Natasha Elizabeth Macnack1, Sulochana Dhital1, Jacob Bushong1 and William R. Raun2, (1)Plant and Soil Science, Oklahoma State University, Stillwater, OK (2)044 N Agricultural Hall, Oklahoma State University, Stillwater, OK
Price deductions related to low grain protein content (GPC) are a real concern in the grain industry. The ability to predict GPC mid-season could potentially allow producers to adjust nitrogen (N) fertilizer rates in time to correct for low GPC. Normalized difference vegetation index (NDVI) values derived from ground based optical sensors have successfully been used to monitor plant N status and predict grain yield. However, there has been little success relating sensor readings to GPC. The objective of this study was to predict GPC using NDVI measurements, applied N rates, cumulative rainfall and average air temperature (Tavg) in a statistical model. Grain yield and GPC data from 1999 to 2012 from Oklahoma State University long-term winter wheat (Triticum aestivum L.) fertility trials at Lahoma, Stillwater, and Altus, OK were used. Precipitation and temperature data were downloaded from the Mesonet. Multiple linear regression was used to determine the variables that best predicted GPC. Results will show that grain yield and GPC increase with increasing N rates. Also, NDVI by itself cannot reliably predict GPC. However, the addition of cumulative rainfall to the model improves the prediction of GPC and could thus be utilized to improve N fertilizer recommendations when GPC is expected to be low.