Wednesday, November 4, 2009
Convention Center, Exhibit Hall BC, Second Floor
The soybean (Glycine max., L. Merr) growth model (SoySim) combines existing approaches for photosynthesis simulation (Farquhar type framework) and biomass accumulation and partitioning (similar to the WOFOST model) with several new components: simulation of flowering based on floral induction and post-induction processes, leaf area index simulation with a logistic function, integration of canopy assimilation using a beta function, and yield simulation based on assimilate- and phenology-driven seed number and individual seed growth. Total above ground dry matter (TDM) and seed yield simulations were validated against the observed field data from Lincoln (NE), Mead (NE), Whiting (IA), and West Lafayette (IN). In each of these field studies representing X site-year-genotype observations, agronomic management was optimized to achieve growth with minimal limitations from pests, nutrients, or other controllable factors. Simulated TDM and yield (13% m.c.) at the validation sites with the SoySim model had an RMSE of 1.37 and 0.46 Mg ha-1, respectively. The proposed model has relatively small requirements for genotype-specific input parameters and yet provides reasonable accuracy in simulating growth and yield under optimum growth conditions across a wide range of geography, sowing dates, and yields, which ranged from 2.5 Mg ha-1 to 6.4 Mg ha-1. The potential of SoySim model as a field management tool for optimizing soybean yield through crop management decisions reflects its simpler input requirements and robustness in simulation of total above ground dry matter and yield across wide range of yields in the corn-belt region of the Unites States. For practical application, the model is also equipped with the forward looking capability of phenology and growth simulation and coupled with an irrigation scheduling decision aid.