Delta yield predicts nitrogen fertilizer requirements for corn in US production systems

Abstract Predicting crop nitrogen (N) fertilizer needs is a major challenge in contemporary agriculture. Despite the success of current N recommendation tools, environmental concerns over N pollution from agriculture, and the adoption of improved corn ( Zea mays L.) technologies with enhanced N efficiencies highlight the need for more accurate N fertilizer recommendation systems. Here, we aimed to develop a methodology to predict corn N requirements based on delta yield (dY = maximum yield−unfertilized yield). To develop this delta yield‐based nitrogen (dY‐based N) tool, we selected 486 quadratic‐plateau corn yield response to N curves (from 732 N rate trials across northern US) to calculate dY and N fertilizer required to reach the yield plateau (N x ). The economic optimum nitrogen rate (EONR) was calculated using different fertilizer:crop price ratios (PR). The response curve outputs were then partitioned into calibration and validation sets. The calibration set was used to select linear models to predict N x based on dY, resulting in nine state, agroecosystem region, and irrigation‐specific sub‐models. These sub‐models predicted N x of the validation set with a mean absolute error (MAE) of 33.0 kg N ha −1 . Predicted values from the site‐year quadratic‐plateau response fits were used to improve further predictions’ outcomes. Predictions of EONR based on dY had a lower MAE than the predictions of N x , ranging between 19.9 and 25.4 kg N ha −1 depending on the PR, highlighting the system's predictive power. The exclusion of non‐responsive and linear‐response trials in our proposed dY‐based approach enables future model refinement to improve EONR prediction accuracy across a broader range of yield responses to fertilizer‐N rates. The proposed dY‐based N system, which integrates both economic and agronomic inputs (including management, environmental effects on soil N supply, and maximum yields), could help to reduce N losses and provide functional benefits for N optimization.

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Publication Details

Journal
Agronomy Journal
Published
2025-09-01
DOI
https://doi.org/10.1002/agj2.70150
Primary Topic
Crop Yield and Soil Fertility
Type
article
Field-Weighted Citation Impact
0.00

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article

Delta yield predicts nitrogen fertilizer requirements for corn in US production systems

Gurpal S. Toor, Jerry L. Hatfield, Hanna Poffenbarger, Sarah M. Hirsh et al.
Agronomy Journal
Crop Yield and Soil Fertility
article

Delta yield predicts nitrogen fertilizer requirements for corn in US production systems

Gurpal S. Toor, Jerry L. Hatfield, Hanna Poffenbarger, Sarah M. Hirsh, Jarrod O. Miller, Daniel J. Quinn, Sotirios V. Archontoulis, Wade E. Thomason, Nicole M. Fiorellino, Mauricio Tejera‐Nieves, Ray R. Weil, Sam J. Leuthold, John T. Spargo, Chad D. Lee, Raziel A. Ordóñez, Zachary P. Sanders, Laila A. Puntel, Michael J. Castellano, Kathleen E. Arrington, Tony J. Vyn, Amy L. Shober, Matthew D. Ruark, Jason Philip Kaye, John H. Grove, Zoelie Rivera‐Ocasio, Charles M. White, Andrew M. Lefever, Javed Iqbal, Charles A. Shapiro, Louis Thorne, Sarah Tierney, Gerasimos Danalatos
article en

Abstract

Abstract Predicting crop nitrogen (N) fertilizer needs is a major challenge in contemporary agriculture. Despite the success of current N recommendation tools, environmental concerns over N pollution from agriculture, and the adoption of improved corn ( Zea mays L.) technologies with enhanced N efficiencies highlight the need for more accurate N fertilizer recommendation systems. Here, we aimed to develop a methodology to predict corn N requirements based on delta yield (dY = maximum yield−unfertilized yield). To develop this delta yield‐based nitrogen (dY‐based N) tool, we selected 486 quadratic‐plateau corn yield response to N curves (from 732 N rate trials across northern US) to calculate dY and N fertilizer required to reach the yield plateau (N x ). The economic optimum nitrogen rate (EONR) was calculated using different fertilizer:crop price ratios (PR). The response curve outputs were then partitioned into calibration and validation sets. The calibration set was used to select linear models to predict N x based on dY, resulting in nine state, agroecosystem region, and irrigation‐specific sub‐models. These sub‐models predicted N x of the validation set with a mean absolute error (MAE) of 33.0 kg N ha −1 . Predicted values from the site‐year quadratic‐plateau response fits were used to improve further predictions’ outcomes. Predictions of EONR based on dY had a lower MAE than the predictions of N x , ranging between 19.9 and 25.4 kg N ha −1 depending on the PR, highlighting the system's predictive power. The exclusion of non‐responsive and linear‐response trials in our proposed dY‐based approach enables future model refinement to improve EONR prediction accuracy across a broader range of yield responses to fertilizer‐N rates. The proposed dY‐based N system, which integrates both economic and agronomic inputs (including management, environmental effects on soil N supply, and maximum yields), could help to reduce N losses and provide functional benefits for N optimization.

Agronomy JournalVol. 117(5)
University of Nebraska–Lincoln (US), Oklahoma State University (US), Pennsylvania State University (US), University of Wisconsin–Madison (US), University of Kentucky (US), Iowa State University (US), Purdue University West Lafayette (US), University of Maryland Eastern Shore (US), Noldus Information Technology (NL), University of Maryland Extension (US), National Laboratory for Agriculture and the Environment (US), University of Maryland, College Park (US), University of Delaware (US), Colorado State University (US)
Pennsylvania State University, National Institute of Food and Agriculture
Openalex Percentile: Top 68%
Crop Yield and Soil Fertility
DOIOpen Access PDFOpenAlex
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