Application of the CERES-Wheat model for within-season prediction of winter wheat yield in the United Kingdom

Mechanistic crop growth models have many potential uses for crop management. These models can aid in preseason and within-season management decisions for cultural practices such as fertilizer and irrigation applications and pest and disease management. When making these management decisions, maximizing yield and net return as a function of inputs and production costs is one of the fundamental goals. Reliable yield forecasting within the growing season would enable improved planning ad more efficient management of grain production, handling, and marketing. The objective of this study was to determine if the dynamic simulation model CERES-Wheat could be used to forecast final grain yield and crop biomass within the growing season for environmental and management conditions in the United Kingdom (UK). Experimental data for three seasons and four sites were used for model calibration and evaluation. A stochastic approach was applied, based on multiple years of weather data generated with the weather generator SIMMETEO. Yield forecasts were conducted for five different developmental stages within the growing season. For each forecast date, observed weather data were used up to the forecast date and supplemented with generated weather data until final harvest was predicted. Eighty-nine different sequences of generated weather data were used for each forecast. Predicted grain yield had a root mean square difference (RMSD) ranging from 0.95 t ha(-1) for the first forecast date to 0.68 t ha(-1) for the final forecast date while the RMSD for total predicted biomass ranged from 3.59 to 2.09 t ha-1. An analysis of predicted final grain yield and biomass for all forecast dates showed a significant difference for the first three sample dates up to flag leaf appearance. No significant difference was found for the forecasts conducted at the anthesis stage (paired t test: p = 0.73 for grain yield and p = 0.32 for biomass) and milk stage (p = 0.79 for grain yield and p = 0.22 for biomass). This study showed that using only stochastically generated weather data to substitute measured data could provide a reliable forecast for wheat (Triticum aestivum L.) grain yield starting in June until the remainder of the season for conditions in the UK.