Simulating Maize Response to Split-Nitrogen Fertilization Using Easy-to-Collect Local Features

Abstract

Maize (Zea mays) is a high-nitrogen (N)-demanding crop potentially contributing to nitrate contamination and emissions of nitrous oxide. The N fertilization is generally split between sowing time and the V6 stage. The right split N rate to apply at V6 and minimize environmental damage is challenging. Our objectives were to (1) predict maize response to added N at V6 using machine learning (ML) models; and (2) cross-check model outcomes by independent on-farm trials. We assembled 461 N trials conducted in Eastern Canada between 1992 and 2022. The dataset to predict grain yield comprised N dosage, weekly precipitations and corn heat units, seeding date, previous crop, tillage practice, soil series, soil texture, organic matter content, and pH. Random forest and XGBoost predicted grain yield accurately at the V6 stage (R2 = 0.78–0.80; RSME and MAE = 1.22–1.29 and 0.96–0.98 Mg ha−1, respectively). Model accuracy up to the V6 stage was comparable to that of the full-season prediction. The response patterns simulated by varying the N doses showed that grain yield started to plateau at 125–150 kg total N ha−1 in eight out of ten on-farm trials conducted independently. There was great potential for economic and environmental gains from ML-assisted N fertilization.

Keywords

• key features
• machine learning
• minimum dataset
• mixed model
• multi-environment fertilizer trials
• random forest