The overall goal of this research was to determine how the inclusion of weather forecasting impacts in-season crop model predictions. This forecasted weather data along with the current and historic (previous 35 years) data were combined to drive Agricultural Production Systems sIMulator (APSIM) in-season forecasts of corn and soybean crop yield and phenology in Iowa, USA. We tested a combination of short-term weather forecasts from the Weather Research and Forecasting Model (WRF) to predict in season weather variables, such as, maximum and minimum temperature, precipitation, and radiation at four different forecast lengths (14 days, 7 days, 3 days, and 0 days). However, few evaluations have been conducted to determine the effectiveness of including weather forecasts, as opposed to using historical/climatology data, into crop models. Accurately forecasting crop yield in advance of harvest could greatly benefit decision makers.
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