Ciat crop modeling_18may11

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  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Nutrient composition of plant parts.
  • Ciat crop modeling_18may11

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

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