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Process-based crop simulation models in support of global economic modeling,[object Object],weather,[object Object],plant above ground,[object Object],soil,[object Object],and water,[object Object],plant below ground,[object Object],Ricky Robertson,[object Object],Environment and,[object Object],Production Technology,[object Object],Division,[object Object],International,[object Object],Food Policy,[object Object],Research Institute,[object Object],at CIAT: 18 May 2011,[object Object]
Processed based crop models try to mimic how plants respond to their environment,[object Object],Growth of roots/stems/leaves/fruits,[object Object],Water use/stress,[object Object],Nutrient extraction,[object Object],Sunshine,[object Object],Rain/Irrigation,[object Object],Soil properties,[object Object],This presentation draws from experience with the DSSAT family of crop models from the user perspective,[object Object]
DSSAT consists of several framework pieces working together . . .,[object Object],weather,[object Object],plant above ground,[object Object],soil and water,[object Object],plant below ground,[object Object]
. . . each of which require parameters and data,[object Object],real or generated,[object Object],variety attributes,[object Object],soil characteristics,[object Object],initial conditions,[object Object],planting,[object Object],date,[object Object]
The outputs can be thought of in two ways,[object Object],Dynamic: each day’s growth, water usage, etc.,[object Object],Response: end results as determined by inputs,[object Object],HWAH = 4400.30,[object Object],NUCM =  264.95,[object Object],NLCM =  113.41,[object Object],ETCM =  773.63,[object Object],For economic modeling, “response” dominates, but sometimes “dynamic” is important (water management),[object Object]
By running repeatedly, such models can be run globally when data are available,[object Object],CSIRO/A2/2050,[object Object],maize,[object Object],990001,[object Object],HC27,[object Object],rainfed,[object Object],90 day spin up,[object Object],25% moisture content,[object Object],“April”,[object Object],All of the details must be specified for each location,[object Object]
By running repeatedly, such models can be run globally when data are available,[object Object],CSIRO/A2/2050,[object Object],maize,[object Object],990001,[object Object],HC27,[object Object],rainfed,[object Object],90 day spin up,[object Object],25% moisture content,[object Object],“April”,[object Object],All of the details must be specified for each location,[object Object]
As users, there are interesting “what ifs” that can be done,[object Object],Changes in yield under different climates (rainfed maize 990001; baseline/2000 to CSIRO/A1/2050/369ppm CO2),[object Object],Highest yielding variety by location,[object Object],(irrigated rice, choosing among DSSAT generic varieties),[object Object]
The robustness or accuracy of results depend on the pieces,[object Object],weather,[object Object],Location specific data,[object Object],Environment models,[object Object],Plant models,[object Object],plant above ground,[object Object],soil,[object Object],and water,[object Object],plant below ground,[object Object],As a user, I can only look for obviously strange results,[object Object]
Data quality hinges on availability, geographic coverage, and consistency,[object Object],weather,[object Object],Downscaling of climate data to local scales (to include sunshine and rainfall distribution; microclimates),[object Object],Soils probably provide the greatest opportunity for improvement,[object Object],plant above ground,[object Object],soil,[object Object],and water,[object Object],plant below ground,[object Object]
Users can make some observations to help model developers,[object Object],weather,[object Object],Global scale modeling sometimes exposes strange behavior (e.g., root water extraction in rice),[object Object],Calibration of varieties depends on quality and variability in experimental data (e.g., maize yield is highest around Ames, IA),[object Object],plant above ground,[object Object],soil,[object Object],and water,[object Object],plant below ground,[object Object]
The yield projections are incorporated into the IMPACT economic model at a regional level,[object Object],IMPACT runs on geographical units known as Food Production Units or FPUs,[object Object]
GCM/SRES scenario climate results are down scaled to 0.5 degree/5 minute resolution,[object Object],2000 June average minimum temperature,[object Object],2050  CSIRO/A2 June average rainfall,[object Object],Monthly averages are from Thornton and Jones’s FutureClim; daily weather is from DSSAT’s SIMMETEO,[object Object]
Planting months are chosen based on current and future climate conditions (a rule-based system),[object Object],2000 Rainfed planting month,[object Object],2050 CSIRO/A2 Rainfed planting month,[object Object]
Soils are represented by 27 generic soil profiles based on the harmonized FAO soil datasets,[object Object],Soil profiles color coded by location,[object Object],Soil data must be matched to DSSAT-style soil profiles,[object Object]
The remaining inputs fall under management practices,[object Object],Choice of crop variety,[object Object],Rainfed versus irrigated sources of water,[object Object],Planting densities, row spacing, and transplanting details,[object Object],Fertilizer types, amounts, and application dates,[object Object]
DSSAT generates projected yields for each location,[object Object],2000 Rainfed maize yield,[object Object],2050 CSIRO/A2 Rainfed maize yield,[object Object]
Parallelization of the DSSAT runs results in major time savings,[object Object],serial,[object Object],parallelized,[object Object],roughly 1½ weeks on 80 processors,[object Object],(5 crops,,[object Object],rainfed/irrigated,,[object Object],13 climates,,[object Object],15 arc-minute resolution),[object Object],would take,[object Object],roughly 96 weeks on a single processor,[object Object]
SPAM 2000 areas are used to weight the projected yields when aggregating to FPUs,[object Object],Rainfed maize physical area in 2000,[object Object],The Spatial Production Allocation Model data are available from http://mapspam.info/,[object Object]
Vector FPU boundaries are placed over top of the raster yield projections,[object Object],2000 Rainfed maize yield with FPU boundaries,[object Object],in South Asia,[object Object]
Projected yields from DSSAT are aggregated up to the FPU-level for use in IMPACT,[object Object],By crop and rainfed/irrigated...,[object Object],Find total SPAM area within FPU,[object Object],Find total production (SPAM area × DSSAT yield) within FPU,[object Object],Compute area-weighted-average yield as total production / total area,[object Object]

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Editor's Notes

  1. Nutrient composition of plant parts.
  2. Nutrient composition of plant parts.
  3. Nutrient composition of plant parts.
  4. Nutrient composition of plant parts.
  5. Nutrient composition of plant parts.
  6. Nutrient composition of plant parts.
  7. Nutrient composition of plant parts.
  8. Nutrient composition of plant parts.
  9. Nutrient composition of plant parts.
  10. Nutrient composition of plant parts.
  11. Nutrient composition of plant parts.
  12. Nutrient composition of plant parts.
  13. Nutrient composition of plant parts.
  14. Nutrient composition of plant parts.
  15. Nutrient composition of plant parts.
  16. Nutrient composition of plant parts.
  17. Nutrient composition of plant parts.
  18. Nutrient composition of plant parts.
  19. Nutrient composition of plant parts.