetic
improvement of such characters.
The investigation was carried out
during 2009 to 2011 at Research
Farm of Bidhan Chandra Krishi
Viswavidyalaya, Kalyani, Nadia,
West Bengal, India under the
research field of All India
Coordinated Research Project on
Vegetable Crops, situated at 23.50
N latitude and 890 E longitude at a
mean sea level of 9.75m.
Development of F1 hybrids and
field trials
Seeds of lines CLN2762-A,
CLN2498-D and CLN2777-C
imported from Asian Vegetable
Research and Development Centre,
304
the anthers were separated that
night to dry and the pollen were
extracted the following morning
for pollination, which was done
from 8 to 10 a.m. Each parental
line was crossed with each tester
separately. Hybrid seed were
extracted by the fermentation
method (Rashid and Singh, 2000).
The red ripe finely chopped fruits
were kept for overnight for
fermentation in a plastic bucket.
This process removes the mucilage
and makes the seeds free from
adhering pulp which settles down
at the bottom. Then it was washed
thoroughly with clean water in the
next day morning. Seeds which
were floated in the water along
with pulp were discarded and the
decanted seeds were taken out,
dried and stored in desiccators for
sowing in the next season.
In the next year, the same
method was followed for raising
the seedlings of 6 parental lines
and 9 hybrids and one-month-old
seedlings were transplanted in the
2nd week of September, 2010. The
parents and hybrids were planted in
a randomized complete block
design with 3 replications at 60 ×
60 cm spacing with 36 plants for
each replication in a 3.6 × 3.6 m
plot. The plant protection measures
against early blight and tomato leaf
curl virus diseases were taken up in
time. Observations were recorded
from fifteen randomly selected
plants from each parental line and
hybrid on plant height (cm), days
to 50% flowering, numbers of
flower cluster plant-1, numbers of
fruit flower cluster-1, numbers of
fruit plant-1, fruit weight (g), polar
diameter of fruit (cm), equatorial
diameter of fruit (cm), total soluble
solids (o brix) (by digital hand
Taiwan and the testers DVRT-2,
BCT-53 and BCT-110 of Indian
origin were used for the study.
Seed beds were prepared in a sandy
loam soil and were 20 cm high and
1.0 m wide. Weathered cowdung
manure at 4 kg/m2 was mixed into
the beds. Beds were drenched with
formaldehyde (4.0%) and covered
with polythene sheet for 10 days to
avoid damping off disease. Seeds,
after treatment with Thiram (3 g/kg
of seed), were sown during the 3rd
week of August, 2009 at a shallow
depth 5 cm apart and covered with
finely sieved well rotten leaf mold
(leaves left to decompose for two
year) which acts as soil improver
and to prevent the soil drying out.
After sowing, beds were covered
with straw until germination which
normally takes five to seven days
and hand watered regularly up to
1st week of September, 2009.
Nursery beds were covered with
200 μm ultraviolet (UV)-stabilized
polyethylene film supported by
bamboo poles with open sides to
protect seedlings from rain and
direct sunlight. Seedlings were
hardened by withholding water 4
days before transplanting. Twenty
five days old seedlings were
transplanted to the main field
during 2nd week of September,
2009. Three lines and three testers
were planted in each plot consisted
of 20 plants spaced by 60 cm in 2
rows, each of which are 6 m long.
Management
practices
for
cultivation were followed as per
Chattopadhyay et al. (2007).
During full boom, crossing was
carried out. Flowers of each line
were emasculated between 4.00
and 5.30 p.m. Male parent flower
buds that would open the following
day were picked in the afternoon,
305
refractometer), titratable acidity (%
in terms of citric acid) (AOAC
1984), and fruit yield plant-1 (kg).
Plant height, numbers of flower
cluster plant-1, numbers of fruit
flower cluster-1 were recorded after
last appearance of flower cluster;
days to 50% flowering was taken
when 50% plants bear first flower;
numbers of fruit flower cluster-1,
numbers of fruit plant-1 were
counted after fruit setting of last
flower cluster; fruit weight, polar
and equatorial diameter of fruit,
total soluble solids, titratable
acidity were recorded from the
latest-set (youngest) fully ripened
fruits; and fruit yield plant-1 was
computed by adding the weight of
total fruits from different pickings
from each of the reference plant in
each entry.
Statistical analyses
Data were analyzed with the line x
tester model of genetic analysis
(Kempthorne, 1957). Heterosis
over better-parent (Heterobeltiosis)
was estimated in terms of per cent
increase or decrease of the F1
hybrid over its better-parent (Hayes
et al., 1965) using the following
formula.
Heterobeltiosis (%) = [(F1-BP)/BP]
x 100
Significance of better parent
heterosis was determined following
the “t” test suggested by Wynne et
al. (1970).
S.E. (BP) = √ (2 EMS/r); t value =
Heterobeltiosis / S.E. (BP)
Calculated t was tested against
table value of t at error d.f. for test
of significance where,
MP= Mean of mid-parent in the
cross;
BP = Mean of better-parent in the
cross;
EMS = Error mean square;
r = Number of replication;
d.f. = Degrees of freedom.
Combining ability analysis was
carried out according to Nadarajan
and Gunasekaran (2008) based on
Griffing’s (1956) fixed effect
model using the following formula:
Yij = m + gi +gj + sij + rij + 1/bcΣΣ ijkl
where
i, j = 1, 2……...n;
k = 1, 2,……b.
l = 1, 2,.............c;
Yij is the mean of i × j genotype
over k and l;
m is the population mean;
gi is the GCA effect of the ith
parent; gj is the GCA effect of the
jth parent; sij is the SCA effect; rij is
the reciprocal effect; and
1/bcΣΣ ijkl is the mean error effect.
SPAR I (developed by the
Indian
Agricultural
Statistics
Research Institute, New Delhi,
India) software was used for
statistical analysis. Data were
(Panse and Sukhatme, 1984).
Heterobeltiosis was computed by
using computer software program
Windowstat 8.0 (developed by
Indostat Services 18, Ameerpet,
Hyderabad, India).
306
proportion of additive genetic
variance in the total genetic
variance was much less for these
traits. However, the proportion of
additive genetic variance for TSS
content of fruit was in equal
magnitude with that of total genetic
variance. In such case, both
additive and non-additive gene
actions were important for the
conditioning of TSS content of
fruit.
RESULTS
Nature of gene action
The
general
and
specific
combining ability are the main
criteria of rapid genetic assaying of
the test genotypes under line x
tester analysis. Combining ability
variances were significant for line
x tester in most of the characters
(Table 1). Line x tester component
of genetic variation was not
significant for days to 50%
flowering. Two variances (GCA
and SCA) showed wide range of
variation for all the characters
studied. Additive variance (α2A)
and dominance variance (a2D) can
be calculated at F=l (tomato being
self pollinated crop) from GCA and
SCA variances (Nadarajan and
Gunasekaran, 2008). In this
prediction α2D = 2 α2GCA and α2H
= α2SCA. A general trend of the
genetic control of the characters
can be ascertained from these
estimates of additive and nonadditive variance components. The
relative importance of the additive
and non-additive genetic effects for
these characters was reflected by
the predictability ratio α2 D/ (α2 H
+ α2 D) as per Baker (1978). The
closer this ratio is to unity, the
greater the predictability based on
GCA alone.
The results presented in
Table
2
indicated
that
predominance of non-additive gene
action was evident for control of
characters namely, plant height,
days to 50% flowering, number of
flower clusters plant-1, number of
fruits flower cluster-1, number of
fruits plant-1, fruit weight, polar
diameter, equatorial diameter, fruit
acidity and fruit yield plant-1 as the
GCA and per se performance
The per se performance and GCA
effects of the parents used in the
study for eleven characters are
given in Table 3. After the
assessment of overall picture of
GCA effects, it appeared that the
parents differ in their GCA.
Among the lines, the highest
significant and positive GCA
effects had shown by CLN2498-D
for maximum number of characters
namely, numbers of flower cluster
plant-1, numbers of fruit flower
cluster-1, numbers of fruit plant-1,
fruit weight, polar diameter,
equatorial diameter, fruit acidity
and fruit yield plant-1.
307
Table 1. Analysis of variance for combining ability of eleven characters of tomato.
Source of
variation
(d.f.)
Replication
(2)
Mean Sum of Square for parents and hybrids for the character
PH
D50F
NFCPP
NFPC
NFPP
FW
PD
ED
TSS
ACD
FYPP
0.06
0.42
0.01
0.00
0.01
0.20
0.00
0.0004
0.002
0.0004
0.0001
Lines
(2)
405.83*
13.81**
20.13
0.40
666.44
444.20*
1.38
1.84
1.84**
0.004
6.18**
Testers
(2)
220.89
9.15*
5.82
0.09
62.44
127.30
0.24
0.10
0.10
0.01*
0.12
Line x Tester
(4)
74.82**
2.37
21.55**
0.20**
383.15**
123.30**
0.73**
0.63**
0.70**
0.003**
0.44**
Error
(28)
0.02
1.07
0.01
0.0001
0.021
0.11
0.0001
0.0001
0.01
0.0001
0.0001
PH=Plant height; D50F= Days to 50% flowering; NFCPP= Numbers of flower cluster plant-1; NFPC= Numbers of fruit flower cluster-1; NFPP= Numbers of fruit plant-1;
FW= Fruit weight; PD= Polar diameter; ED= Equatorial diameter; TSS=Total soluble solids; ACD= Acidity; FYPP= Fruit yield plant-1
*, ** Significant at P <0.05 and 0.01, respectively.
308
Table 2. Estimates of component of variance for eleven characters of tomato.
Component of
genetic variance
PH
D50F
NFCPP
NFPC
NFPP
FW
PD
ED
TSS
ACD
FYPP
σ2 GCA
6.63
0.25
-0.24
0.001
-0.52
4.51
0.002
0.009
0.05
0.0001
0.08
σ2 D (2 x σ2 GCA)
13.25
0.51
-0.48
0.003
-1.04
9.02
0.004
0.02
0.91
0.0002
0.15
64.69
1.95
5.75
0.07
124.59
68.14
0.26
0.27
0.50
0.002
0.60
σ2 H/ σ2 D
4.88
3.85
-12.06
28.38
-119.86
7.55
61.69
14.23
5.54
9.00
3.97
Predictability
Ratio
σ2 D/ (σ2 H + σ2 D)
0.17
0.21
0.09
0.03
0.01
0.12
0.02
0.07
0.64
0.10
0.20
σ2 H (σ2 SCA)
PH=Plant height; D50F= Days to 50% flowering; NFCPP= Numbers of flower cluster plant-1; NFPC= Numbers of fruit flower cluster-1; NFPP= Numbers of fruit plant-1;
FW= Fruit weight; PD= Polar diameter; ED= Equatorial diameter; TSS=Total soluble solids; ACD= Acidity; FYPP= Fruit yield plant-1
309
Table 3 Estimates of general combining ability effects and per se performance (in parentheses) in six parents over nine F1s.
Parents
CLN2762-A
CLN2498-D
CLN2777-C
DVRT-2
BCT-53
BCT-110
SE (gi)
PH
D50F
NFCPP
NFPC
NFPP
FW
PD
ED
TSS
ACD
FYPP
6.91**
(81.07)
-0.41**
-0.48
(42.00)
-0.93*
0.06*
(12.63)
1.46**
0.10**
(3.53)
0.14**
1.59**
(44.57)
7.70**
-0.69**
(62.07)
7.34**
-0.30**
(4.23)
0.44**
-0.37**
(4.17)
0.50**
0.78**
(4.30)
-0.30**
0.02**
(0.47)
0.01*
0.01**
(2.78)
0.82**
(75.47)
(42.00)
(15.03)
(3.43)
(51.07)
(64.77)
(4.32)
(4.76)
(3.50)
(0.41)
(3.33)
-6.50**
1.41**
-1.53**
-0.24**
-9.29**
-6.66**
-0.14**
-0.13**
-0.49**
-0.03**
-0.84**
(61.47)
-0.36**
(44.67)
-1.15**
(12.30)
0.75**
(3.36)
-0.11**
(41.20)
0.40**
(44.93)
-3.41**
(3.72)
-0.19**
(3.80)
0.06**
(3.40)
-0.05
(0.43)
0.04**
(1.85)
-0.14**
(56.67)
-4.77**
(41.67)
0.41
(17.40)
-0.85**
(2.47)
0.02**
(42.70)
-2.81**
(43.93)
4.03**
(4.13)
0.08**
(4.13)
-0.12**
(3.80)
0.25**
(0.47)
-0.03**
(1.88)
0.07**
(60.40)
(44.00)
(19.13)
(2.69)
(51.40)
(39.30)
(3.40)
(3.75)
(5.20)
(0.41)
(2.02)
5.12**
(80.57)
0.05
0.70*
(45.00)
0.34
-0.10**
(16.30)
0.03
0.09**
(3.39)
0.003
2.41**
(55.27)
0.05
-0.62**
(64.47)
0.11
0.11**
(4.41)
0.004
0.06**
(4.17)
0.004
-0.20**
(3.50)
0.04
-0.01**
(0.44)
0.004
0.07**
(3.52)
0.004
PH=Plant height; D50F= Days to 50% flowering; NFCPP= Number of flower cluster plant-1; NFPC= Number of fruit flower cluster-1; NFPP= Number of fruit plant-1;
FW= Fruit weight; PD= Polar diameter; ED= Equatorial diameter; TSS=Total soluble solids; ACD= Titratable Acidity; FYPP= Fruit yield plant-1
*, ** Significant at p<0.05 and 0.01, respectively.
310
Next to CLN2498-D, significantly
positive GCA effects for seven
characters namely, plant height,
numbers of flower cluster plant-1,
numbers of fruit plant-1, TSS, fruit
acidity, and fruit yield plant-1 was
shown by CLN2762-A. Among the
testers, significant and positive GCA
effects had shown by BCT-110 for
six characters like plant height,
numbers of fruit plant-1, polar
diameter, equatorial diameter, and
fruit yield plant-1, while significant
GCA effects had shown by BCT-53
and DVRT-2 for five and four traits,
respectively. Negatively significant
GCA effects for days to 50%
flowering were displayed by
CLN2498-D and DVRT-2. The
highest per se performance for fruit
yield was shown by BCT-110, while
the lowest per se for days to 50 %
flowering was exhibited by DVRT2. While considering the traits (TSS
and acidity of fruit) suitable for
processing, two parents namely,
BCT-53 (5.20o brix and 0.41%) and
CLN2762-A (4.30o brix and 0.47%)
satisfied the requirements of quality
attributes for processing.
showing a heterosis of 5.63% over
better parent.
Good
cross
showing
heterobeltiosis for days to 50%
flowering was CLN2498-D x
DVRT-2 (-3.22%). Selection of
hybrids showing negative heterosis
over their better parents for this
character may be useful for
developing
early
commercial
hybrids. The line DVRT-2 emerged
as the one of the earliest parents
(41.67 days). The best heterotic
cross CLN2498-D x DVRT-2 (40.33
days) showed negative heterosis of 3.22% over better parent, providing
the best population for selecting
early plant type.
The
maximum
heterobeltiosis for numbers of
flower cluster plant-1 was exhibited
in desired direction by CLN2498-D
× BCT-110 (34.15%) followed by
CLN2762-A × DVRT-2 (16.48%)
and CLN2498-D × DVRT-2
(15.71%). Best hybrid as derived
from this investigation was CLN2498D×BCT-110
(21.87
flowers/cluster) showing a heterosis
of 34.15% over better parent.
The
maximum
and
significant
heterobeltiosis
for
numbers of fruit flower cluster-1 was
exhibited by CLN2498-D × BCT110
(12.44%)
followed
by
CLN2762-A × BCT-110 (8.13%)
and CLN2498-D × BCT-53 (2.53%).
Best hybrid as derived from this
investigation was CLN2498-D ×
BCT-110
(3.86
fruits/cluster)
better parent.
Heterobeltiosis and SCA
The per se performance of crosses,
per cent of heterobeltiosis, range of
heterobeltiosis and estimates of SCA
effects for eleven characters are
given in Table 4. The maximum
significant heterobeltiosis for plant
height was exhibited by CLN2762-A
× BCT-110 (5.63%) followed by
CLN2777-C × BCT-53 (4.34%) and
CLN-2762-A × DVRT-2 (4.19%).
Best hybrid for plant height as
derived from this investigation was
CLN-2762-A × BCT-110 (85.63cm)
311
Table 4. Heterobeltiosis and estimates of SCA effects for eleven characters in
tomato hybrids.
Characters
Plant height
(cm)
Days to
50%
flowering
Numbers of
flower
cluster
plant-1
Numbers of
fruit flower
cluster-1
Numbers of
fruit plant-1
Fruit
weight (g)
Polar
diameter
(cm)
Range of
heterobeltiosis
Specific
combining ability
effects (per se
performance)
0.68** (85.63)
Better cross/crosses
Heterobeltiosis
(%)
CLN-2762A×BCT-110
5.63**
CLN-2777C×BCT-53
4.34**
CLN-2762A×DVRT-2
4.19**
CLN-2498D×DVRT-2
-3.22**
-3.21 to 2.40
-0.07 (40.33)
CLN-2498D×BCT-110
34.15**
-17.44 to
34.15
2.50** (21.87)
CLN-2762A×DVRT-2
16.48**
1.65** (20.27)
CLN-2498D×DVRT-2
15.71**
0.12 * (20.13)
CLN-2498D×BCT-110
12.44**
CLN-2762A×BCT-110
8.13**
0.17** (3.81)
CLN-2498D×BCT-53
2.53**
-0.09** (3.52)
CLN-2498D×BCT-110
52.59**
CLN-2762A×DVRT-2
51.98**
4.08** (67.33)
CLN-2498D×DVRT-2
33.62**
-1.53** (68.23)
CLN-2777C×BCT-53
CLN-2498D×BCT-53
3.12**
0.98**
-25.29
3.12
CLN-2498D×BCT-53
21.76**
-12.45 to
21.76
CLN-2777C×BCT-110
3.33**
0.40** (4.55)
CLN-2498D×DVRT-2
2.62**
-0.01 (4.43)
-14.39 to 5.63
2.48** (64.13)
4.99** (84.47)
-12.77 to 12.44
-15.98 to 52.59
to
0.18** (3.86)
12.56** (84.33)
-2.32** (46.33)
2.74** (65.40)
-0.56** (5.26)
312
Table 4. (cont’d)
Characters
Equatorial
diameter
(cm)
TSS (o brix)
Titratable
acidity (%)
Fruit yield
plant-1 (kg)
Better cross/crosses
Heterobeltiosis
(%)
Range of
heterobeltiosis
Specific
combining ability
effects (per se
performance)
CLN-2777C×BCT-110
15.84**
-8.88 to 15.84
0.60** (4.83)
CLN-2498D×DVRT-2
6.30**
0.20** (5.06)
CLN-2498D×BCT-53
2.45**
0.20** (4.88)
CLN-2762A×DVRT-2
31.01**
CLN-2498D×BCT-110
20.00**
0.36** (4.20)
CLN-2777C×BCT-110
8.57**
0.15* (3.80)
CLN-2762A×DVRT-2
17.02**
CLN-2498D×DVRT-2
13.48**
0.01 (0.53)
CLN-2498D×BCT-110
12.12**
0.02** (0.49)
CLN-2777C×BCT-53
38.78**
CLN-2498D×DVRT-2
23.50**
0.26** (4.12)
CLN-2498D×BCT-110
20.17**
0.17** (4.23)
-20.51 to
31.01
-3.57 to 17.02
-33.33 to
38.78
0.57** (5.63)
0.02** (0.55)
0.40** (2.80)
*, ** Significant at P <0.05 and 0.01, respectively
The range of heterosis for numbers
of fruit plant-1 varied between 15.98% and 52.59% over better
parent. The maximum significant
heterobeltiosis was exhibited by
CLN2498-D × BCT-110 (52.59%)
followed by CLN2762-A × DVRT-2
(51.98%) and CLN2498-D ×
DVRT-2 (33.62%). Best hybrid as
CLN2498-D × BCT-110 (84.33
fruits per plant) showing a heterosis
of 52.59% over better parent. Better
crosses showing heterobeltiosis for
fruit weight were CLN2777-C x
BCT-53 (3.12%) followed by
CLN2498-D x BCT-53 (0.98%).
Best hybrid for fruit weight on the
basis of per se performance was
CLN2498-D x BCT-53 (65.40 g)
showing 0.98% heterosis over better
parent. Best parent was CLN2498-D
(64.77g), which has been utilized to
develop best hybrid and some other
good crosses.
The maximum significant
heterobeltiosis for polar diameter of
fruit was exhibited by CLN2498-D x
BCT-53 (21.76%) followed by
CLN2777-C x BCT-110 (3.33%)
and CLN2498-D x DVRT-2
(2.62%). Best hybrid as derived
from
this
investigation
was
CLN2498-D x BCT-53 (5.26 cm)
better parent.
313
Equatorial diameter of fruit
is a character related with size of the
fruit and the desirable heterosis is a
positive one. Good crosses for this
character were CLN2777C x BCT110
(15.84%)
followed
by
CLN2498-D x DVRT-2 (6.30%) and
CLN2498-D x BCT-53 (2.45%).
Best hybrid for this character as
CLN2498-D x DVRT-2 (5.06 cm)
showing a heterosis of 6.30 % over
better parent.
The range of heterosis for
total soluble solids (TSS) was from 20.51% to 31.01% over better
parent. Good crosses showing
heterobeltiosis for this character
were CLN2762-A x DVRT-2
(31.01%), CLN2498-D x BCT-110
(20.00%) and CLN2777-C x BCT110 (8.57%). Best hybrid for TSS
was CLN2762-A x DVRT-2 (5.63°
brix) with a heterosis of 31.01%
over better parent. Best parent on the
basis of mean performance was
BCT-53 (5.20° brix) as presented in
Table 3.
The maximum significant
and positive heterosis for fruit
acidity over better parent was shown
by
CLN2762-A
x
DVRT-2
(17.02%) followed by CLN2498-D
x DVRT-2 (13.48%) and CLN2498D x BCT-110 (12.12%). Best hybrid
for fruit acidity was CLN2762-A x
DVRT-2 (0.55%) having 17.02%
heterosis over better parent. On the
basis of mean value, DVRT-2 and
CLN2762-A (0.47%) were selected
as most promising parents as
presented in Table 3.
The range of heterosis for
fruit yield plant-1 was from -33.33%
to 38.78% over better parent. Good
crosses showing heterobeltiosis for
this character were CLN2777-C x
BCT-53 (38.78%), CLN2498-D x
DVRT-2 (23.50%) and CLN2498-D
x BCT-110 (20.17%). Best hybrid
was CLN2498-D x BCT-110 (4.23
kg per plant) with a heterosis of
20.17% over better parent. Best
parent on the basis of mean
performance was BCT-110 (3.52 kg)
as reflected in Table 3.
Specific combining ability
effects represents dominance and
epistatic components of genetic
variation which are not fixable but
the crosses with high SCA effects
involving good general combiner
parents can be exploited in future
heterosis breeding program.
None of the crosses showed
negative significant superiority for
days to 50 % flowering. Out of the 9
crosses, 5 hybrids each showed
significant SCA effects for plant
height, number of flower clusters
plant-1, equatorial diameter and fruit
yield plant-1 in the desired direction.
Four
hybrids
each
showed
significant SCA effects for numbers
of fruit cluster-1 and fruit weight.
Three hybrids each exhibited
significant SCA effects for numbers
of fruit plant-1, TSS and fruit acidity.
The cross CLN2498-D x BCT-110
showed significant positive SCA
effects for numbers of flower cluster
plant-1, numbers of fruit cluster-1,
numbers of fruit plant-1, TSS, fruit
acidity and fruit yield plant-1 and
non-significant negative SCA effects
for days to 50% flowering and it was
3rd highest for yield plant-1 in respect
to SCA effects coupled with the
highest per se performance. The
cross CLN2777-C x BCT-53 also
showed significantly positive SCA
effects for plant height, numbers of
flower cluster plant-1, numbers of
fruit flower cluster-1, numbers of
fruit plant-1, fruit acidity, fruit yield
plant-1 and negative SCA effects for
314
conducted. The successful breeding
methods will be those that
accumulate the genes to form
superior
gene
constellations
interacting in a favorable manner.
These findings suggested heterosis
breeding as the best possible option
for improving the above traits of
tomato. The response of both
additive and non-additive gene
actions for the control of TSS
content of fruit has also been
reported (Rai et al., 2005; Sharma et
al., 2006). In this case use of a
population improvement method in
the form of diallel selective mating
(Jensen, 1970) or mass selection
with concurrent random mating
(Redden and Jensen, 1974) might
lead to release of new varieties with
higher TSS in tomato.
Restricted
recurrent
selection by inter-mating the most
desirable segregants followed by
selection might also be a useful
breeding strategy for the exploitation
of both additive and non-additive
types of gene action. On the contrary
to this study, some researchers
(Makesh et al., 2002; Cheema et al.,
2003; Brar et al., 2005; Rai et al.,
2005; Sharma et al., 2006; Saidi et
al., 2008; Mondal et al., 2009) while
examining genetic control of days to
50% flowering, plant height,
acidity, polar diameter, equatorial
diameter, and fruit yield plant-1
found both additive and non-additive
gene effects to be involved. On the
other hand, Garg et al. (2007) and
Mondal et al. (2009) reported the
importance of non-additive genetic
effects for TSS content of fruit.
Some differences in interpretation in
the present study might have arisen
from difference in genotype used
and environment under which the
days to 50% flowering and it was
highest for yield plant-1 in respect to
SCA effects coupled with high per
se performance. Cross CLN2498-D
x DVRT-2 also showed significantly
positive SCA effects for numbers of
flower cluster plant-1, fruit weight,
equatorial diameter and fruit yield
plant-1 and negative SCA effects for
days to 50% flowering and it was
second highest for yield plant-1 in
respect to SCA effects coupled with
high per se performance. The cross
CLN2762-A x DVRT-2 also showed
significant SCA effects for fruit
yield plant-1 and other component
characters in the desired direction
with high per se performance.
DISCUSSION
While studying the nature of gene
action governing eleven traits, it has
been observed that overwhelming
non-additive
gene
action
is
responsible for the control of all the
traits studied except TSS content of
fruit for which preponderance of
both additive and non-additive gene
actions was evident. The response of
non-additive gene action for the
conditioning of plant height, days to
50% flowering and fruit weight
(Ahmad et al., 2009); number of
flower cluster plant-1 (Bhatt et al.,
2001); number of fruits flower
cluster-1 (Bhatt et al., 2001; Makesh
et al., 2002), number of fruits plant-1
(Garg et al., 2007; Ahmad et al.,
2009); acidity (Garg et al., 2007;
Virupannavar et al., 2010), and fruit
yield plant-1 (Mahendrakar et al.,
2005; Garg et al., 2007; Singh et al.,
2010)
have
been
reported
irrespective of the parental materials
used, methods followed and
environments in which experiments
315
trial was conducted because
estimates of gene effects are proved
to change with environment and
genotypes.
When the parents were
assessed for their general combining
ability for eleven traits, two lines
‘CLN2498-D’ and ‘CLN2762-A‘
were identified as good combiners
for both yield and processing quality
related traits and the tester ‘BCT110’ was selected as good combiner
for yield and its component
characters. Since high gca effect is
related to additive and additive ×
additive interaction and represents
the fixable components of genetic
variance, ‘CLN2498-D’, ‘CLN2762A’ and ‘BCT-110’ could be used
effectively in tomato breeding for
high yield and better processing
quality. Significant negative GCA
effects of days to 50% flowering for
the line ‘CLN2498-D’ and for the
tester ‘DVRT-2’ indicating these
line and tester have additive genetic
effects resulting in lower value for
this trait. In earlier study Ahmad et
al. (2009) reported significant
negative GCA effects for days to
50% flowering in tomato.
Out
of
nine
cross
combinations, four crosses for plant
height, one cross for days to 50%
flowering, three crosses for numbers
of flower cluster plant-1, four crosses
for numbers of fruit flower cluster-1,
eight crosses for numbers of fruit
plant-1, two crosses for fruit weight,
four crosses for polar diameter, three
crosses for equatorial diameter, five
crosses for TSS, six crosses for fruit
acidity, and six crosses for fruit
yield plant-1 exhibited significant
heterobeltiosis in desired direction.
According to Jinks (1983), the
prerequisite for a high, uniform, and
stable heterotic effect is the correct
gene content, which can be
assembled in the homozygous state
or if the appropriate alleles are
completely
dominant
as
a
heterozygote
without
affecting
performance. All crosses except one
(CLN2777-C x BCT-110) produced
negative
or
low
positive
heterobeltiosis
for
equatorial
diameter of fruit. This indicates that
high-performing parents having poor
gca may not produce highly
heterotic crosses.
It may be
concluded
that
a
superior
performance of the hybrids for
equatorial diameter of fruit depends
on the gca of the parents involved.
Positive and significant heterosis
over better parent for plant height
(Prashant, 2004; Tiwari and Lal,
2004; Ashwini and Vidyasagar,
2005); numbers of flower cluster
plant-1 (Prashant, 2004; Tiwari and
Lal, 2004; Ashwini and Vidyasagar,
2005); numbers of fruit flower
cluster-1 (Harer et al., 2006; Gul et
al., 2010); numbers of fruit plant-1
(Thakur et al., 2004; Tiwari and Lal,
2004; Gul et al., 2010); fruit weight
(Tiwari and Lal, 2004); polar and
equatorial diameter (Sharma et al.,
2001); TSS content (Shrivastava,
1998; Bhatt et al., 2001); fruit
acidity (Makesh et al., 2002); and
fruit yield plant-1 (Bhutani et al.,
1973;
Kanthaswamy
and
Balakrishnan, 1989; Yadav et al.,
1989;
Bhatt
et
al.,
1999;
Nagaraj,1995; Tiwari and Lal, 2004)
have been reported with dissimilar
parents and environments.
The perusal of analyses
revealed that the cross combinations
which showed maximum significant
heterobeltiosis for fruit yield plant-1
also exhibited heterosis over better
parent for numbers of fruit plant-1.
Therefore, it appeared that heterosis
316
for fruit yield per plant could be
ascribed mainly to heterosis
observed for numbers of fruit plant-1.
There was a reasonable ground to
suggest that the heterotic expression
for fruit yield plant-1 in cross
combination CLN2498-D x BCT110 was due to additive and additive
x additive type of gene effects as the
cross combination involved parents
with best general combining ability.
Progress in improving the desired
trait will be slow if the parental
selection is based on per se
performance alone. Absence of
significant heterosis in most crosses
could be explained by the internal
cancellation
of
heterosis
components.
Normally sca effects do not
contribute much to improvement of
self-pollinated crop like tomato.
However, crosses showing desirable
specific, along with good general
combining ability could be utilized
in
breeding
programs.
Such
programs would be more effective if
two of the parents are a good
combiner or any one of them is a
poor combiner. In the present study,
two crosses namely, CLN2777-C x
BCT-53 and CLN2498-D x DVRT-2
were identified as good specific
combiners for their high SCA effects
for fruit yield plant-1 and other
contributing characters and the cross
CLN2498-D x BCT-110 was
selected for high SCA effects for
TSS and acidity content of fruit
along with fruit yield. One parent
involved in these combinations had
a high gca effect and high per se
performance for several characters.
Therefore, parents with High × High
or High × Poor gca effects could
produce desirable transgressive
segregants in advance generations
because additive gene effects present
in the good combiner and
complementary epistatic effects in
the F1 may act in the same direction
to
maximize
desirable
plant
attributes.
CONCLUSIONS
The present study found the
importance of non-additive gene
effects in governing fruit yield and
most of the yield attributes in
tomato. However, both additive and
non-additive gene effects to be
involved for control of TSS content
of fruit. Among the lines,
‘CLN2498-D’
and
‘CLN2762A’were identified as most promising
combiners for fruit yield along with
good processing traits. Among the
testers, ‘BCT-110’ was found to be
the best general combiners for fruit
yield and its component characters.
These three parental materials could
be used further in tomato
hybridization programs. Results of
the crosses of CLN2777-C x BCT53 and CLN2498-D x DVRT-2
could be exploited commercially
because of high fruit yield and the
cross CLN2498-D x BCT-110 could
be utilized for better processing
qualities.
ACKNOWLEDGEMENTS
Authors are grateful to Asian Vegetable
Research and Development Centre, Taiwan
and Professor Pranab Hazra, Department of
Vegetable Crops, BCKV, West Bengal,
India for providing genetic materials to
conduct this study.
317
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321
RESEARCH ARTICLE
SABRAO Journal
44 (2) 322-338, 2012
DIALLEL STUDIES AND HERITABILITY ESTIMATES USING
HAYMAN’S APPROACH IN UPLAND COTTON
SUNDAS BATOOL1* and NAQIB ULLAH KHAN2
1
Department of Plant Breeding and Genetics, PMAS Arid Agric. Uni., Rawalpindi, Pakistan
Department of Plant Breeding and Genetics, The University of Agriculture, Peshawar, Pakistan
*
Corresponding Author E-mail: [email protected]
2
SUMMARY
The research work comprises genetic analysis, heritability and genetic advance for
various quantitative traits in 6 × 6 F1 diallel cross of Gossypium hirsutum L. carried out
at The University of Agriculture, Peshawar, Pakistan. Adequacy of additive-dominance
model was found partially suitable for all traits. Greater values of dominance (H1, H2)
than additive (D) genetic components of variance revealed that traits were
predominantly controlled by non-additive type of gene action. Unequal proportions of
positive (U) and negative (V) alleles in loci (H2<H1) with asymmetrical distribution of
genes was observed in the parental cultivars (H2/4H1<0.25 and F different to zero) for
all the traits. The ratio of H2/4H1 was below than maximum value (0.25) for all traits,
which arises when U = V = 0.5 over all loci. Overall, the broad sense heritabilities
were higher than narrow, however, half of the traits showed moderate narrow sense
heritabilities with appreciable genetic advance. Positive correlation coefficient between
Wr + Vr and parental means (y) for monopodia plant-1, bolls sympodia-1 and seed cotton
yield, and negative correlation for sympodia plant-1 and boll weight enunciated that
parental genotypes containing recessive and dominant genes, respectively and were
responsible for increased mean values. According to covariance of additive vs. dominance
effects in arrays, cultivars CIM-707, CIM-496 and CIM-554 owned maximum
dominant genes, while recessive genes were gained by genotypes CIM-499, CIM-473
and CIM-506. Cultivars CIM-506 and CIM-554 performed better in their specific F1
hybrids. Overall, the F1 hybrids CIM-506 × CIM-554, CIM-473 × CIM-554, CIM-554
× CIM-506 and CIM-554 × CIM-707 manifested best genetic potential for majority of
the traits and can be used for hybrid cotton. However, due to desirable narrow sense
heritability, selection will be effective in segregating populations.
Keywords: Diallel analysis, additive dominance model, heritability, genetic
gain, upland cotton
Manuscript received: May 5, 2012; Decision on manuscript: July 20, 2012;
Manuscript accepted in revised form: August 13, 2012
Batool & Khan (2012)
INTRODUCTION
Cotton (Gossypium hirsutum L.)
being a cash crop has a profound
influence on economy of Pakistan.
It maintained prominence and pride
by contributing a handsome foreign
exchange through export and raw
material to 2000 local textile and
ginning units besides millions of
farmers, traders and labourers
earning
livelihood,
directly/
indirectly from cotton and boosting
up the national economy. But from
last two decades Pakistan’s seed
cotton yields are stagnant and
lower than other cotton growing
countries. During 2010-2011,
cotton was grown on 2.689 million
hectares
and
seed
cotton
productions was 11.460 million
bales with average yield of 713 kg
ha-1 (FBS, 2010). Low cotton
production may be due to biotic
and abiotic stresses and decline in
crop area. A critical examination of
the alarming situation of cotton
production demands improvement
of the cotton plant with mellifluous
combinations of the desirable traits
in a single genotype.
Plant breeders have great
interest in quantitative genetics as
most of the yield related and fiber
quality traits are polygenic.
Quantitative traits have constant
variation in a population and can
be tailored significantly by the
environment. Crop development
requires the capability to identify
and select high performing
genotypes in a population. Some
important marvels also cause
complexities when breeder makes
selection for improvements in
quantitative traits (Ragsdale and
Smith, 2007; Khan and Hassan,
2011; Khan, 2011). Conventional
breeding has sustainable base in the
present era of molecular breeding,
and application of molecular
markers must be certified through
traditional breeding. Transgressive
segregation depends upon the
categorizing of genotypes having
potential of transmitting desirable
traits in specific genotypic
combinations.
Diallel
analysis
and
additive dominance models are the
established
mechanisms
of
conventional
breeder
to
comprehend allelic and non-allelic
gene action, nature and magnitude
of genetic variance used by
genotypes
in
specific
combinations. Gene action is
described in statistical terms as
additive, dominant and epistatic
effects and their interactions with
environmental
factors.
Consequently, the use of genotypes
with desirable genetic components
is a continuous pre-requisite for
synthesis
of
physiologically
efficient and genetically superior
genotypes showing promise for
increased production per unit area
under a given set of environments
(Hussain et al., 1998; Esmail et al.,
1999; Khan et al., 2009a; Khan and
Hassan, 2011). All such endeavors
need some genetic information and
knowledge about the type of gene
action
involved
in
various
agronomic and quality traits.
In quantitative genetics,
about diallel a comprehensive work
on the assumptions of additivedominance
model,
genetic
mechanism
and
genetic
components of variance which
control the various traits in cotton
under
diverse
environmental
conditions has been advocated
(Hayman, 1954a, b; Verhalen et
323
al., 1971; Mather and Jinks, 1982;
Luckett et al., 1989; Tang et al.,
1993, 1996; McCarty et al., 1996,
2004a,b; Godoy and Palomo, 1999;
Hussain et al., 1999; Khan, 2003;
Mehetre et al., 2003; Ragsdale,
2003; Lukonge, 2005; Mei et al.,
2006; Wu et al., 2006; Aguiar et
al., 2007; Esmail, 2007; Khan et
al., 2007, 2009a; Ragsdale and
Smith, 2007; Aguado et al., 2008,
2010; Basal et al., 2009; Khan and
Hassan, 2011).
Therefore, this research
work was carried out by using
Hayman’s approach with the
objectives to study the additivedominance model to see the data and
design
adequacy,
genetic
components of variance, heritability,
genetic gain and inheritance patterns
(additive Vs. dominance) for various
morpho-yield traits in 6 × 6 F1
diallel crosses of upland cotton.
Plant material and experimental
procedure
The research work comprised of
diallel analysis in 6 × 6 F1 diallel
cross of G. hirsutum L. was
conducted at The University
Agriculture, Peshawar, Pakistan.
Peshawar, Pakistan lies between
34°, 02′ North latitude and 71°, 37′
East longitude. Six diverse upland
cotton genotypes (CIM-473, CIM496, CIM-499, CIM-506, CIM-554
and CIM-707) were procured from
Central Cotton Research Institute
(CCRI) Multan, Pakistan varying
in pedigree, year of release and
morph-yield traits (Table 1), were
crossed in a complete diallel
fashion during crop season 2008.
The 30 F1 hybrids and their six
parents were grown in a
randomized complete block (RCB)
design during crop season 2009.
All the genotypes (F1s + parental
cultivars) were planted in a single
row measuring five meter, with
three replications. The plant and
row spacing were 30 and 75 cm,
respectively. The thinning was
performed after 15 to 20 days and
at the time when plants gained the
height around 10 to 15 cm to
ensure single plant per hill.
Recommended cultural practices
were followed for all the entries.
Picking was made during the
months of November on individual
plant basis.
Traits
measurement
statistical analysis
and
Data were recorded on monopodia
and sympodia per plant, bolls per
sympodia, boll weight and seed
cotton yield per plant and were
appropriate for RCB design
according to Steel and Torrie
(1980).
Diallel analysis, its assumptions
and test of adequacy
After getting significant means
variations in F1s and parental
populations, the genetic analysis
was
performed
following
Hayman’s and Mather’s diallel
approach with concept of D and H
genetic components of variation for
additive and dominance variances,
respectively (Hayman, 1954a, b;
Verhalen et al., 1971; Mather and
Jinks, 1982). The validity of
information from a group of
genotypes obtained from the diallel
324
method is based on the following
assumptions:
(a)
Diploid
segregation
of
chromosomes.
(b) Homozygosity of parents.
(c) Absence of reciprocal effects.
(d) Absence of epistasis.
(e) No multiple allelism.
(f) Independent distribution of
genes among parents.
To fulfill the assumptions
such as absence of epistasis, no
multiple allelism and independent
assortment of genes, the data were
tested through three scaling tests
(regression analysis, t2 test and
arrays analysis of variance Wr+Vr
and Wr-Vr) to evaluate the
adequacy
of
the
additivedominance model for the data.
According to Mather and Jinks
(1982) the regression coefficient is
expected to be significantly
different from zero and not from
unity. Failure of this test means the
presence of epistasis. If non-allelic
interaction is present, Wr+Vr must
change from array to array and
similarly Wr-Vr will vary among
arrays. Non-significant values of t2
test confirm the presence of no
nonallelic interaction and signify
that genes are independent in their
action for random association in
genotypes. Failure of these three
tests completely invalidates the
additive-dominance
model.
However, if even one test meets the
assumptions, then the additivedominance model is considered to
be partially adequate.
Estimation
of
components of variance
genetic
The genetic components of
variation, their ratio along with
standard error and correlation
coefficient were estimated as
follows:
— D: additive genetic variance; F1 =
[D = Volo-E (Volo = Variance of
the parents)] where E is the expected
environmental
component
of
variation.
— H1: dominance variance [H1 =
Volo-4Wolo1+4V1L1-(3n-2)E/n,
(Wolo = Mean covariance between
the parents and the arrays)], where
V1L1 is mean variance of arrays,
and n is number of parental
cultivars.
— H2: H1 [1-(u-v) 2], where u and v
are the proportions of positive and
negative genes, in the parents.
— F: Mean of Fr values over arrays
= 2Volo-4Wolo1-2(n-2)E/n, where
Fr is the covariance of additive and
dominance effects in a single array.
F is positive where dominant genes
are more frequent than recessive.
—h2:4(ML1-MLo)2-4(n-1)E/n2;
Dominance effect (as algebraic sum
over all loci in heterozygous phase
in all crosses). When frequency of
dominant and recessive alleles is
equal, then H1 = H2 = h2.
Significance of h2 confirms that
dominance is unidirectional.
—E:
Expected
environmental
component of variation
E=[
Error S.S.+ Reps.S.S.
]
d.f.
Number of replications
From these estimates, the following
genetic ratios were determined.
— H1 / D : Denotes the “average
degree of dominance”, if the value
of this ratio is zero, there is no
dominance; if value equal to 1 then
there is complete dominance; If it is
greater than zero but less than unity
(1), there is partial dominance; and if
it is greater than 1, it denotes overdominance.
325
— H2/4H1: Denotes the “proportion
of genes with positive and negative
effects in the parents”, and if the
ratio is equal to 0.25, indicates
symmetrical distribution of positive
and negative genes.
— 4DH1 + F / 4DH1 - F : Denotes the
“proportion of dominant and
recessive genes in the parents”: If
the ratio is 1, the dominant and
recessive genes in the parents are in
equal proportion; if it is less than 1,
it indicates an excess of recessive
genes; but being greater than 1, it
indicates excess of dominant genes.
— h2/H2: Denotes the “number of
gene groups/genes, which control
the
character
and
exhibit
dominance”.
—Correlation coefficient: Negative
value of correlation coefficient (r)
indicates dominant genes, while if
its value is positive then recessive
genes are responsible for the
phenotypic expression of the trait.
∑ XY Correlation Coefficient (r) =
( ∑ X)( ∑ Y)
n
( ∑ X 2 ) - ( ∑ X ) 2 ( ∑Y 2 ) - ( ∑Y ) 2
×
n
n
Heritability
The narrow sense heritability (h2) in
F1 generation was calculated for
each character according to Mather
and Jinks (1982).
F1 narrow sense Heritability ( h 2 ) =
1
1
)D + ( ) H 1 - (
2
2
1
1
1
( )D + ( ) H 1 - (
2
2
4
(
1
1
) H 2 - ( )F
2
2
1
) H 2 - ( )F + E
2
Genetic advance
When broad sense heritability (H2)
estimates are available, progress
from selection can be predicted for
any breeding system, since
expected gain (genetic advance) is
a
function
of
heritability.
Therefore, such guided selection
produces genetic advance. This
change is of great interest to plant
breeders, since it modifies the
population mean. The H2 was
calculated for each character
according to Hayman (1954a, b) and
Mather and Jinks (1982). The
magnitude of genetic advance from
selection under 10% selection
intensity (1.755) and genetic
advance as a percent of the sample
mean was calculated for each
character
in
F1
generation
according to Breese (1972).
F1 broad sense Heritability ( H 2 ) =
σ2g
σ 2 ph
Genetic Advance = K. σ 2 ph .h2(bs)
Genetic Advance % =
G.A
× 100
X
Genetic Variance ( σ 2 g) =
MSG - MSE
r
Phenotypic Variance ( σ 2 ph) =
MSG
r
Where MSG = genetic mean square
of ANOVA; MSE = phenotypic
(error) mean squares of ANOVA;
r = number replications; X =
population mean; σ2ph = standard
deviation of phenotypic variation.
Performance of F1s and their
parents
highly
significant
differences
among the 30 F1’s and their six
parental cultivars for almost all the
traits except boll weight where the
mean variations were merely
significant (Table 2). Monopodia
326
per plant varied from 0.38 to 1.00
among the parental genotypes
while 0.22 to 2.00 in their F1
hybrids (Figure 1). The lowest
monopodia per plant were recorded
in F1 hybrid CIM-496 × CIM-499
(0.22), which was also at par with
two parental cultivars and 11 F1
hybrids ranging from 0.31 to 1.00.
The F1 hybrid CIM-506 × CIM-499
(2.00)
showed
maximum
vegetative branches per plant and
was found at par with seven other
F1 genotypes ranged from 1.18 to
1.40. Monopodia per plant were
mostly found negatively correlated
with seed cotton yield; therefore
the breeders are interested in less or
no monopodia.
Sympodia per plant varied
from 11.00 to 18.00 among the
cultivars, while 12.00 to 24.00 in
F1 cross combinations (Figure 2).
Maximum sympodia revealed by F1
hybrid CIM-506 × CIM-554
(24.00) and were also found at par
with 10 other hybrids with range of
16.80 to 23.41. Lowest number of
fruiting branches per plant was
shown by cultivar CIM-499, and
found at par with six other
genotypes ranged from 12.00 to
13.00. All other genotypes showed
medium values for sympodia per
plant. Sympodia per plant have
direct relationship with seed cotton
yield.
Bolls per sympodia varied
from 1.54 to 2.00 among the
parental cultivars, while 1.52 to
4.00 in their F1 diallel hybrids
(Figure 1). Maximum bolls per
sympodia were recorded in F1
hybrid CIM-506 × CIM-554 (4.00).
Four other F1 cross combinations
with range of 2.50 to 3.38 followed
the said promising F1 hybrid.
Minimum bolls per sympodia were
revealed by F1 cross combinations
CIM-499 × CIM-707 (1.52) and
CIM-707 × CIM-506 (1.53) and
were found at par with three
parental cultivars and 18 F1 hybrids
having range of 1.53 to 2.00. Other
genotypes have medium number of
bolls per sympodia.
Boll weight ranged from
2.89 to 3.33 g among the parental
cultivars, while 3.22 to 3.88 g in
their F1 hybrids (Figure 2). Highest
boll weight was recorded in F1
hybrid CIM-554 × CIM-473 (3.94
g) and was found at par with three
other F1 hybrids (CIM-473 × CIM554, CIM-496 × CIM-707, CIM499 × CIM-554) ranged from 3.88
to 3.83 g. Lowest boll weight was
observed in parental genotype
CIM-554 (2.89 g) and was also at
par with 20 other genotypes ranged
from 3.21 to 3.53 g. The remaining
genotypes showed medium boll
weight.
Seed cotton yield per plant
ranged from 46.77 to 109.56 g
among the parental cultivars, while
53.68 to 190.88 g in F1 hybrids
(Figure 3). Maximum seed cotton
yield per plant (190.88 g) was
owned by F1 hybrid CIM-506 ×
CIM-554. It was also found at par
with three F1 hybrids i.e. CIM-473
× CIM-554 (162.12 g), CIM-554 ×
CIM-506 (151.29 g) and CIM-707
× CIM-496 (149.12 g). Lowest
seed cotton yield was noted in
parent cultivar CIM-499 (46.77 g)
and F1 hybrid CIM-499 × CIM-707
(53.68 g). Other genotypes showed
medium values for seed cotton
yield per plant. Overall, the F1
hybrids CIM-506 × CIM-554,
CIM-473 × CIM-554, CIM-554 ×
CIM-506 and CIM-554 × CIM-707
manifested best genetic potential
for majority of the traits.
327
Table 1. Breeding material used in 6 x 6 F1 diallel cross of upland cotton.
Cultivars
Parentage
Breeding centre
CIM-473 CIM-402 × LRA 5166
CIM-496 CIM-425 × 755-6/93
CIM-499 CIM-433 × 755-6/93
CIM-506 CIM-360 × CP 15/2
CIM-554 2579-04/97 × W-1103
CIM-707 CIM-243 × 738-6/93
CCRI, Multan, Pak.
CCRI, Multan, Pak.
CCRI, Multan, Pak.
CCRI, Multan, Pak.
CCRI, Multan, Pak.
CCRI, Multan, Pak.
Release
Yield GOT Fiber length
(year) (kg ha-1) (%)
(mm)
2002
3000
39.7
29.5
2005
3000
41.1
29.7
2003
3000
40.0
29.6
2004
3000
38.6
28.7
2009
4241
41.5
28.5
2004
3000
39.0
32.2
Table 2. Mean squares for morpho-yield traits in 6 × 6 F1 diallel cross of upland cotton.
Mean squares
Variables
CV %
Reps.
Genotypes
Error
Monopodia plant-1
0.078
0.42**
0.019
17.90
Sympodia plant-1
0.76
28.79**
2.043
9.18
Bolls sympodia-1
0.16
1.06**
0.10
14.85
Boll weight
0.03
0.15*
0.08
8.06
Seed cotton yield plant-1
839.50
3009.42**
673.97
26.13
-1
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Bolls sympodia
Monopodia plant-1
-1
CIM-707 × CIM-554
CIM-707 × CIM-506
CIM-707 × CIM-499
CIM-707 × CIM-496
CIM-707 × CIM-473
CIM-707
CIM-554 × CIM-707
CIM-554 × CIM-506
CIM-554 × CIM-499
CIM-554 × CIM-496
CIM-554 × CIM-473
CIM-554
CIM-506 × CIM-707
CIM-506 × CIM-554
CIM-506 × CIM-499
CIM-506 × CIM-496
CIM-506 × CIM-473
CIM-506
CIM-499 × CIM-707
CIM-499 × CIM-554
CIM-499 × CIM-506
CIM-499 × CIM-496
CIM-499 × CIM-473
CIM-499
CIM-496 × CIM-707
CIM-496 × CIM-554
CIM-496 × CIM-506
CIM-496 × CIM-499
CIM-496 × CIM-473
CIM-496
CIM-473 × CIM-707
CIM-473 × CIM-554
CIM-473 × CIM-506
CIM-473 × CIM-499
CIM-473 × CIM-496
CIM-473
Monopodia plant & Bolls sympodia
-1
*, ** Significant at P ≤ 0.05 and P ≤ 0.01.
6 x 6 F1 diallel cross of upland cotton
Figure 1. Mean values for monopodia plant-1 and bolls sympodia-1 in 6 × 6 F1
diallel cross of upland cotton.
328
-1
CIM-707 × CIM-554
CIM-707 × CIM-506
CIM-707 × CIM-499
CIM-707 × CIM-496
CIM-707 × CIM-473
CIM-707
CIM-554 × CIM-707
CIM-554 × CIM-506
CIM-554 × CIM-499
CIM-554 × CIM-496
CIM-554 × CIM-473
CIM-554
CIM-506 × CIM-707
CIM-506 × CIM-554
CIM-506 × CIM-499
CIM-506 × CIM-496
CIM-506 × CIM-473
CIM-506
CIM-499 × CIM-707
CIM-499 × CIM-554
CIM-499 × CIM-506
CIM-499 × CIM-496
CIM-499 × CIM-473
CIM-499
CIM-496 × CIM-707
CIM-496 × CIM-554
CIM-496 × CIM-506
CIM-496 × CIM-499
CIM-496 × CIM-473
CIM-496
CIM-473 × CIM-707
CIM-473 × CIM-554
CIM-473 × CIM-506
CIM-473 × CIM-499
CIM-473 × CIM-496
CIM-473
Seed cotton yield plant
200
-1
150
100
50
Seedcotton yield plant (g)
CIM-707 × CIM-554
CIM-707 × CIM-506
CIM-707 × CIM-499
CIM-707 × CIM-496
CIM-707 × CIM-473
CIM-707
CIM-554 × CIM-707
CIM-554 × CIM-506
CIM-554 × CIM-499
CIM-554 × CIM-496
CIM-554 × CIM-473
CIM-554
CIM-506 × CIM-707
CIM-506 × CIM-554
CIM-506 × CIM-499
CIM-506 × CIM-496
CIM-506 × CIM-473
CIM-506
CIM-499 × CIM-707
CIM-499 × CIM-554
CIM-499 × CIM-506
CIM-499 × CIM-496
CIM-499 × CIM-473
CIM-499
CIM-496 × CIM-707
CIM-496 × CIM-554
CIM-496 × CIM-506
CIM-496 × CIM-499
CIM-496 × CIM-473
CIM-496
CIM-473 × CIM-707
CIM-473 × CIM-554
CIM-473 × CIM-506
CIM-473 × CIM-499
CIM-473 × CIM-496
CIM-473
NS
NS
Partially adequate
NS
NS
Partially adequate
NS
NS
Partially adequate
NS
NS
Partially adequate
NS
NS
Partially adequate
NS
Monopodia
plant-1
0.12NS
-0.17
±0.41
b0 = -0.42
b1 = 2.83*
Sympodia
plant-1
Bolls
sympodia-1
1.15NS
Boll weight
0.06NS
Seed cotton
yield plant-1
29.45**
0.38
±0.26
0.15
±0.20
0.62
±0.34
0.12
±0.09
b0 = 1.50NS
b1 = 2.29*
b0 = 0.74NS
b1 =4.28*
b0 = 1.85NS
b1 = 1.13NS
b0 = 1.40NS
b1 = 9.98*
4.23NS
Wr-Vr
Wr+Vr
Conclusion
Arrays ANOVA
Variables
T test
Regression
analysis
(t value of b)
b/S.E b0, b1
2
15
-1
Sympodia plant
-1
Boll weight
30
25
20
10
5
0
Sympodia plant & Boll weight (g)
Figure 2. Mean values for sympodia plant-1 and boll weight in 6 × 6 F1 diallel
cross of upland cotton.
250
0
Figure 3. Mean values for yield plant-1 in 6 × 6 F1 diallel cross of upland cotton.
Table 3. Additive-dominance model for various traits in 6 × 6 F1 diallel cross of upland
cotton.
*, ** = Significant at P ≤ 0.05 and P ≤ 0.01, NS = Non-Significant
329
Table 4. Genetic components of variance for morpho-yield traits in upland cotton.
Components of
variance
D
H1
H2
F
h2
E
H1 / D
H2/4H1
4DH1 + F / 4DH1 - F
2
h /H2
Heritability (ns)
Heritability (bs)
Genetic advance
r(Wr+Vr/VP)
Monopodia
plant-1
Sympodia
plant-1
Bolls
sympodia-1
Boll
weight
Yield
plant-1
0.05
±0.03
0.28*
±0.07
0.22*
±0.06
0.07
±0.06
0.05
±0.04
0.72
±0.01
2.42
0.20
1.87
0.24
0.25
0.96
0.63 #
(80.76%)
0.54
5.72*
±1.46
12.97*
±3.70
10.76*
±3.30
1.78
±3.56
2.28
±2.22
0.72
±0.55
1.51
0.21
1.23
0.21
0.47
0.89
5.00 #
(31.38%)
-0.27
0.10
±0.07
0.56*
±0.17
0.45*
±0.15
0.10
±0.16
0.01
±0.10
0.03
±0.03
2.34
0.20
1.53
0.03
0.29
0.84
0.94 #
(43.52%)
0.40
0.05*
±0.02
0.20*
±0.05
0.13*
±0.04
0.11*
±0.05
0.18*
±0.03
0.01
±0.01
2.12
0.16
3.86
2.80
0.04
0.78
0.19 g
(5.41%)
-0.75
297.27
±523.21
2741.44*
±1328.21
2388.64*
±1186.53
-476.30
±1278.20
1456.58
±798.61
175.93
±197.75
3.04
0.22
0.58
0.61
0.42
0.87
43.91 g
(43.31%)
0.56
*Parameter value is significant when it exceeds 1.96 after dividing it with its standard error
*, ** = Significant at P ≤ 0.05 and P ≤ 0.01, N.S. = Non-Significant
Table 5. Covariance of additive and dominance effects (Fr values) in arrays for traits.
Fr values
Cultivars
Monopodia
plant-1
Sympodia
plant-1
Bolls
sympodia-1
Boll
Weight
Seed cotton
yield plant-1
CIM-473
0.11
-2.88
0.27
0.15
-103.44
CIM-496
0.12
13.97
0.28
0.16
1856.14
CIM-499
0.03
-6.16
0.16
0.17
-29.34
CIM-506
-0.02
-0.96
-0.32
0.14
-2984.07
CIM-554
0.11
1.74
0.02
-0.12
-1995.16
CIM-707
0.08
4.98
0.20
0.17
398.06
Mean Fri
0.07
1.78
0.10
0.11
-476.30
330
Tang et al. (1993), Khan et al.
(2007), Basal and Turgut (2005),
Wu et al. (2006), Aguiar et al.
(2007) and Khan and Hassan
(2011) also observed larger genetic
variability in F1 populations and
their parental genotypes for
morpho-yield traits and its role in
managing the seed cotton yield.
Diallel analysis
Adequacy of additive-dominance
model was tested through three
scaling tests (arrays analysis, t2 test
and regression analysis) (Table 3).
Analysis of variance of arrays
revealed that Wr+Vr and Wr-Vr
were non-significant for all the five
traits indicated an absence of
dominance with no nonallelic
interaction and the genes were
found independent in their action
for random association and same
was
also
confirmed
by
nonsignificant value of t2 test
except for seed cotton yield.
However, regression coefficient (b)
was found nonsignificantly differed
from zero and significantly vary
from unity which didn’t fulfill the
assumptions of additive-dominance
model and hence model became
partially adequate for all the traits.
Hussain et al. (1999), Ahmad et al.
(2000), Khan et al. (2007, 2009a),
Ali et al. (2008), Ali and Awan
(2009) and Khan and Hassan
(2011) studied the nature of gene
action in cotton and found that
additive-dominance model was
partially adequate for majority of
the traits. However, Godoy and
Palomo (1999), Khan (2003), Khan
et al. (2007) and Aguado et al.
(2008, 2010) mentioned that
differences between Wr and Vr
indicated that additive-dominance
model was adequate for seed cotton
yield and its components. Results
revealed no epistasis with lack of
dominance and showing that genes
were independent in their action
with random association among the
parents. Verhalen et al. (1971) and
Khan et al. (2007, 2009a) also
detected no epistasis in genetic
analysis of cotton. However,
Mehetre et al. (2003) observed
significant epistatic gene effects.
For monopodia per plant,
the dominance components were
significant, while other components
of variance (D, F, h2, E) were nonsignificant (Table 4). Additive
component (D) was less than both
dominance components (H1, H2) and
average degree of dominance
( H1 / D = 2.42) was being greater
than
unity
indicated
over
dominance. Unequal values of H1
and
H2 indicated
dissimilar
distribution of positive and negative
genes as authenticated by ratio of
H2/4H1 (0.20). Quite analogous
results were reported by Ahmad et
al. (1997, 2000) and Khan (2003)
and reported nonadditive type of
gene action for vegetative branches
in upland cotton. However, the
present results are in contradiction
with findings of McCarty et al.
(1996), Khan et al. (2005) and
Khan and Hassan (2011) and
noticed additive type of gene action
for monopodia, which may be due
to different breeding material used
under varied environments.
High estimate of broad
sense heritability (0.96) was
observed, however the narrow sense
heritability (0.25) was low for
monopodia per plant (Table 4). The
genetic advance under 10%
selection was 0.63#, while its value
as percent of the population mean
331
was 80.76%. McCarty et al. (1996)
and Khan and Hassan (2011)
reported similar ratio of heritability
and genetic advance in F1 and F2
populations. Significant positive
correlation coefficient (r = 0.54)
between Wr+Vr and parental means
(y)
enunciated
that
parents
containing recessive genes were
responsible for increased monopodia
while those with dominant genes
were responsible for decreased
monopodia in F1 generation. The
covariance
of
additive
and
dominance effects in arrays for
monopodia per plant revealed that
cvs. CIM-496, CIM-473, CIM-554,
CIM-707 and CIM-499 earned
maximum positive Fr values (0.03
to 0.12) and contain maximum
dominant genes (Table 5), while
parental
genotype
(CIM-506)
possessed minimum and negative
Fr value (-0.02) had maximum
recessive genes.
Genetic components of
variance for F1 sympodia per
plant revealed that additive and
dominance genetic components
were significant while F, h2 and
E were nonsignificant (Table 4).
Both dominance components
were greater than additive
component and average degree of
dominance ( H1 / D = 1.51) was
also greater than unity showing
dominance type of gene action.
The H1 value was greater than H2
indicating that positive and
negative genes were not equally
distributed as also verified by
ratio of H2/4H1 (0.21). Positive
value of F and h2 revealed that
dominant genes were more
frequent than recessive and were
in increasing position in parental
cultivars for F1 sympodia, and the
same also confirmed by ratio
4DH1 + F / 4DH1 - F (1.23). McCarty
et al. (1996) mentioned similar
results for sympodia in both
generations. These results were
also in accordance with the
findings of Khan et al. (2005)
and Khan and Hassan (2011) as
reported non-additive type of
gene action for sympodia per
plant. However, Ahmad et al.
(1997, 2000) and McCarty et al.
(2004a, b) found that additive
type of gene action was
responsible for inheritance of this
trait. The contradictory findings
for fruiting branches might be
due to different breeding material
studies
under
diverse
Estimates of high broad
sense (0.89) and moderate narrow
sense (0.47) heritabilities were also
recorded for sympodia per plant
(Table 4). The genetic advance
under selection was 5.00#, while its
value as percent of the population
mean was 31.38%. However, the
moderate narrow sense heritability
along with genetic gain revealed
that sympodia could be increased
and maintained through simple
selection
in
segregating
populations. Significant negative
correlation coefficient (r = -0.27)
between Wr+Vr and parental means
clarified that parents contained
maximum dominant genes were
responsible for increased sympodia,
while those with recessive genes
governed minimum sympodia in F1
generation. The covariance of
additive and dominance effects in
arrays (Table 5) manifested that
parental cultivars i.e. CIM-496,
332
CIM-707 and CIM-554 obtained
maximum positive Fr values (1.74
to 13.97) and hold maximum
dominant genes. However, the
genotypes CIM-506, CIM-473 and
CIM-499 (-0.96 to -6.16) possessed
minimum and negative Fr values,
had maximum recessive genes.
In case of bolls per
sympodia, the dominance genetic
components of variance (H1, H2)
were significant, while D, h2, F and
E were nonsignificant (Table 4).
Dominance components dominated
additive component and average
degree of dominance ( H1 / D =
2.34) was being greater than unity
articulated
the
presence
of
and H2 indicating asymmetric
distribution of positive and negative
genes as also confirmed by the ratio
of H2/4H1 (0.20). Positive F value
(0.10) indicated excess of positive
genes in increasing position due to
positive value of h2 (0.01) and the
same was confirmed by the ratio
4DH1 + F / 4DH1 - F (1.53). Bolls per
sympodia were controlled by
dominant genes because of less and
nonsignificant value of additive
component
as
compared
to
dominance components of variation,
with no epistasis and the genes were
independent in their action for
random dispersal and blending. The
results are in quite corroboration to
the findings of Khan (2003), Mei et
al. (2006) and Khan et al. (2009a, b)
who concluded that bolls per
sympodia were controlled nonadditively and contributed large
dominance
effects.
However,
Ahmad et al., (1997) and Hussain et
al., (1998, 1999) had noticed
additive type of gene action for the
inheritance of bolls per fruiting
branch. Such contradictory findings
could be attributed to the variations
in the genotypes used and
High broad sense (0.84) and
low
narrow
sense
(0.29)
heritabilities were estimated for
bolls per sympodia (Table 4).
Genetic advance under selection was
0.94#, while as percent of the
population mean, the value was
43.52%. Aguado et al. (2008)
reported similar ratio of heritability
in F1 populations. Significant
positive correlation coefficient (r =
0.40) between Wr+Vr and parental
means manifested that parents with
recessive genes were responsible for
increased bolls per sympodia in F1
generation. In covariance of additive
and dominance effects in parental
genotypes for bolls per sympodia in
F1 generation (Table 5) revealed that
parental cultivars viz; CIM-496,
CIM-473, CIM-707, CIM-499 and
CIM-554 (0.02 to 0.28) obtained
maximum positive Fr values and
had maximum dominant genes,
while one parental genotype (CIM506) manifested minimum and
negative Fr value (-0.32), had
maximum recessive genes.
For boll weight, almost all
the components of variance (D, H1,
H2, h2 and F) were significant while
E was nonsignificant (Table 4).
Dominance components surpassed
the additive component of variance
and average degree of dominance
than unity which formulated the
presence of dominance. Unequal
values of H1 and H2 revealed
asymmetric distribution of positive
and negative genes and the same
also confirmed by the ratio of
H2/4H1 (0.16). Positive value of F
(0.11) exhibited excess of positive
333
genes with increasing position due
to significant positive value of h2
(0.18) and the same was verified by
the ratio 4DH1 + F / 4DH1 - F (3.86).
Due to significant values of
additive and dominant components
of genetic variations, it was
presumed that inheritance of boll
weight was administered by both
type of genes, however it was
predominantly
controlled
by
dominant genes due to their higher
magnitude. Present results were in
line with the findings of Tang et al.
(1996), Ahmad et al., (1997),
Godoy and Palomo (1999), Basal
and Turgut (2005), Mei et al.
(2006), Esmail (2007), Basal et al.,
(2009) and Gamal et al., (2009)
and concluded non-additive type of
gene action with over-dominance
for boll weight. However, Luckett
et al., (1989), Tang et al. (1993),
McCarty et al., (1996, 2004a & b),
Hussain et al., (1998), Yuan et al.
(2005), Wu et al., (2006), Aguiar et
al. (2007), Aguado et al. (2008)
and Ali and Awan (2009)
mentioned that additive effects
were substantial for boll weight.
The inconsistent views may be due
to different breeding material and
climatic conditions under which
the experiments were conducted.
Low narrow (0.04) and high
broad sense (0.78) heritabilities were
obtained for boll weight (Table 4).
The genetic advance under selection
was 0.19 g, while its value as
percent of the population mean was
5.41%. Similar magnitude of
heritability and genetic advance
were also reported by Tang et al.
(1996), Godoy and Palomo (1999)
and Hussain et al. (1999).
Significant negative correlation
coefficient (r = -0.75) between
Wr+Vr and parental means
pronounced that parents with
dominant genes were responsible for
increased boll weight in F1
additive vs. dominance effects in
parental genotypes for boll weight in
F1 generation revealed that parental
cultivars viz; CIM-499, CIM-707,
CIM-496 and CIM-473 and CIM506 (0.14 to 0.17) by having
maximum dominant genes showed
also maximum positive Fr values
(Table 5), while one parental
genotype (CIM-554) possessed
minimum and negative Fr value (0.12), had maximum recessive
genes.
In seed cotton yield, except
dominance, all other components
of variance (D, h2, F and E) were
found nonsignificant (Table 4).
Dominance components dominated
the additive component and
average degree of dominance
than one suggested presence of
and H2 indicated asymmetric
distribution of positive and
negative genes as confirmed by
ratio of H2/4H1 (0.22). Negative
value of F (-476.30) indicated
excess of recessive genes in
increasing position due to positive
value of h2 (1456.58) and was
confirmed
by
the
ratio
4DH1 + F / 4DH1 - F (0.58). Tang et al.
(1996), Basal and Turgut (2005),
Khan et al. (2005), Esmail (2007)
and Aguado et al. (2008) also
mentioned that dominant gene
effects were higher than additive
for yield and reported nonadditive
type of gene action for seed cotton
yield. However, Tang et al. (1993),
McCarty et al. (1996), Ahmad et
al. (1997), Godoy and Palomo
334
(1999), Hussain et al. (1999), Khan
(2003), Lukonge (2005), Wu et al.
(2006), Aguiar et al. (2007),
Lukonge et al. (2007) and Khan et
al. (2007) findings revealed
additive type of genetic control for
yield. The discrepancies with
respect of phenotypic manifestation
of this complex parameter might be
due to different genotypes studied
under various environments.
Moderate narrow (0.42) and
high broad sense (0.87) heritabilities
were observed for seed cotton yield
(Table 4). The genetic advance
under selection was 43.91 g, while
its value as percent of the population
mean was 43.31%. However, due to
moderate narrow sense heritability
and appreciable genetic advance, it
was concluded that seed cotton
yield could be improved through
intensive selection in segregating
populations. Esmail et al. (1999),
Kumaresan et al. (2000) and Khan
and Hassan (2011) reported high
heritability for seed cotton yield.
Significant positive correlation
coefficient (r = 0.56) between
Wr+Vr and parental means
manifested that parents with
recessive genes were responsible for
decreased seed cotton yield in F1
additive vs. dominance effects in
parental genotypes for seed cotton
yield per plant in F1 generation
(Table 5) revealed that parental
cultivars i.e. CIM-707 and CIM-496
(308.06 and 1856.14) obtained
maximum positive Fr values and
possess maximum dominant genes,
while the genotypes CIM-473,
CIM-554, CIM-506, and CIM-499
(-29.34 to -2984.07) possessed
minimum and negative Fr values,
had maximum recessive genes.
CONCLUSION
Results revealed that all the traits
were predominantly controlled by
non-additive type of gene action.
Overall,
the
broad
sense
heritabilities were higher than
narrow, however, half of the traits
showed moderate narrow sense
heritabilities with appreciable
genetic advance. The F1 hybrids
CIM-506 × CIM-554, CIM-473 ×
CIM-554, CIM-554 × CIM-506
and
CIM-554
×
CIM-707
performed better for majority of the
traits and could be used for hybrid
cotton. However, on the basis of
narrow sense heritability, the
selection
in
segregating
populations will also be effective.
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338
RESEARCH ARTICLE
SABRAO Journal
44 (2) 339-348, 2012
EFFECT OF COMMON SALT (NaCl) SPRAYS TO OVERCOME
THE SELF-INCOMPATIBILITY IN THE S-ALLELE LINES OF
Brassica oleracea var. capitata L.
SAURABH SINGH1* and VIDYASAGAR1
1
Department of Vegetable Science and Floriculture, CSK Himachal Pradesh Krishi
Vishvavidyalaya, Palampur, H.P., India – 176062
*Corresponding author’s E-mail: [email protected]
SUMMARY
Among the various methods for the maintenance of S-allele parental lines, the use of
NaCl solution sprays has been reported as the most convenient and economic. The
present investigation deals with the objective of ascertaining the concentration and
number of sprays of common salt (NaCl) effective for temporary breakdown of selfincompatibility in the S-allele lines (1-4-6 and II-12-4-7) of cabbage. The S-allele lines
were grown at the Experimental Farm and the pollinations viz. BP (selfing in bud
stage) and OP (selfing in freshly opened flowers) were carried out manually in the
plants enclosed with insect proof nylon nets. There was seed-set in BP on all the test
plants during 2009-2010, but erratic seed-set in OP after NaCl sprays in the S-allele
line I-4-6 probably due to too long time lag between spray and pollination and also the
use of relatively lesser volume of spray (25 ml/plant). However, in II-12-4-7, there was
practically no seed-set in OP probably due to the presence of stronger S-allele. The
results obtained during second experimental year (2010-2011) proved that NaCl sprays
(3% and 5%) are effective in temporary breakdown of self-incompatibility. Both the
concentrations of NaCl spray were at par in the S-allele line I-4-6 whereas, 5%
concentration was significantly superior in the S-allele line II-12-4-7 for seed-set in
OP. Seed-set in OP across flowering regimes were at par in the both S-allele lines. All
the three times of NaCl spray were at par for seed-set in OP in the S-allele line I-4-6
whereas, NaCl sprays made 15 minutes after and 15 minutes before plus after
pollination were significantly superior to common salt sprays made 15 minutes before
pollination in II-12-4-7.
Keywords: Cabbage, self-incompatibility, common salt (NaCl), seed production, open
pollination, bud pollination
Manuscript received: February 28, 2012; Decision on manuscript: August 28, 2012; Manuscript
INTRODUCTION
Cabbage (Brassica oleracea var.
capitata L.) is one of the most
important Cole crops being grown
throughout the world and has
originated from wild cabbage
(Brassica oleracea var. oleracea L.).
The
genetic
mechanism
of
sporophytic self-incompatibility has
proved effective in hybrid seed
production of Cole crops. For
continuous production of hybrid
seeds, maintenance of parental selfincompatible (SI) lines is one of the
basic requirements. Earlier, the
seeds of S-allele lines were
produced by manual sib mating in
bud stage (Cabin et al., 1996).
However, bud pollination is time
consuming and suitable for the
production of small seed quantity
only. Similarly, exposing plants to
high CO2 concentration (3-5%) and
relative humidity (100%) in air tight
growth chamber for production of
seeds of parental S-allele lines
(Palloix et al., 1985) also require
costly infrastructure. Off late, a new,
inexpensive and easy to use method
was given by Liao (1995), in which
sodium chloride solution was used
to
breakdown
the
selfincompatibility in Brassica spp.
including cabbage.
However, the use of sodium
chloride (NaCl) solution in Cole
crops has not yet been investigated
in India. Hence the present
investigation was undertaken with
the objective of ascertaining the
concentration and number of sprays
of common salt for temporary
breakdown of self-incompatibility in
the S-allele lines of cabbage.
The investigation was undertaken at
the Experimental Farm of the
Department of Vegetable Science
and Floriculture during 2009-2011.
The S-allele lines of cabbage i.e. I-46 and II-12-4-7 were used to study
the effect of common salt (NaCl)
solution in temporary breakdown of
self-incompatibility. The experiment
was conducted for two years, 20092010 and 2010-2011. During 20092010, the effect of NaCl solution
sprays in overcoming the selfincompatibility was observed after
24 hours to 96 hours of spray as
reported by earlier researchers up to
120 hours of spray. Based upon the
first year results (2009-2010), the
effect of NaCl solution sprays in
breakdown of self-incompatibility
was observed 15 minutes before
pollination and 15 minutes after
pollination during 2010-2011 as per
the findings of Liao (1995). During
2009-2010 seeds of S-allele lines of
cabbage were sown in the nursery on
10th
September,
2009
and
transplanting of seedlings was
carried out on 20th October, 2009 at
spacing of 60 x 45 cm in open field
conditions. For the year 2010-2011,
nursery sowing of the seeds of Sallele lines, produced through
selfing in bud stage and sib mating
in control plants, was done on 1st
September, 2010 and transplanting
was carried out on 13th October,
2010 at 60 x 45 cm spacing. All the
recommended package of practices
was followed to ensure the proper
growth of plants. Cross cuts to heads
were given from 2nd fortnight of
February to early March to hasten
bolting. The plants of self
incompatible lines were enclosed
with the insect proof nylon net
340
Singh & Vidyasagar (2012)
enclosures to prevent out crossing
by pollinators. The concentrations of
common salt (3% and 5%) and
control treatment (no spray) were
same in both the experimental years.
Three plants per treatment and 15-20
flowers/buds/pollination were taken.
During the first year (20092010), the spray quantity of NaCl
solution was 25 ml/plant/spray and
three sprays of NaCl solution were
made. The first spray was made at
15-20 per cent flowering and
subsequent sprays were made after
every four days. The pollinations OP
(selfing in freshly opened flowers)
and BP (selfing in bud stage) were
started from one day after spray
(1DAS) up to 4DAS. The S-allele
lines were also maintained through
manual sib mating in bud stage. For
the second experimental year (20102011), spray quantity of NaCl
solution
increased
to
50
ml/plant/spray. The spray of
common salt (NaCl) solution was
made 15 minutes before, 15 minutes
after and 15 minutes before plus
after OP (selfing in freshly opened
flowers) and BP (selfing in bud
stage) pollinations during the three
flowering regimes i.e. 25-50%, 5075% and >75% flowering. Average
number of seeds per siliqua in each
treatment
were
counted
and
recorded. Data were analysed as
suggested by Panse and Sukhatme
(1985).
RESULTS
In the first year (2009-2010) the
average number of seeds per siliqua
obtained as the mean of three plants
in OP (selfing in freshly opened
flowers) and BP (selfing in bud
stage) treatments in the S-allele lines
I-4-6 and II-12-4-7 after the
common salt sprays (3% and 5%)
are presented in tables 1 to 2. All the
plants set seeds in BP (selfing in bud
stage) treatment confirming the
viability of male and female gametes
of the test plants. With the one spray
of 3% common salt (NaCl) solution
in the S-allele line I-4-6, seed-set in
OP (selfing in freshly opened
flowers) treatment was recorded
only when the pollinations were
carried out one day after spray (0.56
seeds/siliqua). With two sprays of
3% NaCl solution seed-set in OP
treatment was obtained when the
pollinations were carried out two
days after spray (0.16 seeds/siliqua),
3
days
after
spray
(0.03
seeds/siliqua) and 4 days after spray
(0.13 seeds/siliqua). No seed-set in
OP treatment was noted in any of
the plants which had been given 3
sprays of common salt (Table 1).
With one spray of 5 per cent
common salt (NaCl) seed-set in OP
treatment was recorded only when
the pollinations were carried out one
day after spray (0.10 seeds/siliqua).
Similarly with two sprays of 5%
NaCl solution some seed-set in OP
treatment was obtained (0.43
seeds/siliqua) only when the
pollinations were carried one day
after spray. With three sprays of 5 %
common salt no seed-set was
obtained in OP treatment on any of
the plants when the pollinations
were carried out up to 4 days after
spray. The control treatment (no
Statistical analysis
Statistical analysis of experimental
data was accomplished by Analysis
of
Variance
in
completely
randomized block design (CRBD)
using CPCS-1 software (Cheema
and Singh, 1990).
341
spray) was common for 3% and 5%
common salt spray treatments. There
was seed-set in BP treatments
whereas no seed-set was obtained in
OP treatments on the control plants
(Table 1).
The results obtained in the
S-allele line II-12-4-7 during 20092010 with 3% and 5% common salt
(NaCl) spray are presented in Table
2. All the plants produced seeds in
BP treatment confirming the
viability of male and female gametes
of the test plants. No seed-set in OP
treatment was recorded in any of the
plants which had been given one
spray, two sprays and three sprays of
3 per cent common salt (NaCl)
solution when the pollinations were
carried out up to 4 days after spray.
With one spray of 5% NaCl solution
in the S-allele line II-12-4-7, only
0.05 seeds/siliqua were obtained in
OP treatment only when the
pollinations were carried out one
day after spray. No seed-set in OP
treatment was noticed in any of the
plants which had been given two
sprays and three sprays of 5% NaCl
solution (Table 2). There was seedset in BP treatments whereas no
seed-set was obtained in OP
treatments on the control plants
(Table 2).
During the experimental
year 2010-2011, the average number
of seeds/siliqua obtained as the
mean of three plants in OP (selfing
in freshly opened flowers) and BP
(selfing in bud stage) treatments in
the S-allele lines I-4-6 and II-12-4-7
after 3% and 5% common salt
(NaCl) sprays and no spray (control)
treatment are presented in Tables 3
to 4. All the plants set seeds in BP
treatment confirming the viability of
male and female gametes of the test
plants. Good amount of seed-set was
obtained in OP treatment on all the
test plants, across the flowering
regimes (25-50%, 50-75% and
>75% flowering), after the common
salt (NaCl) sprays (3% and 5%)
made 15 minutes before pollination,
15 minutes after pollination and 15
minutes before plus after pollination
in the S-allele lines I-4-6 and II-124-7. However, no seed-set was
recorded in OP treatments during
each of the flowering regimes, on
any of the plants in the control
treatment (no spray) in both the Sallele lines I-4-6 (Table 3) and II-124-7 (Table 4) in which pollinations
were carried out along with the
plants sprayed with common salt
solution. The effect on phenotype of
NaCl sprays was also observed with
respect to silique length. It was
recorded that the length o silique in
the plants treated with NaCl solution
sprays was more and were well
developed than the untreated plants.
Hence the silique developed after
salt sprays were having more
number of seeds. However further
studies are needed to pinpoint the
effect at biochemical and molecular
level.
342
Table 1. Average number of seeds/siliqua in OP and BP after 3% and 5% spray of NaCl solution along with control (no spray) in the Sallele line I-4-6 (2009-2010).
Conc.
No. of
sprays
1
2
3
NS(C)
3%
1 DAS
OP
0.56
0.00
0.00
0.00
BP
4.96
2.11
3.27
3.18
2 DAS
OP
0.00
0.16
0.00
0.00
5%
3 DAS
BP
1.38
2.71
1.67
0.56
OP
0.00
0.03
0.00
0.00
BP
1.34
3.16
1.51
1.27
4 DAS
OP
0.00
0.13
0.00
0.00
BP
1.80
1.84
1.05
2.14
1 DAS
OP
0.10
0.43
0.00
0.00
BP
1.97
1.05
1.55
3.18
2 DAS
OP
0.00
0.00
0.00
0.00
3 DAS
BP
1.38
1.31
1.32
0.56
OP
0.00
0.00
0.00
0.00
BP
2.33
1.44
1.64
1.27
4 DAS
OP
0.00
0.00
0.00
0.00
BP
2.48
1.14
2.11
2.14
Table 2. Average number of seeds/siliqua in OP and BP after 3% and 5% spray of NaCl solution along with control (no spray) in the Sallele line II-12-4-7 (2009-2010).
Conc.
No. of
sprays
1
2
3
NS(C)
3%
1 DAS
OP
0.00
0.00
0.00
0.00
BP
1.45
1.29
1.22
1.09
2 DAS
OP
0.00
0.00
0.00
0.00
BP
1.52
1.75
1.25
1.34
5%
3 DAS
OP
0.00
0.00
0.00
0.00
BP
1.68
1.34
1.40
1.18
4 DAS
OP
0.00
0.00
0.00
0.00
BP
1.85
1.43
1.03
1.21
OP – Self pollination in open flower stage, BP – Self pollination in bud stage
DAS- Days after spray, NS – No spray (control)
343
1 DAS
OP
0.05
0.00
0.00
0.00
BP
1.47
1.36
1.28
1.09
2 DAS
OP
0.00
0.00
0.00
0.00
BP
2.21
1.50
1.11
1.34
3 DAS
OP
0.00
0.00
0.00
0.00
BP
1.45
1.47
1.13
1.18
4 DAS
OP
0.00
0.00
0.00
0.00
BP
2.35
1.40
1.16
1.21
Table 3. Average number of seeds/siliqua in OP and BP upon NaCl spray (3% and 5%) along with control (no spray) during different
flowering regimes in the S-allele line I-4-6 (2010-2011).
Conc.
3%
5%
Flowering
Regimes
15 ( b )
OP
BP
15 ( a )
OP
BP
15 ( b + a )
OP
BP
25-50%
50-75%
>75%
1.63
4.86
4.15
3.23
5.33
1.40
5.12
3.54
3.31
4.27
5.05
6.14
4.38
5.83
2.66
6.97
4.43
3.26
NS ( C )
OP
BP
0.00
0.00
0.00
2.27
2.79
1.76
15 ( b )
OP
BP
15 ( a )
OP
BP
15 ( b + a )
OP
BP
NS ( C )
OP
BP
3.50
1.68
1.33
2.63
7.55
3.38
3.56
3.94
3.91
0.00
0.00
0.00
3.06
2.41
4.11
5.25
6.94
4.23
2.93
4.12
3.57
2.13
2.44
3.38
Table 4. Average number of seeds / siliqua in OP and BP upon NaCl spray (3% and 5%) along with control (no spray) during different
flowering regimes in the S-allele line II-12-4-7 (2010-2011).
Conc.
3%
5%
Flowering
Regimes
15 ( b )
OP
BP
15 ( a )
OP
BP
15 ( b + a )
OP
BP
NS ( C )
OP
BP
15 ( b )
OP
BP
15 ( a )
OP
BP
15 ( b + a )
OP
BP
NS ( C )
OP
BP
25-50%
50-75%
>75%
1.11
0.32
0.48
0.46
1.33
1.33
0.66
1.35
1.56
0.00
0.00
0.00
1.10
0.54
0.62
0.54
2.42
2.65
2.60
1.24
2.09
0.00
0.00
0.00
3.86
0.75
3.71
3.28
1.57
3.35
2.95
2.52
0.72
3.23
2.43
1.81
2.43
2.74
1.87
4.76
2.37
5.02
3.37
2.91
3.33
4.37
3.20
2.43
OP – Self pollination in open flower stage, BP – Self pollination in bud stage, NS (C) – No water spray (control)
15 (b) - Spray of NaCl 15 minutes before pollination
15 (a) - Spray of NaCl 15 minutes after pollination, 15 (b+a) - Spray of NaCl 15 minutes before + 15 minutes after pollination
344
Table 5. Number of seeds/siliqua as per completely randomized block design
(CRBD) recorded in OP treatments in the S-allele lines I-4-6 and II 12-4-7.
Treatments
Concentration
3%
5%
CD ( at 5% level )
Flowering regimes
25-50%
50-75%
>75%
CD (at 5 % level )
Time of sprays
15 ( b )
15( a )
15( b+a )
CD ( at 5% level )
Seeds/Siliqua
I-4-6
3.58
3.44
NS
Seeds/Siliqua
II-12-4-7
0.94
1.48
0.442
3.22
4.43
2.88
NS
1.02
1.18
1.44
NS
2.78
3.92
3.83
NS
0.68
1.42
1.53
0.54
Table 6: Number of seeds/siliqua as per CRBD recorded in OP treatments in
the interaction (flowering regime x time of NaCl spray) in the S-allele line II12-4-7
Time of spray
Flowering regime
25-50%
50-75%
>75%
15 ( b )
15 ( a )
15 ( b+a )
1.10
0.43
0.53
0.48
1.82
1.95
1.46
1.30
1.85
CD: 0.9364
OP- Self pollination in open flower stage,
CD- Critical difference
15 (b) - Spray of NaCl 15 minutes before pollination
15 (a) - Spray of NaCl 15 minutes after pollination
15 (b+a) - Spray of NaCl 15 minutes before + 15 minutes after pollination
findings are inconsistent with those
of Fu et al. (1992) who had
reported higher compatibility index
than the control (no spray) in the
self-incompatible
plants
of
Brassica napus even up to 120
hours of common salt spray.
The findings are also
inconsistent with Kucera (1990)
and Kucera and Cerny (1991) who
had succeeded in maintaining the
selfincompatible plants/lines of
DISCUSSION
Erratic seed-set recorded during
2009-2010 in OP treatments in the
S-allele line I-4-6
after the
may be attributed to too long time
lag between spray and pollinations
and also to relatively lesser
quantity (25 ml/plant) of NaCl
solution sprayed on t the plants of
the S-allele line. The present
345
cauliflower
and
kohlrabi
respectively, with sodium chloride
(3%) solution sprays when
pollinations were carried out 0.51.0 hour before or after NaCl
spray. The findings are also
inconsistent with Koprna et al.
(2005) and Kucera et al. (2006)
who verified the seed production of
S-allele lines by spraying flowers
with 5% and 3% NaCl solution
respectively. The reason of getting
practically no seed-set during
2009-2010 in OP treatments on the
S-allele line II-12-4-7 despite
common salt sprays could be the
presence of stronger S-allele in this
S-allele line as compared to the Sallele line I-4-6.
During 2010-2011, good
amount of seed-set was obtained in
OP treatment on all the test plants,
across the flowering regimes (2550%,
50-75%
and
>75%
flowering), after the common salt
(NaCl) sprays (3% and 5%) made
15 minutes before pollination, 15
minutes after pollination and 15
minutes
before
plus
after
pollination in the S-allele lines I-46 and II-12-4-7. The present
findings (2010-2011), in which
variable seed-set in OP (selfing in
open flower stage) carried out
before/after /before plus after
were recorded, are in accordance
with Kucera (1990), Kucera and
Cerny (1991), Liao (1995) and
Chaozhi et al. (2009) who had
worked on temporary breakdown
of
self-incompatibility
with
common salt sprays in cauliflower,
kohlrabi, cabbage and B. napus
respectively.
Kucera
(1990)
recorded 4.3 seeds per pod on the
self-incompatible
plants
with
common salt spray of 3% made
0.5-1.0 hour after self pollination
as compared to 0.4 seeds per pod in
the untreated plants. Kucera and
Cerny
(1991)
found
3%
concentration of common salt
spray better than 1.5%. Spray
before pollination was effective in
one of the lines only. Liao (1995)
obtained variable number of seeds
in self pollination in different Sallele lines of cabbage sprayed with
3 per cent and 5 per cent common
salt and pollinations made 10-15
minutes before and after spray. In
one of the S-allele lines (No. 86193-5-3), 5% NaCl sprays proved
better. In two of the S-allele lines
i.e. No.15 and No.220, there were a
very few seeds even after NaCl
sprays probably due to the
presence of stronger S-alleles in
these lines. Chaozhi et al. (2009)
recorded 7.03 seeds/siliqua after 35 % spray of NaCl on the S-allele
line S-1300 of B. napus followed
by hand pollination. Similarly,
Wang et al. (2012) also reported
the use of NaCl for overcoming
self-incompatibility in non heading
Chinese cabbage.
Analysis of variance for
the seed-set in OP during 20102011 in the S-allele line I-4-6
revealed that no source of variation
(concentration of common salt,
flowering regimes and time of
common salt spray) was significant
(Table 5). The coefficient of
variation (CV) was rather very
high (72.55%) which may be due
to wide plant to plant variations in
seed-set on the test plants. It is
quite explainable since the present
pollinations were carried out in
insect proof nylon net enclosures
under field conditions with no
control over temperature and
humidity/rains.
Environmental
346
common salt spray were at par in
the S-allele line I-4-6 whereas 5%
concentration was significantly
superior in the S-allele line II-12-47 for seed-set in OP. The NaCl
sprays made 15 minutes after and
15 minutes before plus after
pollination
were
significantly
superior to common salt sprays
made 15 minutes before pollination
in the S-allele line II-12-4-7. In
general, seed-set in BP on the
plants receiving common salt
sprays were higher than the control
plants (no spray).
factors especially temperature
(Gonai and Hinata, 1971) and
humidity (Carter and McNeilly,
1975) have been reported to
influence the level of selfincompatibility
in
Brassica
oleracea. Analysis of variance for
the seed-set in OP in the S-allele
line II-12-4-7 revealed that the
common salt concentration of 5%
was significantly superior to 3%
concentration which may be
attributed to the presence of
stronger S-allele in the S-allele line
II-12-4-7. The common salt sprays
made 15 minutes after pollination
and 15 minute before plus after
pollination were at par with each
other but significantly better than
common salt sprays made 15
minutes before pollination (Table
5). Liao (1995) has reported that
the number of seeds/siliqua depend
on the kind of inbred line and
degree of incompatibility, which
might be the reason for getting
relatively
less
number
of
seeds/siliqua in the S-allele line II12-4-7 as compared to the S-allele
line I-4-6. The interaction between
flowering regimes and time of
common spray was significant in
the S-allele line II-12-4-7 (Table
6). The interactions of the
flowering regime >75% with
common salt sprays made 15
minutes after pollination and 15
minutes
before
plus
after
pollination were at par but
significantly superior to common
salt sprays made 15 minutes before
pollination.
It can be concluded that
proved effective in temporary
breakdown of self-incompatibility
in the S-allele lines I-4-6 and II-124-7. Both the concentrations of
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RESEARCH ARTICLE
SABRAO Journal
44 (2) 349-355, 2012
ESTIMATION OF IODINE AND ANTIOXIDANT ACTIVITY
COUPLED WITH YIELD ATTRIBUTING TRAITS IN
CABBAGE HYBRIDS
V. PANDEY, A. CHURA, M.C. ARYA and Z. AHMED
Defence Institute of Bio Energy Research, Field Station, Pithoragarh- 262501 Uttarakhand, India
SUMMARY
Seventeen hybrids of cabbage were evaluated for quantitative and qualitative traits.
This humble vegetable is a rich source of a number of phytonutrients which help boost
our defence mechanism. The objective of our study was to identify hybrids rich in
iodine content and anti-oxidant activity. So that, they can be used supplements to meet
out the deficiency syndromes viz. anemia, weight loss and constipation and iodine
deficiency etc. Cabbage Hybrid 3 recorded the maximum yield (423 q/ha) followed by
SIR (340 q/ha). On the basis of iodine concentration hybrid Varun recorded
7.41µg/100g followed by Green Flash (6.40). Cabbage hybrid 3 exhibited anti-oxidant
activity of 1.20 IC50 value followed by Cabbage Hy 4 (1.45 IC50).
Key words: Iodine, antioxidant activity, yield attributing traits, cabbage
Manuscript received: March 6, 2012; Decision on manuscript: September 19, 2012; Manuscript
INTRODUCTION
Vegetables being one of the
important components of food have
a vital contribution both as dietetic
and protective agents. Production
of good quality vegetables is the
primary factor of commercial
vegetable cultivation for better
economic returns. It is the chemical
composition which mainly plays a
crucial role for determining the
quality of the vegetables which
makes them worth acceptable for
consumption. Cabbage (Brassica
oleracea var. capitata) belongs to
family Brassicaceae, seems to be
rich in anti-oxidants and are in first
line of defence against cancer.
Cabbage is one of the vegetables
that are highly promoted for
prevention of cancer. Each layer of
cabbage is packed with an
abundance of natural anti-oxidant
properties, that helps us fight
cancer. Both animal model studies
and epidemiological data in
humans have tended to confirm the
protective role of cabbage on the
development of cancer. Dietary
cabbage has been reported to
inhibit
chemically
induced
mammary tumoringenesis in rats
(Wattenberg, 1983) and (Bresnik et
al, 1990). Cabbage is nutrient
packed and low in calories. It is
impressive with its high content
levels of calcium, iron, potassium,
sulphur and phosphorus. It is high
in vitamin A, B1, B2, B6, C, E, K
and folic acid (Salunkhe and
Salunkhe. 1974). It is also a rich
source of iodine and thus aids in
the proper functioning of brain and
nervous system, hypothyroidism
and pregnancy related problems
(Key et al., 1992; Appleby et al.,
1999). To meet the full dietary
needs of a common man, there is a
greater
need
of
enhancing
production potential by growing
hybrids and high yielding varieties.
The productivity of cabbage is
much higher; with a yield potential
of 400-500 q/ha especially if they
are grown from hybrid seeds.
There is a great scope for growing
cabbage in the valley areas of the
western
Himalayas
where
irrigation facilities are available.
Among vegetables grown in the
hilly regions, the cabbage occupies
a premier position. T he nutritional
quality of cabbage produced in
hills is also superior to that
produced in the plains due to cold
climatic conditions. Keeping the
above mentioned factors in view,
the present investigations were
designed on hybrid cultivars to
evaluate their performance with
regard to yield components and
quality parameters and to identify
cabbage hybrids with high iodine
and anti-oxidant activity, which
will be of great significance for
human health.
Seventeen cabbage hybrids namely
Cabbage Hy 1, Cabbage Hy 2,
Quisto,
Kranti,
DARL-801,
DARL-802, CH 21, Green Flash,
SIR, CH 2200, Speed 50, Krishna
and Varun, collected from different
State Agricultural Universities and
private seed companies were
evaluate in randomized complete
block design with three replications
during 2010-11 and 2011-12 at
Experimental Field of Defence
Institute of Bio Energy Research,
Pithoragarh, Uttarakhand (India).
Hybrid Krishna and Varun were
used as check. The experimental
site is located at an altitude of 1550
msl (middle hills) in western
Himalayas. The plants were spaced
60 cm apart between and within
rows. Net plot size was 6.0 m2.
Recommended cultural practices
were adopted to maintain the
optimum growth and stand of the
crop. Five randomly selected plants
were harvested for recording data
on biological yield (kg), head
length (cm), head circumference
(cm), non wrapper leaves (no.),
stalk length (cm), net head weight
(kg) and yield/plot (kg). The data
recorded on yield/plot (kg) of each
hybrid was converted to yield
(q/ha) before statistical analysis of
the data (Gomez and Gomez, 1984)
The iodine content and antioxidant
activities of each hybrid were
analyzed from marketable heads
selected from the crop grown
350
Pandey et al. (2012)
during the second year (Table 1).
Selected heads of each hybrid was
cut into small pieces and oven
dried at 40 0C and then grinds to
make
fine
powder.
Iodine
estimation was done by arseniccerium redox method, based on the
principal
of
quantitative
determination of micro amounts of
Iodine, catalytic reduction of ceric
(Ce +4) to Cerous (Ce+3) ion by
iodine. A suitable amount of dried
sample (usually containing 0.040.08 µg of the iodine) was taken in
a Pyrex test tube (15 x 125 mm).
The concentration of iodine varies
sample to sample. In initial stage,
the amount of dried sample taken
should
contain
iodine
concentration in the range of .04.08
µg.
After
digestion,
incineration and extraction, the
reduction of ceric to cerous was
read
in
a
UV-VIS
spectrophotometer at 420 nm
(Brown and Hutchinson 1949).
Standard solution of KI (Potassium
Iodide) containing 0.0 to 0.16 µg of
iodine per milliliter was run
simultaneously. A straight line
response was obtained by plotting
concentration of iodine in µg
against
reading
on
spectrophotometer. Using this
standard graph, the values for any
unknown sample could be read.
Antioxidant activity of each hybrid
was assayed using DPPH method
(Hatano et al., 1989).
RESULTS
significant variance for all the
characters studied among the
hybrids grown in middle hill
climatic conditions of Uttarakhand.
The biological yield per head was
found to be highest for Cabbage
Hybrid 3 (2.553 kg) followed by
Cabbage Hybrid 4 (2.330 kg) and
Cabbage Hybrid 4 (2.496 kg). The
length of head ranged from 18.1326.82. Cabbage Hybrid 3 recorded
maximum head length (26.82 cm)
followed by Cabbage Hybrid 2
(26.45 cm) and DARL 801 (24.78
cm).
Maximum
head
circumference was exhibited by
Cabbage
Hybrid
3
(60.63)
followed by SIR (58.40) and
DARL 801 (58.33). Number of non
wrapper leaves ranged from 9.617.2. Maximum number of outer
leaves was recorded in Cabbage
Hybrid 4 whereas hybrid SIR
recorded minimum number of
outer leaves (9.6). The length of
stalk varied from 9.6-11.9 cm and
hybrid CH 21 recorded minimum
stalk length (9.6). For the character
net head weight, Cabbage Hybrid 3
exhibited 2.174 kg weight followed
by Cabbage Hybrid 6 (1.922 kg).
Highest yield per plot was recorded
by Cabbage Hybrid 3 (25.416 kg)
followed by Cabbage Hybrid 6
(22.00 kg). Cabbage Hybrid 3
recorded yield 423.00 q/ha. The
Iodine content (µg/100g) range and
mean of seventeen samples were
1.60-7.41 and 4.55 µg/100g
respectively.
Hybrid
Varun
exhibited highest iodine content
(7.41µg/100g) followed by CH2200 (6.41µg/100g) and Green
Flash (6.40 µg/100g). The DPPH
(1, 1-diphenyl-2picryl- hydrazyl )
method was used for estimating
antioxidant activity. IC50 is the
inhibition concentration 50. If this
value will be low for the hybrid,
the anti oxidant activity of that
hybrid will be high. It ranged from
1.45-5.40 IC50. Among hybrids,
351
Cabbage Hy 3 recorded maximum
anti-oxidant activity of IC50 value
(1.20), followed by Cabbage Hy 4
having (1.45 IC50) value (Figure 1).
DISCUSSION
The importance of food based
approach
for
preventing
micronutrient malnutrition has
become widely accepted. Cabbage
as a vegetable plays a very
important role in human diet.
Being the corner stone of health,
supplying us with a wealth of
vitamins, minerals, fibers and
carbohydrates, it has assumed
utmost importance after the
discovery of phyto chemicals and
their strong antioxidant potential in
scavenging free radicals (Holland,
1991). Due to the presence of
iodine in cabbage, this vegetable
has become very important. Iodine
is absent in the soil of many
Himalayan and alluvial regions and
high land pressure has eroded top
soil and with it iodine, that would
be consumed with the foods grown
there. Antioxidants are related with
reduced cancer risks. A large
compositional diversity among
hybrid cultivars can be exploited
by breeders to breed superior
varieties (Oliveira et al, 1999).
These hybrids can be used for
further studies and as a source of
antioxidant richness for improving
the present day cultivars. Certainly
it will be a new investigation on
the set of materials under the
environmental
conditions
of
middle hills of Uttarakhand.
8
7
6
5
4
3
2
1
S
IR
.2
22
S
pe 00
ed
K 50
ris
hn
a
V
ar
un
H
C
C
C
ab
ba
g
ab e H
ba y
C g e .1
ab
ba Hy
C g e .2
ab
ba Hy
C g e .3
ab
ba Hy
C g e .4
ab
ba Hy
g .5
e
H
y.
Q 6
ui
st
o
K
D ran
A
R ti
L
D .8 0
A
R 2
L.
8
01
G CH
re
e .21
n
Fl
as
h
0
Iodine (µg/100g)
Antioxidant activity (IC50)
Figure 1. Iodine concentration (µg/100g) and antioxidant activity (IC50) Value of
cabbage hybrids
352
Table 1. Performance of cabbage hybrids.
Sn
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Treatments
Cabbage Hy 1
Cabbage Hy 2
Cabbage Hy 3
Cabbage Hy 4
Cabbage Hy 5
Cabbage Hy 6
Quisto
Kranti
DARL 802
DARL-801
CH 21
Green Flash
SIR
CH 2200
Speed 50
Krishna ©
Varun ©
CD @1%
CD @5%
CV
Biological yield (kg)
2010-11
2011-12
1.566
1.653
2.595
2.043
2.620
2.486
2.662
2.330
2.215
2.491
1.898
2.043
2.129
2.196
1.825
2.010
1.980
2.183
2.150
1.910
0.750
1.573
2.110
1.900
1.651
2.546
1.366
1.760
1.016
1.590
1.700
1.803
2.000
2.116
0.716
0.851
0.516
0.633
15.476
18.355
Mean
1.610
2.319
2.553
2.496
2.353
1.970
2.163
1.918
2.081
2.030
1.162
2.000
2.098
1.563
1.303
1.752
2.058
2010-11
19.86
26.96
27.50
21.83
22.26
22.33
18.76
21.60
22.20
24.55
21.23
20.50
18.93
16.53
16.06
20.53
20.45
3.177
2.289
5.775
Head length (cm)
2011-12
20.86
25.93
26.13
21.86
25.20
24.33
21.26
22.10
22.00
25.00
20.00
21.20
26.00
20.20
20.16
19.83
19.56
3.171
2.359
6.316
Mean
20.36
26.45
26.82
21.85
23.29
23.71
20.00
21.85
22.10
24.78
20.62
20.85
22.46
18.37
18.13
20.18
20.00
Head circumference (cm)
2010-11
2011-12
Mean
45.80
48.86
47.33
57.26
59.00
58.13
59.86
61.40
60.63
59.13
50.00
54.56
52.16
57.13
54.65
46.76
56.80
51.78
45.80
51.73
48.76
49.56
41.83
45.69
54.00
54.33
54.17
57.20
59.46
58.33
45.20
49.40
47.30
49.50
50.80
50.15
57.20
59.60
58.40
49.50
51.76
50.15
52.11
50.00
51.10
51.36
49.66
50.50
58.20
50.06
54.13
1.418
1.482
1.184
1.102
9.493
8.460
Non wrappers leaves (no)
2011-11
2011-12
Mean
12.2
10.6
11.4
10.6
9.3
9.9
11.5
10.8
11.2
17.2
17.2
17.2
13.3
9.5
11.4
13.0
11.4
12.2
18.4
16.6
17.5
14.8
11.7
13.3
11.5
12.4
11.9
10.8
12.3
11.6
12.8
11.2
11.6
10.3
8.0
12.0
12.0
10.6
9.6
16.2
14.7
15.5
10.2
9.7
9.9
9.3
10.4
9.8
11.2
10.1
10.7
1.893
2.317
1.382
1.723
6.042
9.110
353
Table 1 (cont’d). Performance of cabbage hybrids
SN
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Treatments
Cabbage Hy 1
Cabbage Hy 2
Cabbage Hy 3
Cabbage Hy 4
Cabbage Hy 5
Cabbage Hy 6
Quisto
Kranti
DARL 802
DARL-801
CH 21
Green Flash
SIR
CH 2200
Speed 50
Krishna ©
Varun ©
CD @1%
CD @5%
CV
Stalk length (cm)
2010-11
5.90
7.46
11.03
10.56
7.60
6.10
9.59
7.13
10.00
11.80
5.00
8.00
6.30
8.90
6.10
7.00
7.20
1.436
1.035
7.234
2011-12
6.53
7.80
10.20
10.20
7.53
5.93
9.86
7.53
10.13
12.16
6.26
8.13
7.33
6.63
6.60
7.73
7.63
1.482
1.102
8.460
Net head weight (kg)
Mean
6.2
7.6
10.6
10.4
7.6
6.0
9.7
7.3
10.1
11.9
5.6
8.1
6.8
7.8
6.2
7.4
7.4
2010-11
1.300
1.630
1.940
1.220
2.076
1.363
0.910
1.196
1.200
1.600
0.908
1.550
1.575
0.950
0.746
1.110
0.920
0.400
0.288
10.678
2011-12
1.150
1.345
1.903
1.150
2.271
1.700
1.256
1.203
1.235
1.865
1.440
1.620
1.890
1.425
1.140
1.235
1.021
0.693
0.515
11.125
Mean
1.225
1.542
1.922
1.535
1.185
2.174
1.532
1.083
1.199
1.218
1.218
1.733
1.174
1.585
1.732
1.118
1.188
Yield /plot
(Kg)
Yield
(q/ha)
Iodine
content
(µg/100g)
Mean
13.165
19.560
25.416
9.580
22.750
22.000
10.916
10.580
9.680
13.750
8.715
17.535
20.416
6.495
9.000
7.625
13.685
219.00
326.00
423.00
159.00
379.00
366.00
181.00
176.00
161.00
229.00
145.00
292.00
340.00
108.00
150.00
127.00
95.00
5.54
2.73
4.28
1.75
4.51
4.95
4.78
3.33
4.00
5.80
5.94
6.40
6.20
6.41
2.91
1.60
7.41
1.629
1.211
15.997
Antioxidant
activity
(IC50) Value
5.40
1.74
1.20
1.45
2.70
2.60
2.31
2.30
1.73
2.50
2.37
2.94
2.37
2.08
2.93
2.02
2.10
0.845
0.628
15.720
354
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Wheeler M and Markin RS
(1990).
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cabbage
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Brown HD, and Hutchinson CS
(1949). Vegetable Science.
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Sons, New York.
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FujitaY
and
Yashuhara A (1988). Effects
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(1991). Vegetables, Herbs
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Key TJ, Thorogood M, Keenan J, and
Long A (1992).
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Oliveira AP, Costa JS, Costa CC
(1999). Performance of six
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355
RESEARCH ARTICLE
SABRAO Journal
44 (2) 356-369, 2012
GENETIC DIVERGENCE STUDY IN AROMATIC RICE (Oryza
Sativa L.)
PRAVEEN SINGH1*, ANIL PANDEY1, S.B. MISHRA1 and
RAJESH KUMAR1
1
Department of Plant Breeding & Genetics, Rajendra Agricultural University,
Pusa (Samastipur)-848125, India
SUMMARY
The genetic divergence study was conducted to estimate the nature and magnitude of
diversity in thirty aromatic rice accessions including fifteen improved varieties during
kharif season, 2008. The divergence analysis including Tocher’s, canonical (vector)
and Euclidian methods indicated the presence of appreciable amount of genetic
diversity in the material. The thirty aromatic rice genotypes were grouped into six
clusters by both Tocher’s and Euclidian methods of divergence study. But the clusters
of both methods were different on the basis of the genotypes and their numbers present
in the cluster. The genetic parameters of all the characters have also been studied. The
result obtained from different methods of divergence study was slightly differs from
each others. The suitable genotypes for the different quantitative and qualitative
characters have been drawn from the all three methods of divergence study. The most
diverse parent combination for the component characters of grain yield have been
identified on the basis of 3D diagram of PCA scores and Euclidian distance matrix,
which were JGL 15336 x Taroari Basmati for effective tillers per plant, Badsabhog x
Rajendra Kasturi for filled grain per panicle, JGL 15336 x Birsamati for grain weight
per panicle and Gandhasala x Pusa Sugandha-3 for leaf area index. These genotypes
were found most suitable genotypes for the respective characters and can be used as
potential donor for future breeding programs.
Keywords: Aromatic Rice, Genetic Divergence, Cluster analysis, PCA
Manuscript received: March 12, 2012; Decision on manuscript: September 22, 2012; Manuscript
Singh et al. (2012)
suitable genetic divergence are
generally the most responsive for
yielding the most promising
segregants. The present study was,
therefore, undertaken to assess the
extent of genetic diversity in 30
aromatic rice genotypes which will
help to select prospective parents
to
develop
transgressive
segregants.
INTRODUCTION
India in well known for its native
wealth of rice genetic resources
and among these the large number
of aromatic varieties cultivated in
different agro-climatic regions of
country. As per the growing
demand of aromatic rice, the
emphasis should be given to the
development of fine grain aromatic
rice for their outstanding quality
traits like aroma, kernel elongation
after cooking, fluffiness and taste.
The importance of aromatic rice
cultivars/strains developed by
different rice research institutes of
the
country
for
different
environment cannot be neglected.
These strains can perform better in
varying environments and also
good in aroma and taste, although
some scientists have claimed that
under specific condition they are
best in comparison of local
varieties.(Nayak, et al. 2004). Thus
a thorough understanding of those
factors, which might affect the
yield in strains and varieties of
aromatic
rice
in
different
environments is important and may
serve as a basis for their rational
utilization for improvement.
Genetic diversity is pre
requisite for any crop improvement
program, as it helps in the
development
of
superior
recombinants (Naik et al., 2006).
Genetic divergences among the
genotypes play an important role in
selection of parents having wider
variability for different characters.
Statistical analysis quantifies the
genetically distance among the
selected genotype and reflects the
relative contribution of specific
taints towards the total divergence.
The crosses between parents with
The present investigation was
under taken during kharif 2008 and
kharif 2009 in the Rice Research
field of Rajendra Agricultural
University, Pusa, Samastipur,
Geographically Pusa is located at
25.290 N latitude and 85.400E
longitude and 51.8 meters above
the mean sea level. The relative
humidity during crop season
ranged from 85 to 94 per cent. The
investigation was carried out in
randomized complete block design
(RCBD) with three replication and
plot size of 1.80 m2 (0.6 m x 3 m)
with spacing of 20 cm and 15 cm
between rows and between plants,
respectively. The experimental
materials consist of thirty aromatic
rice genotypes selected on the basis
of different geographical origin and
genetic background and obtained
from different parts of the country
(i.e. from S.K. University of
Agricultural
Science
and
Technology, Jammu, Central Rice
Research
Institute,
Cuttack,
Directorate of Rice Research ,
Hyderabad,
and
Rajendra
Agricultural
University,
Pusa
Samastipur) (Table 1). The
observations were recorded for
yield
and
yield
attributing
357
characters and quality characters of
aromatic rice used in this study.
To study the genetic
divergence among the genotype
used in the present investigation
and to know the fluctuation in
clustering
pattern
of
those
genotypes,
the
D2
values
(Mahalanobis,
1936)
were
calculated by using Tocher method
described by Rao, 1952 for
grouping the varieties into different
clusters.
Genetic
divergence
analysis using canonical (Vector)
method is a sort of multivariate
analysis where canonical vectors
and roots representing different
axes of differentiation and the
amount of variation accounted for
by each of such axes, respectively,
were derived (Rao 1952). Non
hierarchical Euclidean cluster
analysis (Beale, 1969, Katyal et al.,
1985) was conducted using
computer package (Windostat
version 8.5).
RESULTS
Amongst six clusters formed by
Tocher’s method (Fig. 1), cluster I
was largest (with 15 genotypes)
and cluster III, IV, V and VI were
smallest with one genotype only
(Table 2). The maximum inter
cluster distance was observed
between cluster II and cluster III
there was a large inter cluster
distance between these clusters
(Table 3), which thus may be
utilized under inter varietal
hybridization programme. As per
the Tocher’s method (Table 4)
highest values for tall plant height
(155.061),
Spikelets/panicle
(215.758), filled grain/panicle,
grain weight/panicle, days to 50%
flowering, days to maturity, head
rice recovery and elongation ratio
was observed for cluster II. The
highest cluster mean value for
panicle length, grain length, kernel
L/B ratio, alkali digestion value
and test weight of seed was
observed for cluster IV. The
highest cluster mean value for
effective tillers/plant, kernel length
and kernel length after cooking
was observed for cluster III. The
highest cluster mean value for leaf
area index, harvest index, grain
yield/plot, and grain yield per plant
was observed for cluster V,
whereas, the highest cluster mean
of specific leaf weight was
observed for cluster VI.
All the 30 aromatic rice
genotypes were subjected to nonhierarchical
Euclidian
cluster
analysis using the computer
software (Windostat version 8.5)
divided the genotypes in six
clusters (Fig. 1). Maximum number
of genotypes i.e. 11 grouped in
cluster D and minimum i.e. 2
genotypes in each cluster B and C,
whereas, cluster E has only one
genotype. The clusters D, A, F, B,
C and E comprised of 11, 9, 5, 2, 2
and 1 genotypes, respectively
(Table 2). On the basis of
Euclidian method the highest inter
cluster distance was recorded
between C and F with 2 and 5
genotypes, respectively, where as
minimum between D and F with
11and 5 genotypes, respectively.
The genotypes with high order of
divergence were found in clusters
C and F followed by C and E, C
and D, B and E, A and F, B and C,
B and F, E and F, A and E.
358
Singh et al. (2012)
Table 1. Brief description of the aromatic rice genotypes used in present investigation.
Sl.N
Genotype
Source
Origin centre
Pedigree
1
2
3
4
5
6
7
8
HUR-ASG-GN
Kasturi
Pusa Sugandh 3
Birsamati
Jasmine
Jeerakasala
Gandhasala
Basmati 370
BHU,Varanasi
IARI, New Delhi
IARI, New Delhi
BAU, Ranchi
Thailand
Kerela
Kerela
PAU, Ludhiana
9
Basmati 385
PAU, Ludhiana
Selection from Gr-32
Basmati340/CRR 88-17-1-5
Pusa 1238-1/Pusa 88-6
IR 36/BR 9
KDML 105 Thai variety
Land race from Kerela
Land race from Kerela
Pure line selection of local land
race
TN 1/Basmati 370
10
Ranbir Basmati
SKUAST, Jammu
Selection from Basmati 370
11
Sanwaal Basmati
PAU, Ludhiana
12
RR564
SKUAST, Jammu
13
Taroari Basmati
PAU, Ludhiana
Pure line selection from HBC 19
14
NDUAT, Faizabad
Selection from local Lalmati
15
Narendra
Lalmati
NDR 6235
BAU, Ranchi
BAU, Ranchi
BAU, Ranchi
BAU, Ranchi
BAU, Ranchi
DRR, Hyderabad
DRR, Hyderabad
SKUAST,
Jammu
SKUAST,
Jammu
SKUAST,
Jammu
SKUAST,
Jammu
SKUAST,
Jammu
SKUAST,
Jammu
DRR, Hyderabad
DRR, Hyderabad
NDUAT, Faizabad
16
17
NDR 8018
NDR 6242
DRR, Hyderabad
DRR, Hyderabad
NDUAT, Faizabad
NDUAT, Faizabad
18
19
CR 2603
HUR – ASG –
KN
KJT 4-4-36
RNR 2465-1
RAU 3055
RAU 3036
Rajendra Kasturi
Rajendra
Suwasini
Kalanamak
Badsahbhog
NDR 9542
PAU 3025-50-12
JGL -15336
DRR, Hyderabad
DRR, Hyderabad
CRRI, Cuttack
BHU,Varanasi
Selection from Kalanamak
(Basti)
BPK246/Sabita
(Birdpur)
NDR 8095/Dubraj
DRR, Hyderabad
DRR, Hyderabad
RAU, Pusa
RAU, Pusa
RAU, Pusa
RAU, Pusa
DRR, Hyderabad
DRR, Hyderabad
RAU, Pusa
RAU, Pusa
RAU, Pusa
RAU, Pusa
KJT 9-333/Indrayani
RNR-M7/RNR 19994
T 3/VG 56
T 3/VG 56
Kasturi/Sugandha
Kasturi/Pusa Basmati
DRR, Hyderabad
DRR, Hyderabad
DRR, Hyderabad
BAU, Ranchi
UP
WB
NDUAT, Faizabad
PAU, Ludhiana
Improved local land race
Improved local land race
BKP246/NDR300301/Swarna
Selection of Basmati 370
DRR, Hyderabad
Chattisgargh
JGL 384/Godawari Isukala
20
21
22
23
24
25
26
27
28
29
30
359
Figure 1. Dendrogram showing clusters formed by Tocher’s method and Euclidian
method.
360
Singh et al. (2012)
Table 2. Clustering pattern of 30 genotypes of aromatic rices on the basis of D2
statistics (Tocher’s Method and Euclidian method).
Cluster
Tocher’s Method
Euclidian method
No. of
genotypes
Name of genotypes
No. of
genotypes
Name of genotypes
I A
15
9
HUR-ASG-GN, RNR
2465-1, Jeerakasala,
Gandhasala, Narendra
Lalmati, NDR 6242,
NDR 6235,
Kalanamak, HURASG-KN
II B
11
2
Birsamati, NDR 9542
III C
1
Basmati 385(9), Ranbir
Basmati(10), Basmati
370(8), Kasturi(2),
Sanwaal Basmati(11),
Rajendra Suwasni(25),
RR 564(12), RAU
3036(23), KJT 4-436(20), NDR 8018(16),
Jasmine(4), CR
2603(18), Pusa
Sugandha-3(3), PAU
3025-50(29) and
Narendra Lalmati(14)
Badsabhog(27), JGL
15336(30), RNR 24651(21), HUR-ASG-KN(1),
Gandhasala(7),
Jeerakasala(6), HURASG-GN(19), NDR
6235(15), NDR 6242(17),
NDR 9542(28) and
Kalanamak(26)
Taroari Basmati(13)
2
IV D
1
RAU 3055(22)
11
V E
1
Birsamati(5)
1
Badsabhog, JGL
15336
Kasturi, Rajendra
Suwasni, RR 564,
Sanwaal Basmati,
NDR 8018, KJT 4-436, CR 2603, Basmati
385, Ranbir Basmati,
Basmati 370, RAU
3036
Rajendra Kasturi
VI F
1
Rajendra Kasturi(24)
5
361
Jasmine, PAU 302550, Pusa Sugandha-3,
Taroari Basmati.
Table 3. Mean inter and intra cluster distance among six clusters in aromatic rice on the basis of
D2 statistics (Tocher’s Method and Euclidian method).
Cluster I
Cluster A
Cluster II
Cluster B
Cluster III
Cluster C
Cluster I
Cluster A
Cluster II
Cluster B
Cluster III
Cluster C
Cluster IV
Cluster D
Cluster V
Cluster E
Cluster VI
Cluster F
265.58
1332.87
1312.57
2980.52
746.07
2781.18
792.15
2513.37
815.15
4253.48
1093.91
5777.23
577.90
1643.68
3012.44
4863.78
2992.60
2751.15
1434.30
5811.05
1680.18
4778.41
0.00
297.54
504.52
6973.65
1402.08
7242.19
1364.47
11965.76
0.00
556.77
872.90
3142.13
2567.38
1542.84
0.00
0.00
2086.49
4755.19
Cluster IV
Cluster D
Cluster V
Cluster E
Cluster VI
Cluster F
On the basis of cluster mean
values, maximum divergence for
plant height was exhibited by
cluster A; earliness for days to 50%
flowering and days to maturity in
cluster D; effective tillers/plant,
leaf area index, specific leaf weight
in cluster E; grain weight/panicle,
harvest index, alkalie digestion
value, grain yield per plot in cluster
B; spikelets/panicle, filled grain
/panicle, grain yield/plant, head
rice recovery, elongation ratio in
cluster
C;
and
maximum
divergence for six characters
namely panicle length, grain
length, kernel L/B ratio, test weight
of seed, kernel length and kernel
length after cooking were exhibited
by genotypes in cluster F (Table 4).
0.00
971.88
The
results
obtained
from
canonical vector method for
divergence study revealed that the
first principal component (λ1)
absorbed and accounted for
maximum (37.881%) proportion of
variability and remaining once
accounted for progressively lesser
and lesser amount of variation
(14.646, 11.706, 7.649, 6.991,
5.403 and 4.337) for λ2, λ3, λ4, λ5,
λ6 and λ7, respectively (Table 5).
In Z1, highest element value (0.326) was observed for grain
length; in Z2 the maximum
element value was found for days
to maturity (0.399) and leaf area
index (-0.394) and in Z3 filled
grain per panicle with element
value -0.385 was the highest.
362
Singh et al. (2012)
Table 4. Mean value of six clusters for 21 characters in aromatic rice based on Tocher’s and Euclidian cluster analysis.
Plant
Height
(cm)
Panicle
length
(cm)
Effective
tillers/
plant
Spikelets/
panicle
Filled
grain/
panicle
Grain
weight/
panicle
Leaf
area
index
Specific
leaf
weight
Grain
length
(mm)
Kernal
L/B
ratio
Harvest
index
(%)
Alkali
digestion
value
Grain
yield /
plot (g)
Grain
yield/plant
(g)
100
seeds
weight
(g)
Days to
50%
flowering
Days to
maturity
Head rice
recovery
(%)
Kernel
length
(mm)
Cluster I
126.56
26.37
15.56
150.56
83.89
1.41
4.47
0.007
10.33
4.34
22.75
2.93
494.52
19.32
1.80
109.91
131.40
58.78
7.74
9.23
1.19
Cluster A
157.22
26.28
13.07
198.11
127.89
1.94
3.80
0.009
7.79
3.04
23.96
3.33
539.77
23.82
1.58
117.41
139.81
62.48
5.88
7.56
1.29
Cluster II
155.06
24.94
12.57
215.76
142.82
1.96
5.26
0.008
7.23
2.85
25.85
3.58
521.70
24.07
1.44
120.21
142.64
62.87
5.48
7.35
1.35
Cluster B
109.33
24.60
15.33
185.00
107.17
2.05
11.63
0.005
9.02
3.41
32.01
6.00
643.00
28.87
1.70
118.33
140.83
60.44
6.46
7.90
1.22
Cluster III
144.67
25.83
26.33
105.00
56.67
0.92
10.21
0.005
11.25
4.86
22.30
2.67
458.29
19.50
1.78
114.33
137.67
55.64
9.65
10.64
1.10
Cluster C
153.33
20.18
16.00
289.83
227.33
1.79
8.04
0.006
5.65
2.41
28.31
2.50
409.70
29.90
1.01
121.17
142.33
63.23
4.27
6.78
1.58
Cluster iv
116.67
26.93
6.67
132.67
75.67
1.61
9.27
0.008
13.30
5.89
20.93
6.33
377.33
10.00
2.00
110.67
130.33
52.62
8.66
9.64
1.11
Cluster D
127.24
26.16
14.06
153.00
82.58
1.42
4.39
0.007
10.03
4.26
22.85
2.94
473.76
16.90
1.74
110.24
131.73
59.29
7.60
9.27
1.21
Cluster V
98.00
24.87
15.00
191.33
110.67
1.95
12.99
0.004
10.45
4.26
30.37
5.33
636.33
27.39
1.80
113.67
136.67
58.07
7.05
8.42
1.19
Cluster E
112.67
20.63
17.67
213.33
133.67
1.19
2.83
0.012
5.94
2.50
27.91
2.67
470.67
22.16
0.82
116.00
140.67
62.35
8.48
9.40
1.10
Cluster vi
112.67
20.63
17.67
213.33
133.67
1.19
2.83
0.012
5.94
2.50
27.91
2.67
470.67
22.16
0.82
116.00
140.67
62.35
8.48
9.40
1.10
Cluster F
124.67
26.73
16.93
129.00
68.80
1.32
7.09
0.006
11.93
5.11
22.32
3.67
490.75
18.70
1.93
112.27
134.20
55.54
8.76
9.72
1.11
363
Cooked
kernel
length
(mm)
Elongation
ratio
Table 5. Canonical vectors which supply best linear function of variates, value of
canonical roots and percentage of variation absorbed by respective roots.
1
Vector
2
Vector
3
Vector
4
Vector
5
Vector
6
Vector
7
Vector
7.955
3.076
2.458
1.606
1.468
1.135
0.911
% Var. Exp.
37.881
14.646
11.706
7.649
6.991
5.403
4.337
Cum. Var. Exp.
37.881
52.527
64.233
71.882
78.872
84.275
88.612
Plant height (cm)
0.147
0.140
0.351
0.313
0.103
0.218
0.411
Panicle
(cm)
-0.209
0.059
0.124
0.199
-0.422
0.104
-0.026
Effective
tillers/plant
0.045
0.102
-0.349
0.330
0.238
0.454
0.113
Spiklets/panicle
0.311
0.013
0.048
-0.241
-0.034
0.072
0.297
Filled
grain/panicle
0.134
-0.101
-0.385
-0.226
-0.419
-0.227
-0.039
Grain
weight/panicle
(g)
0.201
-0.258
-0.094
0.359
-0.316
-0.017
0.045
Leaf area index
-0.051
-0.394
-0.113
-0.291
0.003
0.494
0.017
Specific
weight
-0.041
-0.325
0.196
0.114
0.362
-0.482
0.059
-0.326
-0.106
-0.096
0.130
-0.101
0.053
0.041
Kernal L/B ratio
-0.233
0.107
-0.112
-0.248
-0.139
-0.225
0.466
Harvest
(%)
0.234
-0.343
0.020
0.072
0.036
0.081
-0.123
-0.070
0.244
0.321
-0.138
-0.274
0.168
-0.450
-0.300
0.055
0.001
0.166
-0.263
-0.013
0.222
0.276
-0.005
-0.192
0.250
-0.175
-0.239
0.127
Eigen
(Root)
Grain
(mm)
Value
length
leaf
length
index
Alkali
value
digestion
Grain
(g)
yield/plot
Grain yield/plant
(g)
364
Singh et al. (2012)
Table 5. (cont’d)
1
Vector
2
Vector
3
Vector
4
Vector
5
Vector
6
Vector
7
Vector
100
seeds
weight (g)
-0.323
-0.029
0.104
0.170
-0.132
0.046
0.092
Days to
flower
-0.218
-0.163
0.290
-0.293
0.057
0.069
0.276
Days to maturity
-0.121
0.399
-0.048
0.045
0.227
-0.200
-0.158
Head
rice
recovery %
0.269
0.315
0.128
0.094
-0.090
-0.007
-0.016
Kernel
(mm)
-0.277
0.025
-0.260
0.175
0.090
-0.016
0.073
Cooked kernel
length (mm)
-0.227
0.024
-0.354
0.007
0.226
0.023
-0.212
Elongation ratio
0.139
0.372
-0.246
-0.263
0.038
0.100
0.246
50%
length
Figure 2. Three dimensional representation of genotypes using 3 principal
components based on canonical variates.
365
DISCUSSION
Knowledge of genetic divergence
in any crop is essential for
selecting
useful
parent
for
hybridization, so, that maximum
heterosis could be utilized and
useful segregants could be selected
from
transgressive
breeding
programme. The greater parental
diversity the greater the chance of
developing
higher
yielding
breeding lines (Joshi and Dhawan
1966; Ananda and Murty 1968). In
crop like aromatic rice D2 study
were also conducted by Pradhan
and Mani (2005), Singh et al.
(1996), Zaman et al. (2005),
Sharma et al. (2002), Sharma et al.
(2008), Chaudhary and Sarawgi
(2002), Naik et al. (2006), Roy et
al. (2002), Nayak et al. (2004),
Gupta et al. (1999), Ravindra et al.
(2006), and Awasthi et al. (2005).
On the basis of results obtained in
this study a high degree of genetic
divergence was observed. It is
evident as more number of cluster
(six) formed by the 30 aromatic
rice genotypes and high rang of
values of inter and intra cluster
distance. The greater the diversity
of parents, the greater chance of
obtaining high heterosis (Zaman et
al. 2005). The pattern of
distribution of genotypes within
various clusters was random and
independent
of
geographical
isolation (Sharma et al. 2002). So
there is no association between the
geographical
distribution
and
genetic diversity (Sharma et al.
2008). Similar findings were also
observed in the present study. The
varieties originated from PAU,
Ludhiana were grouped in different
clusters and varieties from
NDUAT, Faizabad were also
showed independent clustering
pattern. For grouping of varieties
into various clusters, two methods
namely Tocher and Euclidean
method (Rao, 1952) have been
utilized. Although D2 statistics
using
Tocher
method
for
classifying the genotypes is useful
in general but non-hierarchical
Euclidian cluster analysis (based
on Wards minimum variance
dendrogram) (Figure 1) more
critically identifies sub clusters of
the major groups at different levels
and offers additional opportunity
than that of Tocher method to plant
breeders
in
planning
of
hybridization program aimed at
crop improvement.
The relative association
among the different genotypes is
presented in the form of Wards
minimum variance dendrogram
which was prepared using the
rescaled distance. The resemblance
coefficient between two genotypes
is the value at which their branches
join. The dendrogram elaborate the
relative magnitude of resemblance
among the genotypes as well as the
clusters. It is clear from the perusal
of wards minimum variance
dendrogram that “fence sitter”
single genotype, grouped by
Tocher method in cluster III
(Taroari Basmati) and cluster IV
(RAU 3055) were precisely
accommodated in cluster F
exhibiting more similarity (less
variance) between Tarori Basmati
and RAU 3055. Similarly, single
genotype, grouped by Tocher
method in cluster V (Birsamati)
was accommodated in cluster B
with
NDR
9542
exhibited
similarity
between
them.
Geographical and genetic diversity
exhibited
no
correspondence
366
Singh et al. (2012)
vectors
Z1,
Z2
and
Z3,
respectively. Jagadev et al. (1991)
reported
that
the
character
contributing maximum to the
divergence should be given greater
emphasis for deciding the type of
cluster for purpose of further
selection and the choice of parents
for hybridization.
Genetic
divergence
between genotypes is measured in
terms of spatial distance and
resulted in formation of three
dimensional (3D) based on three
PCA scores (λ1, λ2 and λ3 graphs)
as depicted in Fig.2. Three
principal factors scores were used
to plot all the 30 aromatic rice
genotypes using PCA1, PCA2 and
PCA3 i.e. 3D plot which accounted
for most important component
traits namely grain length, days to
maturity and filled grain per
panicle. Similar type of study was
also carried out by Khalequzzaman
et al. (2005), who reported 2D
diagram using two PCA scores
reflecting the relative position of
the genotypes and distributed the
genotypes in six clusters. Amongst
30 aromatic rice genotypes, studied
in the present study, exhibited great
extent of genetic diversity on the
basis of 3D diagram based on PCA
scores and Euclidian distance
matrix, which reflected highest
diversity between RAU 3055 and
Badsabhog,
while
minimum
genetic diversity between HURASG-GN and Jeerakasala. The
cross combinations were formed
for important component characters
of yield involving suitable parents
which were also found diverse for
the particular characters. Those
were JGL 15336 X Taroari
Basmati for effective tillers per
plant, Badsabhog X Rajendra
between them as genotypes from
one and different geographic
reasons are grouped together,
which might be due to free
exchange of genetic material from
different regions. Principal factors
were carried out using principal
component (PC) method for factor
extraction. Differentiation among
populations occurs in stages, or in
other words in different axes of
differentiation which accounts for
total divergence. Theoretically as
many as axes of differentiation can
be envisaged as there are characters
contributing to total variation, but
it is not absolutely. It is possible
that most of the variation is
accounted for by the first two or
more axes of differentiation. In the
present investigation only the first
three principal components showed
eigen values more than one and
cumulatively
they
explained
64.233% variability (Table 5). The
first principal component explained
37.881 per cent of the total
variation and the second and third
principal components explained
14.646 per cent and 11.706 per
cent
variation,
respectively.
However, divergence in canonical
method was mainly estimated on
the basis of these three vectors.
The study through canonical
analysis revealed that on the basis
of two axes the cumulative
percentage of variation absorbed
by λ1 + λ2 was 52.527% where as
there are three effective axes Z1,
Z2 and Z3 where λ1 + λ2 + λ3 =
64.233%. In Z1 grain length; in Z2
days to maturity and leaf area
index and in Z3 filled grain per
panicle contributed maximum to
the total divergence at primary,
secondary and tertiary axes of
differentiation based on canonical
367
Kasturi for filled grain per panicle,
JGL 15336 X Birsamati for grain
weight per panicle (g), Gandhasala
X Pusa Sugandha-3 for leaf area
index (cm2) and JGL 15336 X
Sanwaal Basmati for harvest index
(%). These cross combinations can
be recommended for making
further research strategies and
breeding programs.
ACKNOWLEDGEMENT
Author is thankful to all teaching and
non-teaching staffs of Department of
Plant Breeding, RAU, Pusa for their
support in conductance of the
investigation.
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369
RESEARCH ARTICLE
SABRAO Journal
44 (2) 370-381, 2012
ANALYSIS OF YIELD TRAITS REGARDING VARIABILITY,
SELECTION PARAMETERS AND THEIR IMPLICATION FOR
GENETIC IMPROVEMENT IN WHEAT (Triticum aestivum L.)
VIKRANT SINGH1, 3*, RAM KRISHNA1, LOKENDRA SINGH1
and SANJAY SINGH2
1
Department of Genetics and Plant Breeding, C.S.A. University of Agriculture and Technology,
Kanpur, U.P., India.
2
Division of Genetics, Indian Agricultural Research Institute, Pusa, New Delhi-110012.
3
Present Address: Department of Genetics and Plant Breeding, G.B.Pant University of
Agriculture & Technology, Pantnagar, India.
SUMMARY
Genetic analyses were carried out of 28 genotypes (7 Parents + 21 F1s) through diallel
mating design excluding reciprocals in bread wheat. Analysis of variance reflected
appreciable variability among the parents as well as among the crosses for almost all
the characters. Component analysis revealed that dominance gene action was prevalent
in most of the traits. High estimates of heritability (h2) and genetic advance were
found only for duration of reproductive phase. Rest of the traits exhibited differential
combinations. Grain yield per plant was significantly correlated with number of
productive tillers per plant, spike length, biological yield, harvest index and test
weight. In path coefficient analysis, higher desirable direct effects were observed for
grain yield in biological yield, harvest index and duration of reproductive phase,
indicating the high value of selection parameters should be given due importance
during selection.
KEY WORDS: Bread wheat, correlation coefficient, diallel, genetic advance,
heritability and path coefficient analysis.
Manuscript received: April 16, 2012; Decision on manuscript: October 10, 2012; Manuscript
accepted in revised form: November 4, 2012.
INTRODUCTION
Wheat is the first most important
food crop in the world while in India
it stands at second position just after
rice which contributes nearly one
third of total food grains production.
At global level, India ranks second
largest wheat producing nation with
13.4% global wheat production after
Singh et al. (2012)
China which contributes 17.7% to the
world wheat production. The other
major wheat producing countries are
Russian Federation, United States of
America and Canada and these 5
countries together contribute more
than half of the global wheat
production. Wheat contribution has
increased over years while its
contribution in the early fifties was
less
than
10%
of
today’s
contribution. To fulfill the increasing
food demand of the world population,
wheat production and productivity
must be increased (USDA, 2012).
The increase in yield
potential has always been of
fundamental importance in wheat
breeding
programmes.
Genetic
analyses of wheat yield improvement
had shown that grain yield is
determined by component traits, and
is a highly complex character
(Adams, 1967; Blum, 1988). Also,
the analyses showed that genes for
yield per se do not exist (Grafius,
1959). Therefore, knowledge about
the nature and magnitude of gene
effects of yield traits and their
expression
are
of
paramount
importance in formulating an
efficient
breeding
programme.
Exploitation of variability in wheat is
regarded as a breakthrough in the
field of wheat improvement for
developing superior varieties. For the
development of superior varieties
through selection using high yielding
trait combinations, it is essential to
evaluate
available
promising
genotypes for yield and its
component traits.
Diallel analysis is most
effective with proven merits for
ascertaining the systematic genetic
architecture of metric traits within a
short period. In diallel technique,
analysis of variance components,
heritability,
genetic
advance,
correlation coefficient and path
coefficient are of considerable use in
gathering precise picture of genetic
architecture. Keeping in view,
genetic variability, mode of gene
action and association of yield
contributing traits was studied.
Seven parents (Table 1) were crossed
in
diallel
fashion
excluding
reciprocals during rabi, 2007-2008.
The resulting 21 F1s along with 7
parents were evaluated in randomized
block design with three replications
at Crop Research Form of C. S. A. U.
A. & T. Kanpur, U. P. during rabi,
2008-2009. The entries were sown in
3 rows of 3 meter length with interand intra- row spacing of 25 cm and
15
cm,
respectively.
All
recommended agronomic practices
were adopted in order to raise normal
crop. Observations on five normally
selected competitive plants were
recorded for 14 traits and their mean
values were used for the analyses.
The analysis of variance
(ANOVA) was based on the model
given by Panse and Sukhatme
(1967). Coefficient of heritability (in
narrow sense) in F1 generation based
on
component
analysis
was
calculated
as
proposed
by
Crumpacker and Allard (1962). The
genetic advance was worked out by
the formula proposed by Robinson et
al. (1949). The genotypic and
phenotypic correlation coefficients
were calculated as suggested by AlJibouri et al. (1958). In path analysis,
direct, indirect as well as residual
effects were calculated. All the
analyses works were done in
Microsoft Excel as per the procedure
371
given by Singh and Chaudhary
(1985).
Variability and gene action
Significant differences were observed
among the treatments (parents and
their F1s) revealing existence of
variability for all the traits while
highly significant variability due to
soil heterogeneity was also found
(Table 2).
Non-significant value of t2
for nine traits out of fourteen traits in
F1 generation (Table 3) indicated the
validity of hypothesis. Significant
value of “t2” indicated the failure of
one or more assumptions of diallel
analysis which might be due to
sampling error. Non-significant value
of regression coefficient from unity
suggested the absence of gene
interaction for some traits while for
others, regression coefficient differed
significantly from unity which
suggested the presence of gene
interaction (Table 3). Presence of
gene interaction in bread wheat has
also been reported by Pooran Chand
(1999) and Mehta et al. (2000).
The estimates of components
of variation along with their
corresponding standard errors and
related statistics are presented in
Table 3. Significant value of both D̂
component as well as of dominance (
Ĥ 1) component (for some traits) and
of only Ĥ 1 component (for some
traits) content indicated the role of
both additive as well as dominance
gene action and of only dominance
gene action, respectively, for that
corresponding traits. Predominance
of non-additive gene action in bread
wheat was also reported by
Dimitrijevic et al. (1995) for number
of grains per spike, Tsenov (1996)
for grain yield and test weight and
Pandey et al. (1999) for grain yield.
The estimates of ( Ĥ 1/ D̂ )0.5 were
higher than unity for all traits except
days to maturity and duration of
reproductive phase which indicated
over-dominance while for remaining
characters expressing average degree
of dominance as lesser than unity and
greater than zero indicated partial
dominance. Similar findings were
reported by Gupta et al. (1988) and
Singh et al. (1991) for protein
content and grain yield and
Srivastava et al. (1992) for seed
hardness. The estimated values of Ĥ
2/4 Ĥ 1 were lesser than 0.25 for all
the characters which indicated that
the distribution of alleles was not
balanced among the parents. The
result is in conformity with that of
Singh et al. (1991). The ratios of
dominant and recessive alleles,
expressed by (4 D̂ Ĥ 1)0.5 + F̂ / (4 D̂
Ĥ 1)0.5 – F̂ , were more than unity for
all the characters which indicated the
presence of an excess of dominant
genes in the parents for these
characters. The estimates of ĥ 2/ Ĥ 2
being more than unity for biological
yield and grain yield indicated the
influence of more than one gene pairs
in the control of these traits while the
estimates less than unity for rest of
the traits suggested the involvement
of single gene group in the
inheritance of these traits and it might
be due to complementary gene
interaction causing depression in
ratio.
372
Singh et al. (2012)
Heritability and genetic advance
The general mean and range of
variability of F1s was greater than
their corresponding parents for all the
traits (except days to flowering, days
to maturity and seed hardness as in
range of variability, only). The high
genetic advance with high heritability
was found only for duration of
reproductive phase, medium genetic
advance with medium heritability for
plant height spike length, number of
spikelets per spike and number of
grains per spike while rest traits
showed
indefinite
combination
(Table 4). The results indicated that
useful variability in progenies
developed through hybridization can
be properly utilized and selection of
suitable genotypes on the basis of
different genetic parameters can be
done to get high yield and protein
content in bread wheat. The finding
of present investigations are in
accordance to the finding of Singh et
al. (1991), Garg and Pal (1991) for
grain yield and number of tillers per
plant, Srivastava (1992), Zhang et al.
(1996), Dimitrijevic et al. (1995),
Uddin et al. (1997) for number of
spikelets per spike and Khan et al.
(1992), Dimitrijevic et al. (1995),
Uddin et al. (1997) for number of
grains per spike. Panse (1957)
emphasized that if trait is governed
by non-additive gene action it may
give high heritability and low genetic
advance.
Correlation coefficients
Table 5 revealed a close agreement
between genotypic and phenotypic
correlation in most of the traits thus
indicating low environment influence
on the degree of association hence
forth reference will be made only to
genotypic correlation. Genotypic
correlation coefficients were, in
general, slightly higher than the
corresponding phenotypic correlation
coefficients.
Among
various
component traits namely number of
productive tillers per plant, biological
yield, harvest index, spike length and
test weight exhibited significant
positive association with grain yield,
indicating that grain yield and these
traits has the same physiological
basis for their expression. Sharma et
al. (2006), Muhammad et al. (2007)
and Kumar et al. (2008) also reported
similar result in bread wheat.
Path coefficients
The traits days to flowering, duration
of reproductive phase, number of
grains per spike, biological yield,
harvest index, test weight, seed
weight, seed hardness, number of
productive tillers per plant and
protein content showed positive
direct effect on grain yield while rest
traits showed negative effect on grain
yield (Table 6). These findings were
in accordance to the findings of
Sharma et al. (2006).
All the characters showed a
indirect effect on grain yield through
other characters except someone. So,
the characters which were showing
positive indirect effect on grain yield
except days to flowering, days to
maturity and plant height (where
negative effect is desired) should also
be
improved
to
cause
an
improvement in grain yield per plant.
The findings were in accordance to
the findings of Vivek-Sharma et al.
(2006). The residual effect in path
analysis was very low (i.e. 0.10). It
indicated that most of the yield’s
contributing characters were included
in the study.
373
Table 1. Details about the genotypes used in study.
S. No.
Genotypes
Parentage
Centre developed
1
K 9465
B1153/CB85
2
K 9423
HP 1633/Kalyan Sona/UP 262
3
K 2021
B1153/DSN72
4
K 7903
HP 1982/K816
C.S.A.U.A.&T.
Kanpur, India
C.S.A.U.A.&T.
Kanpur, India
C.S.A.U.A.&T.
Kanpur, India
C.S.A.U.A.&T.
Kanpur, India
5
HD 2733
ATTILA/3/TUI/CARC//CHEN/CHTO/4//ATTILA
I.A.R.I., New Delhi,
India
2001
6
HD 2285
I.A.R.I., New Delhi,
India
1985
7
HUW 234
36896/./CJ54/P4160E/3/HUAR/4/KAL’S’/NP852/
4/PJ’S’/P14/KT54B/3/K65/6/HD2160/7/SL’S’/MP
852/4/PJ’S’/P14//KT54B/3/K65/5/2*SKA
NP826/CNO//SPRW/3/NP826/CNO
B.H.U.,
India
1984
Varansi,
Year of
release
1997
Suitability
2005
Irrigated, late sown
-
..............................
2002
Irrigated, Late and very late
sown, Terminal heat tolerant
(National check)
Irrigated, Timely sown,
Moderately resistant to smut
and rust
Irrigated, Late and Very late
sown, resistant to rust
Rainfed, Late sown
Irrigated,
late
sown,
Resistant to shoot fly
Source: AICRP on wheat, 2003-2012.
374
Singh et al. (2012)
Table 2. Analysis of variance (ANOVA) for 14 characters in bread wheat.
Mean Sum of Squares
Source of
variation
d.f.
Days to
flowering
Days to
maturity
Number
of productive
tillers per
plant
8.8**
Plant
height
(cm)
Spike
length
(cm)
Number
of spikelets per
spike
Number
of
grains/
spike
Biological
yield (g)
Harvest
index
(%)
Test
weight
(g)
Seed
hardness
(kg/seed)
Protein
content
(%)
Grain
yield
(g)
12.7**
Duration
of reproductive
phase
(days)
14.9**
Replications
2
13.2**
11.1**
11.8**
14.4**
10.2**
21.8**
3.7**
2.9**
0.2**
0.2**
13.1**
Treatments
27
54.0**
420.5**
264.2**
9.9**
178.3**
3.9**
3.5**
91.9**
143.1**
263.4**
67.7**
1.7**
1.0**
42.1**
Parents
6
95.0**
984.0**
521.7**
0.9**
114.7**
3.3**
1.6**
62.7**
112.6**
166.6**
13.1**
2.2**
1.6**
13.0**
F1s
20
41.1**
247.7**
190.9**
9.8**
204.6**
3.6**
3.4**
102.1**
115.7**
230.8**
72.9**
1.7**
0.8**
20.5**
Parents Vs
F1s
Error
1
66.4**
493.9**
183.8**
66.5**
33.0**
14.2**
16.7**
63.2**
871.7**
1497.5**
291.8**
1.4**
0.3**
647.0**
54
0.1
0.1
0.1
0.1
0.1
0.0
0.1
0.4
0.1
0.2
0.1
0.0
0.0
0.5
* Significant at 5% level, ** Significant at 1% level
375
Table 3. Value of regression parameters and variance components for 14 characters in bread wheat.
Characters
b
SEb
Regression parameters
(b-0)/SEb
(b-1)/SEb
Variance components
t2
Days to flowering
0.7
0.2
3.8*
-1.6
0.8
Days to maturity
1.0
0.1
9.2**
0.2
0.2
Duration of reproductive phase
0.9
0.1
9.4**
-1.4
1.2
Number of productive tillers per plant
0.0
0.1
0.0
-13.4**
42.8**
Plant height (cm)
-0.2
0.1
-1.9
-10.7**
15.3*
Spike length (cm)
0.8
0.4
2.4
-0.5
0.2
Number of spikelets/spike
0.6
0.4
1.6
-1.2
0.0
Number of grains/ spike
-0.2
0.1
-1.8
-9.0**
9.3*
Biological yield (g)
0.3
0.2
1.1
-3.1*
1.8
Harvest index (%)
0.4
0.3
1.3
-1.8
0.2
Test weight (g)
0.0
0.1
0.2
-7.0**
10.7*
Seed hardness (kg/seed)
0.9
0.4
2.3
-0.4
0.3
Protein content (%)
0.7
0.5
1.3
-0.6
0.6
Grain yield (g)
0.3
0.1
2.2
-6.5**
14.1*
b = Regression coefficient, SEb = Standard error of b, (b-0)/SEb = b from zero, (b-1)/SEb = b from unity,
and v are the proportion of + ve and –ve genes, respectively in parents,
Dominance effect,
Ê
D̂
Ĥ1
Ĥ 2
F̂
31.6**
+4.3
328.0**
+16.3
173.9**
+13.9
0.3
+1.7
38.2
+41.2
1.1*
+0.3
0.5
+0.4
20.8
+33.5
37.5*
+13.9
55.5
+28.1
4.3
+9.9
0.7**
+0.1
0.5**
+0.1
4.2
+3.1
43.4**
+10.2
178.6**
+39.2
167.0**
+33.5
15.0*
+4.1
310.9*
+99.1
6.0**
+0.8
4.2**
+0.9
146.1
+80.5
190.6**
+33.5
387.8**
+67.6
904.5*
+23.7
3.3**
+0.3
1.1**
+0.3
51.0**
+7.3
34.3*
+9.0
147.2**
+34.6
154.0**
+29.5
14.2**
+3.6
223.9
+87.3
5.3**
+0.7
3.9**
+0.8
127.5
+71.0
143.5**
+29.5
321.3**
+59.5
82.7*
+20.9
2.1**
+0.3
0.8*
+0.2
43.1**
+6.5
21.6
+10.2
173.0**
+39.1
89.6*
+33.4
0.7
+4.1
95.8
+98.8
1.6
+0.8
0.1
+0.9
31.3
+80.2
56.4
+33.4
93.1
+67.3
0.9
+23.6
1.7
+0.3
0.5
+0.3
7.8
+7.3
D̂ = Additive variance, Ĥ1
= Dominance variance,
Ĥ 2
ĥ 2
Ê
12.4
+6.1
92.1*
+23.2
34.3
+19.8
12.4**
+2.4
6.1
+58.7
2.6**
+0.5
3.1**
+0.5
11.7
+47.7
162.6**
+19.8
279.4**
+40.0
54.5*
+14.0
0.3
+0.2
0.1
+0.2
120.7**
+4.3
0.0
+1.5
0.0
+5.8
0.0
+4.9
0.0
+0.6
0.0
+14.6
0.0
+0.1
0.0
+0.1
0.1
+11.8
0.0
+4.9
0.1
+9.9
0.0
+3.5
0.0
+0.1
0.0
+0.0
0.2
+1.1
= H1 [1-(u-v)] where u
2
F̂ = Mean of Fr over the array, where Fr is the covariance of additive and dominance effects in a single array, ĥ =
= Environmental variance, * Significant at 5% level, ** Significant at 1% level
376
Singh et al. (2012)
Table 4. Grand mean, per cent narrow sense heritability, genetic advance, parental mean and genetic advance in % of parental mean for 14 characters in
bread wheat.
Characters
Days to flowering
Days to maturity
Duration of reproductive phase (days)
Number of productive tillers per plant
Plant height (cm)
Spike length (cm)
Number of spikelets per spike
Number of grains per spike
Biological yield (g)
Harvest index (%)
Test weight (g)
Seed hardness (Kg/seed)
Protein content (%)
Grain yield (g)
Grand mean
67.2
113.6
47.0
8.5
69.4
10.5
20.4
57.7
31.6
51.7
39.0
10.8
12.2
16.1
Narrow sense heritability
[h2 (%)]
59.0
98.3
69.2
1.9
15.1
19.8
10.5
15.3
21.8
15.8
4.6
30.1
44.5
8.7
Genetic advance
Parental mean
8.7
24.4
19.3
3.7
15.9
2.3
2.1
11.3
14.2
19.3
9.8
1.5
1.1
7.5
65.7
109.4
44.5
7.0
68.3
9.8
19.3
56.2
26.0
44.4
35.8
10.6
12.1
11.3
Genetic advance (% of
parental mean)
13.3
22.3
43.5
53.1
23.2
23.7
10.8
20.1
54.6
43.4
27.3
14.3
9.0
66.8
377
Table 5. Genotypic (upper diagonal) and phenotypic (lower diagonal) correlation coefficient among the characters in 7 x 7 diallel cross of F1s in bread
wheat.
Traits
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
rg rp
0.7**
0.4**
0.1
-0.0
0.5**
0.1
-0.1
-0.2
0.2
-0.2
0.1
-0.2
0.0
2
0.7**
1.0**
0.0
-0.2
0.2
0.0
-0.1
-0.1
-0.0
-0.2
0.1
-0.1
-0.0
3
0.4**
1.0**
0.0
-0.3*
0.0
-0.0
-0.1
0.0
-0.1
-0.2
0.2
0.0
-0.1
4
0.1
0.0
0.0
-0.1
-0.0
0.5**
0.2
0.5**
0.0
-0.1
-0.2
-0.1
0.4**
5
-0.0
-0.2
-0.3*
-0.1
0.1
0.2
0.2
-0.1
-0.1
0.3*
0.2
0.1
-0.2
6
0.5**
0.2
0.0
-0.0
0.1
0.4**
0.3*
0.1
0.3*
0.4**
0.0
-0.0
0.3*
7
0.1
0.0
-0.0
0.5**
0.2
0.4**
0.6**
0.2
0.2
0.2
-0.0
0.1
0.3*
8
-0.1
-0.1
-0.1
0.2
0.2
0.3*
0.6**
0.1
0.0
0.0
-0.2
0.1
0.0
9
-0.2
-0.1
0.0
0.5**
-0.1
0.1
0.2
0.1
-0.3*
0.3*
0.1
0.1
0.7**
10
0.2
-0.0
-0.1
0.0
-0.1
0.3*
0.2
0.0
-0.3*
0.2
-0.1
0.1
0.5**
11
-0.2
-0.2
-0.2
-0.1
0.3*
0.4**
0.2
0.0
0.3*
0.2
0.2
0.0
0.4**
12
0.0
0.1
0.2
-0.2
0.2
0.1
-0.0
-0.2
0.1
-0.1
0.2
-0.1
0.0
13
-0.2
-0.1
0.0
-0.1
0.1
-0.1
0.1
0.1
0.1
0.1
0.1
-0.1
14
0.0
-0.0
-0.1
0.4**
-0.2
0.3*
0.3*
0.0
0.7**
0.5**
0.4**
0.0
0.1
0.1
1=Days to flowering, 2=Days to maturity, 3=Duration of reproductive phase, 4=Number of productive tillers per plant, 5=Plant height (cm), 6=Spike length (cm),
7=Number of spikelets per spike, 8=Number of grains per spike, 9=Biological yield (g), 10=Harvest index (%), 11=Test weight (g), 12=Seed hardness (kg/seed),
13=Protein content (%), 14=Grain yield (g), * Significant at 5% level, ** Significant at 1% level, rg = Genotypic coefficient; rp = Phenotypic coefficient
378
Singh et al. (2012)
Table 6. Genotypic (g) and phenotypic (p) direct and indirect effects of yield component characters on yield.
Traits
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1p
0.7
-1.3
0.6
0.0
0.0
-0.0
0.0
0.0
-0.2
0.2
-0.0
0.0
0.0
0.0
g
0.1
-0.1
0.0
0.0
0.0
-0.0
0.0
0.0
-0.2
0.2
-0.0
0.0
0.0
0.0
2p
0.5
-1.9
1.4
0.0
0.0
-0.0
0.0
0.0
-0.0
-0.0
-0.0
0.0
0.0
-0.0
g
0.1
-0.1
0.0
0.0
0.0
-0.0
0.0
0.0
-0.0
-0.0
-0.0
0.0
0.0
-0.0
3p
0.3
-1.8
1.5
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
-0.0
0.0
0.0
-0.1
g
0.1
-0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
-0.0
0.0
0.0
-0.1
4p
0.0
-0.1
0.0
-0.0
0.0
0.0
-0.0
0.0
0.5
0.0
0.0
-0.0
0.0
0.4**
g
0.0
0.0
0.0
0.0
0.0
0.0
-0.0
0.0
0.4
0.0
0.0
0.0
0.0
0.4**
5p
-0.0
0.4
-0.4
0.0
-0.1
-0.0
-0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
-0.2
g
0.0
0.0
-0.0
0.0
-0.1
0.0
-0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
-0.2
6p
0.4
-0.4
0.0
0.0
-0.0
-0.1
-0.0
0.0
0.1
0.2
0.0
0.0
0.0
0.3*
g
0.1
-0.0
0.0
0.0
-0.0
-0.0
-0.0
0.0
0.1
0.2
0.0
0.0
0.0
0.3
7p
0.1
-0.0
-0.1
0.0
-0.0
-0.0
-0.0
0.0
0.2
0.2
0.0
0.0
0.0
0.3
g
0.1
0.0
0.0
0.0
-0.0
-0.0
-0.1
0.0
0.2
0.2
0.0
0.0
0.0
0.3
8p
-0.0
0.1
-0.1
0.0
-0.0
-0.0
-0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
g
-0.0
0.0
-0.0
0.0
-0.0
-0.0
-0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9p
-0.2
0.1
0.1
0.0
0.0
-0.0
-0.0
0.0
0.9
-0.2
0.0
0.0
0.0
0.7**
g
-0.0
0.0
0.0
0.0
0.0
0.0
-0.0
0.0
0.9
-0.2
0.0
0.0
0.0
0.7**
10p
0.2
0.0
-0.2
0.0
0.0
-0.0
-0.0
0.0
-0.3
0.8
0.0
0.0
0.0
0.5**
g
0.0
0.0
-0.0
0.0
0.0
-0.0
-0.0
0.0
-0.2
0.7
0.0
0.0
0.0
0.5**
11p
-0.2
0.4
-0.2
0.0
-0.1
-0.0
-0.0
0.0
0.3
0.2
0.1
0.0
0.0
0.4*
g
-0.0
0.0
-0.0
0.0
-0.0
-0.0
-0.0
0.0
0.2
0.1
0.1
0.0
0.0
0.4*
12p
0.0
-0.3
0.3
0.0
-0.0
0.0
0.0
-0.0
0.1
-0.1
0.0
0.0
0.0
0.0
g
0.0
-0.0
0.0
0.0
-0.0
0.0
0.0
0.0
0.1
-0.1
0.0
0.0
0.0
0.0
13p
-0.2
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
-0.0
0.1
g
-0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.1
1=Days to flowering, 2=Days to maturity, 3=Duration of reproductive phase, 4=Number of productive tillers per plant,
5=Plant height (cm), 6=Spike length (cm), 7=Number of spikelets per spike, 8=Number of grains per spike, 9=Biological
yield (g), 10=Harvest index (%), 11=Test weight (g), 12=Seed hardness (kg/seed), 13=Protein content (%), 14=
Correlation-coefficient with the grain yield, * Significant at 5% level, ** Significant at 1% level, Note: Under lined digits
denote the direct effects, Residual effect is 0.10.
379
From findings of this study, it could
be recommended that the exploitation
of both additive and non-additive
genetic variances (days to flowering,
days to maturity, duration of
reproductive phase, spike length,
biological yield, seed hardness and
protein content) through recurrent
selection scheme with modification
like selective diallel or bi-parental
mating would be more effective for
further improvement in the generated
material. The traits such as duration
of reproductive phase showed high
heritability with high genetic advance
indicating selection will be 100 per
cent effective while some traits such
as number of productive tillers per
plant, biological yield, harvest index,
and grain yield showed high genetic
advance with low or medium
heritability which will also exhibit
good selection response. The
characters such as number of
productive tillers per plant, spike
length, biological yield, harvest index
and test weight showed positive and
significant correlation coefficient
with grain yield indicating that we
can improve grain yield per plant by
improving these traits as well as
emphasis should be given on these
traits for the selection of elite
genotypes from the segregating
population.
ACKNOWLEDGEMENTS
The authors thank to the Directorate
Research, C.S.A. University of Agriculture
and Technology, Kanpur, U.P., India for
providing necessary facilities to carry out the
present investigation as a part of M.Sc. Thesis
research work.
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381
RESEARCH ARTICLE
SABRAO Journal
44 (2) 382-397, 2012
GENETIC DISTANCE AND HETEROTIC PATTERN AMONG
SINGLE CROSS HYBRIDS WITHIN WAXY MAIZE (Zea mays L.)
KITTI BOONLERTNIRUN1*, PEERASAK SRINIVES1,
PRAMOTE SARITHNIRAN2 and CHOOSAK JOMPUK1
1
Department of Agronomy, Faculty of Agriculture at Kamphaeng Saen, Kasetsart University,
Thailand
2
Department of Horticulture, Faculty of Agriculture at Kamphaeng Saen, Kasetsart University,
Thailand.
*
Corresponding author: [email protected]
SUMMARY
Waxy maize is a traditional Asian vegetable, consumed similar to sweet maize as
cooked green ears. A promising hybrid breeding program in Thailand has started based
on germplasm derived from old local landraces, but the genetic diversity and heterotic
pattern must be better understood to efficiently plan and use the existing germplasm.
The genetic distance and heterotic pattern was studied for a diallel without reciprocal
crosses, based on nine hybrids and evaluated in four environments in Thailand.
Inbreeding depression was high in green ear yield (38%) and marketable dehusk yield
(53%), but low in ear length (9%) and ear width (5%). Heterosis of 36 double cross
combinations ranged from -33 to 8% for marketable dehusk yield. Troyer’s genetic
diversity (TGD) estimated from the relationship between heterosis and inbreeding
depression varied from 0.40 to 1.16. This indicates that waxy maize hybrids had a
diverse genetic background with a good potential for improvement by breeding. The
degrees of relatedness did not agree with sources of the hybrids. Variation in GCA was
significant in green ear yield, marketable dehusk yield and ear size, corresponding
variation in SCA was significant for marketable dehusk yield and ear length. By cluster
analysis based on SCA for marketable dehusk yield matrix, two promising distinct
clusters of heterotic patterns were identified for a targeted hybrid breeding. A very high
positive correlation (r = 0.99) was found between TGD and heterosis across four
environments. The TGD also positively correlated with SCA (r = 0.63).
Keyword: heterosis, combining ability, genetic diversity
Manuscript received: May 25, 2012; Decision on manuscript: September 19, 2012; Manuscript
Boonlertnirun et al. (2012)
heterosis are high per se
performance, good adaptation, low
inbreeding depression, high general
combining ability (GCA) and
specific combining ability (SCA),
and different genetic backgrounds
(Melchinger and Gumber, 1998).
Diallel analysis has been used in
estimating GCA and SCA effects
(Griffing, 1956; Gardner and
Eberhart, 1966) and establishing
heterotic
groups
in
maize
(Mungoma and Pollak, 1988;
Dickert and Tracy, 2002; Soengas
et al., 2003). Moll et al. (1965)
showed that heterosis in maize
increases with increasing genetic
distance only up to an optimum
level. Troyer et al. (1988) offered a
method for measuring genetic
diversity between hybrids based on
relative heterosis of the hybrid by
hybrid cross. The method assumed
that heterosis was caused by some
degree of dominance without
epistasis. The genetic distance
evaluated by this method agrees
well with the pedigrees of the
hybrids. This method has been
used to estimate genetic diversity
of field maize hybrids and to
classify the hybrids into heterotic
groups (Jompuk et al., 2000; Souza
et al., 2001; Phumichai et al.,
2008; Balestre et al., 2008).
Genetic distance estimated by
Troyer’s equation is positively
correlated with heterosis and
specific
combining
ability
(Phumichai et al., 2008; Balestre et
al., 2008). Troyer et al. (1988)
stated that grain yield is the best
trait for accurate estimation of
genetic diversity in field maize.
While commercial yield of waxy
maize is a compound trait of ear
weight, quality and appearance
which are focused by the breeders.
INTRODUCTION
Waxy maize as fresh vegetable has
been developed and cultivated all
over South East and East Asia
since centuries, with expanding
local and exporting markets.
Superior new hybrid varieties have
been quickly replacing local
varieties during the past decade.
However, germplasm pools with
promising heterotic patterns must
be available for inbred line
extraction. From field maize it is
known that inbred lines were
derived
from well
adapted
germplasm, either from synthetic
populations or single crosses with a
narrow genetic base (Hallauer et
al., 1988). Single crosses are the
best sources of germplasm because
they have passed rigorous tests in
high-yielding
agronomic
environments, carrying a great
proportion of fixed favorable.
Consequently, inbred extraction
from this source has been a general
practice (Balestre et al., 2008).
This might apply to the present
Asian waxy maize hybrids as well;
therefore going back to old
landraces would be an error at the
present advanced stage without
analyzing genetic variability in
single crosses first. However,
germplasm developed from single
crosses can lead to reduction in
variability (Hallauer and Miranda,
1988) and may cause genetic
vulnerability (Troyer et al., 1988).
Selection of germplasm for hybrid
development
is
commonly
practiced in genetically diverse
heterotic
groups.
Divergent
population improvement usually
targets to increase yield and
heterosis (Hallauer et al., 1988).
The desirable criteria to determine
383
Nine waxy maize hybrids were
obtained from different sources.
NSW, TSW and GR484 were from
Bangkok Seed Industry Company,
KWSX91
from
Kasetsart
University,
KKU1116
and
KKU2901 from Khon Kaen
University, BW852 from East West
Seed Company, WPP004 from Jia
Tai Seed Company and PEK from
Sweet Seed Company. They were
grown
and
crossed
during
December 2007 to February 2008
in a half diallel mating manner to
produce thirty-six double crosses.
The F1 hybrids were also selfpollinated resulting in nine selfed
progenies. Nine hybrid parents (H),
thirty six double crosses (DCs) and
nine selfed progenies (S) were used
as the entries in yield testing.
January, 2009). The soil type was
Rhodic Kandiustox. The second
location was Agronomy Farm,
Faculty of Agricultural Technology
and Agro-industry, Rajamangala
University
of
Technology
Suvarnabhumi, Phranakhon Si
Ayutthaya province, the central
region of Thailand, at the latitude
of 14o36’N and longitude of
100o60’E in dry season (December,
2008 – January, 2009) and early
rainy season (May-June, 2009).
The soil type of this location was
Vertic
Endoaquept.
The
experimental design in each
environment was a randomized
complete
block
with
two
replications. Each plots consisted
of two rows, 5 m long with a 0.25
× 0.75 m2 of plant row spacing,
resulting in 5.3 plants/m2. Weeds
were controlled by spraying
atrazine at 2.3 L ha-1 as a preemergence control, and hand
weeding thereafter. Fertilizer was
basally applied with 46.8 kg ha-1
each of N, P2O5 and K2O, which
was provided by compound
fertilizer, formula 15-15-15, at the
rate of 312.5 kg ha-1 and top
dressed with urea at the rate of
312.5 kg ha-1 (143.8 kg ha-1 of N)
at 20 days after emergence
Field evaluation
Data analysis
The experiment was conducted in
two locations each in two seasons.
The first location was Suwan Farm
(National Corn and Sorghum
Research
Center)
Nakhon
Ratchasima
province,
the
northeastern region of Thailand at
the latitude of 14o30’N and
longitude of 101o0’E in rainy
season (August-September, 2008)
and dry season (December, 2008 –
Forty plants per plot were
harvested at 20 days after silking
stage. The entries were recorded on
green yield (GY), marketable
dehusk yield (MDY), ear length
(EL), ear width (EW) and number
of seed rows per ear (NSR).
Analysis
of
variance
was
performed for each trait and
combined
across
four
environments. Variation among
The objective of this study was to
evaluate genetic distance and
heterotic pattern among single
cross commercial waxy maize
hybrids potentially used as
germplasm for further breeding
improvement.
Plant materials
384
were considered fixed effects.
Genotype
×
environment
interaction was used as the
denominator
for
testing
significance among the entries.
UPGMA dendrogram based on
SCA matrix was performed to
classify heterotic pattern among
nine waxy maize hybrid varieties.
Inbreeding
depression
(IBD) was determined from IBD =
(H – S)/H, while mid-parent
heterosis (MPH) = (DC – MP)/MP
where H is the trait average across
four experiments of the hybrid
varieties; S is the trait average of
selfed progeny of the hybrids, and
DC is the trait average of the
double cross combinations, MP =
(P1 + P2)/2 in which P1 and P2 are
the trait average of the hybrid
parents.
Troyer’s genetic distance
(TGD) between hybrid parents in
each
replication
within
environments was calculated from
the equation TGD = 1- (H-DC)/(HS). Where H is the trait means of
the two hybrid parents, DC is the
double cross trait mean and S is the
trait mean of the selfed progenies
(Troyer et al., 1988). Analysis of
variance for TGD was performed
across environments.
Pearson
correlation
coefficients
determined
from
relationships among DC means
across four environments, specific
combining ability, heterosis and
Troyer’s genetic distance indicated
relationship
between
genetic
distance and heterosis. Statistic
analyses were performed using R
(R Core Team, 2012).
entries was partitioned into nonorthogonal
contrast
between
hybrids (H) and selfed-progenies
(S) (df = 1), the variation among H,
S (each with df = 8) and hybrid
cross or double cross (DCs) (df =
35). The analysis II and analysis III
of Gardner and Eberhart (1966)
were employed to estimate genetic
information of the hybrid parents
and their crosses. The analysis II
was based on fitting the hybrid (H)
and hybrid cross (DC) means to the
linear model:
Yij =µv + ½ (vi +vj)
+ γ( h + hi+ hj + sij ),
where Yij is trait mean of an entry;
µv is the overall mean of the trait;
vi and vj are estimates of the
varietal effects for parental hybrid i
and j, respectively; h is the
average heterosis contributed by
the DC; hi and hj are heterotic
effects for hybrids i and j,
respectively; and sij is the specific
heterosis that occurs when hybrid i
is mated to hybrid j; γ= 0 when i =
j (i.e. H) and γ = 1 when i ≠ j (i.e.
DC).
The analysis III was
performed based on fitting the
hybrid cross (DC) means:
Yij =µc+ gi + gj + sij ,
where Yij is the mean of the cross
between hybrid i and j; µc is the
overall mean of the DC; gi and gj
are general combining ability
effects and sij is specific combining
ability effect of the hybrid parents.
Environments
and
replications within environment
were considered as random effects
while the other sources of variation
385
RESULTS
Combined analysis of variance
The combined analysis of variance
revealed differences among the 54
waxy maize entries in all traits. The
entries × environment interactions
were also significant in all traits
(Table 1). The comparison of
hybrids versus selfed-progenies (H
Vs S) measured an average
inbreeding depression (IBD) was
significant for yield and ear size.
Based on Analysis II and III of
Gardner and Eberhart (1966), the
data from hybrids and hybrid
crosses or double crosses (DCs)
revealed difference in trait means
and general combining ability
(GCA) effects among varieties.
However, specific combining
ability (SCA) effects were not
different in some traits. There was
variation in heterosis (hij) of all
traits, except for green yield (GY).
Average heterosis ( h ) was
significant in only marketable
dehusk yield (MDY), while
heterotic effect of each variety (hj)
was different in ear length (EL),
ear width (EW) and MDY.
Environments × vj interactions and
environments × gi interactions were
significant for all traits, but
environments × hij interactions
were significant for only yield
traits.
Inbreeding depression
GY and MDY differed among
hybrids. GY ranged from 10.5 t ha1
in TSW to 14.5 t ha-1 in PEK,
while MDY ranged from 6.43 t ha-1
in KWSX91 to 9.49 t ha-1 in PEK.
Comparison between hybrids and
selfed-progenies
showed
that
hybrids gave higher yield than
selfed-progenies in all varieties,
whereas ear size was significantly
different in some varieties.
Inbreeding depression resulted in
yield loss from 30.4% to 48.3% for
GY and 44.3% to 61.2% for MDY
(Table 2). The average inbreeding
depression for EL and EW were
9.5% and 4.7%, respectively.
Variation among varieties and
general combining ability effects
Varieties and GCA effects were
significantly different in all traits.
The Env × GCA interaction in our
study was also significant. GY and
MDY averaged over all varieties
were 12.52 and 8.05 t ha-1,
respectively. PEK, GR484, WPP,
BW and KKU1116 showed
positive varietal effects, i.e. these
hybrids gave higher GY and MDY
over varietal average (Table 3).
GCA effects of PEK and NSW
showed the highest GY and MDY,
whereas those of TSW were the
lowest. It could be explained that
PEK and NSW were good
combiners for GY and MDY.
Average EL, EW and NSR of the
hybrid varieties were 16.31 cm,
4.19 cm and 13.47 rows,
respectively. WPP showed the
widest and shortest ear, as well as
the best general combiner for EW
and NSR but the worst GCA effect
for EL. Correlations between GCA
and variety effect were high for EL
(r = 0.83**) and EW (r = 0.97**),
but non-significant for GY, MDY
and NSR (Table 3).
386
Table 1. Mean squares from combined analysis of variance and Gardner and
Eberhart (1966) diallel analysis for yield and ear size of nine hybrid parents
(H), nine selfed progenies (S) and 36 double crosses (DC) tested in four
environments.
Mean squares
SOV
df
GY 1
MDY
EL
EW
NSR
Env (E)
3
222.01 **2 65.72 ns 39.71 *
5.47 **
9.27 **
Rep/Env
4
5.17
13.75
2.91
0.17
0.39
Entry
53
29.32 ** 20.68 ** 11.69 ** 0.78 **
6.33 **
Hybrids (H)
8
10.62 *
7.62 * 20.11 ** 1.96 ** 12.02 **
Selfs (S)
8
5.09 *
2.03 ns 14.10 ** 0.98 **
7.41 **
H vs S
1
88.83 ** 73.96 ** 88.36 ** 1.56 **
4.48 **
Double Crosses
35
7.64 **
5.61 **
4.93 ** 0.43 **
4.97 **
(DC)
H and DC
44
8.25 **
6.84 **
7.61 ** 0.70 **
6.14 **
(H&DC)
Varieties (vj)
8 23.44 ** 15.35 ** 33.37 ** 3.45 ** 28.15 **
GCA (gj)
8 17.89 ** 13.86 ** 17.32 ** 1.66 ** 18.16 **
Heterosis (hij)
36
4.87 ns
4.94 **
1.88 ** 0.09 **
1.25 ns
Average ( h )
1 10.54 ns
43.54 ** 1.46 ns 0.00 ns
0.18 ns
Variety (hj)
8
5.06 ns
6.13 *
4.06 ** 0.17 **
2.02 **
Specific (sij)
27
4.60 ns
3.16 **
1.25 ** 0.07 ns
1.06 *
Env x Entry
159
3.29 **
1.62 **
0.84 *
0.06 **
0.77 **
Env x H
24
3.48 **
2.45 **
1.16 *
0.06 *
0.70 ns
Env x S
24
1.53 ns
1.19 ns
1.04 *
0.08 **
0.64 ns
Env x H vs S
3
0.46 ns
0.35 ns
0.25 ns 0.08 ns
0.39 ns
Env x DC
105
3.38 **
1.48 **
0.73 ns 0.05 *
0.80 **
Env x H&DC
132
3.49 **
1.64 **
0.81 *
0.05 **
0.78 **
Env x Varieties
24
4.80 **
2.72 **
1.72 ** 0.10 ** 1.60 **
Env x GCA
24
3.68 **
2.27 **
1.30 ** 0.07 *
1.29 **
Env x Heterosis
108
3.20 **
1.40 **
0.61 ns 0.04 ns
0.59 ns
Env x Average
3
7.42 **
0.58 ns
1.11 ns 0.03 ns
0.73 ns
Env x Variety
24
2.37 ns
2.00 **
0.74 ns 0.04 ns
0.38 ns
Env x Specific
81
3.29 **
1.25 *
0.55 ns 0.05 ns
0.65 ns
Pooled Error
212
1.56
0.90
0.62
0.04
0.38
Total
431
CV (%)
10.91
14.09
4.86
4.69
5.36
1
GY = green yield, MDY = marketable dehusk yield, EL = ear length, EW = ear width, NSR =
number of seed rows per ear.
2
ns = non significant; *,** = significant at P < 0.05 and P < 0.01, respectively
different in MDY, EL and EW.
Specific heterosis (sij) or SCA was
different among double crosses
(DCs) for MDY and EL (Table 1).
The average heterosis ( h )
for MDY was negative (-0.87)
(Table 3), MDY averaged over 36
DC was 7.18 t ha-1 which was less
than that of the hybrid parents,
which gave 8.05 t ha-1 most likely
Components of heterosis in yield
related traits
Heterosis parameter (hij) was
different among the double cross
hybrids for MDY, EL and EW. A
non-orthogonal
partitioning
revealed that average heterosis ( h )
was significant only in MDY, while
heterosis (hj) among hybrids was
387
due to inbreeding depression in the
DC. BW/PEK was the highest
yielding DC (9.17 t ha-1), with the
heterosis of 2.52%. The MDY of
BW/NSW,
PEK/WPP
and
NSW/PEK were over 8 t ha-1 and
their heterotic effects were 7.29%, 7.84% and -7.00%, respectively
(Table 4). The specific heterosis for
MDY ranged from -1.14 to 1.08 t
ha-1 (Table 5). BW/PEK, NSW/BW
and WPP/PEK gave high yield and
exhibited positive significance for
specific heterosis. The amount of
heterosis in DC was probably due
to the degree of genotypic
difference between the parental
single crosses.
Heterotic effect of a variety
in crosses can be measured as a
deviation from average heterosis
(Murray et al., 2003). KWSX91
had the highest varietal heterotic
effect (1.01 t ha-1) for MDY (Table
3), while its per se performance was
the lowest at 6.43 t ha-1 (Table 4).
WPP had high positive heterotic
effect for EL (0.46 cm) and
negative effect for EW (-0.18 cm)
and NSR (-0.45). We found
negative
correlation
between
variety heterosis and variety effect
in all traits, with particularly high in
EL (r = -0.73**) and EW (r = 0.88**) (Table 3).
Classification of heterotic groups
among the hybrid parents
Diallel cross designs have been
widely used to evaluate the
performance of crosses among
inbred lines or populations. We
clustered the hybrid parents
through SCA matrix and UPGMA.
UPGMA dendogram based on the
SCA matrix arranged members into
each group. Group classification of
hybrid varieties resulted in less
than average of SCA members
within group as compared to those
of between groups. This revealed
that heterotic pattern among 9
hybrids could be classified into 2
clusters. Cluster I comprised 5
hybrids which WPP and TSW were
assigned into subgroup I-1,
whereas KKU1116, KKU2901 and
PEK were in subgroup I-2. Cluster
II was divided to be 2 subgroups,
i.e. NSW and GR484 in subgroup
II-1, and BW and KWSX91 in
subgroup II-2 (Figure 1). The SCA
average of DC from which the
parents belong to Cluster I and
Cluster II were -0.36 and -0.21 t ha1
, respectively, but the average
SCA of DC which the parents were
in the other clusters was 0.21 t ha-1.
Comparison of Troyer’s genetic
distance
Troyer’s genetic distance (TGD)
based on marketable dehusk yield
(MDY) was significantly different,
ranging from 0.40 between
KKU1116 and KKU2901, up to
1.21 between KKU1116 and
KWSX91. There were only two
pairs of hybrid which showed TGD
index lower than 0.5, while 24
pairs showed the index of higher
than 0.75. The average index of
MDY across the experiment was
0.83 (Table 5). TGD of
KKU1116/KKU2901
and
WPP/TSW were low with the
values of 0.40 and 0.50,
respectively (Table 5).
388
Table 2. Mean performance of nine parental waxy maize hybrids and inbreeding depression (%IBD) in yield and ear size.
Hybrids
GY 1 (t ha-1)
H
2
S
MDY(t ha-1)
%IBD
EL (cm)
H
S
%IBD
H
3
S
EW (cm)
%IBD
NSR
H
S
%IBD
H
S
%IBD
-0.6ns
13.2
12.9
2.3ns
NSW
12.13
8.10
33.2**
7.93
4.15
47.6**
16.6
15.9 4.7ns
4.0
4.0
BW
13.39
6.93
48.3**
8.40
3.28
61.0**
15.9
14.6 8.2**
4.3
4.0
6.7*
12.6
11.9
5.9ns
WPP
12.48
7.13
42.9**
8.63
3.35
61.2**
13.3
11.9 10.0**
5.4
4.8
11.2**
16.5
15.0
9.1**
PEK
14.51
9.64
33.6**
9.49
4.90
48.4**
17.9
16.3 8.6**
4.1
3.9
4.3ns
12.9
12.8
0.4ns
GR484
12.79
7.56
40.9**
8.74
3.69
57.8**
18.2
15.3 15.6**
4.1
3.8
8.2**
12.9
12.6
2.5ns
KWSX91
11.46
7.65
33.3**
6.43
3.39
47.3**
16.5
14.8 10.5**
4.3
4.1
3.3ns
13.3
12.7
4.1ns
KKU1116
12.44
7.94
36.2**
8.24
3.69
55.2**
17.4
15.2 12.4**
4.1
3.9
4.5ns
14.1
13.5
4.3ns
KKU2901
13.03
7.96
38.9**
7.94
3.60
54.6**
16.5
15.1 8.6**
3.9
3.8
2.8ns
12.5
12.4
0.8ns
TSW
10.45
7.28
30.4**
6.66
3.71
44.3**
14.5
13.5 7.1*
3.5
3.5
1.8ns
13.2
14.2
-7.6*
12.52
7.8
37.5
8.05
3.75
53
16.3
14.8 9.5
4.2
4.0
4.7
13.5
13.1
2.4
Mean
LSD.05
4
1.79
1.25
0.90
0.23
0.87
LSD.01
2.36
1.61
1.19
0.32
1.15
1
GY = green yield, MDY = marketable dehusk yield, EL = ear length, EW = ear width, NSR = number of seed rows per ear.
2
H = the trait average across four experiments of the hybrid varieties, S = the trait average of selfed progeny of the hybrids.
3
ns =non significant; *, ** = significant at P < 0.05 and P <0.01, respectively
4
LSD.05 and LSD.01 are used to compare between varietal means of both H and S in each trait at P < 0.05 and P < 0.01, respectively
389
Table 3. Variety effects (Vi), GCA effects (GCAi) and variety heterosis (hj) of parental waxy maize hybrids and correlation
between variety effects and GCA effects (r(V,GCA)) and variety heterosis (r (V,h)) in yield and yield component traits.
GY (t ha-1)
MDY(t ha-1)
EL (cm)
EW (cm)
NSR
Vi GCAi
hi
Vi GCAi
hi
Vi GCAi
hi
Vi GCAi
hi
Vi GCAi
NSW
-0.39
0.58 0.77 -0.12
0.60 0.66
0.33
0.56 0.40 -0.19 -0.02 0.08 -0.27
0.12
BW
0.87
0.12 -0.32 0.35
0.31 0.13 -0.37 -0.19 0.00 0.09
0.04 0.00 -0.82 -0.62
WPP
-0.04
0.06 0.08 0.58
0.12 -0.16 -3.05 -1.07 0.46 1.18
0.41 -0.18
3.03
1.06
PEK
1.99
0.82 -0.17 1.44
0.65 -0.07
1.56
0.83 0.04 -0.10
0.00 0.05 -0.57 -0.24
GR484
0.27 -0.46 -0.59 0.69 -0.35 -0.69
1.86
0.04 -0.90 -0.07 -0.13 -0.10 -0.57 -0.45
KWSX91
-1.06
0.32 0.85 -1.62
0.20 1.01
0.20
0.23 0.13 0.06
0.00 -0.03 -0.17 -0.20
KKU1116
-0.08 -0.24 -0.20 0.19 -0.53 -0.63
1.09
0.13 -0.42 -0.05 -0.01 0.02
0.63
0.40
KKU2901
0.51 -0.13 -0.38 -0.11 -0.22 -0.16
0.23 -0.05 -0.17 -0.25 -0.08 0.04 -0.97 -0.59
TSW
-2.07 -1.07 -0.04 -1.39 -0.77 -0.08 -1.84 -0.47 0.45 -0.67 -0.21 0.12 -0.27
0.52
2
Mean (µv, µc, h ) 12.52 12.09 -0.43 8.05
7.18 -0.87 16.31 16.47 0.16 4.19
4.20 0.01 13.47 13.42
3
F-test
**
**
ns
**
**
*
**
**
**
**
**
**
**
**
SE 4
1.25 0.36 1.01 0.95
0.27
0.77
0.78
0.22
0.63 0.19
0.06
0.16
0.71
0.20
0.62ns
0.38ns
0.83**
0.97**
0.28ns
r (Vi,GCA)
ns
ns
r (Vi,,h)
-0.45
-0.54
-0.73*
-0.88**
-0.39 ns
1
GY = green yield, MDY = marketable dehusk yield, EL = ear length, EW = ear width, NSR = number of seed rows per ear.
2
µv = parental hybrid mean, µc = double cross mean , h = average heterosis
3
ns =non significant; *, ** = significant at P < 0.05 and P < 0.01, respectively
4
SE standard error of the column mean
hi
0.25
-0.20
-0.45
0.05
-0.16
-0.11
0.08
-0.11
0.65
-0.06
ns
0.58
390
Table 4. Marketable dehusk yield (tha-1) of 36 double crosses (above diagonal)
and mid-parent heterosis (%) of double crosses (below diagonal).
NSW
NSW
BW
7.29
WPP
-6.04
PEK
GR484
BW
WPP
PEK
GR484
KWSX91
KKU1116
KKU2901
TSW
8.76
7.78
8.1
6.77
7.51
7.27
8.12
7.31
9.17
6.57
7.06
7.34
6.64
6.15
8.35
7.23
7.64
6.59
7.47
5.39
7.69
6.84
6.98
7.25
6.53
6.38
7.08
6.85
7.92
7.77
6.67
5.41
5.92
7.88
-7.46
-7.00
2.52
-7.84
-18.78
-23.34
-16.75
7.58
-16.84
KWSX91
4.60
-4.79
1.46
-3.39
-13.91
KKU1116
-10.08
-11.78
-21.87
-22.84
-24.85
7.98
KKU2901
2.33
-18.73
-9.84
-19.91
-15.11
8.14
-33.13
TSW
0.21
-18.33
-29.50
-10.22
-11.04
1.91
-20.54
6.43
-11.92
LSD.05,and LSD.01 to compare dehusk yield between double cross combinations and parental hybrids are
1.25 and 1.61 t ha-1, respectively.
Table 5. SCA (sij) for marketable dehusk yield of 36 double crosses (above
diagonal), and Troyer’s genetic distance (TGD) averaged across four
environments (below diagonal), of nine waxy maize hybrid varieties.
NSW
NSW
BW
WPP
PEK
GR484
KWSX91
KKU1116
0.69
-0.13
-0.33
-0.66
-0.47
0.03
0.55
0.31
0.25
1.04
-0.58
-0.62
0.41
-0.62
-0.56
0.40
0.28
0.14
-0.18
0.38
-1.14
0.11
-0.32
-0.47
-0.63
0.20
-0.50
0.08
0.46
0.81
0.62
0.08
-1.01
0.07
BW
1.20
WPP
0.88
0.88
PEK
0.88
1.07
0.91
GR484
0.70
0.64
0.77
0.67
KWSX91
1.10
0.95
1.03
0.93
0.83
KKU1116
0.80
0.82
0.70
0.59
0.62
1.21
KKU2901
1.04
0.68
0.84
0.64
0.78
1.16
0.40
TSW
1.05
0.72
0.50
0.81
0.77
1.05
0.57
1.08
KKU2901
TSW
0.25
0.78
SE (Sij) = 0.87 SE(TGDij) = 0.10
F-test : Env (ns), TGD (**), Env x TGD (ns)
LSD.05 and LSD.01 for Troyer’s genetic distance of double cross combinations are 0.28 and 0.37,
respectively
391
Table 6. Correlation coefficient (r) among marketable dehusk yield (MDY),
heterosis, SCA, and TGD in double cross combinations .
Heterosis
SCA
TGD
MDY
Heterosis
SCA
0.78**
0.66**
0.77**
0.63**
0.99**
0.62**
ns = non significant; ** significant at P < 0.01
Figure 1. UPGMA dendogram of 9 hybrid parents base on SCA.
392
The parental lines of
KKU1116/KKU2901
were
developed from the same source,
while those of WPP/TSW were
from different sources. Although
the
parents
of
NSW/TSW
originated from the same source,
they had high TGD of 1.05.
WPP/TSW
double
cross
combination was low in heterotic
effect. Both hybrids were classified
in subgroup I-1 based on the SCA
matrix. TGD index revealed that
parental lines of TSW and WPP
were closely related. KKU1116,
KKU2901 and PEK in subgroup I2 were closely related and their
heterosis
of
double
cross
combinations,
KKU1116/KKU2901,
PEK/KKU1116
and
PEK/KKU2901 were low. The
parental lines of KWSX91 were
distantly related with parental lines
of the other hybrids.
Relationship between
distance and heterosis
DISCUSSION
The comparison of H versus S,
which provided a measure for an
average Inbreeding depression, was
significant (P < 0.01) for yield and
ear size traits. Interaction between
hybrids versus selfed-progenies (H
vs S) and environment was not
significant;
it
meant
that
environments similarly affected
inbreeding depression of hybrids.
Inbreeding depression was more
serious in yield than that in the
other traits. Inbreeding depression
in the F2 may be due to
accumulation of recessive alleles in
homozygous state (Saleh et al.,
1993). Varieties and GCA effects
were significantly different in all
traits. The variations in variety and
GCA components were the results
of both additive and dominant
genetic effects (Dickert and Tracy,
2002; Murray et al., 2003). The
interactions between varieties and
GCA effects and environments in
yield were significant due to
sequence changes in each different
environment (Medici et al., 2004).
PEK and NSW showed the highest
GCA effects of yield. It could be
explained that PEK and NSW were
good combiners for yield. WPP
showed the highest GCA effects of
ear width and number of seed row.
Large GCA effects of the parents
are mainly due to additive and
additive × additive gene actions
(Griffing,
1956).
Heterosis
parameter (hij) was different among
the double cross hybrids for
marketable dehusk yield, ear length
and ear width. It was the result of
dominant
gene
action
and
difference in gene frequencies
between parental varieties (Murray
et al., 2003). The average heterosis
genetic
The marketable dehusk yield
(MDY)
performance
was
positively correlated with heterosis
(r = 0.78**), SCA (r = 0.66**) and
TGD (r = 0.77**) (Table 6). TGD
and SCA were moderately
correlated (r = 0.62**). Genetic
divergence among parents was
required for high heterosis.
Combinations of distance
measured base on pedigree and
phenotypic traits are useful for
determining genetic
distance,
therefore resources have to be
considered
in
measuring
phenotype.
393
( h ) for marketable dehusk yield
was negative, most likely due to
inbreeding depression in the DC. A
similar result was observed in grain
yield of field maize (Phumichai et
al., 2008). While the average
heterosis ( h ) for ear size was not
significant. The amount of heterosis
in DC was probably due to the
degree of genotypic difference
between the parental single crosses.
Falconer and Mackay (1996) stated
that the expression of heterosis
depended on the difference in allele
frequency of the parents and
dominant effect at various loci.
Correlations between GCA and
variety effect were high for ear
length and ear width, but no
significance was observed for yield.
Additive and additive ×
additive gene actions are important
for ear size of waxy maize. Hybrid
parents have a few genes and show
similar allele frequency for ear size.
Line per se method is suitable for
improving ear size, especially ear
width but is not suitable to improve
yield because variety effect based
on additive and dominant gene
which was different in allele
frequency of the parents and
dominant effect at various loci.
Diallel cross designs have
been widely used to evaluate the
performance of crosses among
inbred lines or populations. Thereby
GCA and SCA are determined in
order to assign genotypes to
heterotic groups (Hallauer and
Miranda, 1988; Melchinger and
Gumber, 1998). The positive SCA
implied that the parents were in the
different heterotic groups (Revilla et
al., 2002; Pswarayi and Vivek,
2008). UPGMA dendogram based
on the SCA matrix revealed that
heterotic pattern among 9 hybrids
could be classified into 2 clusters
(Figure 1). Most of DCs with
parents within one cluster had
negative SCA but some DCs such as
WPP/PEK had positive SCA (Table
5). While most of DCs with parents
in different clusters showed positive
SCA but some DCs such as
BW/TSW had negative SCA.
However, the average of SCA
within cluster finally showed
negative and valued the lowest.
Positive SCA and the highest value
were observed in the average of
SCA in different clusters. UPGMA
dendogram based on the SCA
matrix could be clearly classified
into 4 sub groups. These results may
be useful for the breeder who wants
to start a waxy maize breeding
program. Practically, to develop
hybrid variety parental lines are
usually obtained from genetically
divergent heterotic groups (Hallauer
et al., 1988).
Theoretically, TGD values
range from 0 to 1 depending on the
percentage of relatedness of the
four parental inbred lines of both
hybrids (Troyer et al., 1988). The
average TGD index based on
marketable dehusk yield across the
experiments was 0.83, revealed
that waxy maize hybrids improved
in Thailand had rather high
diversity (Table 5). TGD and SCA
were moderately correlated (r =
0.62**). TGD of crosses were
mostly correlated with heterotic
groups. The obvious observation
were WPP, TSW in subgroup I-1
and KKU1116, KKU2901 and
PEK in subgroup I-2 which were
closely
related.
WPP/TSW,
KKU1116/KKU2901,
PEK/
KKU1116 and PEK/KKU2901
showed low TGD index. The
394
their selection practices which vary
in
consuming
quality
and
agronomic traits. TGD was not a
good measure of diversity between
the single cross parents and was
not consistent to reveal relationship
between the germplasm sources
(Phumichai et al., 2008)
relationship among the parental
lines of hybrids is important to
support the plant breeder’s decision
to sustain variation of his/her
breeding population.
In field corn, TGD could
be used to predict source of hybrid
parents (Troyer et al., 1988) such
as Jompuk et al. (2000) who
assessed TGD in field maize and
concluded that hybrids of the same
source showed high degree of
relatedness, while hybrids of
different sources had more diverse
parents with a tendency toward
high degree of relatedness.
However, that finding was not
clear in waxy maize because some
hybrids from different sources
were low TGD index, while
hybrids originated from the same
source had high TGD. Choukan et
al. (2006) found that closely
related inbred lines by pedigree
showed high genetic diversity if
different lines were selected in
different
environments.
The
genetic
divergence
between
different germplasm sources was
low. This might be due to
continuous exchange of genetic
materials among the waxy maize
breeders
in
Thailand.
Ear
appearance is an important trait to
be
focused
in
germplasm
improvement. Different ear shapes
and qualities of hybrids released
from the same source were
available upon consumers’ need in
each location. The companies then
emphasize
their
breeding
objectives on consuming quality
and yield potential. Thus the TGD
indices may not reveal relationship
between the germplasm sources. It
is possible that inbred lines used as
parents of hybrids in each breeding
program are different depending on
CONCLUSIONS
Waxy maize hybrids had a diverse
genetic background. They displayed
potential as germplasm in hybrid
breeding program. In this study,
two distinct clusters were identified
based on SCA. Cluster I comprised
5 hybrids (WPP, TSW, KKU1116,
KKU2901 and PEK), while cluster
II had 4 hybrids (NSW, GR484,
BW
and
KWSX91).
This
relationship suggested that breeders
can improve two separated sets of
waxy maize through reciprocal
recurrent
selection
method.
Advanced farmers who desire to
keep their own population for
reducing seed cost can produce
double cross population by
themselves from some hybrids,
such as between BW and PEK in
this study
ACKNOWLEDGEMENTS
The authors would like to thank
Rajamangala
University
of
Technology
Suvarnabhumi
for
supporting research budget. Special
thanks were extended to National
Corn and Sorghum Research Center of
Kasetsart University, Plant Breeding
Research Center for Sustainable
Agriculture of KhonKhaen University,
Sawankaloke Research Station of
Bangkok Seed Industry Co. Ltd. for
providing the seeds used in this study.
395
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397
RESEARCH ARTICLE
SABRAO Journal
44 (2) 398-405, 2012
HAPLOTYPE ANALYSIS FOR Pup1 LOCUS IN RICE
GENOTYPES OF NORTH EASTERN AND EASTERN INDIA TO
IDENTIFY SUITABLE DONORS TOLERANT TO LOW
PHOSPHORUS
WRICHA TYAGI1*, MAYANK RAI1, and AIBANSHAN
DOHLING1
1
School of Crop Improvement, College of Post Graduate Studies, Central Agricultural
University, Umroi Road, Umiam, Meghalaya, India-793103
SUMMARY
Phosphorous (P) is a major constraint for crop productivity and plants have developed
several mechanisms to adapt to low P availability. Being a macronutrient essential for
plant growth and development, understanding the uptake and utilization of P is crucial
for attaining better P use efficiency. In rice, Phosphorous Uptake 1 (Pup1) locus is
known to be involved in efficient P uptake and markers specific to Pup1 locus have
been developed. We tested a set of 6 markers on 60 diverse rice genotypes adapted to
acidic soils of North Eastern and Eastern part of India. Our results show that Kasalath
like allele at Pup1 locus is present in 13 (21%) rice genotypes already adapted to these
stress prone regions, whereas it was detected in only about 10% of the analyzed
irrigated/lowland genotypes. Four genotypes- Sahbhagi Dhan, Dagaddeshi, Pynthor
and Paijong, adapted to North Eastern and Eastern part of India have been identified as
potential donors for future breeding programmes using Marker Assisted Selection
(MAS).
Keywords: acid soils, low phosphorous tolerance, rice, North Eastern India, Pup1.
Manuscript received: July 20, 2012; Decision on manuscript: August 23, 2012; Manuscript
Communicating Editor: C. N. Neeraja
INTRODUCTION
Phosphorous (P) is a major
component of energy currency of
cell and a macronutrient essential
for plant growth and development.
Therefore,
understanding
the
uptake and utilization of P is
crucial for attaining better P use
efficiency. Approximately 5.7
billion hectares of arable land lack
sufficient P available for plant and
Tyagi et al. (2012)
almost 50% of rice soils are Pdeficient worldwide (Batjes, 1997).
In rice, a major QTL Pup1 located
on chromosome 12 explaining 78.8
% of the total phenotypic variance
for phosphorus uptake has been
found to be associated with
tolerance to P deficiency and
efficient P uptake in low
phosphorus soil (Wissuwa et al.,
1998; 2002). Kasalath, a Pup1
donor variety has a 278 Kb INDEL
and NILs (Near Isogenic Lines)
with the QTL showed an increase P
uptake (Heuer, 2009; Wissuwa et
al., 2002) and also 2-to 4- fold
grain weight per plant (Chin et al.,
2010).
Using a marker-assisted
backcrossing approach, Pup1 has
been successfully introgressed into
two irrigated rice varieties, namely
IR64 and IR74 and three
Indonesian
upland
varieties,
namely Dodokan, Situ Bagendit,
and Batur (Chin et al., 2011).
Preliminary data from this study
indicates the potential of Pup1 to
work across different genetic
backgrounds and environmental
conditions.
Screening for P-efficient
rice varieties has its own problems,
as the phenotypic screening in
problem soils, particularly acidic
soils is often limited due to other
stresses
(e.g.
iron
toxicity,
aluminum toxicity) which retard
root
growth
and
restrict
phenotyping. However, with the
development of Pup1 based
molecular markers, one can screen
for varieties carrying the desired
locus as well as performing well in
the field. Studies pertaining to
interaction of Pup1 with other
stresses prevalent in problem acidic
soils could be an interesting area
for future studies.
The current study was
undertaken to evaluate haplotypes
of Pup1 locus across the adapted
rice varieties of Eastern and NorthEastern
Hill
region.
Crop
productivity in this region of India
is severely hampered due to poor
utilization of phosphorus due to
low pH owing to soil acidity.
Identification of rice genotypes
carrying the Pup1 locus and
already adapted to acidic soils will
pave way for effective breeding
programs suited to agro-climatic
needs to this region of India.
A set of 60 rice genotypes
representing the overall diversity in
terms of geographic distribution
and adaptability to acidic soils of
the region under consideration
were selected (Figure 1; Table 1).
Further,
known
international
checks for Pup1 i.e. Kasalath, IR
1552 and IR64 obtained from
International
Rice
Research
Institute (IRRI), were also used in
the study.
Genomic
DNA
was
extracted using CTAB method.
Approximately, 15-20 ng of
genomic DNA was used as
template for performing PCR
analysis with markers reported for
Pup1 locus (K41, K42, K43, K46,
K52 and K59) using PCR
conditions as mentioned in Chin et
al. (2010). Briefly, standard PCR
was carried out with the following
profile: 5 min at 94°C, 30 cycles:
30 s at 94°C, 45 s at 58°C, 60 s at
72°C, followed by 10 min at 72°C
for a final extension. Genomic
399
DNA (15–20 ng) was used as
template in a total volume of 20 µl
(5 pmol each primer, 2 µl PCR
buffer [100 mM Tris–HCl, 500
mM KCI, 15 mM MgCl2, pH 9.0,
0.01% gelatin], 1 µl of 10 mM
dNTPs, and 0.5 unit Taq
polymerase (Sigma; D1806). PCR
products were size fractionated in
1.4% agarose gels and stained with
ethidium bromide.
Out of the 6 Pup1-based
markers used in our study,
haplotype classification of the
genotypes was based on results
obtained for markers K41, K43 and
K46. The choice of markers for
haplotype classification is based on
the r2 values obtained by GGT 2.0
analysis as described in Chin et al.
(2011). Chin and co-workers had
identified a core set of the six most
informative Pup1 markers- K29-1,
K29-3, K41, K43, K45, and K46-1.
As the markers K29-1 and K29-3
lie outside the Kasalath specific
INDEL region and marker K43
targets the transposable element,
these were excluded from the
current study.
three core markers as mentioned in
materials and methods; K-group,
having Kasalath alleles for all the
three markers and N-group, having
non-Kasalath alleles for any one of
the core marker. Based on the
Kasalath allele frequency, 13
genotypes were grouped into Kgroup and 47 genotypes into Ngroup (Table 1). Pup1 locus was
present in 17% of the analyzed
lowland genotypes, whereas 28%
of upland genotypes carried it.
Twenty five per cent of the
landraces analyzed (10/40) carried
the Pup1 locus. Most of these were
landraces/varieties performing well
in the acidic soil regions of Eastern
and North Eastern India. Our data
showed perfect match with earlier
report with respect to genotypes
(Kasalath, N22, IR20) previously
analysed for Pup1 locus with an
exception of the variety N22.
RESULTS
In the present study, rice genotypes
adapted to acidic soils of Eastern
and North Eastern India were
analysed using 6 dominant markers
(Figure 2) located in a large
Kasalath-specific INDEL (Chin et
al., 2010). The germplasm
surveyed in the current study
showed presence of Kasalath-type
allele at all the marker loci in 20%
of the germplasm. However, the
classification of rice genotypes into
two major groups was based on
presence of Kasalath alleles for the
Figure 1. Geographic distribution of rice
germplasm. Various germplasm including
landraces, varieties and advanced breeding
lines collected predominantly from North
Eastern and Eastern India are indicated by
dots. Region of India affected by acidic soil
is shown in grey. Adapted from Sharda et
al. (2009).
400
Tyagi et al. (2012)
Table 1. Pup1 genotype data on 60 rice genotypes.
Landrace/impr
oved
line/variety
Upland/lowland
K
Improved
line/Variety
Lowland/upland
N
Improved
line/Variety
Lowland
K
Improved
line/Variety
Upland
N
Improved
line/Variety
Upland
N
Improved
line/Variety
Lowland
N
Improved
line/Variety
Lowland
K
Landrace
Lowland
K
Improved
line/Variety
Upland
N
Improved
line/Variety
Upland
N902
K
Landrace
Upland
Local 3
N
Landrace
Lowland
Posimot
N
Landrace
Lowland
Laljagli
K
Landrace
Lowland
N861
N
Landrace
Upland
Theruvii
K
Landrace
Lowland
Col4
N
Landrace
Upland
N
Improved
line/Variety
Lowland
N
Improved
line/Variety
Lowland
N
Improved
line/Variety
Lowland
N
Improved
line/Variety
upland
N
Improved
line/Variety
upland
Pynthor
K
Landrace
Lowland
Paijong
K
Landrace
Lowland
Name
Kasalath
IR20
Sahbhagi
Dhan
Virendra
Sneha
Abhishek
Dagardeshi
RPCL115
RPCL116
CauR1
IRR1552
IR8
Anjali
N22
K K K
4 4 4
1 2 3
K K K K
4 4 5 5 Allele
6 8 2 9 type
401
Table 1. (continued)
Name
Chakahou
Poreiton
Chakahou
Ambui
K
4
1
K K
4 4
2 3
K K K K
4 4 5 5 Allele
6 8 2 9 type
Landrace/impr
oved
line/variety
Upland/lowland
N
Landrace
Lowland
N
Landrace
Lowland
Sali
K
Landrace
Lowland
Sadabahar
N
Landrace
Lowland
N
Improved
line/Variety
Lowland
Mynri
N
Landrace
Lowland
ARR09
N
Landrace
Upland
Theke
K
Landrace
Upland
Deku
N
Landrace
Lowland
Khezhoru
N
Landrace
Lowland
Mallabik
N
Landrace
Lowland
Taroi
basmati
N
Improved
line/Variety
Lowland
Kalanamak
N
Landrace
Lowland
Shasharang
N
Landrace
Lowland
Pokkali
K
Landrace
Lowland/upland
Nagaland
Special
N
Landrace
Upland
HazariDhan
N
Landrace
Lowland
K39
N
Variety
Upland
Lampnah
N
Landrace
Lowland
Emma
N
Landrace
Lowland
Balwai
N
Landrace
Lowland
Nonabokra
N
Landrace
Lowland
Tengo
K
Landrace
Upland
Drum
N
Landrace
Lowland
N
Improved
line/Variety
Upland
N
Improved
line/Variety
Upland
Atley
N
Landrace
upland
Basphod
N
Landrace
Lowland
Kamlesh
Kuch Never
Kazul krer
402
Tyagi et al. (2012)
Table 1. (cont’d)
Landrace/impr
oved
line/variety
Upland/lowland
N
Landrace
Lowland
KbaLum
N
Landrace
Upland
Kbaeithati
N
Landrace
Lowland
Dalwar
N
Landrace
Lowland/upland
Ayangleima
N
Landrace
Lowland
Khawleih
N
Landrace
Upland
N
Improved
line/Variety
Lowland
Takyer
N
Landrace
Upland
Italica
livorno
N
Improved
line/Variety
Upland
Name
Lapia
Kalinga463
K K K
4 4 4
1 2 3
K K K K
4 4 5 5 Allele
6 8 2 9 type
Note: K41, K42, K43, K46, K48, K52 and K59 refer to dominant makers across Pup1 as mentioned in Chin
et al. (2010). “N” and “K” denote Nipponbare and Kasalath haplotype based on K41, 43 and K46-1 markers.
Figure 2. Representative gel pictures showing variation across Pup1 locus using Pup1specific markers in nine rice genotypes. 1, 2, 3, 4, 5, 6, 7, 8 and 9 represent Kasalath,
N902, Dagaddeshi, SahbhagiDhan, Virendra, CAUR1, RPCL115, IR8, and IR1552,
respectively. All markers used for this study are dominant and amplify only Kasalath
(K) alleles. The absence of PCR products indicates IR 1552 (I) or non-Kasalath alleles.
403
DISCUSSION
The present study showed that the
Kasalath-type alleles for most of
the
markers
surveyed
are
conserved in the genotypes adapted
to poor soils present in the Eastern
and North Eastern India suggesting
that the Pup1 locus has been under
positive selection. This region of
India is considered to be hot spot
for rice diversity (Nagarajan, 2008)
and a large number of rice
landraces are still cultivated using
traditional practices, therefore
possibility of finding better/ novel
alleles of Pup1 exists. While this
work was being finalized for
publication, a paper reporting
underlying
gene
for
Pup1,
OsPsTol1, has been published
(Gamuyao et al., 2012). The
landraces identified in the present
study could be part of the future
panel of genotypes for allele
mining across OsPsTol1 as well as
genes flanking OsPsTol1 like
“dirigent”. However, it will be
difficult to use these genotypes
directly as variety as most of these
being landraces, have poor
agronomic performance. Since the
varieties released for other parts of
India may not necessarily perform
well in the Eastern and North
Eastern India, utilization of
identified genotypes as donor of
Pup1 will pave way for developing
high yielding rice varieties suited
for low pH acidic soils regions of
India where P deficiency in soil is
major
factor
limiting
rice
productivity.
The results of our study
using Pup1 markers were in
congruence with Chin et al. (2011)
with respect to common genotypes
used with the exception of N22. It
has been previously reported that
eight different accessions of the
variety N22 are registered in the
IRRI gene-bank (Chin et al., 2011).
The N22 accession used in the
present study carried non-Kasalath
alleles, which seems to be different
from the one used in the previous
study with presence of two
Kasalath alleles.
Our
results
led
to
identification
of
complete
Kasalath-like haplotype in 11 rice
genotypes. Out of these 11
genotypes, 9 are landraces
predominantly from North Eastern
Hill region of India (except Pokkali
and Dagaddeshi). Only 2 improved
lines i.e. RPCL115 and Sahbhagi
Dhan carried the haplotype.
Interestingly many of the lowland
landraces i.e. Dagaddeshi, Laljagli,
Sali, Pynthor and Paijong carry the
haplotype
suggesting
the
favourable alleles for phosphorus
deficiency tolerance have been
selected not only under upland
conditions but also in poor lowland
soils.
The study has led to
identification of genotypes like
Sahbhagi
Dhan,
Dagaddeshi,
Pynthor and Paijong as ideal donor
parents for Pup1. Field trials have
already
confirmed
good
performance of Sahbhagi Dhan in
the rainfed upland areas of
Jharkhand. On the other hand,
Dagaddeshi has been identified as
drought
tolerant
landrace
performing well in rain-fed
conditions of Raipur (personal
communication, G. Chandel).
Pynthor and Paijong are landraces
of Meghalaya showing promise in
field conditions (unpublished data).
Knowledge of Pup1 haplotype in
these genotypes will be critical for
404
Tyagi et al. (2012)
future breeding programmes suited
to Eastern and North Eastern parts
of India where rice productivity is
less than 40% of national average
due to acidic soil and poor
availability of phosphorus as one
of the major factors.
ACKNOWLEDGEMENTS
The authors are grateful to Gayle
Alisha Kharshing and Clarissa
Challam for technical assistance.
Authors are also grateful to Drs. T.
Mohapatra and A.K. Singh, NAIP, for
their support and helpful suggestions
during the study. This work is fully
supported by the National Agricultural
Innovative Project (NAIP; GRANT#
C30033/415101-036).
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Bustamam M, Prasetiyono J,
Moeljopawiro S, Wissuwa M,
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R, Ismail A, Wissuwa M,
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J, Pesaresi P, Catausan S,
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Kanamori H, Matsumoto T,
De Leon T, Ulat VJ, Ismail
AM, Yano M, et al. (2009).
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sequence
analyses of the major
quantitative
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(2002). Substitution mapping
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405
RESEARCH ARTICLE
SABRAO Journal
44 (2) 406-417, 2012
MOLECULAR DIVERSITY AMONG SELECTED Momordica
cochinchinensis (Lour.) Spreng ACCESSIONS USING RAPD
MARKERS
N. BOOTPROM1, P. SONGSRI1,*, B. SURIHARN1, P.
CHAREONSAP2, J. SANITCHON1 and K. LERTRAT1
1
Plant Breeding Research Center for Sustainable Agriculture, Department of Plant Science and
Agricultural Resources, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002,
Thailand
2
Plant Genetic Conservation Project Office, The Royal Chitralada Palace, Dusit, Bangkok
10303, Thailand
SUMMARY
Spiny bitter gourd or gac fruit (Momordica cochinchinensis (Lour.) Spreng) is a minor
cucurbitaceous vegetable crop in spite of having considerable nutritional and medicinal
properties. Although there have been some reports on its genetic diversity based on
molecular markers, no work has been conducted to estimate genetic diversity in this
crop. The aim of this research was to study genetic diversity and genetic relatedness of
spiny bitter gourd collected in Thailand and Vietnam using RAPD markers. In the
present study, 25 accessions of M. cochinchinensis including from different parts of
Thailand and Vietnam were analyzed for diversity study at molecular levels. Genomic
DNA was extracted from young healthy leaves following the procedure of DNA Trap I
(DNA Technology Laboratory, Kasetsart University Kamphaeng Saen Campus,
Nakhon Pathom). Pair-wise comparison of genotypes was calculated as per the
procedure of Jaccard. Dendrogram was constructed using the unweighted pair group
method with arithmetic averages (UPGMA) and the computation for multivariate
analysis was done using the computer program NTSYS-pc Version 2.0. Among 36
random decamer primers screened, 11 were polymorphic and informative enough to
analyze these genotypes. A total of 176 markers generated of which 175 (99.43%)
were polymorphic and the number of bands per primer was 16.00 out of them 15.91
were polymorphic. The coefficients of genetic similarity within spiny bitter gourd
accessions were from 0.63 to 0.90, suggesting a wide genetic base for the genotypes.
The spiny bitter gourd accessions were grouped into eight major clusters. This study
could also clearly identify male and female genotypes using molecular markers, and
this information is very useful for crop breeding and crop production.
Keywords: gac fruit, breeding, cluster analysis, germplasm, genetic diversity
Bootprom et al. (2012)
2005). Spiny bitter gourd aril has
lycopene/gram over 70 of times
higher than that in tomato, and the
lycopene has been well recognized
as health beneficial phytochemical.
Fatty acids in spiny bitter gourd
make β-carotene more bioavailable
than that of the synthetic form
(Vuong et al., 2002). Conversely,
consumption of certain β-carotenerich foods has been shown to
produce little increase in plasma βcarotene or retinol concentrations
(Vuong et al., 2002).
In Vietnam, the densely
red gac placenta is used for
coloring glutinous rice (Aoki et al.,
2002; Kubola and Siriamornpun,
2011), which is called “xoi gac” in
Vietnam word, and many recipes
with gac as a colorant are served at
special occasions such as new
years and weddings (Ishida et al.,
2004; Kubola and Siriamornpun,
2011). The seeds are used in
traditional
Chinese
medicine
(Kubola and Siriamornpun, 2011).
In Thailand, immature fruits and
young shoot are consumed as
vegetable, served with chili pastes
or cooked in several curry recipes
(Kubola and Siriamornpun, 2011)
and also use aril for health drinks
and cosmetics.
The potential utilizations
of this underutilized crop are as
raw material for natural colorant
industry, food additive and
functional food products in several
countries
because
of
high
carotenoids in its placenta. In order
to improve this crop for industrial
utilization, many agronomic traits
such as high carotenoids and yield
should be improved through
breeding. Because of high and
uniform phyto-chemical content is
necessary
for
large
scale
INTRODUCTION
Spiny bitter gourd (Momordica
cochinchinensis (Lour.) Spreng) or
gac fruit is an underutilized
tropical vegetable crop in Asia,
which is known under different
names in different countries such
as Gac in Vietnam, Fak kao in
Thailand, Bhat Kerala in India,
Moc Niet Tu in China and Mak
kao in Laos (Kubola and
Siriamornpun, 2011). It is a
member of perennial dioecious
cucurbit family (Sanwal et al.,
2011). The crop has long been used
as a food and traditional medicine
in East and Southeast Asia
(Iwamoto et al., 1985; Kubola and
Siriamornpun, 2011).
Placenta or aril (seed
membrane) and fruit oil are
excellent sources of bio-accessible
carotenoids (lycopene and beta
carotene) (Vuong et al., 2002;
Aoki et al., 2002; Voung and King,
2003; Ishida et al., 2004; Vuong et
al., 2006). Lycopene concentration
in the aril is as high as 2,227 μg/g
of fresh weight, and the aril also
contains 17-22% of fatty acids by
weight (Vuong and King, 2003;
Ishida et al., 2004). Oil extracted
from the spiny bitter gourd aril
showed
a
total
carotenoid
concentration of 5,700 μg/ml, of
which 2,710 μg being β-carotene,
the oil also consisted of high levels
of vitamin E (Vuong and King,
2003). The fatty acids in the aril
are important for the absorption of
fat-soluble nutrients including
carotenoids in a diet typically low
in fat (Vuong et al., 2002). Spiny
bitter gourd, thus, provides an
acceptable source of high levels of
valuable antioxidants that have
good bioavailability (Burke et al.,
407
commercial production for use as
raw material for functional food
products. Therefore, the first
accession of germplasm collection
mission was carried out in
Thailand and Vietnam, and some
accessions
were
collected.
However, genetic diversity and
genetic relationships among these
landraces
has
not
been
investigated, and this information
is very important for genetic
improvement of this crop.
As spiny bitter gourd has
low variation for morphological
characters possibly due to variation
in environment, we are interested
in using molecular markers to
investigate genetic diversity and
genetic relatedness of these
accessions, and we selected
random amplified polymorphic
DNA (RAPD) for this purpose as
this method is rather simple, rapid
and cost-effective compared to
other molecular markers (William
et al., 1990; Rafalski and Tingey,
1993; Dey et al., 2006).
To the best of our
knowledge, genetic diversity and
genetic relatedness based on
molecular markers in spiny bitter
gourd have not been reported to
date. However, RAPD technique
has been used successfully in other
related species such as in bitter
gourd (Dey et al.,2006; Behera et
al., 2008; Paul et al., 2010),
watermelon (Lee et al., 1996; Dey
et al., 2006), melon (Silberstein et
al., 1999; Garcia-mas et al., 2000;
López-Sesé et al., 2003; Dey et al.,
2006; Sensoy et al., 2007; Yildiz et
al., 2011), pumpkin (Gwanama et
al., 2000; Dey et al., 2006), ash
gourd (Sureja et al., 2006; Dey et
al., 2006; Verma et al., 2007;
Pandey et al., 2008; Resmi and
Sreelathakumary, 2011), Squash
(Tsivelikas et al., 2009) and
Momordica spp. (Bharathi et al.,
2012)
The
previous
investigations in other related
species are convincing for using
RAPD technique in our project.
The objective of this research was
to study genetic diversity and
genetic relatedness among 25
landraces of spiny bitter gourd
collected in Thailand and Vietnam
using RAPD markers. This
information is very useful for
genetic improvement of this crop.
Plant material
In the first accession of a genetic
survey for collecting spiny bitter
gourd germplasm in Thailand and
Vietnam, 25 accessions were
collected from Thailand and
Vietnam and some information of
the accessions were recorded at
collection sites (Table 1). These
accessions were grown in October
2010 at Fruit Crops Research
Station, Faculty of Agriculture,
Khon Kaen University, Thailand,
and used for RAPD analysis. The
plants spacing was 2 x 6 m. The
chemical fertilizer is applied at 15,
45, and 90 days after transplanting.
Pests and diseases were controlled
by
weekly
applications
of
insecticide and fungicide.
DNA extraction
Genomic DNA was extracted from
young healthy leaves following the
procedure of DNA Trap I (DNA
Technology Laboratory, Kasetsart
408
employed to calculate Jaccard’s
similarity coefficient (GS):
University
Kamphaeng
Saen
Campus, Nakhon Pathom), and the
DNA samples were run in 1%
agarose gel to check the quality.
Thirty six RAPD primers were
used for screening polymorphism
in spiny bitter gourd (Table 2).
𝑎
𝑛 − 𝑑′
where a is the number of positive
matches, d the number of negative
matches and n is the total sample
size (Jaccard, 1908). Genetic
distance (GD) between pairs of the
accessions were estimated as GD =
1 - GS. A dendrogram was
constructed using the Unweighted
Pair
Group
Method
with
Arithmetic averages (UPGMA)
and
the
computation
for
multivariate analysis was done
using the computer program
NTSYSpc software Version 2.0
(Rohlf, 1998).
RAPD analysis
The amplification reactions were
set at a final volume of 20 µL
contained 15 ng genomic DNA, 5x
PCR buffer, 25 mM MgCl2, 10
mM dNTPs, 5 µM primer and 0.5
U Tag DNA Polymerase. DNA
amplification was carried out in a
DNA Thermal Cycler. DNA
denaturation was done at 94oC for
5 min; followed by a 43 cycle
amplification (94 oC, 30 sec; 32oC,
1 min; 72 oC, 2 min) and final
extension step at 72 oC for 5 min
and finally the amplified product
was brought down at 10 oC. The
amplified products were run in 2%
agarose gel in 1X TBE buffer. Gel
electrophoresis was performed at
constant voltage of 75V for 2.30
hrs and stained with ethidium
bromide for 30 min. The RAPD
banding
patterns
were
photographed using the gel
documentation system of Biorad.
RESULTS
Banding patterns
Eleven decamer primers from 36
primers gave reproducible DNA
polymorphisms and the numbers of
polymorphic bands ranged from
250 bp to 2000 bp. The sample of
polymorphic bands for OPW03
primer was shown in Figure 1, and
the details of the primers producing
polymorphic bands are presented in
Table 2. These 11 primers
generated a total of 176
reproducible bands. The total
numbers of bands ranged from 10
to 23 and the polymorphic bands
ranged from 10 to 23. Most
primers were 100% polymorphic
except
for
OPF10 (92%).
Maximum number of polymorphic
bands (23) was obtained with the
primers OPW03. The following
primers were not polymorphic:
Data analyses
Reproducible DNA bands, i.e.
bands present in both repetitions of
individual sample were scored
manually. Weak bands with
negligible intensity were excluded
from final data analysis. Band
profiles for each parent were
scored in a binary mode with 1
indicating its presence and 0 in
dicating its absence. Pairwise
comparisons
of
genotypes
409
OPC05, OPC07, OPC09, OPC11,
OPC12, OPF04, OPF05, OPF06,
OPF09, OPF13, OPF16, OPW01,
OPW05,
OPW06,
OPW07,
OPW08,
OPW09,
OPW11,
OPW13,
OPW16,
OPW18,
OPW19, OPW20, OPX01, OPX04,
and OPX06.
Coefficient of similarity
The coefficients of genetic
similarity within spiny bitter gourd
genotypes were from 0.63 to 0.90.
Genetic similarity coefficient
(0.79) was lowest between the
accession KKU ac. 09-008(F) from
Thailand and the accession KKU
ac. 10-094(M) from Vietnam,
whereas the similarity coefficient
(0.90) was highest between the
accession KKU ac. 10-040(F) and
the accession KKU ac. 10-043(F).
Both of them were collected in
Thailand.
Other
accessions
collected in Thailand also showed
high genetic similarity.
Cluster analysis
The dendrogram constructed based
on RAPD analysis using UPGMA
(NTSYS-PC) shown in Figure 2.
The spiny bitter gourd accessions
were grouped into eight major
clusters. Cluster 1 had one
accession (KKU ac. 10-094(M))
from Vietnam. Cluster 2 had one
from Thailand. Cluster 3 had one
from Thailand. Cluster 4 had KKU
ac. 10-090(M), KKU ac. 09087(M), KKU ac. 10-040(M),
KKU ac. 09-034(M), KKU ac. 09033(M), KKU ac. 09-018(M), and
KKU
ac.
09-003(M)
from
Thailand. Cluster 5 had one
accession (KKU ac. 10-094(F))
from Vietnam. Cluster 6 had one
accession (KKU ac. 10-087(F))
from Thailand. Cluster 7 had KKU
ac. 10-077(F), KKU ac. 10-049(F),
KKU ac. 10-043(F), KKU ac. 10040(F), KKU ac. 10-038(F), KKU
ac. 09-036(F) and KKU ac. 09034(F)) from Thailand, and
Cluster 8 had KKU ac. 09-016(F),
KKU ac. 09-013(F), KKU ac. 09019(F), KKU ac. 09-018(F), KKU
ac. 09-030(F) and KKU ac. 09008(F) from Thailand.
Sex expression associated with
RAPD markers
It is interesting to note that the
dendrogram clearly separated male
and female genotypes into distinct
clusters. Male genotypes formed
clusters 1, 2, 3 and 4, whereas
female genotypes consisted of
clusters 5, 6, 7 and 8. These
markers are potentially used for
distinguishing male and female
genotypes. This finding will be of
great benefit for crop breeding and
crop production because sex of the
plant can be known at seedling
stage, and, therefore, the waiting
time for sex expression at
flowering stage is considerably
reduced.
410
Table 1. List of spiny bitter gourd accessions used in this study, sites of collection, fruit characters and leaf characters.
Accessions
KKU ac.09-008 (F)
KKU ac.09-013 (F)
KKU ac.09-016 (F)
KKU ac.09-018 (F)
KKU ac.09-019 (F)
KKU ac.09-030 (F)
KKU ac.09-034 (F)
KKU ac.09-036 (F)
KKU ac.10-038 (F)
KKU ac.10-040 (F)
KKU ac.10-043 (F)
KKU ac.10-049 (F)
KKU ac.10-077 (F)
KKU ac.10-087 (F)
KKU ac.10-094 (F)
KKU ac.09-003 (M)
KKU ac.09-018 (M)
KKU ac.09-033 (M)
KKU ac.09-034 (M)
KKU ac.10-040 (M)
KKU ac.10-087 (M)
KKU ac.10-090 (M)
KKU ac.09-036 (M)
KKU ac.09-032 (M)
KKU ac.10-094 (M)
Collection sites
Chiang Mai Province, Thailand
Kalasin Province, Thailand
Yasothon Province, Thailand
Ratchaburi Province, Thailand
Kalasin Province, Thailand
Chaiyaphum Province, Thailand
Phetchabun Province, Thailand
Tak Province, Thailand
Nakhon PathomProvince, Thailand
Vietnam
Yasothon Province, Thailand
Kanchanaburi Province, Thailand
Tak Province, Thailand
Nakhon Pathom Province, Thailand
Samut Songkhram Province, Thailand
Vietnam
Fruit characteristics
round shape, small weight
elliptical with pointed end shape, medium weight
flattened (oblate) shape, extra weight
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
(M) = Male and (F) = Female; NA = not available
411
Leaf characteristics
auriculate
palmate
palmate
palmate
palmate
palmate
palmate
palmate
palmate
palmate
palmate
palmate
palmate
palmate
lobed
palmate
palmate
palmate
palmate
Palmate
palmate
palmate
palmate
palmate
lobed
Table 2. Details of 11 polymorphic RAPD primers.
Primers
Sequence
Total bands
Polymorphic bands
Percentage
OPC08
5'-TGGACCGGTG-3'
18
18
100
OPC15
5'-GACGGATCAG-3'
10
10
100
OPC19
5'-GTTGCCAGCC-3'
18
18
100
OPC20
5'-ACTTCGCCAC-3'
17
17
100
OPF01
5'-ACGGATCCTG-3'
19
19
100
OPF03
5'-CCTGATCACC-3'
11
11
100
OPF07
5'-CCGATATCCC-3'
14
14
100
OPF10
5'-GGAAGCTTGG-3'
12
11
92
OPF12
5'-ACGGTACCAG-3'
16
16
100
OPW03
OPX01
5'-GTCCGGAGTG-3'
5'-CTGGGCACGA-3'
Total
23
18
176
23
18
175
100
100
99
1
2
3
4
5
6 7
8
9 10 11 12 13 14 15 16 17
2000 bp
1500 bp
1000 bp
500 bp
200 bp
100 bp
Figure 1. RAPD polymorphism amongst Momordica cochinchinensis (Lour.) Spreng
genotypes detected with primer OPW03. M = primer OPW03; 1 = KKU ac.09-008(F);
2 = KKU ac.09-013(F); 3 = KKU ac.09-016(F); 4 = KKU ac.09-018(F); 5 = KKU
ac.09-019(F); 6 = KKU ac.09-030(F); 7 = KKU ac.09-034(F); 8 = KKU ac.09-036(F);
9 = KKU ac.10-038(F); 10 = KKU ac.10-040(F); 11 = KKU ac.10-043(F); 12 = KKU
ac.10-049(F); 13 = KKU ac.10-077(F); 14 = KKU ac.10-087(F); 15 = KKU ac.10094(F); 16 = KKU ac.10-094(M); 17 = KKU ac.09-003(M)
412
008(F)
030(F)
018(F)
019(F)
013(F)
016(F)
034(F)
036(F)
038(F)
040(F)
043(F)
049(F)
077(F)
087(F)
094(F)
003(M)
018(M)
033(M)
034(M)
040(M)
087(M)
090(M)
036(M)
032(M)
094(M)
008(F)
0.63
0.70
0.77
0.84
VIII
VII
VI
V
IV
III
II
I
0.90
Coefficient
Figure 2. Dendrogram showing genetic relatedness of 25 spiny bitter gourd accessions collected in Thailand and Vietnam using 11 RAPD primers.
413
DISCUSSION
Random amplified polymorphic
DNA (RAPD) technique was used
to study genetic diversity and
genetic relatedness in 25 landraces
of spiny bitter gourd collected in
Thailand and Vietnam. The
diversity and genetic relatedness of
the germplasm are indicated by
banding patterns of the primers,
coefficients of similarity and the
dendrogram that was constructed
based on the marker information.
Banding patterns
Out of the 36 primers used, 11
primers
were
polymorphic,
accounting for 99.43% of total
primers. The degree of RAPD
polymorphism was higher than
those reported in melon 18%
(Garcia-mas et al., 2000), pumpkin
23% (Gwanama et al., 2000) and
ash gourd 28% (Sureja et al.,
2006). The results might indicate
that genetic diversity in these
accessions of spiny bitter gourd
was rather high compared to the
results of the related species.
However, percentage of
polymorphic primers observed in
this study was rather low compared
to other study in the related
species. Using 26 primers, Ferriol
et al. (2003) observed 57%
polymorphic bands from a total of
92
consistent
bands.
The
differences in the results of
different studies were due to the
fact that the materials used in the
studies were in the same species or
in different species.
Coefficient of similarity
The coefficients of genetic
similarity within spiny bitter gourd
accessions were from 0.65 to 0.91.
The wide range of similarity values
suggests that the germplasm
collection represents a genetically
diverse population. The direct
comparison of the same species
was not possible as this study
seemed to be the first investigation
in spiny bitter gourd.
However, Sureja et al.
(2006) used 26 random decamer
primers to analyse diversity among
nine parental lines of Benincasa
hispida and found a low range of
dissimilarity (0.056 to 0.179),
suggesting a narrow genetic base
among nine inbreds of ash gourd.
Gwanama et al. (2000) also
reported the range (0.13 to 0.41) of
dissimilarity in 31 landraces of C.
moschata using 31 random
primers. The comparison suggested
that genetic variation in this
germplasm was rather high, and the
germplasm is useful for further
improvement of this crop.
Cluster analysis
RAPD markers could reveal eight
distinct clusters of 25 spiny bitter
gourd accessions. RAPD markers
were
more
effective
than
morphological characters by visual
observation in identifying genetic
diversity in spiny bitter gourd
accessions. The accession from
Vietnam (KKU ac.10-094 (F) and
KKU ac.10-094 (M)) in cluster 1
and 5 based on RAPD analysis had
similar leaf shape, and in the
accession from Thailand of cluster
2, 3, 4, 6, and 7 had palmate leaf
shape and cluster 8 had palmate
and auriculate leaf shape (Table 1).
In case of fruit shape, the accession
414
from Vietnam (cluster 5) had
flattened (oblate) shape and extra
fruit weight different to the
accession from Thailand (cluster 6,
7 and 8) almost had elliptical with
pointed end shape and medium
fruit weight, except KKU ac. 09008(F) had round shape, small fruit
weight. The accessions in Thailand
were mostly grouped in the same
clusters, and a male accession from
Vietnam was in the isolated cluster
by RAPD analysis.
The main findings in this
study are that RAPD markers could
clearly separate male and female
accessions into distinct clusters.
Further investigations on gene
mapping for this trait to identify
markers associated with male and
female genotypes are necessary
and others desire characters such as
high beta-carotene and high
lycopene yields. This information
is useful for crop breeding and
production because male and
female plants can be known at
seedling stage.
ACKNOWLEDGEMENTS
This work was supported by the Higher
Education Research Promotion and
National Research University Project of
Thailand, Office of the Higher Education
Commission, through the Food and
Functional Food Research Cluster of Khon
Kaen University and support in part by the
Plant Breeding Research Center for
Sustainable Agriculture, Faculty of
Agriculture, Khon Kaen University, the
Thailand Research Fund (TRF), the
Commission for Higher Education (CHE)
and Khon Kaen University (KKU)
providing financial supports to this
research through the Distinguish Research
Professor Grant of Professor Dr. Aran
Patanothai.
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Volume 44 No. 2 December 2012

Transcription

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