Site‐Specific Covariates Affecting Yield Response to Nitrogen of Late‐Sown Maize in Central Argentina

Core Ideas We used linear mixed effects models to explore maize yield response to applied N. Final models accurately described the observed data (R2 = 0.93). Best models indicated that yield response to applied N depended on soil N and soil type. Information is useful to optimize management decisions on N fertilizer rates. Resulting models are better than traditional ones based on ordinary least squares. Optimizing fertilizer rates is a common problem in modern agriculture. Frequently used response models ignore basic statistical assumptions and do not allow quantifying the effects of variables influencing yield response to fertilizer, generating uncertainty in fertilizer rate recommendations. We used linear mixed‐effects models to explore maize (Zea mays L.) yield response to applied nitrogen (N) in late sowings, and we tested different predictors for explaining yield responses across sites. Data included yield response trials to applied N at 17 different environments (combination site × year) with four to five N rates replicated twice in each trial. The best model (Model A) that included significant effect of N rate applied, sowing date, and soil N‐NO3 at sowing described grain yield variations with high accuracy (R2 = 0.93). Another best model (Model B) showed that soil type as additional variable affected significantly yield response to applied N. The final model indicated that the overall response across sites was characterized by a linear coefficient of 67 kg grain ha−1 per additional kg N ha−1 applied and a quadratic coefficient of −0.37 kg grain ha−1 per additional kg N ha−1 applied. Across all sites, soil N‐NO3 at sowing (explored range from 34 to 356 kg N ha−1) explained 46% of the variability in the linear yield response to applied N. We proposed a method and generated statistical models with site specific covariates that can help optimize farmers’ decisions on the use of optimal N fertilizer rates.