Utilizing Soil Moisture Data With Optical Sensors to Determine Nitrogen Recommendations in Winter Wheat.

When utilizing optical sensors to make nitrogen (N) fertilizer recommendations in winter wheat, in-season grain yield potential is one parameter needed at the time of crop sensing. Currently estimating in-season grain yield potential uses some measure of crop biomass, such as normalized difference vegetation index (NDVI) along with growing degree days (GDD’s) from planting to sensing. The objective was to incorporate soil moisture data from the Oklahoma Mesonet to improve the ability to predict final grain yield in-season. Crop NDVI, GDD’s that were adjusted based upon if there was adequate water for crop growth, and the amount of soil profile (0-80cm) water were incorporated into a multiple linear regression model to predict final grain yield. Twenty two site-years of N fertility trials with in-season yield predictions for growth stages ranging from Feekes 3 to 10 were utilized to calibrate the model. Three models were developed, one for all soil types, one for loamy textured sites, and one for coarse textured sites. The models were validated with 11 site-years of sensor and weather data. The results indicated there was no benefit to having separate models based upon soil types. Typically, the models that included soil moisture, more accurately predicted final grain yield. Across all site years and growth stages, yield prediction estimates that included soil moisture had an R²= 0.49, while the current model without a soil moisture adjustment had an R²=0.40. The yield prediction model which incorported soil moisture data for all soil types was then evaluated by determining mid-season N fertilizer recommendations from 34 mid-season N fertilizer response trials and comparing those values to the agronomic optimum N rates.  Yield predictions that included soil moisture parameters performed similarly to current methods at determining optimum N rates. In conclusion, including soil moisture parameters improved the ability to predict grain yield at mid-season; however, this improvement did not significantly influence the mid-season N fertilizer recommendation.