Evaluation of static and sensor-based nitrogen recommendation models for winter wheat

Abstract

Nitrogen (N) management is crucial to increase winter wheat production. Improved prediction of the economic optimal N rates (EONR) for winter wheat could increase N use efficiency and farmers' profits. However, characterization of the variability in the EONR and the performance of existing dynamic and static N recommendation models have been limited. The objectives were (i) to characterize winter wheat yield and protein content response to N rate and timing and (ii) to evaluate the performance of static and dynamic N recommendation models. Nine experiments across 2 years were conducted in eastern Nebraska to evaluate N rate recommendation models and N application timing. Dynamic models included Oklahoma State University, Holland and Schepers, and Kansas State University remote sensing-based N recommendations. Static N recommendation models included empirical equations from the University of Nebraska–Lincoln and Kansas State University. The EONR ranged across site-years from 57 to 150 kg N ha⁻¹, yield at the EONR from 3.33 to 7.51 Mg ha⁻¹, and protein at the EONR from 11.1% to 16.4%. There was no significant effect of the timing of N application on grain yield and protein content. Dynamic N recommendations performed better than static models based on an average difference from the observed EONR (±14.8 kg N ha⁻¹ and 46.0 ± 83 kg N ha⁻¹, respectively). Further testing of N winter wheat recommendation models is needed to better inform winter wheat growers about N management and fine-tuned N recommendations to current management practices.