Application of the DSSAT-CERES-Maize Model for

Application of the CERES-Maize Model
for Climate Change Impact Assessment
and Decision Support in Corn
Production
Dr. Orlando Balderama
Isabela State University
Echague, Isabela Philippines
Paper presented at ICT- Asia Conference and
Workshop, 25-26 May, 2015, SEARCA College
Los Banos Laguna
Presentation Outline
•
•
•
•
•
•
Background
Objectives
Methodology
Results and Discussion
Conclusion
Way Forward: Development of Farmer
Decision Support System for Corn Farmers
Background, Isabela Philippines
Highest Corn
Producer, 25% of
National Production
Most Area of Corn
Farm, 26% of
National Area
Agricultural lands of Isabela by Agro-zone and
its vulnerability to climate change impact
A result of GIS analysis
showed that 43% or 13
million hectares of the
country will be under
dryland environment as a
consequence of climate
change (Obien, 2008).
Isabela province is mostly
affected with 432,916
hectares
El Niño in 2010
• 4 billion pesos
drought damage
in agriculture
• 2 billion pesos in
Corn
• Isabela province
suffered the
biggest
Climate Change Projection 2020 and 2050,
Isabela Philippines
% change in Rainfall
Simulated DecFeb
Year
Benchmark
year (19912000)
MarMay
JunAug
Increase in Temperature
Sep- DecNov Feb
MarMay
JunAug
SepNov
-
-
-
-
-
-
-
-
2020
Projections
3.9
-8.6
5.1
13.5
0.8
0.9
0.9
0.8
2050
Projections
25.1
-29.2
8.7
1.7
2
2.1
2.1
1.9
Source: PAGASA, 2011
Smarter Agriculture
The Philippine National Program on Weather and Crop
Forecasting
OBJECTIVE
COMPONENTS
Objectives
Overall goal of the study is to assess impacts of climate
change to corn production in Isabela, Philippines using
Ceres-Maize simulation model.
Specific objectives are as follows:
• Determine genetic coefficients of Dekalb 9132 corn
cultivar (long duration hybrid corn, 90% of farmers uses
this variety);
• Validate the capability of the model in flood plains and
upland corn production areas;
• Estimate corn production considering future climate
change environment scenarios
Methodology
• Site Selection and setting up of field experiment
(dry and wet season)
• Setting up of weather monitoring station
• DSSAT Model Calibration and Validation
• Analysis of production performance under
various climate change scenario
• Develop a Farmer Decision Support System
Framework
Site and Field Experimental Data
• There were four (4) field sites established in each of the three
agro-zones representing in Isabela to represent flood plain, rolling
and hilly corn areas;
• Automatic weather stations were installed in each site to monitor
daily climate data. The minimum input weather data required to run
the model are the rainfall, minimum and maximum temperature and
solar radiation;
• Soil profile in each site were characterize. The locations of these
field sites were as follows:
1)Villa Imelda - rolling terrain, with rain gauge, humidity, temp,
wind speed & direction, and 3 soil moisture sensors
2)Sindon Bayabo - hilly terrain, with 3 soil moisture sensors
3)Cabisera 10 - flood plain with 3 soil moisture sensors
4)CVRC San Felipe - Flood plain with rain gauge and 3 soil
moisture sensors
Physical and Chemical Properties of
Corn Farms in the Project Site
Location
CVRC,
San Felipe
Cabisera 10
Villa Imelda
Sindon
Bayobo
Corn Farmer
Research Center
Charlito Servilla
Eulalio Paredes
Amado Bolda
Agrozone
Alluvial plain
Alluvial plain
Rolling
Hilly
Slope (%)
≥1
≥1
8-18
18-30
Soil depth
(cm)
>100
>100
>100
65
Texture
Silty clay loam
Loam
Clay
Sandy clay loam
over clay
Soil reaction
(pH)
5.6 (MA)
4.84 (VSA)
4.66 (VSA)
5.0 (VSA)
Organic
matter (%)
0.41 (VL)
0.16 (VL)
0.59 (VL)
0.05 (VL)
Phosphorus
(mg/kg)
8.3 (L)
6.99 (L)
15.96 (M)
0.4 (VL)
Potassium
(cmol/kg)
0.18 (VL)
0.24 (L)
0.3 (L)
0.36 (L)
MA = medium acid
VL = very low
VSA= very strongly acid
L= low
M= medium
Project Site
Instrumentation and Data Monitoring
Experimental layout for rainfed and
irrigated (500 sq.m. at CVRC, San Felipe,
Ilagan City, Isabela)
Seed Selection and Crop Data Monitoring
• Maize hybrid, Dekalb 9132, was selected for the calibration that
represents highly productive simple hybrids grown in the area;
• Local daily climate data and soil information for the each site were
gathered and monitored;
• Phenological events were recorded in reference to date of
planting;
• Biomass data was gathered every 10 days after emergence until
harvest by destructive random sampling from the sampling area.
Samples were oven dried for 48 hours at 70°C and weighed. A
harvest area of 5 meter by 3 meter was designated in the middle of
each plot.
Results and Discussion
Derivation/Calibration of Crop Coefficient for Dekalb 9132
Definition
Variable
1 Thermal time from seedling emergence to
the end of the juvenile phase
P1
degree days above TBASE 266.4
during which the plant is
not responsive to
changes in photoperiod
2 Extent to which development is delayed for
each hour increase in photoperiod above
the longest photoperiod at which
development proceeds at a maximum rate
(which is considered to be 12.5 hours)
P2
expressed as days
3 Thermal time from silking to physiological
maturity
P5
4 Maximum possible number of kernels per
plant
G2
expressed in degree days 850.3
above a base
temperature of 8øC
928
5 Kernel filling rate during the linear grain
filling stage and under optimum conditions
G3
mg/day
6 Phylochron interval; the interval in thermal
time between successive leaf tip
appearances
Unit
PHINT degree days
Coeff.
0.114
16.47
45
Statistical tools to assess the capability of the
model (comparison of actual vs. simulated)
R2
d-Stat.
CVRC (Irrigated)
0.94
0.92
CVRC (Rainfed)
0.79
0.72
Cabisera 10
0.92
0.88
Villa Imelda
0.71
0.86
Sindon Bayabo
0.91
0.95
Site
Observed and predicted phenological
events from the five plots
Emergence
SITE
Obs
Sim
Beginning of
Grain Filling
Silking
Obs
Sim
Obs
Sim
Physiological
Maturity
Obs
Sim
CABISERA 10
3
4
57
52
66
66
115
106
DA CVRC
(IRRIGATED)
5
6
66
68
74
78
110
111
DA CVRC
(RAINFED)
5
6
66
68
74
78
110
111
VILLA IMELDA
3
4
58
58
69
66
115
109
SINDON BAYABO
3
4
58
56
65
69
114
106
Simulated and observed grain yield during
the wet season cropping, kg/ha
SITE
PLANTING DATE
OBSERVED SIMULATED
CVRC IRRIGATED
June 12 2014
8800
9163
CVRC RAINFED
June 12 2014
8213
8777
CABISERA10
June 2 2014
9867
10202
VILLA IMELDA
July 3 2014
5190
5024
June 27 2014
7167
7566
SINDON BAYABO
Modeled and Actual Biomass and
Yield (Irrigated Experiment)
Dry Aboveground Biomass, Kg/ha
25000
20000
15000
10000
5000
0
50
-5000
100
Days after Planting
150
Modeled and Actual Biomass and
Yield (Rainfed Experiment)
Dry Aboveground Biomass, Kg/ha
7000
6000
5000
4000
3000
2000
1000
0
-1000
50
100
Days after Planting
150
Yield projections for 2014-2015 El Nino for
Dec15 and Jan1 Planting (Presented at El
Nino Forum, Nov. 2014)
Normal year (1991-2000)
2014-2015 (projected)
4500
4400
Yield, kg/ha
4300
4200
4100
4000
3900
3800
3700
3600
Dec 15
Jan 1
Yield projections for Dec 1 and Jan 1 planting dates using normal year
(1991-2000) and 2014-2015 projected weather data
Yield projections for wet and dry season
planting using normal year (1991-2000)
Yield (Normal year)
12000
Yield, kg/ha
10000
8000
6000
4000
2000
0
Dry season (Jan 1)
Wet season (Jun 1)
Projected dry season yield for the year
2020 and 2050
Yield, kg/ha
Projected yield
4500
4000
3500
3000
2500
2000
1500
1000
500
0
Normal year (19912000)
2020 Projections
2050 Projections
The irrigated (left) and rainfed (right)
plots 50 days after planting (Feb 26, 2014)
The irrigated (left) and rainfed (right)
plots during harvest 122 days after
planting (May 9, 2014)
The harvested yield of corn from the
irrigated and rainfed plots.
CONCLUSIONS and RECOMMENDATIONS
• Calibration of these coefficients was successful as
manifested by close agreement between actual and
simulated biomass and phenological events;
• The model predicted the actual corn biomass production
and phenological stages as indicated by statistical
analysis performed with acceptable error;
• Without intervention, corn yield would be reduced by
up to 44% in 2020 and 35% in 2050 due to change in
rainfall amount and rise in temperature;
• A roadmap should be develop with institutional plan to
develop a farmer decision support system using our R&D
result
Development and Application
of Farmer Decision Support
System (FDSS): Way Forward
Proposed FDSS Framework
FDSS Architecture
Mga impormasyon makukuha sa FDSS by SMS
(Goal: Increased yield by at least 30%)
• Pinakamagandang
araw ng pagtatanim
• Pinakamainam na
dami at petsa ng
paglalagay ng abono
• Dami at petsa ng
patubig
• Kailan ang pagsibol,
pagusbong, paglaki
ng halaman
• Pagtantya ng dami
ng ani