Evaluation of Crop Sensor Algorithms for on-the-Go Variable Rate N Application for Corn in NY.

Proximal sensors are increasingly used to measure the Normalized Difference Vegetation Index (NDVI) and manage nitrogen (N) for a large range of crops. Algorithms developed to estimate yield and within-field N needs are site specific, reflecting local soil types, field management, and soil fertility levels. Our objective was to evaluate the performance of existing algorithms from other states for use in New York. Two types of field trials were established. To evaluate the relationship between NDVI and final season yield, four trials were implemented in 2015 with five N rates applied at planting (0, 45, 89, 134 and 178 kg ha^-1) in four replications. These fields were scanned for Normalized Difference Vegetation Index (NDVI) from the V3 leaf stage to V12 (9 rounds) to determine the optimum timing of scanning. The second approach included five trials in 2014 and seven in 2015 with six N rates (0, 45, 89, 134 and 178 kg ha^-1 applied mid-season, plus an N-rich treatment that included 270 kg ha^-1 of total N applied split between planting and mid-season), to evaluate the ability of the different algorithms to predict the most economic rate of N (MERN) for each field. The 2015 field trials are ongoing but results of the 2014 growing season suggest that existing algorithms developed at Virginia Tech and Oklahoma State University can be used in New York once input parameters are calibrated for location growing conditions. The calibration includes the development of an exponential model to predict yield potential using the NDVI and the adjustment of the Response Index (RI) used to calculate the maximum yield that can be achieved.