Improving in-Season Estimation of Yield Using Soil Moisture Data to Make Nitrogen Fertilizer Recommendations in Winter Wheat.

With rising fertilizer costs and an increasing public awareness of non-point source pollution, accurate in-season nitrogen (N) fertilizer recommendations are critical. Previous studies have demonstrated that soil moisture data combined with a vegetative crop characteristic may be used for estimating crop yield potential, resulting in more accurate N fertilizer recommendations. The objective of this study was to utilize sensor-based normalized difference vegetative index (NDVI) values along with site temperature and soil moisture data to estimate grain yield potential in winter wheat (Triticum aestivum L). Grain yield and NDVI values were collected from three long term soil fertility trials in Oklahoma from 2003 to 2011. Site growing degree days (GDD), water-stress days (WSD), and soil profile moisture to 80 cm was derived from archived Oklahoma Mesonet weather data and USDA-NRCS Soil Survey data. Multivariate statistical models to predict yield potential were developed using the parameters listed above. Significant correlations with grain yield were higher for the prediction models that included NDVI, GDD, WSD, and soil profile moisture together.  Yield potential prediction models will be validated using collected field measurements and weather data from the 2011-2012 crop year (results to be presented).  Accurate mid-season soil moisture data combined with GDD and NDVI data has the potential to improve the prediction of final grain yield.