Crop Growth Models with Fewer Cultivar-Specific Inputs to Enhance Use in Research and Decision Support.

Simulation of crop phenology is critical for accurate estimates of yield. Some models require several genotype-specific coefficients (GSCs) as input to get phenology right, and often these coefficients are difficult to estimate and are not stable across environments for the same cultivar or hybrid. Other models were developed with the goal of using as few genotype-specific coefficients as possible, and ensuring that those required are widely available. This presentation will argue that the latter type of models is crucial for enhancing and facilitating use of crop simulation models for research and decision-support in commercial crop production, and we provide examples in support of this position. Use of several GSCs is problematic for the following reasons: (i) rapid turnover of commercial cultivars and hybrids makes it difficult to keep up with coefficients for newly released varieties, (ii) commercial seed companies typically do not measure or provide information about such coefficients for their products, (iii) coefficients calibrated for a given cultivar in a given location or region may not be useful in another environment, and (iv) crop models with GSCs to evaluate effects of climate change on crop yield must be used with caution because environmental conditions used to develop and calibrate the GSCs are quite different from future climatic conditions to be simulated. This presentation will describe the GSCs and the underlining mechanisms in Hybrid-Maize and SoySim, which are two examples of crop models with minimum number of GSCs compared to several other widely used crop models. Evaluation against measured data from high-yield experimental sites in the U.S. Corn Belt demonstrate that Hybrid-Maize and SoySim had a similar or better accuracy in simulating seed yield compared to these other models despite using fewer GSCs. These models also were robust in simulating growth and seed yield response to yearly weather variation and different management such as plant population density and planting date, which is important for use in research and decision support.