GPFARM plant model parameters: complications of varieties and the genotype x environment interaction in wheat

The USDA-ARS Great Plains Framework for Agricultural Resource Management (GPFARM) decision support system was developed to assist Great Plains producers in making economically viable and environmentally sound strategic plans for whole farm and ranch systems. A major user requirement for GPFARM is to supply the default plant parameters required to simulate crop growth. Developing this plant parameter database is difficult because varietal differences, caused by a genotype by environment (G x E) interaction, increases parameter uncertainty and variability. This article examines species-based plant parameter sets for simulating winter wheat (Triticum aestivum L.) yield responses, explores the significance of the G x E interaction on simulating varietal grain yield, and investigates whether simple adjustments to a species-based plant parameter database can improve simulation of varietal differences across environments. Three plant parameter sets were evaluated against observed yield data for six locations in eastern Colorado: (1) the Default parameter set used best estimates from EPIC-based plant parameter databases, (2) the Dryland Agroecosystems Project (DAP) parameter set further calibrated the default plant parameters against observed yield data for Colorado, and (3) the Theory parameter set modified DAP parameters based on whether irrigated or dryland conditions were simulated. The Theory parameter set simulated yield the best when pooling varieties over environments and locations. However, no parameter set could simulate all the different varietal yield responses to environmental conditions (irrigated or dryland) due to the diverse G X E interactions. The Theory parameter set best simulated the wheat variety TAM 107 across diverse locations, with little bias for either irrigated or dryland conditions. Simple adjustments to a few plant parameters based on whether dryland or irrigated conditions were simulated improved the species-based plant parameter approach used in GPFARM. However, until a better mechanistic representation of the G x E interaction is incorporated into existing plant growth models, opportunities for improving yield response to environmental conditions and management will be limited.