Development of a nitrogen recommendation tool for corn considering static and dynamic variables.

Highlights • Dynamic factors affected the economic optimum N rate to corn more than static factors. • Precipitation, residue amount, soil depth, nitrate content, and heat stress explained 60% of EONR variability. • We developed a new N rate forecasting tool for rainfed corn production systems. • Across 51 N-trails, the observed EONR was about 50% higher than current N rates used in Central-West Buenos Aires, Argentina. Abstract Many soil and weather variables can affect the economical optimum nitrogen (N) rate (EONR) for maize. We classified 54 potential factors as dynamic (change rapidly over time, e.g. soil water) and static (change slowly over time, e.g. soil organic matter) and explored their relative importance on EONR and yield prediction by analyzing a dataset with 51 N trials from Central-West region of Argentina. Across trials, the average EONR was 113 ± 83 kg N ha−1 and the average optimum yield was 12.3 ± 2.2 Mg ha−1, which is roughly 50% higher than the current N rates used and yields obtained by maize producers in that region. Dynamic factors alone explained 50% of the variability in the EONR whereas static factors explained only 20%. Best EONR predictions resulted by combining one static variable (soil depth) together with four dynamic variables (number of days with precipitation > 20 mm, residue amount, soil nitrate at planting, and heat stress around silking). The resulting EONR model had a mean absolute error of 39 kg N ha−1 and an adjusted R2 of 0.61. Interestingly, the yield of the previous crop was not an important factor explaining EONR variability. Regression models for yield at optimum and at zero N fertilization rate as well as regression models to be used as forecasting tools at maize planting time were developed and discussed. The proposed regression models are driven by few easy to measure variables filling the gap between simple (minimum to no inputs) and complex EONR prediction tools such as simulation models. In view of increasing data availability, our proposed models can be further improved and deployed across environments. [ABSTRACT FROM AUTHOR]