EVALUATION AND IMPROVEMENT OF CROP MODELS USING REGIONAL CULTIVAR TRIAL DATA

Multi-location cultivar trials are a primary source of information for plant breeding and agronomic research. To use cultivar trial data to evaluate model performance, there are many issues that need to be resolved for determining model inputs (e.g., weather, soil characteristics, cultivar coefficients, and cultural practices) as well as appropriate methods of analysis for model evaluation. Our objectives were to better understand methodological issues, limitations, and potential uses of cultivar trial data for evaluation and improvement of crop models. Cultivar trial data were obtained from “The Soybean Uniform Tests: Northern States and Southern States” publications for over 60 locations from the years 1970 to 1990. The plant process model SOYGRO Version 5.42 was used to simulate the cultivar trial results. The original SOYGRO model had a large bias in simulation of yield with latitude. Different hypotheses of temperature effects on photosynthesis, pod addition rate, and seed growth were tested to explain bias in model simulations. Results showed that the mean square error of prediction could be decreased to 52% of its original value by changing the temperature functions. While calibration removed the model bias with latitude and temperature, the model underpredicted yield in high and overpredicted yield in low yielding environments. This suggests that other factors such as soil fertility and pest problems not included in the model are important. The use of a large number of widely-located cultivar trials succeeded in testing the model over a very broad range of weather conditions, was useful in refining relationships originally used in the model, and has led to improvements in a subsequent soybean model release, the CROPGRO-Soybean model.