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

Authors

Laila A. Puntel, University of Nebraska-LincolnFollow
Agustin Pagani, Clarion Inc., Buenos Aires
Sotirios V. Archontoulis, Iowa State UniversityFollow

ORCID IDs

Laila A. Puntel https://orcid.org/0000-0001-6455-510X

Agustin Pagan https://orcid.org/0000-0003-3934-1059

Sotirios V. Archontoulis https://orcid.org/0000-0001-7595-8107

Document Type

Article

Date of this Version

3-18-2019

Citation

Published in European Journal of Agronomy 105 (2019), pp 189–199

doi 10.1016/j.eja.2019.01.003

Comments

Copyright © 2019 Elsevier B.V. Used by permission.

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.

Includes supplemental figures and table. Excel model attached below as additional file.