On Farm Research to Evaluate Sensor Algorithms for Corn N Management in NY.

Proximal (active) sensing has been increasingly used to provide information about canopy properties in a large range of crops. Algorithms that estimate within-field nitrogen (N) needs from sensor data need to be calibrated and validated to represent local soils, management and growing conditions. In this study we conducted field trials across New York farms in 2014 (5 trials) and 2015 (7 trials) to evaluate the ability of existing algorithms from other states to calculate the most economic rate of N (MERN) in corn (Zea mays L.) produced for grain or silage. A randomized complete block design was used with four blocks. Treatments included 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 split between planting and mid-season). In addition to the on farm trials, four trials were implemented in 2015 on research stations; three included five N rates applied at planting (0, 45, 89, 134 and 178 kg ha^-1) in four replications and one had two rates of manure and two rates of compost replicated five times. Corn was scanned for Normalized Difference Vegetation Index (NDVI) at every growth stage between V4 and V11 to determine the optimum timing of scanning and develop the exponential model to predict yield using NDVI data. Preliminary results suggest (1) the best time to scan is at V7, and (2) existing algorithms developed at Virginia Tech and Oklahoma State University can be used to predict end-of-season yield and N needs of corn in New York once yield potential and response index (RI) equations are calibrated for the state’s growing conditions.