Validating Weather and Sensor-based N Prediction Models for Michigan Corn Production


08 Mar 2017

2016 Annual Report

2016 Corn IPNI Sensor Final Report.pdf2016 Corn IPNI Sensor Final Report.pdf

Results have been published:

Determining corn nitrogen rates using multiple prediction models
Jeff Rutan & Kurt Steinke


Weather uncertainty and soil spatial variability impact nitrogen (N) cycling and corn (Zea mays L.) growth, making accurate N predictions a challenge. Field studies were conducted in Lansing, Michigan, to evaluate a computer model (i.e., Adapt-N), a preseason year-based model (i.e., maximum return to N [MRTN]), and a crop sensor model (i.e., active canopy sensor with algorithm) for recommending corn N rates. To determine site-specific economic optimum N rates (EONR), five N rates were also applied (0, 33%, 66%, 133%, and 166% of the suggested MRTN) as starter + sidedress (SD) at V4. In a wet year (i.e., 2015), Adapt-N increased V8 SD N rates 35 kg N/ha relative to the MRTN V4 SD N application. Although the greater rate of N may have provided additional yield protection, no statistical yield differences were observed between the two models. The MRTN model increased partial factor productivity (PFP) 20% relative to Adapt-N. Limited expression of V8 corn N deficiency reduced crop sensor total N rates (21–56 kg N/ha) and yield (0.82–1.05 Mg/ha) relative to other models. In a drier year (i.e., 2016), N demand was reduced (EONR 64 kg N/ha less than 2015), resulting in similar corn response to all three models. Despite differences in actual corn N rate recommendations, all three models resulted in similar economic net returns across study years.