Wheat regression models that account for the effect of weather are developed to forecast wheat yield and quality. Spatial lag effects are included. Wheat yield, protein, and test weight level are strongly influenced by weather variables. The forecasting power of the yield and protein models was enhanced by adding the spatial lag effect. Out of sample forecasting tests confirm the models’ usefulness in accounting for the variations in average wheat yield and qualities.