International Development 1982 – 1993 • No continuation of financial support since 1993 University of Hawaii (Dr. Uehara & Dr. Tsuji) • Michigan State University • University of Florida • International Fertilizer Development Center (IFDC)
countries based on a systems analysis approach To use computer models and data in agricultural sciences in contrast to a traditional agronomic approach (trial and error) Understanding Prediction Control & Manage (H. Nix, 1983)
plants, soil, weather and management interactions • Morphological and phenological development • Photosynthesis, respiration, partitioning and growth • Root water and nitrogen uptake • Stress effects on growth processes Predict growth, yield, timing (Outputs)
all programs, tools and utilities 15+ programs, utilities and tools • Mixture of languages, including Fortran, Visual Basic, Delphi, Excel, etc. Experimental, crop, weather, soil, pest, genotype and economic data files Developed by scientific programmers
simulation model PNUTGRO : Peanut simulation model BEANGRO : Common bean simulation model • 1989 CROPGRO : A generic grain legume model for soybean, peanut, common bean, chickpea, cowpea and other crops • 1994 + 1998
Model CROPGRO module for soybean, peanut, common bean and other grain legumes CROPGRO module for cotton CROPGRO module for tomato, bell pepper, green beans and cabbage CROPGRO module for bahia and brachiaria
CERES modules for maize, rice, sorghum and millet CROPSIM-CERES for wheat and barley Substor module for potato CANEGRO module for sugarcane IXIM module for maize ORYZA2000 for rice APSIM-Asseng for wheat
parameters and functions • Defines the response of a crop to environmental conditions, including temperature, solar radiation, CO2 and photoperiod, as well as plant composition and other functions and parameters.
coefficients • Defines coefficients for groups of cultivars that show similar behavior and response to environmental conditions. Cultivar coefficients • Cultivar and variety specific coefficients, such as photothermal days to flowering & maturity, sensitivity to photoperiod, seed size, etc.
and soil characteristics • Weather (daily) • Management • Cultivar characteristics Model calibration for local variety Model evaluation with independent data set Can be used to perform “what-if” experiments
3500 4000 25 27 29 31 33 Max Temp Average (C) Yield (kg/ha) Simulated Yields 0 500 1000 1500 2000 2500 3000 3500 4000 25 27 29 31 33 Max Temp Average (C) Yield (kg/ha) Relationship between seasonal average max temperature and soybean yield: Observed (Georgia yield trials) and Simulated
models Increasing interest by granting agencies to support model improvements and applications Decrease in personnel due to retirements and career changes Need for collaboration among modeling groups?