Delta yield predicts nitrogen fertilizer requirements for corn in US production systems
| Author(s) | Ordóñez, Raziel A. | |
| Author(s) | White, Charles M. | |
| Author(s) | Spargo, John T. | |
| Author(s) | Kaye, Jason P. | |
| Author(s) | Ruark, Matthew | |
| Author(s) | Iqbal, Javed | |
| Author(s) | Shapiro, Charles A. | |
| Author(s) | Thomason, Wade E. | |
| Author(s) | Fiorellino, Nicole M. | |
| Author(s) | Thorne, Louis A. | |
| Author(s) | Shober, Amy | |
| Author(s) | Grove, John H. | |
| Author(s) | Hirsh, Sarah M. | |
| Author(s) | Weil, Ray R. | |
| Author(s) | Castellano, Michael J. | |
| Author(s) | Archontoulis, Sotirios V. | |
| Author(s) | Hatfield, Jerry J. | |
| Author(s) | Lee, Chad D. | |
| Author(s) | Quinn, Daniel J. | |
| Author(s) | Sanders, Zachary P. | |
| Author(s) | Rivera-Ocasio, Zoelie | |
| Author(s) | Tierney, Sarah | |
| Author(s) | Arrington, Kathleen E. | |
| Author(s) | Lefever, Andrew M. | |
| Author(s) | Tejera-Nieves, Mauricio | |
| Author(s) | Danalatos, Gerasimos G. | |
| Author(s) | Puntel, Laila A. | |
| Author(s) | Poffenbarger, Hanna | |
| Author(s) | Leuthold, Sam | |
| Author(s) | Miller, Jarrod | |
| Author(s) | Toor, Gurpal S. | |
| Author(s) | Vyn, Tony J. | |
| Date Accessioned | 2025-09-17T18:51:14Z | |
| Date Available | 2025-09-17T18:51:14Z | |
| Publication Date | 2025-09-05 | |
| Description | This article was originally published in Agronomy Journal. The version of record is available at: https://doi.org/10.1002/agj2.70150 This is an open access article under the terms of the Creative Commons Attribution License, (https://creativecommons.org/licenses/by/4.0/) which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). Agronomy Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy. | |
| 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 (Nx). 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 Nx based on dY, resulting in nine state, agroecosystem region, and irrigation-specific sub-models. These sub-models predicted Nx 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 Nx, 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. Plain Language Summary We developed a delta yield-based nitrogen (dY-based N) approach to predict N requirement for U.S. corn production systems. Using 486 N response trials, we modeled N rates to reach yield plateaus and found that dY effectively predicts both agronomic (Nx) and economic optimum N rate (EONR) requirements. These submodels predicted Nx of the validation set with a mean absolute error (MAE) of 33.0 kg N ha−1. Predictions of EONR based on dY had a lower MAE than the predictions of Nx, ranging between 19.9 and 25.4 kg N ha−1 depending on the fertilizer:grain price ratios (PR). This approach can support sustainable fertilizer use by integrating agronomic and economic factors, including soil N supply, management practices, and yield potential, offering a practical tool for improving N use efficiency and reducing environmental impact. | |
| Sponsor | National Institute of Food and Agriculture,Grant/Award Number: PENW-2018-09028;Natural Resources Conversation Service,Grant/Award Number: NR20-08G010;Pennsylvania State University, Grant/Award Numbers: Project-PEN04764,Accession-1025969 | |
| Citation | Ordóñez, R. A., White, C. M., Spargo, J. T., Kaye, J. P., Ruark, M., Iqbal, J., Shapiro, C. A., Thomason, W. E., Fiorellino, N. M., Thorne, L. A., Shober, A., Grove, J. H., Hirsh, S. M., Weil, R. R., Castellano, M. J., Archontoulis, S. V., Hatfield, J. J., Lee, C. D., Quinn, D. J., ... Vyn, T. J. (2025). Delta yield predicts nitrogen fertilizer requirements for corn in US production systems. Agronomy Journal, 117, e70150. https://doi.org/10.1002/agj2.70150 | |
| ISSN | 1435-0645 | |
| URL | https://udspace.udel.edu/handle/19716/36625 | |
| Language | en_US | |
| Publisher | Agronomy Journal | |
| dc.rights | Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| Title | Delta yield predicts nitrogen fertilizer requirements for corn in US production systems | |
| Type | Article |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Delta yield predicts nitrogen fertilizer requirements for corn in US production systems.pdf
- Size:
- 2.56 MB
- Format:
- Adobe Portable Document Format
- Description:
- Main article
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 2.22 KB
- Format:
- Item-specific license agreed upon to submission
- Description: