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A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate
Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909184/ https://www.ncbi.nlm.nih.gov/pubmed/29706974 http://dx.doi.org/10.3389/fpls.2018.00436 |
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author | Puntel, Laila A. Sawyer, John E. Barker, Daniel W. Thorburn, Peter J. Castellano, Michael J. Moore, Kenneth J. VanLoocke, Andrew Heaton, Emily A. Archontoulis, Sotirios V. |
author_facet | Puntel, Laila A. Sawyer, John E. Barker, Daniel W. Thorburn, Peter J. Castellano, Michael J. Moore, Kenneth J. VanLoocke, Andrew Heaton, Emily A. Archontoulis, Sotirios V. |
author_sort | Puntel, Laila A. |
collection | PubMed |
description | Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R(2) = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R(2) = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha(−1) (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost. |
format | Online Article Text |
id | pubmed-5909184 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-59091842018-04-27 A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate Puntel, Laila A. Sawyer, John E. Barker, Daniel W. Thorburn, Peter J. Castellano, Michael J. Moore, Kenneth J. VanLoocke, Andrew Heaton, Emily A. Archontoulis, Sotirios V. Front Plant Sci Plant Science Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R(2) = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R(2) = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha(−1) (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost. Frontiers Media S.A. 2018-04-13 /pmc/articles/PMC5909184/ /pubmed/29706974 http://dx.doi.org/10.3389/fpls.2018.00436 Text en Copyright © 2018 Puntel, Sawyer, Barker, Thorburn, Castellano, Moore, VanLoocke, Heaton and Archontoulis. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Puntel, Laila A. Sawyer, John E. Barker, Daniel W. Thorburn, Peter J. Castellano, Michael J. Moore, Kenneth J. VanLoocke, Andrew Heaton, Emily A. Archontoulis, Sotirios V. A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate |
title | A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate |
title_full | A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate |
title_fullStr | A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate |
title_full_unstemmed | A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate |
title_short | A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate |
title_sort | systems modeling approach to forecast corn economic optimum nitrogen rate |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909184/ https://www.ncbi.nlm.nih.gov/pubmed/29706974 http://dx.doi.org/10.3389/fpls.2018.00436 |
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