ciat crop modeling_18may11
DESCRIPTION
TRANSCRIPT
Ricky RobertsonEnvironment andProduction TechnologyDivision
InternationalFood PolicyResearch Institute
at CIAT: 18 May 2011
Process-based crop simulation models in support of global economic modeling
soiland water
plant above ground
plant below ground
weather
Processed based crop models try to mimic how plants respond to their environment
Sunshine
Rain/Irrigation
Soil properties
Growth of roots/stems/leaves/fruits
Water use/stress
Nutrient extraction
This presentation draws from experience with the DSSAT family of crop models from the user perspective
DSSAT consists of several framework pieces working together . . .
soil and water
plant above ground
plant below ground
weather
. . . each of which require parameters and data
soil characteristicsinitial conditions
variety attributes
plantingdate
real or generated
The outputs can be thought of in two ways
For economic modeling, “response” dominates, but sometimes “dynamic” is important (water management)
Dynamic: each day’s growth, water usage, etc.
Response: end results as determined by inputs
HWAH = 4400.30NUCM = 264.95NLCM = 113.41ETCM = 773.63
By running repeatedly, such models can be run globally when data are available
All of the details must be specified for each location
HC27rainfed
90 day spin up25% moisture content
maize990001
“April”
CSIRO/A2/2050
HC27rainfed
90 day spin up25% moisture content
maize990001
“April”
CSIRO/A2/2050
By running repeatedly, such models can be run globally when data are available
All of the details must be specified for each location
As users, there are interesting “what ifs” that can be done
Changes in yield under different climates (rainfed maize 990001; baseline/2000 to CSIRO/A1/2050/369ppm
CO2)
Highest yielding variety by location(irrigated rice, choosing among DSSAT generic
varieties)
The robustness or accuracy of results depend on the pieces
As a user, I can only look for obviously strange results
Location specific data
Environment models
Plant models
soiland water
plant above ground
plant below ground
weather
Data quality hinges on availability, geographic coverage, and consistency
soiland water
plant above ground
plant below ground
weather
Downscaling of climate data to local scales (to include sunshine and rainfall distribution; microclimates)
Soils probably provide the greatest opportunity for improvement
Users can make some observations to help model developers
soiland water
plant above ground
plant below ground
weather
Global scale modeling sometimes exposes strange behavior (e.g., root
water extraction in rice)
Calibration of varieties depends on quality and variability in experimental data (e.g., maize yield is highest
around Ames, IA)
FPU level yield and area projections
FPU boundaries
The yield projections are incorporated into the IMPACT economic model at a regional level
IMPACT runs on geographical units known as Food Production Units or FPUs
GCM/SRES scenario climate results are down scaled to 0.5 degree/5 minute resolution
FPU level yield and area projections
FPU boundaries
2000 June average minimum temperature
2050 CSIRO/A2 June average rainfall
Monthly averages are from Thornton and Jones’s FutureClim; daily weather is from DSSAT’s SIMMETEO
Planting months are chosen based on current and future climate conditions (a rule-based system)
FPU level yield and area projections
FPU boundaries
2000 Rainfed planting month
2050 CSIRO/A2 Rainfed planting month
Soils are represented by 27 generic soil profiles based on the harmonized FAO soil datasets
FPU level yield and area projections
FPU boundaries
Soil profiles color coded by location
Soil data must be matched to DSSAT-style soil profiles
The remaining inputs fall under management practices
FPU level yield and area projections
FPU boundaries
Choice of crop variety
Rainfed versus irrigated sources of water
Planting densities, row spacing, and transplanting details
Fertilizer types, amounts, and application dates
DSSAT generates projected yields for each location
FPU level yield and area projections
FPU boundaries
2000 Rainfed maize yield
2050 CSIRO/A2 Rainfed maize yield
Parallelization of the DSSAT runs results in major time savings
FPU level yield and area projections
FPU boundaries
roughly 1½ weeks on 80 processors
(5 crops,rainfed/irrigated,13 climates,15 arc-minute resolution)
serialparallelized
would takeroughly 96 weeks on a single processor
SPAM 2000 areas are used to weight the projected yields when aggregating to FPUs
FPU level yield and area projections
FPU boundaries
Rainfed maize physical area in 2000
The Spatial Production Allocation Model data are available from http://mapspam.info/
Vector FPU boundaries are placed over top of the raster yield projections
FPU level yield and area projections
FPU boundaries
2000 Rainfed maize yield with FPU boundariesin South Asia
Projected yields from DSSAT are aggregated up to the FPU-level for use in IMPACT
FPU level yield and area projections
FPU boundaries
By crop and rainfed/irrigated...
Find total SPAM area within FPU
Find total production (SPAM area × DSSAT yield) within FPU
Compute area-weighted-average yield as total production / total area