Accurately predicted crop yield is economically valuable for farmers and fro the government, who can better prepare for high or low yields given a crop forecast. The crop environment resource synthesis (CERES) wheat model provides yield predictions given environmental variables, typically using climatological means of temperature and precipitation data. In Oklahoma, long-term records of daily weather are available for each county in the state. for this study, seasonal forecasts were obtained from the Climate Prediction Center. These forecasts were combined with long-term temperature and precipitation data divided into above normal, normal, and below normal categories to match the forecast anomaly estiamtes. The weights assigned to each of these categories were adjusted using probabilities from the long-range forecasts to generate a weighted climate history. Coupling the forecasts with observed weather data provided a more accurate model of potential weather for the growing season as compared to using climatology alone. This model of weather was used in conjunction with the CERES model to predict wheat yield in Oklahoma. Incorporation of the long-range forecast showed little difference in the mean predicted yield. However, results indicated that enhanced climate forecasts can improve the prediction of wheat yield by decreasing random error in the predictions.
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