Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

Abstract

Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

Nitrate leaching reduced with Dynamic‐Adaptive nitrogen management under contrasting soils and tillage

“…Stanford (1973) himself acknowledged that this approach "… largely ignores the dynamic nature of the water-soil-plantnitrogen system". Dynamic simulation models of soil and crop processes have the potential for estimating N dynamics and crop fertilizer needs (Melkonian, van Es, DeGaetano, Sogbedji, & Joseph, 2007;Puntel et al, 2016;Setiyono et al, 2011). Recently, the performance of the dynamic-adaptive Adapt-N simulation tool (Melkonian, van Es, DeGaetano, & Joseph, 2008;van Es et al, 2007b) was evaluated on commercial farms against a Stanford-type method and a static empirical approach known as the Maximum Return To N (Sawyer et al, 2006).…”

A Review of Methods to Improve Nitrogen Use Efficiency in Agriculture

“…Many surveys have reported that most of the producers overestimate the target yield to determine the N requirement [48]. Similarly, studies in Wisconsin [49], Pennsylvania [50], and Ontario [51] have shown the problems of using yield expectation to predict N rate and consequently raise concern over its reliability and use for future N recommendations.…”

How does inclusion of weather forecasting impact in-season crop model predictions?

“…Over the past years the APSIM model has been successfully applied in this region to simulate production (Hammer et al, 2009;Archontoulis et al, 2014a, b;Dietzel et al, 2016;Puntel et al, 2016;Jin et al, 2017) and environmental aspects of US Midwestern corn and soybean cropping systems (Malone et al, 2007;Archontoulis et al, 2016a, b;Basche et al, 2016;Martinez-Feria et al, 2016).…”

“…While there are many physically-based cropping system model platforms available (APSIM; Holzwort et al, 2014), Decision Support System for Agrotechnology Transfer (DSSAT; Jones et al, 2003;Hoogenboom et al, 2015), System Approach to Land Use Sustainability (SALUS; Basso et al, 2010Basso et al, , 2012), we chose to use APSIM based on the combination of numerous detailed calibrations previously conducted in our region (Hammer et al, 2009;Malone et al, 2009;Archontoulis et al, 2014a, b;Archontoulis et al, 2016a, b;Basche et al, 2016;Dietzel et al, 2016;Jin et al, 2016;Martinez-Feria et al, 2016;Puntel et al, 2016) and the wide range of realistic in-season management options available such as when to apply fertilizer, spray chemicals, and harvest (Horie et al, 1992;Lawless & Semenov 2005;Howden et al, 2007).…”