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Database on olive farming and olive industry in the Mediterranean Countries
Olive waste availability
Date:
16/05/2016
Report Number:
Version Number:
Deliverable Number:
Due Date for
Deliverable: Actual
Submission date
Task Leader:
WP 2 - Task 2.1
4
D 2.1
31/05/2013
16/05/2016
ISAFOM
FFW is co-funded by the European Community
Seventh Framework Programme for European Research and
Technological Development (2012-2015) FFWaddresses
“Liquid and gas Fischer-Tropsch
fuel production from olive industry waste: fuel from waste”
Start date: October 2012, duration: 3 Years
Document Dissemination Level PP
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(including the Commission Services).
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consortium (including the Commission Services).
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Confidential, only for members of the
consortium (including the Commission Services).
FFW Deliverable
CL restricted
CL confidential
CL secret
= Classified with the mention of the
classification level restricted "Restraint UE"
=
Classified with the
mention of the classification level
confidential "Confidential UE"
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classification level secret "Secret UE"
Document Information
Title
Lead Author
Contributors
Distribution
Report Number
Database o n o l i v e f a r m i n g a n d o l i v e i n d u s t r y i n
t h e Mediterranean Countries - Olive waste availability.
ISAFoM
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Document History
Date
31/05/2013
30/09/2014
28/07/2015
29/03/2016
Version
1
2
3
4
Prepared by
GM,ER
GM, MB
GM, ER, MB
VC, MB,GM
Organization
ISAFOM- UNIPG
ISAFOM- UNIPG
ISAFOM - UNIPG
ISAFOM
Notes
Acknowledgement
The work described in this publication was supported by the European Community’s Seventh
Framework Programme through the grant to the budget of the FFW project, Grant Agreement Number
308733.
Disclaimer
This document reflects only the authors’ views and not those of the European Community. This work
may rely on data from sources external to the members of the FFW project Consortium. Members of the
Consortium do not accept liability for loss or damage suffered by any third party as a result of errors or
inaccuracies in such data. The information in this document is provided “as is” and no guarantee or
warranty is given that the information is fit for any particular purpose. The user thereof uses the
information at its sole risk, and neither the European Community nor any member of the FFW Consortium
is liable for any use that may be made of the information.
© Members of the FFW Consortium
2
Summary
Task 2.1 aims at identifying the main sources of biomass from the productive areas in the Countries under
scrutiny and thus providing the necessary basis for the subsequent activities of the FFW project.
For this purpose, the amount of available biomass from olive crop and olive oil production was estimated:
1) at the regional level for Italy, Spain, Greece and Portugal by means of a survey managed through web
and telephone call campaigns. A first database, named “Database on olive farming and olive industry in the
Mediterranean countries: survey”, was developed containing the data gathered with the survey;
2) at the municipal level (1,025 local administrative units) for the Puglia (258 Comuni) and Andalucìa (767
Municipios) regions. A complementary database, named “Database on olive farming and olive industry in
the Mediterranean countries: desk analysis” containing information on olive cultivated area, olive
production, pruning, pomace, total biomass, biomass proximity index, consumption and gross generation of
electricity, employment rate has been realised;
Two Multi-Criteria Analysis have been realized, a) the first one, aimed at mapping the most suitable sites of
possible locations for gasification plants using olive pruning and pomace as raw materials, was developed for
Italy, Spain, Greece and Portugal at the regional level on the basis of information collected by national and
European statistical institutes. This analysis evidenced that Andalucìa in Spain and Puglia in Italy are the two
most suitable regions both in terms of biomass production and as potential locations for gasification plants
using olive pruning and olive pomace as raw materials; b) the second one for testing the dataset (point 2)
and aimed at supplying a tentative and preliminary localisation analysis of suitable locations for gasification
plants within Puglia and Andalucìa; this exercise produced a stable and robust ranking of the top 10
municipalities eligible for gasification plant localization in each of the two regions.
3
INDEX
Summary ..................................................................................................................................................... 3
1. Introduction ............................................................................................................................................... 7
Section I ........................................................................................................................................................ 8
2. The Survey and MCA analysis .................................................................................................................... 8
2.1. The questionnaires ............................................................................................................................. 8
2.2 Biomass availability estimation by Survey response ......................................................................... 10
2.3 Individuation of possible location of Gasification plant at regional level (MCA) .............................. 15
3. The geography of olive plantation and production: descriptive analysis based on official statistics ..... 25
Section II ...................................................................................................................................................... 37
4. A Dataset for the Localisation of Gasification Plants in Andalucía and Puglia ........................................ 37
4.1 Data and Variables............................................................................................................................. 37
4.2 Descriptive Outcomes........................................................................................................................ 41
4.3 A preliminary localisation analysis for gasification plants in Puglia and Andalucía .......................... 46
5. Conclusions .............................................................................................................................................. 49
6. References ............................................................................................................................................... 50
7. Annexes ................................................................................................................................................... 53
Annex 1 - Biomass availability estimation by Survey response: the methodology ................................. 53
Annex 2 – Farm Questionnaire ................................................................................................................ 56
Annex 3 – Mill Questionnaire .................................................................................................................. 61
Annex 4 –Simplified Farm Questionnaire ................................................................................................ 65
Annex 5 – Simplified Mill Questionnaire ................................................................................................. 74
Annex 6 – Data Bases Architecture ......................................................................................................... 80
Tables Index
Table 1 Number of e-mail addresses, responses, rejected and skipped back emails rate
Table 2 Pruning residues and pomace production in PORTUGAL
Table 3: Pruning residues and pomace production in GREECE
Table 4 Pruning residues and pomace production in SPAIN
4
Table 5 Pruning residues and pomace production in ITALY
Table 6 Pedigree Matrix (from Weidema, Wesnæs, 1996)
Table 7 Data quality assessment - Pruning - Italy
Table 8 Data quality assessment - Pomace – Spain
Table 9 MCA Italy data collected (Source data: ISTAT, EUROSTAT, ISMEA)
Table 10 MCA Italy - Effects standardization settings
Table 11 Weights of MCA for Italy
Table 12 MCA PORTUGAL data collected (Source data: INE.pt)
Table 13 MCA- PORTUGAL -Effects standardization settings
Table 14 Weights of MCA for Portugal
Table 15 MCA Greece - data collected (Source data: EL.STAT, EUROSTAT)
Table 16 MCA Greece- Effects standardization Settings
Table 17 Weights of MCA for Greece
Table 18 Data from MCA Spain data collected (Source data: INE.ESP, EUROSTAT, and AICA: Agencia de
Informacion y control alimentarios)
Table 19 MCA Spain- Effects standardization Settings
Table 20 Weights of MCA for SPAIN
Table 21 Localisation index (LI) of olive plantations at region vs. country level
Table 22 Harvested production of olive trees across regions (1000 tons) and concentration indexes (C4
and normalized Herfindal-Hirschman Index_HHI)
Table 23 Descriptive Statistics at municipality level
Table 24 – Multi Criteria Analysis, objectives and alternative weight sets
Table 25 Results of the MCA analysis (ranking of municipalities) and sensitivity analysis, Puglia
Table 26 Results of the MCA analysis (ranking of municipalities) and sensitivity analysis, Andalucía
Table 27 Estimates of biomasses production – Pruning (tons/ha)
Table 28 Estimates of the biomasses production – Pomace (tons/mill/year)
5
Figures Index
Figure 1 MCA ITALY, weighted summation
Figure 2 MCA Portugal weighted summation
Figure 3 MCA Greece weighted summation
Figure 4 MCA Spain, weighted summation
Figure 5 Olive plantations in NUTS2 regions (Land Use, 1000 Hectares)
Figure 6. Within-country specialisation in olive plantations (NUTS2 Regions)
Figure 7: Olive tree yields (Tons per hectare)
Figure 8 Harvested production of olive trees across region and concentration indexes
Figure 9 MCA analysis
Figure 10.1 Distributions of the key variables along municipalities in Puglia and Andalucía
Figure 10.2 Cumulative Distribution Function for the Total Biomass: Puglia vs. Andalucía
Figure 10.3 Cumulative Distribution Function for the Proximity Biomass Index: Puglia vs. Andalucía
Figure 11. A procedure to estimate the production of biomass
Figure 12. Farm Questionnaire
Figure 13. Mill Questionnaire
Figure 14. English Farm Questionnaire
Figure 15. Greek Farm Questionnaire
Figure 16. Portuguese Farm Questionnaire
Figure 17. English Mill Questionnaire
Figure 18. Greek Mill Questionnaire
Figure 19. Portuguese Mill Questionnaire
Figure 20. Database on olive farming and olive industry in the Mediterranean countries: survey
Figure 21. Database on olive farming and olive industry in the Mediterranean countries: desk analysis
6
1. Introduction
The main aim of this Task is to determine the amount of available olive waste that can be prepared to serve
as an input to the synthesis process.
The activities were organised in two stages:
(i)
(ii)
Estimation of the amount of available olive waste in selected Mediterranean Countries with the
aim of providing a dataset and identifying the most suitable regions for the localization of
gasification plants using pruning and olive pomace as raw materials;
Enhancement of the database created in stage (i) by means of data at sub-regional level as a
starting informative basis for the analysis of biomass energy production plant localization at
Municipal level in the regions identified in the previous step of analysis.
The document is organized in two main sections:
(i) The first section of the analysis has been focused on identifying the main areas of biomass production
(olive pruning residues and two-phase and three-phase pomace) and on estimating the available biomass
amounts. With respect to the geographic area of interest of FFW, there are not official primary data available
on the olive and olive oil biomasses production.
Thus two estimates of the biomasses availability were achieved by:
a) A survey carried out at farm and mills level in Portugal, Spain, Italy, Greece, Turkey, Tunisia,
Morocco and Algeria, followed by and estimation based on the survey results. A Multi-Criteria Analysis with
the purpose of mapping the most suitable sites of possible locations for gasification plants was developed
for Italy, Spain, Greece and Portugal at regional level on the basis of information collected by national and
European statistical institutes. In this former study Puglia, in Italy, and Andalucía, in Spain, were identified as
the top two eligible regions both in terms of potential biomass production and as sites for localisation of
gasification plants.
(a).
b) A desk analysis on Eurostat data at regional level aimed at validating the results obtained in point
The second analysis (b) provided results consistent with the previous one (a); both identified
Andalucia in Spain and Puglia in Italy as the two most suitable regions for a possible location of a future
gasification plant using olive pruning and olive pomace as raw materials.
(ii) The second section focused on Puglia and Andalucía and aimed at identifying the main areas of biomass
production (pruning residues from olive crops and pomace) and of potential localisation of gasification plants
within each of the two regions.
This further analysis was focused at sub-regional level, providing data supporting the mapping of potential
sites for localisation of gasification plants at intra-regional level providing also information on the biomass
potential transportation costs. A user-friendly database containing all data used in the analysis and produced
has been developed.
7
Section I
2. The Survey and MCA analysis
To estimate the biomass availability is a difficult task. National statistical data from every target
country show that olive yields vary significantly depending on the year, operating system, planting density,
growing system, climate conditions and the alternation of production typical of the olive tree. Also the oil
production and the proceeding biomass is affected by several factors such as the extraction system, the olive
oil containt in the drupae, etc.
In order to obtain information about farms and mills existing in the target countries, a questionnaire
has been prepared, and e-mail addresses have been selected from stakeholders, professionals, and
companies. Targeted countries: Spain, Portugal, Italy, Greece, Turkey, Tunisia, Algeria and Morocco.
The objective of our survey was to improve information about amount and localization of biomass
wastes. In each targeted country e-mail addresses of olive growers and olive oil producers were collected.
Namely, the e-mail database was created collecting addresses from: Information and Promotion web portals;
Import-export web sites; Agricultural associations web sites; Sales portals (olive oil sale); International olive
oil associative organizations web sites.
2.1. The questionnaires
2.1.1 The first questionnaire
The questionnaire includes both parts dedicated to the farming system and olive oil production activities,
selected by the respondents according to their activity.
The questionnaire includes the usual items concerning personal information and contacts of the
respondents.
The farm-specific questions set concern the size of the farm and the olive growing system; pruning
and harvesting methods adopted.
The mill-specific questions concern with the type of extraction, working capacity of the plant, the
production and management of mill wastes.
The last common section is dedicated to the external energy inputs, to estimate, for instance, the ratio
of production between Diesel and Natural Gas. (Information required by partner KTH).
The text was drafted in different languages (Italian, English, French and Spanish) to reach all the countries
targeted in the survey.
8
A small subsample of respondents to be interviewed was randomly selected in order only to test the
questionnaire. Therefore 200 Italian, but also for testing 25 Moroccan and 25 Tunisian contacts
(approximately 5% of the e-mail addresses were sampled for the pilot test).
The pilot test was administrated by SurveyMonkey. The results of the test confirmed the adequateness
of the questionnaire. Furthermore after a first telephonic contact with the Commercial Departments of the
Embassy of the targeted Countries we asked to spread our survey to producers' associations and/or to
olive and olive oil producers or to supply their e-mail addresses to us.
The questionnaire was delivered to all the potential respondents by means of Survey Monkey, a
platform for Web based surveys.
To implement the visibility of the survey among the olive oil producers and farmers it has been created
an account on “LinkedIn”, the social networking website for people in professional activities. Some olive and
olive oil producers group such as Aceite de Oliva Virgen Extra, extra virgin olive oil, Importers of Food &
Beverage, Olive Global Network, Olive Oil, Olive oil importers and distributors, Premium Extra Virgin Olive
Oil, Spanish olive oil in the world, UC Davis Olive Center were joined on Linkedin.
After the first e-mail campaign, 158 questionnaires were filled out (12 out of 170 contained useless
information).
Table 1 Number of e-mail addresses, responses, rejected and skipped back emails rate
Italian
Spanish
French
English
total
File Folder
e-mail contacts
web link
e-mail contacts
web link
e-mail contacts
web link
e-mail contacts
web link
Sent
1404
1677
201
811
4093
Filled
Response rate
108
7,69
2
48
2,86
1
4
1,99
1
10
1,23
12
170
21.5% of respondents were Spanish, 68,2% Italian, 3.3% Portuguese, 2.5% Greek, 2,5% Tunisian, 1%
Moroccan and 1% Turkey.43,83% of respondents were olive farmers,30,25% were mills, 24,69% were both
olive farm and mills.
During the project, a reminding e-mail has been sent weekly to every contact that did not answer to the
questionnaire. We have also been collecting new e-mail addresses with the helpful assistance of Italian
Embassies in the target Countries (Commercial Offices), Target Countries Embassies in Italy and the
International Olive Oil Council.
9
2.1.2 The second questionnaire
To improve the size of the database, a simplified version of the questionnaires was designed and
translated in Greek, Portuguese and English. Due to the low response rate registered for Turkey, Tunisia,
Algeria and Morocco, the decision of focusing only on the European Countries has been taken.
In the meantime, additional e-mail contacts and telephone numbers of olive farmers and olive oil
producers and their associations were collected. The planned strategy was to address all farmers and millers’
associations directly by phone to ask them to transmit and spread our questionnaire towards their associates.
We contacted 110 Portuguese and 151 Greek Associations among agricultural, production and rural
development organization. To sustain the effectiveness of the contacts, Italians olive oil associations were
also involved to help us in reaching the target countries, obtaining visibility in olive oil ambiances. Despite all
these actions, the database did not increase its information as expected.
Two different phone calls campaigns were therefore performed with farmers and millers with the
contribution of two mother tongue interviewers for Greece and Portugal. A total amount of about 1000 new
telephone numbers and e-mail contacts were collected from different sources on the web and also on the
“paginasmarelas.pt” and “xo.gr”. The first step was to contact producers by e-mail to introduce the
telephonic survey and the week later our interviewers started their activity.
The sample of telephone numbers, and also the e-mail addresses, has been randomly collected on the
internet.
The phone call campaign allowed us to enforce the dataset with data proceeding from 60 mills and 40
farms from Greece, 50 mills and 18 farms from Portugal and 25 mills from Italy.
2.2 Biomass availability estimation by Survey response
Starting from the Survey results, our approach is based on the estimation of the average yield of
biomass (pruning and pomace).
Referring the analysis to real production systems, we propose to estimate the biomass availability by
determining the expected average yield: i.e. the case of pruning the probable yield of biomass per hectare of
the agricultural area utilized in olive cropping, in the case of pomace the probable yield of biomass for mill.
The full methodology is explained in Annex 1.
The following Tables present the estimates achieved by the FFW survey (2013-2015)
To be able to compare our estimation with the other data sources, we expressed the estimated pruning
production proceeding from our estimates in 1000 tons of dry matter.
10
Table 2 Pruning residues and pomace production in PORTUGAL
DISTRICT
CENTRO
ALENTEJO
NORTE
Faro
Lisboa
Area
oliviculture
(1000 ha)
Pruning
average Yield
(tons/Ha)
Estimated pruning production
(1000 tons d.m./year)
mills number
Pomace
average yield
(tons/mill)
EUROSTAT
2013
our
estimation
our calculation (average
moisture content pruning
40%-D 2.3)
INE.PT 2011
our
estimation
174,2
1,3
140,56
107,0
1,3
7,18
120,4
1,3
82,7
1,3
8,9
0,7
1,3
97,15
271,0
66,73
125,0
0,5
2,0
Estimated
pomace
production
(1000
tons/year)
our
calculation
1011,5
274,1
1011,5
6,0
108,2
1011,5
126,4
1011,5
2,0
1011,5
6,1
Table 3: Pruning residues and pomace production in GREECE
REGION
Eastern Makedonia
Area
oliviculture
(1000 ha)
Pruning
average Yield
(tons/Ha)
EUROSTAT
2013
our
estimation
15,1
1,87
moisture content pruning 40%D 2.3)
16,9
mills number
MORE
2008
mv
Central Makedonia
36,2
1,87
40,6
western Makedonia
0,2
1,87
0,2
Ipiros
23,0
1,87
25,8
mv
Central Greece
98,1
1,87
110,1
mv
Western Greece
59,2
1,87
66,4
28,0
1,87
mv
1,87
Thessalia
Ionian islands
Peloponnesus
Attiki
Northern Egeo
Southern Egeo
Kriti
32,0
42,0
196,2
mv
172,4
1,87
1,87
35,9
47,1
1,87
220,1
1,87
0,0
1,87
mv
mv
mv
mv
mv
mv
31,4
mv
0,0
mv
193,4
mv
124
Pomace
average yield
(tons/mill)
our
estimation
0,0
Estimated
pomace
production
(1000
tons/year)
our
calculation
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
0,0
0,0
1205,5
0,0
0,0
0,0
0,0
0,0
0,0
0,0
149,5
11
Table 4 Pruning residues and pomace production in SPAIN
Region
Galizia
Principado de Asturias
Area oliviculture
(1000 ha)
Pruning
average Yield
(tons/Ha)
INE data 2012
0,0
0,0
Pomace
Production
(1000 tons/y)
Pomace
Production
(1000 tons/y)
Mills
number
Pomace
average yield
(tons/mill)
our
estimation
moisture content pruning
40%-D 2.3)
anuario de
estadistica
2013
Report MORE
July 2008
AICA
our
estimation
1,06
0,0
mv
1,06
0,1
0,2
mv
mv
mv
1,06
Cantabria
0,0
1,06
Comunidad Foral de Nabarra
5,5
1,06
Aragon
47,1
Castilla y Leon
7,3
1,06
262,2
1,06
Pais Vasco
La Rioja
Comunidad de Madrid
0,2
4,5
25,7
1,06
1,06
8,1
1,06
23,0
1,06
1514,5
Ciudad Autonoma de Ceuta
0,0
Region de Murcia
Ciudad Autonoma de Melilla
Canarias
0,0
0,1
5,2
mv
17
2463,0
41,9
2463,0
253,7
46,8
4
22
mv
121
2463,0
50,8
mv
133
2463,0
mv
mv
mv
20
251
196
9
5985,4
3961,6
819
1,06
0,0
mv
mv
mv
mv
1,06
0,0
0,1
2463,0
290,1
mv
963,2
1,06
2463,0
2463,0
34,1
mv
0,0
2463,0
19
mv
9,9
mv
1,06
14,6
2463,0
our
calculation
mv
mv
30,0
64,3
mv
2463,0
5,7
79,5
1,06
166,8
mv
4
103
366,1
4,6
mv
mv
228,6
1,06
Andalucía
30,1
1,06
125,0
Illes Balears
2,9
30,0
mv
12,2
Cataluña
101,1
3,5
mv
16,3
359,4
Comunidad Valenciana
0,0
mv
1,06
Castilla-la Mancha
Extremadura
0,0
Estimated
pomace
production
(1000
tons/year)
mv
mv
2463,0
0,0
9,9
54,2
49,3
2463,0
618,2
2463,0
482,7
2463,0
298,0
327,6
22,2
2463,0
2017,2
mv
2463,0
0,0
mv
2463,0
43
mv
2463,0
2463,0
105,9
0,0
0,0
12
Table 5 Pruning residues and pomace production in ITALY
Area
oliviculture
(1000 ha)
Pruning (1000
tons
d.m./year)
Pruning (1000
tons
d.m../year)
Pruning
average Yield
(tons/Ha)
ISTAT 2010
Report ANPA
11/2001
De Gennaro
2011
our
estimation
Piemonte
0,1
mv
0,0
1,6
Liguria
14,8
11,0
6,0
Trentino alto
Adige
mv
0,2
Region
Valle D'Aosta
Lombardia
0,0
2,4
mv
1,6
Virgin pomace
average production
(1000 tons/year)
Virgin pomace
production (1000
tons/year)
Mills number
Pomace
average yield
(tons/mill)
Estimated pomace production
(1000 tons/year)
Report ISPRA
111/2010
Report UNAPOL 2011
De Gennaro 2011
ISMEA Giugno
2013
our estimation
our calculation
0,1
0,0
mv
0,0
mv
1353,0
0,0
1,6
14,2
mv
5,5
10,2
2,2
0,2
1,6
0,0
0,5
mv
0,6
0,0
1,6
mv
1,0
Veneto
4,9
Toscana
93,1
98,0
34,6
Friuli-VeneziaGiulia
0,1
3,6
Virgin pomace
production (1000
tons)
0,1
1,6
1,6
0,0
2,3
4,7
0,1
1,6
26,7
Emilia
Romagna
1,6
3,2
1,0
1,4
1,6
3,1
44,7
62,0
20,1
1,6
42,9
Abruzzo
mv
4,7
0,1
17,0
61,1
57,0
23,0
59,3
367
114,6
381
178,0
125,4
1,6
176,5
419,4
233,0
452,5
47,6
15,9
1,6
34,9
19,4
mv
28,4
Sicilia
158,5
36,4
152,8
64,8
1,6
152,1
mv
358
mv
11,8
92,1
13,4
117,8
mv
13,4
183,9
Sardegna
mv
3035,0
mv
Calabria
30,1
1353,0
mv
508,5
1,6
mv
2,8
11,7
244,0
10,6
0,0
218
465,5
35,0
1353,0
7,8
361,5
31,4
mv
80,9
1,6
Basilicata
0,0
26,0
175,3
69,2
1353,0
12,0
596,0
1,6
mv
mv
26,3
376,7
35,5
13,0
mv
Puglia
92,0
1,6
mv
mv
13,6
72,1
8,2
8,0
mv
Molise
Campania
16,0
1,6
0,3
60,5
4,6
4,6
4,1
85,3
95,0
6,0
1,9
47,4
88,9
8,3
0,0
89,3
27,8
Marche
3,6
1,6
our calculation
(average moisture
content pruning 40%D 2.3)
Umbria
Lazio
31,5
2,0
1,6
Estimated pruning
(1000 tons d.m/year)
39,0
mv
54,0
20,3
13,1
145,7
335
1353,0
1353,0
1353,0
0,0
0,0
0,0
1353,0
484,4
1353,0
453,3
1353,0
1353,0
mv
1353,0
mv
1353,0
888
0,0
1353,0
1353,0
295,0
0,0
0,0
496,6
0,0
515,5
1353,0
1201,5
753
1353,0
1018,8
mv
1353,0
0,0
mv
572
1353,0
1353,0
0,0
773,9
13
The collected estimates have various bases and often diverge from the FFW survey estimates. To provide
a comparison among the sources, we adopted the Pedigree matrix (Weidema and Wesnæs 1996; Ciroth et
al., 2013).
Drawing from Weidema and Wesnæs (1996, pp. 168-169) we summarize the five criteria used to assess
comparatively the available estimates of biomass:

Reliability: The ‘reliability indicator’ relates to the sources, acquisition methods and verification
procedures used to obtain the data

Completeness: The ‘completeness indicator’ relates to the statistical properties of the data: how
representative is the sample, does the sample include a sufficient number of data and is the period adequate
to even out normal fluctuations.

Temporal correlation: ‘temporal indicator’ represents the time correlation between the year of study
(as stated in the data quality goals) and the year of the obtained data.

Geographical correlation: the ‘geographical indicator’ illustrates the geographical correlation
between the defined area (as stated in data quality goals) and the obtained data.

Further technological correlation the ‘technological indicator’ is concerned with all other aspects of
correlation
The data quality indicators scores are illustrated in the following table 6 (Weidema, Wesnæs, 1996, 169).
Table 6 Pedigree Matrix (from Weidema, Wesnæs, 1996)
Indicator score
Reliability
Completeness
Temporal
correlation
1
2
3
Verified data Verified data partly based Not verified data
based on
on assumptions or nonpartly based on
measurements
verified data based on
assumptions
measurements
Representative Representative data from Representative
data from a
a smaller number of sites
data from an
sufficient
but for adequate periods adequate number
sample of sites
of sites but from
over an
shorter period
adequate period
to even out
normal
fluctuations
Less than three
Less than six years
Less than 10 years
years of
difference
difference
difference to
year of study
Geographical
correlation
Data from area
under study
Further
technological
correlation
Data from
Data from processes and
enterprises, materials under study but
processes and from different technology
material under
study
Average data from larger
area in which the area
under study is included
4
5
Qualified estimate
(e.g. by industrial
experts)
Non-qualified
estimate
Representative
data but from a
smaller number of
sites and shorter
period or
incomplete data
from adequate
number of sites and
period
Less than 15 years
difference
Representative
ness unknown
or incomplete
data from
smaller number
of sites and/or
from shorter
periods
Age of data
unknown or
more than 15
years of
difference
Data from area Data from area with
Data from
with similar
slightly similar
unknown area
production
production
or area with
conditions
conditions
very different
production
conditions
Data from
Data on related Data on related
processes and
processes or
processes or
materials under materials but same materials but
study but from
technology
different
different
technology
technology
14
In the Tables 7 and 8 we summarize the scoring resulting from the “pedigree Matrix” assessment.
Table 7 Data quality assessment - Pruning - Italy
Report ANPA De Gennaro 2011 Survey FFW
Reliability
2
4
1
4
2
1
Completeness
3
Geographical correlation
4
Temporal correlation
Further technological correlation
Assessment
2
(2, 3, 4, 4, 2)
5
4
3
1
2
(4, 5, 2, 4, 2)
2
(1, 4, 1, 1, 2)
Table 8 Data quality assessment - Pomace - Spain
Reliability
Completeness
Anuario de Estadistica 2013 Report M.O.R.E. Survey FFW
2
3
1
3
2
3
( 2, 2, 3, 2, 2)
(2, 3, 3, 3, 2)
3
Further technological correlation
2
Assessment
1
2
Temporal correlation
Geographical correlation
2
2
4
1
2
(1, 4, 1, 1, 2)
The assessment scores were assigned by evaluating the data sources with respect to the data quality
indicators. Most of the estimates available in the literature are based upon experts’ knowledge of technology
or experiments and are in some case related to specific geographical areas. This motivates the better
assessment assigned to the FFW survey estimates that however present the limits of statistical
representativeness mentioned in the Report.
2.3 Individuation of possible location of Gasification plant at regional level (MCA)
We carried out a Multicriteria Analysis at the regional level in order identify the potential localization of
gasification plants using olive pruning and olive pomace as raw materials. According to the par. 2 results,
supported also with Survey results, for the MCA we considered four Countries: Italy, Spain, Portugal and
Greece. We took into account the regional level according to the data availability. The outcomes of the
analysis provide information complementary to the remaining part of the study.
We used a simple weighted sum approach applied to different effects depending on data availability at
regional levels in the four Countries. As for the variables related to olive crops and waste, we decided to use
the olive crop area and the number of mills as the proxies for the potential amount of biomasses. This is
preferable to the use of the above estimated amount of biomass, given the difficulties encountered in
15
collecting complete information through our survey and the consequences of that on the reliability of the
estimates already discussed. The choice is, however, neutral with respect to the final outcomes of the MCA,
since by construction the two variables (olive crop area and number of mills) fully encapsulate the variability
at regional level (within countries) of potential biomass. The regions are considered as a potential alternative
location.
In the case of Italy, we used the following effects indicators:







Territorial land as a proxy of the overall potential supply of biomasses;
Residents, as an indicator of the potential demand of energy;
Highways, which indicates the possibilities of transportation;
Energy consumption an indicator of the current level of energy demand;
Protected areas, indicating the areas with zero supply of biomasses and potential sources of
other constraints;
Olive crop area, indicating the potential amount of biomasses from olive crop;
Mills is indicating the potential amount of biomasses from olive oil processing (waste).
The original data are presented in Table 9. The standardization criterions are presented in Table 10, and
the weights adopted in Table 11. Figure 1 illustrates the set of the outcomes.
16
Table 9 MCA Italy data collected (Source data: ISTAT, EUROSTAT, ISMEA)
REGION
Territorial
la nd
Residents
ISTAT
ISTAT
Km
SOURCE
N
2
Highways
Km
ISTAT
Abruzzo
10831,84
1323223
349,17
Calabria
15221,90
1969385
293,34
22452,78
4411921
563,63
Ba silicata
10073,32
Ca mpania
577292
13670,95
ERomagna
FriuliVG
5819858
7862,30
La zio
17232,29
Lombardia
23863,65
Molise
4460,65
Liguria
1225611
5713863
5416,21
Marche
1578532
9883960
9401,38
1549147
314033
Piemonte
25387,07
4405425
Sardegna
24100,02
1652119
Puglia
19540,90
Sicilia
25832,39
Tosca na
22987,04
TrentinoAA
Umbria
8464,33
2427099373,55
5122899293,25
Olive crop
area
Mills
ISTAT
EUROSTAT
ISMEA
1712,46
31,40
Km
3866,97
44,70
2892,16
183,90
2671,88
3,20
3732,17
226,30
9472997108,33
1470,25
26262602456,33
1000 Ha
2
16378002755,43
72,10
0,10
N.
367
753
381
457,44
21452899253,05
3980,66
88,90
598,55
64145196236,82
3722,73
2,40
35,92
1275600772,11
1186,53
13,60
4025,43
376,70
888
4701,49
158,50
572
1303,51
27,80
218
4141,67
4,90
371,85
167,56
815,17
311,48
5901196186,60
6645303180,80
8605199678,64
4530,80
6914844925,34
58,65
113,67
561,31
19234596485,62
5168301526,49
943700549,86
28643506691,16
14,80
1419,61
3985,77
16762896482,87
172,21
444,57
1397,38
23933400848,47
1168890
4904288
Protected
areas
438,20
17900801439,28
128218
18407,42
6137300679,92
647,89
891491
3260,90
Kwh/year
5047435
3721669
13605,50
Valled'Aosta
Veneto
4070534
28,94
Energy
consumption
335
8,30
0,10
36,40
3195,20
93,10
3863,96
988,05
358
Table 10 MCA Italy - Effects standardization settings
Effects
Unit
Standardization
method
Minimum
Range
Maximum
Range
N
maximum
0
9883.9
0
26013
Territorial land
km2
Highways
Km
maximum
0
Km2
maximum
0
Residents
Energy consumption
Kwh/year
Olive crop area
1000 Ha
Protected areas
Mills
N.
maximum
maximum
maximum
maximum
0
0
0
25837
815.2
4701.5
58836.7
1200
17
Table 11 Weights of MCA for Italy
Ra nk order
Weight
Mills
1
0.299
Res idents
2
0.08
Energy Cons umption
2
0.08
Olive crop a rea
1
Territoria l la nd
0.299
2
Highwa ys
0.08
2
Protected a rea s
0.08
2
0.08
The outcomes are presented in the following figure.
Figure 1 MCA ITALY, weighted summation
MCA ITALY: Weighted summation
0,7
0,6
0,5
0,4
0,3
0,2
Valled'Aosta
Molise
FriuliVG
Sardegna
Liguria
Marche
Umbria
Piemonte
ERomagna
Basilicata
TrentinoAA
Lombardia
Abruzzo
Veneto
Toscana
Lazio
Calabria
Campania
Sicilia
0
Puglia
0,1
Sicilia, Puglia and Campania are the most suitable locations for suitable locations of gasification plants.
Good opportunities are also provided by Campania, Lazio, and Toscana. The remaining alternatives appear
less attractive. Territorial areas, Olive crop areas, and Mills are the main factors causing the outcomes
depicted (with Highways in the case of Sicily).
The Indicator Highway is referred to the km of highways existing inside the region. Sicily is an island, for
this reason; the Highway effect gives not the real picture of the potential possibilities of transportation. For
this reason, Puglia is to be considered as the best potential location for future suitable locations of
gasification plants using olive pruning and olive pomace as raw materials in Italy.





In the case of Portugal, we used the following effects indicators:
other constraints
18
Table 12 MCA PORTUGAL data collected (Source data: INE.pt)
SUP
RES
Km2
Aveiro
2800.94
Castelo Branco
6627.47
Coimbra
3973.73
Guarda
5535.31
Leiria
3505.78
Viseu
5009.79
Beja
10263.32
Évora
7393.46
Portalegre
6084.34
Santarém
6718.35
Setúbal
5214.06
Braga
2706.11
Bragança
6598.55
Porto
Viana do Castelo
Faro
208069
441245
173716
459450
394927
161211
173408
127018
475344
788459
866012
148808
1781826
4307.47
223731
4996.79
Lisboa
713578
2331.70
2218.84
Vila Real
n
2816.14
250273
395208
2135992
HIGHW
Km
611
709
752
791
PROTECT
km2
14644.25
38874
14644.25
686
14644.25
17923
971
47980.5
905
926
711
896
966
14644.25
47980.5
47980.5
877
47980.5
16704
896
26679
672
145209
843
44803
836
460
803
35489
47110
Table 13 MCA- PORTUGAL -Effects standardization settings
Unit
SUP
Km2
HIGHW
Km
RES
PROTECT
N
Km2
Standardiza
tion method
Minimum
Range
Maximum
Range
maximum
0,00
2135992,00
-47980,50
14520,90
maximum
maximum
interval
0,00
0,00
10263,32
971,00
19
Table 14 Weights of MCA for Portugal
SUP
We ight
1
0,313
1
RES
HIGHW
Ra nk orde r
PROTECT
1
2
0,313
0,313
0,063
Figure 2 MCA Portugal weighted summation
0,80
0,70
0,60
0,50
0,40
0,30
0,20
0,10
0,00
MCA Portugal, weighted summation
Lisboa seems to be the best solution for the location of a gasification plant, but this outcome is affected
by the lack of official information on olive trees and mills distribution at the local level.
In the case of Greece, we used the following effects indicators:





other constraints;
Olive crop area indicating the potential amount of biomasses from olive crop.
The original data are presented in Table 15. The standardization criterions are presented in Table 16.
Figure 3 illustrates the set of the standardized effects plots.
20
Table 15 MCA Greece - data collected (Source data: EL. STAT, EUROSTAT)
REGION
SUP
Km
SOURCE
RES
2
EL.STAT
EL.STAT
18811,00
1882108
281,33
9203,00
336856
15549,00
547390
14157,00
Western Ma kedonia
9451,00
Ipiros
Thes sa lia
14037,00
Centra l Greece
Ionian isa lnds
2307,00
608182
283689
732762
3808,00
3828434
5286,00
309015
Northern Egeo
3836,00
Southern Egeo
Kriti
8336,00
196,25
257,92
2
88,98
36,20
362,42
23,00
2350,49
98,10
33,04
16,61
41,20
8,25
199231
10,69
623065
1000 ha
EUROSTAT
451,27
342,00
Olive crop
a rea
EL.STAT
186,83
288,67
577903
Attiki
21379,00
679796
Km
196,25
137,00
11350,00
PROTECTKm
361,58
207855
Western Greece
Poloponnesos
Km
EL.STAT
Ea stern Makedonia
Centra l makedonia
N
HIGHW
61,00
15,10
0,20
32,00
42,00
59,20
196,20
28,00
0,00
0,00
172,40
Table 16 MCA Greece- Effects Standardization Settings
SUP
RES
HIGHW
PROTECTKm
OLIVETREE
Unit
Standardization method Minimum Range Maximum Range
Km2
maximum
0
maximum
0
N
maximum
Km2
interval
Km
1000 Ha
maximum
21379
0
3828434
-451.27
0
0
361.58
196.2
Table 17 Weights of MCA for Greece
SUP
RES
Ra nk order
We ight
1
0.2
1
HIGHW
1
OLIVETREE
1
PROTECTKm
1
0.2
0.2
0.2
0.2
21
Figure 3 MCA Greece weighted summation
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
MCA Greece, weighted summation
Peloponnesus, Thessalia and Central Makedonia are the most suitable locations. Among the effects
considered, we found that Olive tree and protected area play an important role.
In the case of Spain, we used the following effects indicators:







Energy consumption an indicator of the current level of energy demand;
other constraints;
Olive crop area indicating the potential amount of biomasses from olive crop;
Mills is indicating the potential amount of biomasses from olive oil processing (waste).
22
Table 18 Data from MCA Spain data collected
(Source data: INE.ESP, EUROSTAT, and AICA: Agencia de Informacion y control alimentarios)
SUP
RES
HIGHW
ELECTinhab
PROTECT
Olive crop
area
MILLS
SOURCE
INE.ESP
INE.ESP
INE.ESP
INE.ESP
INE.ESP
EUROSTAT
AICA
Aragón
47719
1325385
824
15608
47,1
103
4992
1103442
REGION
Km2
Andalucía
87268
Asturias, Principado de
10604
Balears, Illes
Canarias
7447
Cantabria
5321
n
8402305
1061756
2104815
588656
km
Kwh/year
Km2
2686
2299244
140459
447
1333288
64660
322
169880
32773
187
230
1141940
58860
602192
64660
0
0
2353
1420540
100000
Cataluña
32114
7518903
1572
5156864
14119
Extremadura
41634
249276
18118
Madrid, Comunidad de
8028
999
1137836
397
788732
Comunitat Valenciana
1841
945840
17
195712
0
4,5
22
0
0
0
0
11313
1466818
662
País Vasco
7234
2188985
19,51
84963
Ceuta
Melilla
13,77
0
5,5
Murcia, Región de
5045
7285
0
2043196
Rioja, La
101,1
20
1107
640790
0
674460
0
601
2497008
0
1
0
319002
169
84509
2
251
25,7
2748695
10391
359,4
0
29574
Navarra, Comunidad Foral de
42757
19
121
2223308
6454440
7,3
359,4
1430
Galicia
0
196
5004844
766
0
125
23255
1099632
0
9
2494790
2078611
0
819
8,1
94223
79463
1514,5
n
8703
Castilla y León
Castilla - La Mancha
1000 Ha
0
23
0,2
0
133
4
43
4
0
Table 19 MCA Spain- Effects standardization Settings
Unit
SUP
RES
HIGHW
Km2
N
Km
Sta nda rdi za
ti on me thod
Minimum
Ra nge
Ma xi mum
Ra nge
ma ximum
0
8402305
ma ximum
ma ximum
0
0
94223
26 86
ELECTi nha b
Kwh/ye a r
ma ximum
0
5156864
OLIVETREE
1000 ha
ma xi mum
0
1514.5
PROTECTKm
MILLS
Km2
N
ma ximum
ma ximum
0
0
140459
819
23
Table 20 Weights of MCA for SPAIN
SUP
HIGHW
ELECTi nha b
PROTECTKm
MILLS
We ight
1
0.143
1
RES
OLIVETREE
Ra nk order
0.143
1
0.143
1
0.143
1
1
1
0.143
0.143
0.143
Figure 4 MCA Spain, weighted summation
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
MCA Spain, weighted summation
Andalucía is the most suitable locations. All the effects considered are influential in determining this
solution except than for energy consumption.
In the MCA analysis Puglia, in Italy, and Andalucía, in Spain, were identified as the top two eligible regions
both in terms of potential biomass production and as sites for localisation of gasification plants.
24
3. The geography of olive plantation and production: descriptive analysis
based on official statistics
As above mentioned (par.1), an analysis using a crop concentration index (e.g. Singh and Dhillon, 2004)
is now carried out to complement the previous estimates and provide a more comprehensive understanding
of the results.
As well known, the uneven localization of olive plantations within Southern European countries is strictly
driven by the climate and soil conditions of their most Mediterranean regions. Moreover, the area dedicated
to olive trees is almost time invariant in the short and medium term, due to the perennial character of this
crop. These factors explain the large cross-regions variation in the area devoted to olive plantations. Based
on Eurostat statistics for NUTS 2 level regions (the highest detail available)1, figure 5 shows the very high
existing asymmetric distribution of this crop in Spain, where Andalucía employs in olive trees an area (1.5
million of hectares) that is five times larger than that used by the second important region (Castilla-laMancha, about 300,000 hectares). In contrast, olive plantations in Greece are much more evenly distributed
across regions, for example Peloponnesus has about 250,000 hectares dedicated to this crop, whereas the
second most important region, Kriti, reaches 200,000 hectares. Italy and Portugal stand as intermediate
cases: in these countries, the most important region shows an area invested in olive trees which is twice as
big as the one of the region ranking second. This is the case for Puglia and Calabria in Italy and Alentejo and
Norte in Portugal, respectively.
As well known, the information on extent of land cultivated in a given crop only is not enough to depict
the specialization of regional production systems satisfactorily. According to some agricultural geographers
(e.g., Singh and Dhillon, 2004) a crop concentration index could better inform us about the interest in the
production of that crop in a given region. The idea behind this view is that the larger the specific sector of
agriculture at regional level (measured by the percentage of its cropped area with respect to the same
percentage at country level), the more important the learning by doing processes that support efficiency and
productivity per hectare in the crop production. Of course, in the prospect of our study, higher productivity
per hectare also means increasing biomass production.
To measure the within-country crop concentration, we used a standard Localization Index (LI) of olive
plantations at regional level,
LI =
1
OP UAA
∗
UAA
OP
All statistics used in this section come from Eurostat Agriculture database.
25
Figure 5 Olive plantations in NUTS2 regions (Land Use, 1000 Hectares)
Greece
250
200
150
100
2011
2012
50
2013
0
Spain
1.500
1.200
900
600
300
0
2011
2012
2013
26
Puglia
Calabria
Sicilia
Toscana
Lazio
Campania
Abruzzo
Umbria
Basilicata
Sardegna
Liguria
Molise
Marche
Emilia-Romagna
Veneto
Lombardia
Provincia Autonoma di…
Friuli-Venezia Giulia
Piemonte
100
50
0
Valle d'Aosta
Figure 5 continued Olive plantations in NUTS2 regions (Land Use, 1000 Hectares)
Italy
400
350
300
250
200
150
2011
2012
2013
Portugal
200
150
100
50
2011
0
2013
2012
Source: Eurostat, Land Use statistics
27
where OP and OP are the area dedicated to the olive plantations in the NUTS2 region i and in the
country j to which it belongs, respectively; UAA and UAA are the utilized agricultural area in the NUTS2 at
region and country level. Regions for which this indicator is higher than 1 are considered specialized in olive
growing, whereas the opposite holds if the indicator is lower than 1 (de-specialized regions).
Table 1 shows specialized and de-specialized regions in olive growing within the four countries under
scrutiny. Greece is the country in which more than 50% of regions have LI above 1; hence they are
characterized by an agriculture strongly based on olive plantations. In Italy the specialized regions are
localized in the South and in the Centre of the country and amount to 7 out of 21. Despite the limited data
availability, in Portugal three regions with moderate specialization can be observed (Norte, Alentejo and
Algarve). In Spain only a very specialized region emerges, Andalucía, with additional three regions following
(Comunidad Valenciana, Cataluña and Extremadura) and composing the group of specialized regions. On the
whole, we observe in Spain a minority of regions with specialization in olive growing, they are 4 out of 17
(the latter are regions for which information is available).
A ranking of specialized regions according to the rate of LI is displayed in Figure 6. It is worth noting that
in the top ten of most specialized areas we find 6 Greek regions, 3 Italian ones and only 1 Spanish (that is
Andalucía).
By comparing evidence from Figure 6 with the olive tree yields at regional level displayed in Figure 7, we
get a more informative picture about the correspondence between specialized regions and their productivity
per hectare. We can sum up this information by means of a simple exercise. The median of values related to
the 3-years tons per hectare that have been shown for all regions in Figure 7 is 1.83 (tons per hectare). The
bulk of specialized regions reported in Figure 6 had productivity per hectare above this median in at least
one year. More precisely, 15 out of 22 specialized regions charted in Figure 6 fall into this group, whereas
moderately specialized regions, such as Algarve, Alentejo, Extremadura, Cataluña, Comunidad Valenciana,
Norte and Attiki had performances under the median.
28
Table 21 Localisation index (LI) of olive plantations at region vs. country level
Greece
Kriti
Voreio Aigaio
Ionia Nisia
Peloponnesus
Attiki
Dytiki Ellada
Sterea Ellada
Notio Aigaio
Ipeiros
Kentriki Makedonia
Thessalia
Anatoliki Makedonia,
Thraki
Dytiki Makedonia
Spain
Andalucía
Cataluña
Extremadura
Castilla-la Mancha
Comunidad de Madrid
Illes Balears
Región de Murcia
Aragón
La Rioja
Comunidad
Foral
de
Navarra
Canarias
Castilla y León
País Vasco
Galicia
Cantabria
Ciudad Autónoma de Ceuta
Ciudad Autónoma de Melilla
Local.
Index
3.60
3.37
3.35
2.88
2.57
1.85
1.31
1.27
0.36
0.23
0.19
0.15
0.00
Local.
Index
3.03
1.24
1.16
1.13
0.81
0.65
0.48
0.37
0.24
0.21
0.13
0.01
0.01
0.01
0.00
0.00
0.00
na
na
Portugal
Norte
Alentejo
Algarve
Área Metropolitana de Lisboa
Região Autónoma dos Açores
Região Autónoma da Madeira
Local.
Index
1.46
1.10
1.08
0.09
na
na
Italy
Calabria
Puglia
Liguria
Campania
Sicilia
Lazio
Toscana
Abruzzo
Umbria
Molise
Local.
Index
3.39
3.10
2.35
1.30
1.23
1.14
1.01
0.84
0.71
0.66
Basilicata
Piemonte
Marche
Sardegna
Veneto
Emilia-Romagna
Lombardia
Provincia Autonoma di Trento
Valle d'Aosta
Provincia
Autonoma
di
Bolzano
Source: own elaborations on Eurostat data. Note: na stands for not available
0.62
0.32
0.20
0.19
0.05
0.04
0.03
0.02
0.01
0.00
0.00
29
Figure 6 Within-country specialisation in olive plantations (NUTS2 Regions)
Toscana
Algarve
Alentejo
Extremadura
Lazio
Cataluña
Sicilia
Notio Aigaio
Campania
Sterea Ellada
Localisation Index
Norte
Dytiki Ellada
Liguria
Attiki
Peloponnisos
Andalucía
Puglia
Ionia Nisia
Voreio Aigaio
Calabria
Kriti
1,00
1,50
2,00
2,50
3,00
3,50
4,00
Source: own elaborations on Eurostat data.
30
Figure 7 Olive tree yields (Tons per hectare)
Greece
6
5
4
3
2
2001
1
2002
0
2003
Spain
3,5
3,0
2,5
2,0
1,5
1,0
2004
0,0
2006
0,5
2005
Source: Eurostat, yields statistics
The result of highest relevance for this study is that the most specialized and highly performing regions
in terms of olive tree productions, singled out by means of elaborations on official statistics (Eurostat data),
largely corresponds to those regions in which the estimated biomass production is higher (see next section).
31
The Table 22 further supports this evidence by providing information on the harvested production of
olive trees. Of course, for this preliminary descriptive analysis, the harvested production is even a much
better proxy of the biomass production at regional level.
Figure 7 continued Olive tree yields (Tons per hectare)
Italy
2004
Molise
Calabria
Puglia
Abruzzo
Marche
Umbria
Campania
Lazio
Lombardia
Emilia-Romagna
Veneto
Sicilia
Liguria
Sardegna
Toscana
Basilicata
Valle d'Aosta
2005
Piemonte
9
8
7
6
5
4
3
2
1
0
2006
Portugal
1,6
1,4
1,2
1
0,8
0,6
0,4
2004
0
2006
0,2
2005
Source: Eurostat, yields statistics
32
Table 22 Harvested production of olive trees across regions (1000 tons) and concentration indexes (C4 and normalized
Herfindal-Hirschman Index_HHI)
NUTS2 Regions
Production
Galicia
Cantabria
Ciudad Aut. de Ceuta
Ciudad Aut. de Melilla
País Vasco
Canarias
Illes Balears
La Rioja
Com. Foral de Navarra
Comunidad de Madrid
Castilla y León
Región de Murcia
Aragón
Cataluña
Castilla-la Mancha
Extremadura
Andalucía
Spain
C4
HHI
0.00
0.00
0.00
na
na
0.37
0.40
2.27
4.80
11.53
11.80
13.00
20.70
44.53
90.67
114.27
254.17
361.17
4037.30
4966.90
0.96
0.65
Dytiki Makedonia
Anatoliki Makedonia
Notio Aigaio
Attiki
Thessalia
Voreio Aigaio
Kentriki Makedonia
Ipeiros
Ionia Nisia
Sterea Ellada
Dytiki Ellada
Peloponnesus
Kriti
Greece
C4
HHI
NUTS2 Regions
0.20
25.27
27.37
38.07
56.73
64.37
66.57
74.00
125.80
224.33
286.73
619.43
683.43
2292.33
0.79
0.13
Production
NUTS2 Regions
Região Aut. dos Açores
Região Aut. da Madeira
Área Metr. de Lisboa
Algarve
Norte
Alentejo
Portugal
C4
HHI
NUTS2 Regions
Production
na
na
0.30
6.33
89.77
103.30
299.13
0.67
0.29
Production
Valle d'Aosta
0.00
Piemonte
0.03
0.43
Bolzano
0.47
Trento
0.63
Lombardia
4.47
Emilia-Romagna
6.23
Veneto
8.87
Liguria
25.17
Marche
27.77
Basilicata
36.97
Molise
43.10
Sardegna
51.90
Umbria
80.90
Toscana
149.10
Abruzzo
155.23
Lazio
182.07
Campania
225.43
Sicilia
291.53
Calabria
1250.03
Puglia
1367.87
Italy
3908.23
C4
0.80
HHI
0.20
Source: own elaborations on Eurostat data. Note: data on productions are 3-year averages (2001, 2002 and 2003 for Greece; 2004,
2005 and 2006 for the other 3 countries). na stands for not available.
Again, this table identifies as the likely producers of biomasses, as we will see also in the survey-based
analysis (see par. 2.2), regions such as Kriti, Peloponnesus and Dytiki Ellada in Greece, Norte and Alentejo in
Portugal, Puglia, Sicilia and Campania in Italy, and Andalucìa in Spain.
33
Lastly, Table 22 also offers a picture concerning the within-country concentration of production and aims
at endowing us with a preliminary idea about possible location of the gasification plants. The C4 index is
simply the share of the first 4 larger regional-level producers on the total production of the country. The
normalized Herfindal-Hirschmann Index (HHI) is specified as follows:
HHI =
∑
s − 1⁄N
1 − 1⁄ N
where s is the squared share of olive trees production in region i and N is the number of regions within
a specific country. The normalized version of the HHI ranges from 0 to 1. A value close to 1 indicates very
high concentration of the olive tree production across regions and within country.
Table 22 shows a very high concentration of the harvested production in Spain (C4=0.96 and HHI=0.65),
where Andalucía emerge as possible site for a gasification plant. Instead, the low value of HHI in the other 3
countries would suggest that localization in more than one site could be optimal and calls for a thorough
examination that a multi-criteria analysis could better satisfy, as it has been done in the previously of the
study.
34
Figure 8 Harvested production of olive trees across region and concentration indexes
35
Figure 9 MCA analysis
36
Section II
4. A Dataset for the Localisation of Gasification Plants in Andalucía and Puglia
This section aims at describing a dataset on olive crops, production and waste at a detailed territorial
level, which has been created and assembled for the municipalities of Andalucía and Puglia. The two regions
were identified, in the previous steps of the analysis, as the most suitable regions in Spain and Italy,
respectively, for the localisation of gasification plants. The database presented here is intended as a
complementary tool to the previous research and provides a starting point for the analysis of localisation
choices within the above-mentioned two regions on the basis of an initial set of variables. These variables
refer to potential biomass production from olives and olive oil production (pomace and pruning), demand
and supply of electricity, existing renewable energy production, transportation costs and socio-economic
conditions.
This section is organised as follows: in the next paragraph we describe the variables included in the
database and the methods used for their construction. Then we present some descriptive statistics (4.2) and
an application of the database to the study of localisation choices in the two regions (4.3). The latter is solely
intended as a preliminary exercise aimed at showing the potential of the dataset in localisation analysis. The
list of variables that can be provided by means of a desk analysis is indeed limited; however, the dataset can
be easily enriched with further relevant information (such as the constraints imposed by local environmental
and urbanistic legislation and by the revealed preferences of territories in terms of policy objectives).
4.1 Data and Variables
The dataset has the municipality as its territorial unit. This level of territorial detail corresponds to
“Municipios” in Spain and to “Comuni” in Italy. As for the two regions of interest here, the database is
composed of 258 Comuni for Puglia and 767 Municipios in Andalucía. The dataset covers therefore all
municipalities of Puglia and 767 out of 778 municipalities of Andalucía, since for eleven of them the data on
olive crop area, which are our starting point, are missing in the tables supplied by the Spanish National
Statistical Institute.
The variables included in the dataset can be grouped into two main sets: (i) variables related to olive
crop area, olives production and waste from olive production usable as biomass; (ii) variables related to socioeconomic, productive and geographic conditions of the territory, such as demand/supply of energy and
electricity, existing supply of biomass energy, potential biomass transportation costs, labour market
conditions.
37
4.1.1
Set 1
Our starting point for the estimation of the quantity of pomace and pruning available at municipal level
in Puglia and Andalucía is the information about the area used for olive crops at municipal level. This
information is typically available in the Agrarian Census; in our case, we employ the latest available data,
which correspond to 2010 for Puglia (source: ISTAT, Istituto Nazionale di Statistica) and 2009 for Andalucía
(source: INE, Instituto Nacional de Estadistica).
The olive crop area at municipal level is used to estimate the quantity of olives produced in each
municipality. This information (on production of olives) is indeed only available at provincial level, both for
Italy (for the 6 provinces of Puglia; source: ISTAT) and Spain (for the 8 provinces of Andalucía; Source: INE,
Annual Statistics on Areas and Production of Crops). For Andalucía the production of olives is also
distinguished between table olives and oil-press olives; this information is conceptually important since while
for the estimation of pruning the starting point is the whole olive production/cropping area, the estimation
of pomace should ideally be only refereed to oil-press olives production. This distinction is, unfortunately,
not available in the Italian statistics.
The estimated quantity of olives (in tonnes) available at municipal level has been simply estimated
allocating the quantity available at provincial level into the municipalities of the province, assuming that the
share of olive production of each municipality is proportional to its share of olive crop area with respect to
the total olive crop area of the province.
Olive.Prodmunic = Oliv.Prodprov *(Areamunic/Areaprov)
[4.1]
For Andalucía, as, already explained, we were able to estimate the production of table and oil-press olives
separately. In order to avoid distortions due the biannual cycle of olives production, we used as the starting
data for olives production at provincial level the average of the last four years available (from 2008 to 2011).
The quantity of olives produced at municipal level is functional to estimate the availability of biomasses
from olive crops. As regards pruning, the existing empirical literature provides quantitative relationships
between the quantity of olives and pruning for both Andalucía and Puglia.
As regards Andalucía, we follow Civantos (1981) and Medina and Hernandez (2006) and estimate
separately the quantity of Ramón and Leña by means of the following equations:
Ramónmunic = Oliv.Prodmunic *0.88 + 4.76
[4.2]
Leñamunic = Oliv.Prodmunic *0.74-6.48
[4.3]
where the olive production (in tons) includes both table and oil-press olives. Since the pruning is carried
out on a biannual basis, the total amount of annual availability of biomass from pruning is:
Prunmunic= ½ * (Ramónmunic + leñamunic )
[4.4]
38
As regards Puglia, the corresponding quantitative relationships are available in Probio-Regione Puglia
(2012)2, distinguished for two groups of provinces:
Pruning/hamunic = Oliv.Prod/hamunic *0.566 + 1.496
[4.5]
(Foggia, Bari and Barletta-Andria-Trani)
and
Pruning/hamunic = Oliv.Prod/hamunic *0.305 + 1.401
[4.6]
(Taranto, Brindisi and Lecce)
Given the aims of the present project (and the innovativeness of the technical process of biomasses
processing), no adjustments of the quantity of pruning available were made in terms of factors affecting the
actual collection of pruning (such as accessibility of the crops, climatic conditions at the time of pruning, size
distribution on the farms, efficiency of the capital involved in the production processes). Our estimation of
pruning is therefore to be intended as the total quantity of biomass potentially available; in other words, it
is an upper bound estimation.
The estimation of the quantity of pomace was carried out with two different approaches in the two
regions, since while for Puglia the quantity of pomace at provincial level is available in Probio-Regione Puglia
(2012), and could therefore be allocated at municipal level (see below), this was not the case for Andalucía.
For the Spanish region, we made use of the information available in a recent paper by the Agenzia Andaluza
Del Energìa (2013), which provides the composition of the output of olive processing in mills (pag.4). In
particular, it reports that for each tonne of olives 0.083 tons and 0.197 tons are represented by pit (hueso)
and dried pomace (orujillo), the remaining components being extra-vergin olive oil (0.27), oilve pomace oil
(0.010) and water (0.438). Acoording to these coefficients, the quantity of pomace available as biomass
accounts for around 28% of the quantity of olives; this share is in line with other literature, which reported a
share around 25 % (see, for example Abu-Ashour et al., 2010 and the references cited therein). The quantity
of pomace available at municipal level for Andalucia is therefore obtained as:
Pomacemunic=Oliv.prodmunic*0.28 (0.083+0.197)
[4.7]
where the quantity of olives here refers of course to oil-press olives only.
As for the region of Puglia, the starting point was instead the quantity of pomace available at provincial
level, provided by Probio-Regione Puglia (2012) (data referred to the year 2007, since no more recent data
are available)3. The allocation of pomace at municipal level was carried out on the basis of the share of
employment in oils and fats processing firms (the most detailed industry breakdown available) in each
The coefficients of equations 4.5 and 4.6 stem from experimental results carried out by the Department of Agroenvironmental Sciences, University of Bari (see Probio-Regione Puglia (2012).
3
The pomace we considered stems from the traditional extraction methods (cold-pressed and sinolea oil) and from
three-phases oil decanters. By following Probio-Regione Puglia (2012, p.26-28) we kept out the two-phases pomace due
to its high-moisture content that makes it unsuitable for the energy production purposes.
2
39
municipality on the total employment in the same sector in the province, available in the 2011 Italian Industry
and Service Census:
Pomacemunic=Pomaceprov*(Employ.millsmunic/Employ.millsprov)
[4.8]
A summary of the outcomes obtained through these methods of estimation is provided in section 4.2.
4.1.2
Set 2
The second set of variables refers to socio-economic, energy production and geographic conditions of
the territory.
First of all, we included two metrics of demand and supply of electricity at provincial level, as a proxy of
the size of the energy needed for productive and residential purposes and of the supply of energy produced
in the territory. Unfortunately, no data are available at municipal level for both Andalucía and Puglia;
however, we decided not to re-allocate these data among the municipalities since they better represent, at
provincial level, demand/supply of socio-economic contexts in which the municipality is localised. Data are
from Agencia Andaluza de la Energia, Consejeria de Empleo, Empresa y Comercio and refer to 2014 for
Andalucía; from ISTAT, Atlante statistico territoriale delle infrastrutture, and refer to 2013 for Puglia.
The same sources provided the data about the existing supply of energy generated by biomasses (again
at provincial level); this variable is intended as a proxy of the existing potential of biomass production and
also somehow represents a proxy for revealed preferences of territories on methods of energy production.
We then provide an index of optimality of localisation in terms of transportation costs of biomass
produced in the region, defined as the Biomass Proximity Index (BPI):
BPI i =
n1
1
1
Biomassj
å
j
=1
n 1
di, j
[4.9]
where Biomass is the sum of pomace and pruning estimated as explained in section 4.1.1 and di,j is the
distance between each pair of Municipalities derived from a distance matrix in terms of:
- travel time (minutes), for the Municipalities of Puglia (available in ISTAT, Territorio e Cartografia,
see: http://www.istat.it/it/archivio/157423);
- geographical distance for the Municipalities of Andalucía (based on elaborations starting from the
geographical coordinates of the town that identifies the Municipio).
As it is clear from equation [4.9] the index is, for each municipality i the average of the quantity of biomass
available in all j remaining municipalities of the region, weighted by their proximity (the inverse of the
distance) with respect to municipality i. The BPI is therefore the higher the more a municipality is close to
municipalities with larger availability of biomass; in other terms, the higher the index, the more desirable
would be the localisation of a biomass processing plant in terms of input (pruning and pomace)
transportation costs.
Lastly, we introduced a labour market variable at municipal level in order to capture the local socioeconomic conditions. We opted for an employment rate, built as the percentage of employed individuals
40
(aged 15 years and over) out the population between 15 and 64 years. Data were drawn from the latest ISTAT
and INE Population and Housing Census and refer to 2010 and 2011, respectively. To the few Spanish
municipalities for which the data on employment is missing, the employment rate of the corresponding
province was attributed.
As already explained, this is just an initial set of variables that can be used for localisation analysis, which
can be enhanced with other information depending on the purpose and objectives of the analysis.
4.2 Descriptive Outcomes
Table 23 summarizes the main features referring to the municipalities of Puglia (258) and Andalucía (767).
Overall, the central tendency (mean and median) and dispersion (standard deviation and coefficient of
variation) indices display an important skewness and heterogeneity for most of the variables we have taken
into account, especially in the case of Andalucía.
The area devoted to olive crops is on average 1,377 hectares in the municipalities of Puglia and goes up
to 1,771 hectares in the Andalucía’s ones. However, the medians are far lower than the means, 746.84 and
369.19 hectares in Puglia and Andalucía, respectively. This outcome reveals that the municipalities in the high
tail of the distribution significantly influence the average value. Probably these local administrative units are
also the largest ones in terms of total territorial area. In any case, we observe a large dispersion of the olive
crops area around the mean, as indicated by the remarkable standard deviation and by the coefficient of
variation (CV), which is the ratio of standard deviation to the mean. Especially in Andalucía, the olive crop
area across municipalities has a very high-variance distribution, with the standard deviation that almost
doubles the mean (CV=1.99). This heterogeneity even increases in the case of olives production. Again, the
average value is larger in Andalucía (7,312 tonnes) than in Puglia (4,672 tonnes), whereas the opposite holds
in term of medians (2,581 in Puglia versus 1,074 tonnes in Andalucía). The CV for olive production is 2.15 in
Andalucía against 1.31 for the municipalities of Puglia. This outcome can be explained by the highly skewed
distribution in which few Andalucían municipalities, positioned in the high tail, are characterised by modern
high-density olive groves that are located in plain and irrigated areas (Béltran-Esteve, 2013). Although the
implementation of Common Agricultural Policy led to the intensification of this crop and the emergence of
highly intensive farms in more fertile grounds previously used for other crops (Gallardo et al., 2002),
traditional rain-fed olive groves remain predominant in Andalucía (Arriaza et al., 2011; European
Commission, 2012). These facts provide support to the conspicuous heterogeneity we find across
municipalities in this region. It is also worth noting that olive production in Andalucían municipalities between
2008 and 2011 ranged from zero to 131,542 tonnes of olives (min and max values in the Table 4.1,
respectively); the maximum value is nearly three times as large as the quantity that the largest Puglia’s
producer (municipality) carried out in the same period (53,716 tonnes).
The estimated values of pruning, pomace and total biomass are naturally coherent with that we have
found for olive production, even though the dispersion around the mean reported by the coefficient of
variation increases for Andalucía (CV is 2.15, 2.24 and 2.17 for pruning, pomace and total biomass
respectively).
41
Olive Area (hectares)
Mean
Stand.Dev.
Median
CV
Min
Max
Puglia
1,377.70
Andalucía
1,771.36
746.84
369.19
1,773.75
1.29
4.07
14,482.60
3,523.47
1.99
0.00
25,950.10
Proximity Biomass Index
(tonnes/distance unit)
Mean
Stand.Dev
Median
CV
Min
Max
Puglia
Andalucía
20.28
37.75
80.32
84.10
0.25
37.75
141.16
Olive Production (tonnes)
Puglia
4,672.02
2,035.08
0.00
13.26
2.15
12.66
53,716.06
13,1542.00
Electricity consumption
(GWh at NUTS3_level)
Puglia
Andalucía
1,303.37
71.16
2,193.96
280.60
1,074.50
1.31
2,728.96
22.82
Puglia
4,007.01
15,690.08
79.70
0.47
Andalucía
7,312.27
6,110.07
2,581.41
0.48
1,044.74
5,679.43
Table 23 Descriptive Statistics at municipality level
Pruning (tonnes)
Andalucía
5,922.09
236.98
1.41
2.15
1.62
52,069.26
869.64
0.00
106,547.60
Electricity Gross Generation
Total (GWh at NUTS3_level)
5,035.02
0.42
1.03
7,297.83
490.82
2,094.42
1,659.77
2,594.65
Andalucía
1,916.18
12,708.81
Andalucía
3,093.58
Puglia
1,294.14
5,633.09
Puglia
3,973.61
Pomace (tonnes)
4,874.99
3,614.60
435.90
19,882.60
0.00
13,124.63
Total Biomass (tonnes)
Puglia
5,301.15
Andalucía
7,838.27
4,291.70
7,296.59
16,971.58
2.24
1.38
2.17
0.00
36,537.79
Electricity Generation
Renewable (GWh at
NUTS3_level)
2,823.84
13.26
64,902.23
1,166.76
0.00
143,085.00
Employment Rate (%)
Puglia
Andalucía
Puglia
Andalucía
2,269.18
1,285.35
490.08
0.04
0.06
0.62
0.80
0.32
0.08
3,644.64
3,202.20
1,740.30
9,853.00
1,606.89
1,515.56
1,133.70
1,602.70
427.60
753.70
3,881.00
3,089.10
0.49
0.45
0.49
0.46
0.39
0.30
0.63
0.13
0.63
Notes: All statistics are based on 258 municipalities in Puglia and 767 municipalities in Andalucía. CV is the coefficient of variation, which is the ratio (Stand.Dev./Mean).
42
It is interesting to notice very similar average values for the proximity biomass index (PBI) in Puglia and
Andalucía (80.32 versus 79.70). This means that, on average, the municipalities of these two regions share
the same level of attractiveness, measured as tonnes of biomass per minute of transport distance (Puglia) or
tonnes of biomass per coordinates-based distance (Andalucía). Again, differences in the medians and in the
max-min values reveal that all the key variables in our dataset (olive production, biomass and PBI) deserve a
thorough analysis of the distributions, which is carried out below.
Table 23 also reports information on electricity consumption and gross generation, which are proxies for
demand and supply of energy at provincial (NUTS3) level. Although we know that the gross electricity
generation cannot be compared with consumption, we can have a first preliminary idea of the energy balance
in the territories under scrutiny. For example, the value separating the higher half from the lower half of the
municipalities’ distribution in both Puglia and Andalucía, that is the median, shows a gross electricity
generation higher than consumption (3,615 vs. 2,194 GWh in Puglia and 3,202 vs. 3,094 GWh in Andalucía).
In addition, from Table 23 it can be deduced that in terms of medians renewable sources contribute to 35%
of gross electricity generated in Puglia (1,134 to 3,202 GWh) and to 50% of gross electricity generated in
Andalucía (1,603 to 3,094 GWh).
Some information on the socio-economic context can be grasped by the statistics on employment rate at
the municipal level. Both regions under scrutiny here share very low employment rates (on average 45% in
Andalucía and 49% in Puglia) if compared with 64 %, the average of the European Union (28 countries), over
the period 2010-2013 (Eurostat, 2015). Only maximum values (municipalities with 63% of employment rate
in both regions) nearly approach the European Union’s average. This underuse of labour force is rather
homogenous across the administrative units under scrutiny, as signaled by the low values of the CV (0.08 in
Puglia and 0.13 in Andalucía).
Figures 8.1-8.3chart different aspects of the distribution of the key variables included in the dataset and
answer to the need to carefully study the heterogeneity across municipalities, as we mentioned above.
K-density estimations in Figure 8.1 are performed on the log-transformed variables of Table 23 to improve
the graphic readability. First of all, the dispersion coefficients discussed above are confirmed by the
distributions depicted in the Figure 8.1. Especially Andalucía shows for olive crops area, olive production and
olive wastes (pruning and pomace) a low-kurtosis distribution with fatter tails. It means that a number of
municipalities, higher than those in Puglia, have values both far lower and far higher than the modal value.
In contrast, the peakedness we observe for the municipalities of Puglia discloses a more uniform distribution
with thinner tails; this means that the values are less dispersed around the modal value. It is also worth noting
the slight bimodality for pomace in Puglia, indicating two similar size groups of municipalities: on the leftside peak we observe the group with lower pomace production whereas the opposite holds for the right-side
peak.
The cumulative distribution functions depicted in Figures 8.2 and 8.3 inform us about other features of
the distribution of the total biomass and the biomass proximity index. For example, with probability 0.75 (up
to the third quartile of the distribution) the total biomass produced by municipalities is equal or less than
5,000 tonnes and normally the Puglia’s territories slightly dominate the Andalucía ones in providing this olive
wastes. However, beyond 5,000 tonnes, the last quartile (25% of municipalities with higher waste
production) sees a strong dominance of Andalucía municipalities in providing potential biomass. Similarly,
for low values of attractiveness (PBI equal or less than 90 tonnes per distance unit) Puglia’s municipalities are
43
slightly ahead of the Andalucía’s ones. In contrast, in the last tertile, (with probability greater than 0.68) we
find the Spanish municipalities showing the most advantageous PBI index. Thus, also the comparative analysis
of distributions confirms that biomass production and the possibility to minimize the transportation costs
seems to be more concentrated within Andalucía with respect to Puglia.
Figure 10.1 Distributions of the key variables along municipalities in Puglia and Andalucía
Olive Area
Olive Production
Pruning
Pomace
44
Figure 10.2 Cumulative Distribution Function for the Total Biomass: Puglia vs. Andalucía
Figure 10.3 Cumulative Distribution Function for the Proximity Biomass Index: Puglia vs. Andalucía
45
4.3 A preliminary localisation analysis for gasification plants in Puglia and Andalucía
The choice of the site for the localisation of a productive plant is a complex task involving several social,
economic, environmental, political dimensions. A decision-making tool able to support the decision-making
process of interrelation of complex data, leading to a more comprehensive vision of the synthesis, is
therefore necessary. Decision support systems or multi-criteria decision-making methods have been used
with great effectiveness in the areas of energy; particularly, since the beginning of 2000s, the application of
multi-criteria methods to issues of energy production from renewable sources has been increasing (Scott et
al., 2012). In the last decade, studies on the availability of biomass and relative costs of transportation and
transformation to produce renewable energy played a relevant role in supporting policy makers. Examples
of such researches applying the Multi Criteria Analysis (MCA) tools to identify the least cost for the best
suitable area for renewable energy plants for Europe and the US include Panichelli and Gnansounou (2008),
Recchia et al. (2010). Similarly, additionally to cost evaluations, several studies consider the problem of
choosing the best location for renewable energy plant (PhuaMui-How and Minowa, 2005; Zambelli et al.,
2012; Colantoni et al., 2013; Perpifia et al., 2013; Recanatesi et al., 2014).
Multi-criteria decision-making implies a process of assigning values to alternatives that are evaluated
along multi-criteria. Multi-criteria decision-making can be divided into two broad classes of multi-attribute
decision-making and multi-objective decision-making. If the problem is to evaluate a finite feasible set of
alternatives and to select the best one based on the scores of a set of attributes, as it is the case here, it is a
multi-attribute decision making problem. The multi-objective decision-making deals with the selection of the
best alternative based on a series of conflicting objectives.
There are many classifications in place for the extensive formal methods and procedures for handling
multi-criteria-decision making (Yue and Yang, 2007; Kinoshita et al., 2009; Sacchelli et al., 2013). Here we opt
for the weighted sum approach, which allows aggregating the information (indicators) relevant for the
localisation analysis and accounting for their relative importance. The final outcome is a ranking of the socalled alternatives (in our case the municipalities of Puglia and Andalucía, in two different MCA exercises),
which provides an ordering of the most desirable localisation of biomass production plant.
The method presupposes the construction of a so-called evaluation matrix (X), of dimensions j x i, where
j are the indicators or objectives (in our case the indicators based on the variables described in section 4.1)
and i the alternatives (the 767 and 258 municipalities of Andalucía and Puglia, respectively); the element zij
is therefore the value of indicator j for the alternative i.
All indicators have of course to be standardised, such as they vary between 0 and 1:
z ij = (zij – min zij) / (max zij – min zij)
[4.10]
Preferences on the criteria and indicators are expressed as weights that are assigned by the researchers
or the decision makers. The vector Wj provides information on the relative importance of the criteria.
Combining the weights and the indicators generates priority area for best plants location for production
of biomass energy:
Ei = å z ij ´ wj
j
[4.11]
46
Ei is the score assigned to each alternative, which summarizes its performance with respect to the
objectives set and their hierarchy.
The choice of the set of weights is of course an ex ante choice of the researcher and is somehow
arbitrary; however, in order to assess how much the outcomes of the MDA depend on the assumptions about
the weight set, a sensitivity analysis can be carried out by altering the relative importance of the
objectives/indicators.
Our MCA, which is a purely descriptive analysis mainly aimed at illustrating the potentials of the
database for this kind of investigation, has been carried out on the variables derived from the information
illustrated in the previous section and with the sets of weights illustrated in Table 24.
Table 24 Multi Criteria Analysis, objectives and alternative weight sets
pro/con
localisation
Quantit
y of Biomass
(+)
Biomass
Proximity Index
(+)
Electricity
cons/prod gap
(+)
% energy by
renewable resources
(-)
Employme
nt rate
(-)
W1
0.25
0.45
0.10
0.10
0.10
W3
0.20
0.50
0.10
0.10
0.10
W2
W4
W5
0.30
0.20
0.30
0.40
0.40
0.50
0.10
0.10
0.05
0.10
0.15
0.05
0.10
0.15
0.10
The set of variables employed for the MCA first of all includes the quantity of biomass available at
municipal level and the Biomass Proximity Index (as defined in equation 4.9), as proxies of the potential input
available at local level, from both the territory of the municipality and from neighbouring territories. Being
normally these variables crucial in the plant localisation decisions, they are assigned a relatively high weight
in the initial weighting scheme (W1) and in all alternative ones used for the sensitivity analysis. These two
variables are positively correlated with the desirability of plant localisation, since the larger the input
availability the lower the costs of employing them. A third variable, again positively correlated with
desirability of localisation, is a rough indicator of energy demand/supply gap. Data for this variable is at
provincial level (no municipal detail is available) and the higher the gap in the province, the more desirable
is to implement further energy production plants in one municipality belonging to that province. The fourth
variable illustrates the share of energy produced by renewable resources, again at provincial level; in this
case, the lower the indicator, the higher the desirability to locate the biomass processing plant in one of the
municipality of the province, in order to achieve a sort of upward convergence of the shares across the region.
The last indicator describes the labour market performance at local level and, since the localisation of a plant
generates jobs, the lower the employment rate in the municipality, the higher the desirability to localise
there the biomass processing plant.
Tables 25 and 26 describe the outcomes of the MCA with the initial set of weights and with the alternative
ones; we list in the tables the ten municipalities in which our tentative MCA analysis suggests that the
localisation of the gasification plant will be most desirable.
47
Table 25 Results of the MCA analysis (ranking of municipalities) and sensitivity analysis, Puglia
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
W1
Bitonto (Ba)
Palo del Colle (Ba)
Binetto (Ba)
Andria (Bt)
Grumo Appula (Ba)
Bitetto (Ba)
Terlizzi (Ba)
Corato (Ba)
Ruvo di Puglia (Ba)
Molfetta (Ba)
W2
Bitonto (Ba)
Andria (Bt)
Palo del Colle (Ba)
Binetto (Ba)
Grumo Appula (Ba)
Corato (Ba)
Bitetto (Ba)
Terlizzi (Ba)
Ruvo di Puglia (Ba)
Molfetta (Ba)
W3
Palo del Colle (Ba)
Binetto (Ba)
Bitonto (Ba)
Grumo Appula (Ba)
Bitetto (Ba)
Andria (Bt)
Terlizzi (Ba)
Ruvo di Puglia (Ba)
Molfetta (Ba)
Corato (Ba)
W4
Bitonto (Ba)
Palo del Colle (Ba)
Binetto (Ba)
Grumo Appula (Ba)
Bitetto (Ba)
Terlizzi (Ba)
Andria (Bt)
Corato (Ba)
Ruvo di Puglia (Ba)
Molfetta (Ba)
W5
Bitonto (Ba)
Andria (Bt)
Palo del Colle (Ba)
Binetto (Ba)
Grumo Appula (Ba)
Bitetto (Ba)
Terlizzi (Ba)
Corato (Ba)
Ruvo di Puglia (Ba)
Molfetta (Ba)
The outcomes for Puglia reveal that the optimal localisation of the biomass processing plant would be,
according to the set of variables employed, in the municipalities of the province of Bari (9 of the first 10
municipalities of the top ranking) listed in table 25, with the only exception of Andria. The sensitivity analysis
shows that these results are very weakly dependent on the choice of the weighting scheme; while there is
some mobility within the 10 top positions, all five schemes identify the same municipalities as the top ten in
the ranking.
Table 26 Results of the MCA analysis (ranking of municipalities) and sensitivity analysis, Andalucía
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
W1
Úbeda (Ja)
Martos (Ja)
Victoria, La (Co)
Torredonjimeno (Ja)
Jaén (Ja)
Alcaudete (Ja)
Torre del Campo (Ja)
Baena (Co)
Jamilena (Ja)
Lucena (Co)
W2
Úbeda (Ja)
Martos (Ja)
Jaén (Ja)
Baena (Co)
Torredonjimeno (Ja)
Alcaudete (Ja)
Victoria, La (Co)
Lucena (Co)
Baeza (Ja)
W3
Victoria, La (Co)
Martos (Ja)
Úbeda (Ja)
Torredonjimeno (Ja)
Jamilena (Ja)
Alcaudete (Ja)
Jaén (Ja)
Baena (Co)
Baeza (Ja)
W4
Victoria, La (Co)
Martos (Ja)
Úbeda (Ja)
Jamilena (Ja)
Alcaudete (Ja)
Torredonjimeno (Ja)
Jaén (Ja)
Baena (Co)
Lupión (Ja)
W5
Úbeda (Ja)
Martos (Ja)
Victoria, La (Co)
Baena (Co)
Torredonjimeno (Ja)
Jaén (Ja)
Alcaudete (Ja)
Lucena (Co)
Baeza (Ja)
As for Andalucía, Table 26 shows that the most desirable localisation of the plant according to the
variables employed here would be in some municipalities of either the province of Jaén or Cordoba. As it was
the case for Puglia, the list of the top 10 locations in Andalucía is very robust to changes in the weighting
scheme, with some re-ranking and very few new municipalities (Baeza and Lupión) entering the top ten
underweight sets W2-W5. However, the set of the top 8 locations remains unaltered under all weighting
schemes.
As we already emphasized, this analysis is to be considered only as an exercise aimed at illustrating the
potentialities of the dataset presented in the previous section; more proper localisation analyses should take
into account a larger set of information (economic, environmental, political) relevant for the choice.
48
5. Conclusions
The main results stemming from the performed activities can be summarized as follows.





The amount of available biomass from olive crop and olive oil production was estimated at
regional level for Italy, Spain, Greece and Portugal by mean of a survey managed by web and
telephone call campaigns. A user-friendly database was developed containing e-mail
addresses, telephone numbers and the data gathered with the Survey;
A Multi-Criteria Analysis with the purpose of mapping the most suitable sites of possible
locations for gasification plants using olive pruning and olive pomace as raw materials was
developed for Italy, Spain, Greece and Portugal at regional level on the basis of information
collected by national and European statistical institutes;
A second user friendly database has been developed at municipal level (1,025 local
administrative units) for the Puglia (258 Comuni) and Andalucìa (767 Municipios) regions
containing information on olive cultivated area, olive production, pruning, pomace, total
biomass, biomass proximity index, consumption and gross generation of electricity,
employment rate;
A descriptive analysis on variables at sub-regional level showing large heterogeneity in
terms of biomass production across municipalities and between the two regions of Puglia
and Andalucìa has been performed;
A Multicriteria Analysis (MCA) has been carried-out aimed at testing the previous dataset
and at supplying a tentative and preliminary localisation analysis of gasification plants using
olive pruning and olive pomace as raw materials within Puglia and Andalucìa; this exercise
produced a stable and robust ranking of the top 10 municipalities eligible for gasification
plant localization in each of the two regions. For Puglia the top 10 potential localisations are:
Bitonto (Ba), Palo del Colle (Ba), Binetto (Ba), Andria (Bt), Grumo Appula (Ba), Bitetto (Ba),
Terlizzi (Ba), Corato (Ba), Ruvo di Puglia (Ba), Molfetta (Ba). For Andalucia: Úbeda (Ja),
Martos (Ja), La Victoria (Co), Torredonjimeno (Ja), Jaén (Ja), Alcaudete (Ja), Torre del Campo
(Ja), Baena (Co), Jamilena (Ja), Lucena (Co).
49
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52
7. Annexes
Annex 1 - Biomass availability estimation by Survey response: the methodology
Starting from the Survey results, our approach is based on the estimation of the average yield of biomass
(pruning and pomace). Referring the analysis to real production systems, we propose to estimate the biomass
availability by determining the expected average yield: i.e. the case of pruning the probable yield of biomass
per hectare of the agricultural area utilized in olive cropping, in the case of pomace the probable yield of
biomass for mill.
For example. Abu-Ashour and Abu-Qdais (2010) state: “Professionals in olive oil industry estimate the
dry olive waste production to be 25% of the olive production. Hence the amount of olive solid waste
produced in 2005 was around 27.000 tons. As shown in Table 5. about 29% of the olive waste was produced
in the governorate of Irbid.”(p.2231). This method is normally based on technical coefficients drawn from
literature and, sometimes, on field experience. Furthermore it strongly simplifyies the representation of the
production process. Actually the production requires normally more than input, while the method
concentrates on a “critical” input (e.g., the land. in the case of agricultural production). In turn this implies
the implicit assumption that the quantities of all the remaining input are used in a fixed proportion with the
“critical” input considered. In other words, this approach basically considers an idealized production process
which is presumed to account to a large extent for the real processes implemented at the real world units
level. According to this method it is necessary to assume that the fixed production coefficients do not vary
across the productive organization (e.g., the farms) and the territories investigated. Nevertheless this
assumption cannot be easily adopted. Therefore this approach can just provide some tentative, preliminary
information. Our approach is based on the analysis of the data collected by the questionnaires. The idea is to
estimate the probability of each classes of biomass production – as observed by the questionnaire - on the
basis of the sampled units and then using these probabilities to estimate the average yield of biomass
We assume that the biomass production is connected to the size of the agricultural areas utilized by the
cropping system, therefore, the expected average yield in terms of pruning is:
y=
∑ π y
(1)
where: y Is the expected average yield; n is the number of classes of land; π j is the probability of
achieving the yield yj and yj is the yield of the j.th class.
The equation (1)
simply indicates that in a given territory, the average yield biomass can be estimated by knowing: a) the
class of biomass productivity (e.g., 0.0-0.49 tons/ha; 0.5-1.0 tons/hectare etc.) observable in the territory; b)
the probability of each class of productivity.
We assume that the probability π j is a function of the characteristics of the olive production system: the
scale (i.e. the size of the utilized area, the n. of tree, the type of the crop, if specialized or not, the technology
of pruning and so forth). The probability π j indicates, of course, how much it is likely to achieve the j.th level
of yield in the observed productive system. To analyze the quantitative influence of the technological
characteristics of the productive system on the probability π j allows to build up scenarios in which the
variation of those characteristics are put in relation with the expected biomass yields and availability. Figure
9 summarizes the approach proposed in the case of biomass proceeding from pruning.
Figure 11 A procedure to estimate the production of biomass
53
Classification of the biomass yield with respect to the size of the agricultural areas of the cropping system
Estimation of the probability of the yield of each class
Calculation of the expected average yield
Calculation of the biomass production by class and regions
In the case of mills the probability π j is a function of the characteristics of the extraction plant: the type
(i.e. 2 phases or 3 phases ), the capacity of the plant and the volume of production.. The probability π j
indicates, of course, how much it is likely to achieve the j.th level of yield in the observed productive system.
To estimate the probability of each class of production of biomass we refer to the Ordered Logit Models
(Greene 2008).
Table 27 Estimates of the biomasses production – Pruning (tons/ha)
Class of yield
I
ITALY
Average Prob
Yield Class
Yield
yj
0,5
pj
0,20
(yj p j )
0,101
0,281
0,224
0,0653
0,114
0,121
0,0199
0,055
1,25
0,388
0,485
IV
2,25
0,152
0,342
V
VI
VII
VIII
1,75
2,75
3,25
3,75
4
0,128
0,044
0,014
0,029
0,043
pj
(yj p j )
0,5618
II
III
SPAIN
Average Prob
Yield Class
0,046
0,109
0,172
0,2425
0,0728
0,0063
0,0127
0,0187
0,303
0,164
0,021
0,048
0,075
PORTUGAL
Average Prob
Yield Class
pj
(yj p j )
GREECE
Average Prob
Yield Class
pj
(yj p j )
0,3181851
0,1590926
0,1171237
0,0585619
0,1029112
0,1800946
0,1467707
0,2568487
0,0282248
0,0776182
0,0630054
0,1732649
0,3908476
0,1078876
0,0088655
0,0175583
0,0255197
0,4885595
0,2427471
0,0288129
0,0658436
0,1020788
0,3405572
0,1988464
0,0210362
0,0436728
0,0689877
0,4256965
0,4474044
0,0683677
0,163773
0,2759508
Table 28 Estimates of the biomasses production – Pomace (tons/mill/year)
54
Class of
yield
I
ITALY
Average
Prob Yield
(yj pj)
Class
Yield
yj
500
II
1500
IV
5000
III
V
3000
6000
pj
0.582
291.000
0.139
417.000
0.218
0.048
0.013
327.000
240.000
78.000
SPAIN
Average
Prob Yield
(yj pj)
Class
pj
0.286
143.000
0.267
801.000
0.224
0.155
0.068
336.000
775.000
408.000
PORTUGAL
Average
Prob Yield
(yj pj )
Class
pj
GREECE
Average
Prob Yield
(yj pj )
Class
0.699
349.5
0.083
249
0.196
0.019
0.004
pj
0.577
294
0.278
95
0.025
24
0.115
0.005
288.5
417
345
125
30
We used the determined coefficients to have a forecast of the total biomass production at regional level
in some of the targeted Countries using data derived from EUROSTAT and National Official Statistics Institutes
(ISTAT, INE.es, INE.pt, EL.STAT).
55
Annex 2 – Farm Questionnaire
Figure 12 Farm questionnaire
56
Figure 12 continued Farm questionnaire
57
58
59
60
Annex 3 – Mill Questionnaire
Figure 13 Mill questionnaire
61
Figure 13 continued Mill questionnaire
62
63
64
Annex 4 –Simplified Farm Questionnaire
Figure 14 English Farm questionnaire
65
Figure 14 continued English Farm questionnaire
66
Figure 14 continued English Farm questionnaire
67
Figure 15 Greek Farm questionnaire
68
Figure 15 continued Greek Farm questionnaire
69
Figure 15 continued Greek Farm questionnaire
70
Figure 16 Portuguese Farm questionnaire
71
Figure 16 continued Portuguese Farm questionnaire
72
Figure 16 continued Portuguese Farm questionnaire
73
Annex 5 – Simplified Mill Questionnaire
Figure 17 English Mill questionnaire
74
Figure 17 continued English Mill questionnaire
75
Figure 18 Greek Mill questionnaire
76
Figure 18 continued Greek Mill questionnaire
77
Figure 19 Portuguese Mill questionnaire
78
Figure 19 continued Portuguese Mill questionnaire
79
Annex 6 – Data Bases Architecture
Two different applications have been realized each one providing an easy way of consultation, synthesis
and analysis of the data collected. That was to meet the different needs and requirements and to better use
the data related to the biomass which are organized in excel files: in particular, the two applications have
been realized separately and independently due to different structural and architectural configurations
which have been chosen and selected.
Each application has its own database and tools for consultation, according to the different
representations to be offered to the users.
The choice to use an Access db environment, which is embedded in the Microsoft Suite, has been
suggested by the need to avoiding heavy data base managers such as SQLServer or Oracle.
Our purpose was to allow a simple as well as articulated use of data in all the different necessary
combinations. The complexity of the implementation was inversely proportional to the ease of use; so this
choice required an effort and a concentration in engineering the applications to be implemented in harmony
with the demands and needs to be met and satisfied. The second application contains a pivot table which is
the optimal solution for the dynamic analysis of tabular data and is extremely easy to reshape.
Both the applications indicate that the data used were sourced from the internet and they were treated
in line with the privacy law.
The first database, “Database on olive farming and olive industry in the Mediterranean countries:
survey”, provides the following information:
1) 6728 email addresses of olive oil mills, olive farmers and olive oil associations, stakeholders, pellets
producers, etc. in the Mediterranean area;
2) General Information (Nation - Post code – Age – Education); Olive characteristics ( Farm total area
(hectares) - Average age of planting (years) - Olive farming area (ha); Total number of olive trees in the farm;
Irrigation; Average yearly yield in Olives (tons/ha); Pruning; Frequency of pruning; Pruning system; Pruning
season; Average pruning residues production (tons/plant); Olive tree training shape; Olive Varieties (specify
name and percentage of each); Average yearly yield in Olives (Quintals); Total number of olive trees in the
farm; Olive growing; Age of planting; Total area (hectares); Irrigation; More common plant density in the
farm; Age of planting; Total area (Hectares); More common plant density in the farm; Frequency of pruning;
Average yearly pruning residues production (average of the last two years); Pruning residues management
(Type and e %); How many Km is the mill far from the farm?; Timing of olive harvesting; How many Km is the
mill far from the farm?; Do you know any existing service in your area for gathering and removal pruning
residues?; Would you be interested in such a service?; Oil extraction plant; Production capacity of the olive
mill; Average yield of the last two years (Quintals); Use of pomace; Use of the pit once separated; Availability
of specific areas for pomace treatment and storage in the firm (treatment plants, silos, etc.); Pomace average
production in the last three years (ton); How many Km do you need to cover to take pomace to the oil
extraction plant?; Which of the following would encourage you at utilizing olive mills residues for energy
production?
The second database, “Database on olive farming and olive industry in the Mediterranean countries:
Desk Analysis”, has 10 variables related to 1025 Municipalities:
1) Olive area (Hectares), harvested production (tonnes), estimated pruning (tonnes), estimated pomace
(tonnes), total biomass (tonnes), electricity consumption (MWh), electricity grossgeneration total (MWh),
electricity renewable gen (MWh), Proximity biomass index (tonnes/distance unit), employment rate at
province level, employment rate at municipality level.,
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2) Intraregional analysis data Regions: Puglia, Andalucia
PUGLIA:
Foggia: Accadia; Alberona; Anzano di Puglia; Apricena; Ascoli Satriano; Biccari; Bovino; Cagnano Varano;
Candela; Carapelle; Carlantino; Carpino; Casalnuovo Monterotaro; Casalvecchio di Puglia; Castelluccio dei
Sauri; Castelluccio Valmaggiore; Castelnuovo della Daunia; Celenza Valfortore; Celle di San Vito; Cerignola;
Chieuti; Deliceto; Faeto; Foggia; Ischitella; Isole Tremiti; Lesina; Lucera; Manfredonia; Mattinata; Monte
Sant'Angelo; Monteleone di Puglia; Motta Montecorvino; Ordona; Orsara di Puglia; Orta Nova; Panni;
Peschici; Pietramontecorvino; Poggio Imperiale; Rignano Garganico; Rocchetta Sant'Antonio; Rodi Garganico;
Roseto Valfortore; San Giovanni Rotondo; San Marco in Lamis; San Marco la Catola; San Nicandro Garganico;
San Paolo di Civitate; San Severo; Sant'Agata di Puglia; Serracapriola; Stornara; Stornarella; Torremaggiore;
Troia; Vico del Gargano; Vieste; Volturara Appula; Volturino; Zapponeta;
Bari: Acquaviva delle Fonti; Adelfia; Alberobello; Altamura; Bari; Binetto; Bitetto; Bitonto; Bitritto;
Capurso; Casamassima; Cassano delle Murge; Castellana Grotte; Cellamare; Conversano; Corato; Gioia del
Colle; Giovinazzo; Gravina in Puglia; Grumo Appula; Locorotondo; Modugno; Mola di Bari; Molfetta;
Monopoli; Noci; Noicattaro; Palo del Colle; Poggiorsini; Polignano a Mare; Putignano; Rutigliano; Ruvo di
Puglia; Sammichele di Bari; Sannicandro di Bari; Santeramo in Colle; Terlizzi; Toritto; Triggiano; Turi;
Valenzano.
Taranto: Avetrana; Carosino; Castellaneta; Crispiano; Faggiano; Fragagnano; Ginosa; Grottaglie; Laterza;
Leporano; Lizzano; Manduria; Martina Franca; Maruggio; Massafra; Monteiasi; Montemesola; Monteparano;
Mottola; Palagianello; Palagiano; Pulsano; Roccaforzata; San Giorgio Ionico; San Marzano di San Giuseppe;
Sava; Statte; Taranto; Torricella;
Brindisi: Brindisi; Carovigno; Ceglie Messapica; Cellino San Marco; Cisternino; Erchie; Fasano; Francavilla
Fontana; Latiano; Mesagne; Oria; Ostuni; San Donaci; San Michele Salentino; San Pancrazio Salentino; San
Pietro Vernotico; San Vito dei Normanni; Torchiarolo; Torre Santa Susanna; Villa Castelli;
Lecce: Acquarica del Capo; Alessano; Alezio; Alliste; Andrano; Aradeo; Arnesano; Bagnolo del Salento;
Botrugno; Calimera; Campi Salentina; Cannole; Caprarica di Lecce; Carmiano; Carpignano Salentino;
Casarano; Castri di Lecce; Castrignano de' Greci; Castrignano del Capo; Castro; Cavallino; Collepasso;
Copertino; Corigliano d'Otranto; Corsano; Cursi; Cutrofiano; Diso; Gagliano del Capo; Galatina; Galatone;
Gallipoli; Giuggianello; Giurdignano; Guagnano; Lecce; Lequile; Leverano; Lizzanello; Maglie; Martano;
Martignano; Matino; Melendugno; Melissano; Melpignano; Miggiano; Minervino di Lecce; Monteroni di
Lecce; Montesano Salentino; Morciano di Leuca; Muro Leccese; Nardò; Neviano; Nociglia; Novoli; Ortelle;
Otranto; Palmariggi; Parabita; Patù; Poggiardo; Porto Cesareo; Presicce; Racale; Ruffano; Salice Salentino;
Salve; San Cassiano; San Cesario di Lecce; San Donato di Lecce; San Pietro in Lama; Sanarica; Sannicola; Santa
Cesarea Terme; Scorrano; Seclì; Sogliano Cavour; Soleto; Specchia; Spongano; Squinzano; Sternatia;
Supersano; Surano; Surbo; Taurisano; Taviano; Tiggiano; Trepuzzi; Tricase; Tuglie; Ugento; Uggiano la Chiesa;
Veglie; Vernole; Zollino.
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Andria: Andria; Barletta; Bisceglie; Canosa di Puglia; Margherita di Savoia; Minervino Murge; San
Ferdinando di Puglia; Spinazzola; Trani; Trinitapoli.
ANDALUCIA:
Almeria: Abla; Abrucena; Adra; Albánchez; Alboloduy; Albox; Alcolea; Alcóntar; Alcudia de Monteagud;
Alhabia; Alhama de Almería; Alicún; Almería; Almócita; Alsodux; Antas; Arboleas; Armuña de Almanzora;
Bacares; Bayárcal; Bayarque; Bédar; Beires; Benahadux; Benitagla; Benizalón; Bentarique; Berja; Canjáyar;
Cantoria; Carboneras; Castro de Filabres; Chercos; Chirivel; Cóbdar; Cuevas del Almanzora; Dalías; Ejido , El;
Enix; Felix; Fiñana; Fines; Fondón; Gádor; Gallardos , Los; Garrucha; Gérgal; Huécija; Huércal-Overa; Huércal
de Almería; Illar; Instinción; Laroya; Láujar de Andarax; Líjar; Lubrín; Lucainena de las Torres; Lúcar; Macael;
María; Mojácar; Mojonera , La; Nacimiento; Níjar; Ohanes; Olula de Castro; Olula del Río; Oria; Padules;
Partaloa; Paterna del Río; Pechina; Pulpí; Purchena; Rágol; Rioja; Roquetas de Mar; Santa Cruz de Marchena;
Santa Fe de Mondújar; Senés; Serón; Sierro; Somontín; Sorbas; Suflí; Tabernas; Taberno; Tahal; Terque;
Tíjola; Tres Villas , Las; Turre; Turrillas; Uleila del Campo; Urrácal; Velefique; Vélez-Blanco; Vélez-Rubio; Vera;
Viator; Vícar; Zurgena;
Cádiz: Alcalá de los Gazules; Alcalá del Valle; Algar; Algeciras; Algodonales; Arcos de la Frontera; Barbate;
Barrios , Los; Benalup-Casas Viejas; Benaocaz; Bornos; Bosque , El; Castellar de la Frontera; Chiclana de la
Frontera; Chipiona; Conil de la Frontera; Espera; Gastor , El; Grazalema; Jerez de la Frontera; Jimena de la
Frontera; Línea de la Concepción , La; Medina-Sidonia; Olvera; Paterna de Rivera; Prado del Rey; Puerto de
Santa María , El; Puerto Real; Puerto Serrano; Rota; San Fernando; San José del Valle; San Roque; Sanlúcar
de Barrameda; Setenil de las Bodegas; Tarifa; Torre Alháquime; Trebujena; Ubrique; Vejer de la Frontera;
Villaluenga del Rosario; Villamartín; Zahara;
Cordoba: Adamuz; Aguilar de la Frontera; Alcaracejos; Almedinilla; Almodóvar del Río; Añora; Baena;
Belalcázar; Belmez; Benamejí; Blázquez , Los; Bujalance; Cabra; Cañete de las Torres; Carcabuey; Cardeña;
Carlota , La; Carpio , El; Castro del Río; Conquista; Córdoba; Doña Mencía; Dos Torres; Encinas Reales; Espejo;
Espiel; Fernán-Núñez; Fuente-Tójar; Fuente la Lancha; Fuente Obejuna; Fuente Palmera; Granjuela , La;
Guadalcázar; Guijo , El; Hinojosa del Duque; Hornachuelos; Iznájar; Lucena; Luque; Montalbán de Córdoba;
Montemayor; Montilla; Montoro; Monturque; Moriles; Nueva Carteya; Obejo; Palenciana; Palma del Río;
Pedro Abad; Pedroche; Peñarroya-Pueblonuevo; Posadas; Pozoblanco; Priego de Córdoba; Puente Genil;
Rambla , La; Rute; San Sebastián de los Ballesteros; Santa Eufemia; Santaella; Torrecampo; Valenzuela;
Valsequillo; Victoria , La; Villa del Río; Villafranca de Córdoba; Villaharta; Villanueva de Córdoba; Villanueva
del Duque; Villanueva del Rey; Villaralto; Villaviciosa de Córdoba; Viso , El; Zuheros;
Granada: Agrón; Alamedilla; Albolote; Albondón; Albuñán; Albuñol; Albuñuelas; Aldeire; Alfacar; Algarinejo;
Alhama de Granada; Alhendín; Alicún de Ortega; Almegíjar; Almuñécar; Alpujarra de la Sierra; Alquife; Arenas
del Rey; Armilla; Atarfe; Baza; Beas de Granada; Beas de Guadix; Benalúa; Benalúa de las Villas; Benamaurel;
Bérchules; Bubión; Busquístar; Cacín; Cádiar; Cájar; Calahorra , La; Calicasas; Campotéjar; Cáñar; Caniles;
Capileira; Carataunas; Cástaras; Castilléjar; Castril; Cenes de la Vega; Chauchina; Chimeneas; Churriana de la
Vega; Cijuela; Cogollos de Guadix; Cogollos de la Vega; Colomera; Cortes de Baza; Cortes y Graena; Cuevas
del Campo; Cúllar; Cúllar Vega; Darro; Dehesas de Guadix; Deifontes; Diezma; Dílar; Dólar; Dúdar; Dúrcal;
Escúzar; Ferreira; Fonelas; Freila; Fuente Vaqueros; Gabias , Las; Galera; Gobernador; Gójar; Gor; Gorafe;
Granada; Guadahortuna; Guadix; Guajares , Los; Gualchos; Güejar Sierra; Güevéjar; Huélago; Huéneja;
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Huéscar; Huétor de Santillán; Huétor Tájar; Huétor Vega; Illora; Itrabo; Iznalloz; Jayena; Jerez del
Marquesado; Jete; Jun; Juviles; Láchar; Lanjarón; Lanteira; Lecrín; Lentegí; Lobras; Loja; Lugros; Lújar; Malahá
, La; Maracena; Marchal; Moclín; Molvízar; Monachil; Montefrío; Montejícar; Montillana; Moraleda de
Zafayona; Morelábor; Motril; Murtas; Nevada; Nigüelas; Nívar; Ogíjares; Orce; Órgiva; Otívar; Otura; Padul;
Pampaneira; Pedro Martínez; Peligros; Peza , La; Píñar; Pinar , El; Pinos Genil; Pinos Puente; Polícar; Polopos;
Pórtugos; Puebla de Don Fadrique; Pulianas; Purullena; Quéntar; Rubite; Salar; Salobreña; Santa Cruz del
Comercio; Santa Fe; Soportújar; Sorvilán; Taha , La; Torre-Cardela; Torvizcón; Trevélez; Turón; Ugíjar; Valle ,
El; Valle del Zalabí; Válor; Vegas del Genil; Vélez de Benaudalla; Ventas de Huelma; Villamena; Villanueva de
las Torres; Villanueva Mesía; Víznar; Zafarraya; Zagra; Zubia , La; Zújar;
Huelva: Alájar; Aljaraque; Almendro , El; Almonaster la Real; Almonte; Alosno; Aracena; Aroche;
Arroyomolinos de León; Ayamonte; Beas; Berrocal; Bollullos Par del Condado; Bonares; Cabezas Rubias; Cala;
Calañas; Campillo , El; Campofrío; Cañaveral de León; Cartaya; Castaño del Robledo; Cerro de Andévalo , El;
Chucena; Corteconcepción; Cortegana; Cortelazor; Cumbres de Enmedio; Cumbres de San Bartolomé;
Cumbres Mayores; Encinasola; Escacena del Campo; Fuenteheridos; Galaroza; Gibraleón; Granada de RíoTinto , La; Granado , El; Higuera de la Sierra; Hinojales; Hinojos; Huelva; Isla Cristina; Jabugo; Lepe; Linares de
la Sierra; Lucena del Puerto; Manzanilla; Marines , Los; Minas de Riotinto; Moguer; Nava , La; Nerva; Niebla;
Palma del Condado , La; Palos de la Frontera; Paterna del Campo; Paymogo; Puebla de Guzmán; Puerto
Moral; Punta Umbría; Rociana del Condado; Rosal de la Frontera; San Bartolomé de la Torre; San Juan del
Puerto; San Silvestre de Guzmán; Sanlúcar de Guadiana; Santa Ana la Real; Santa Bárbara de Casa; Santa
Olalla del Cala; Trigueros; Valdelarco; Valverde del Camino; Villablanca; Villalba del Alcor; Villanueva de las
Cruces; Villanueva de los Castillejos; Villarrasa; Zalamea la Real; Zufre;
Jaén: Albanchez de Mágina; Alcalá la Real; Alcaudete; Aldeaquemada; Andújar; Arjona; Arjonilla; Arquillos;
Arroyo del Ojanco; Baeza; Bailén; Baños de la Encina; Beas de Segura; Bedmar y Garcíez; Begíjar; Bélmez de
la Moraleda; Benatae; Cabra del Santo Cristo; Cambil; Campillo de Arenas; Canena; Carboneros; Cárcheles;
Carolina , La; Castellar; Castillo de Locubín; Cazalilla; Cazorla; Chiclana de Segura; Chilluévar; Escañuela;
Espelúy; Frailes; Fuensanta de Martos; Fuerte del Rey; Génave; Guardia de Jaén , La; Guarromán; Higuera de
Calatrava; Hinojares; Hornos; Huelma; Huesa; Ibros; Iruela , La; Iznatoraf; Jabalquinto; Jaén; Jamilena; Jimena;
Jódar; Lahiguera; Larva; Linares; Lopera; Lupión; Mancha Real; Marmolejo; Martos; Mengíbar; Montizón;
Navas de San Juan; Noalejo; Orcera; Peal de Becerro; Pegalajar; Porcuna; Pozo Alcón; Puente de Génave;
Puerta de Segura , La; Quesada; Rus; Sabiote; Santa Elena; Santiago-Pontones; Santiago de Calatrava;
Santisteban del Puerto; Santo Tomé; Segura de la Sierra; Siles; Sorihuela del Guadalimar; Torre del Campo;
Torreblascopedro; Torredonjimeno; Torreperogil; Torres; Torres de Albánchez; Úbeda; Valdepeñas de Jaén;
Vilches; Villacarrillo; Villanueva de la Reina; Villanueva del Arzobispo; Villardompardo; Villares , Los;
Villarrodrigo; Villatorres;
Málaga: Alameda; Alcaucín; Alfarnate; Alfarnatejo; Algarrobo; Algatocín; Alhaurín de la Torre; Alhaurín el
Grande; Almáchar; Almargen; Almogía; Álora; Alozaina; Alpandeire; Antequera; Árchez; Archidona; Ardales;
Arenas; Arriate; Atajate; Benadalid; Benahavís; Benalauría; Benalmádena; Benamargosa; Benamocarra;
Benaoján; Benarrabá; Borge , El; Burgo , El; Campillos; Cañete la Real; Canillas de Aceituno; Canillas de
Albaida; Carratraca; Cartajima; Cártama; Casabermeja; Casarabonela; Casares; Coín; Colmenar; Comares;
Cómpeta; Cortes de la Frontera; Cuevas Bajas; Cuevas de San Marcos; Cuevas del Becerro; Cútar; Estepona;
Faraján; Frigiliana; Fuengirola; Fuente de Piedra; Gaucín; Genalguacil; Guaro; Humilladero; Igualeja; Istán;
Iznate; Jimera de Líbar; Jubrique; Júzcar; Macharaviaya; Málaga; Manilva; Marbella; Mijas; Moclinejo;
Mollina; Monda; Montejaque; Nerja; Ojén; Parauta; Periana; Pizarra; Pujerra; Rincón de la Victoria; Riogordo;
Ronda; Salares; Sayalonga; Sedella; Sierra de Yeguas; Teba; Tolox; Torremolinos; Torrox; Totalán; Valle de
83
Abdalajís; Vélez-Málaga; Villanueva de Algaidas; Villanueva de Tapia; Villanueva del Rosario; Villanueva del
Trabuco; Viñuela; Yunquera;
Sevilla: Aguadulce; Alanís; Albaida del Aljarafe; Alcalá de Guadaíra; Alcalá del Río; Alcolea del Río; Algaba
, La; Algámitas; Almadén de la Plata; Almensilla; Arahal; Aznalcázar; Aznalcóllar; Badolatosa; Benacazón;
Bollullos de la Mitación; Bormujos; Brenes; Burguillos; Cabezas de San Juan , Las; Camas; Campana , La;
Cañada Rosal; Cantillana; Carmona; Carrión de los Céspedes; Casariche; Castilblanco de los Arroyos; Castilleja
del Campo; Castillo de las Guardas , El; Cazalla de la Sierra; Constantina; Coria del Río; Coripe; Coronil , El;
Corrales , Los; Cuervo de Sevilla , El; Dos Hermanas; Écija; Espartinas; Estepa; Fuentes de Andalucía; Garrobo
, El; Gelves; Gerena; Gilena; Gines; Guadalcanal; Guillena; Herrera; Huévar del Aljarafe; Isla Mayor; Lantejuela
, La; Lebrija; Lora de Estepa; Lora del Río; Luisiana , La; Madroño , El; Mairena del Alcor; Mairena del Aljarafe;
Marchena; Marinaleda; Martín de la Jara; Molares , Los; Montellano; Morón de la Frontera; Navas de la
Concepción , Las; Olivares; Osuna; Palacios y Villafranca , Los; Palomares del Río; Paradas; Pedrera; Pedroso
, El; Peñaflor; Pilas; Pruna; Puebla de Cazalla , La; Puebla de los Infantes , La; Puebla del Río , La; Real de la
Jara , El; Rinconada , La; Roda de Andalucía , La; Ronquillo , El; Rubio , El; Salteras; San Juan de Aznalfarache;
San Nicolás del Puerto; Sanlúcar la Mayor; Santiponce; Saucejo , El; Sevilla; Tocina; Tomares; Umbrete;
Utrera; Valencina de la Concepción; Villamanrique de la Condesa; Villanueva de San Juan; Villanueva del
Ariscal; Villanueva del Río y Minas; Villaverde del Río; Viso del Alcor;
84
Screens of Databases
Figure 20 Database on olive farming and olive industry in the Mediterranean countries: survey
85
Figure 21 Database on olive farming and olive industry in the Mediterranean countries: desk analysis
86

albanchez almeria information spain

Transcription

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