A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

dc.contributor.author Puntel, Laila A.
dc.contributor.author Sawyer, John
dc.contributor.author Barker, Daniel
dc.contributor.author Thorburn, Peter J.
dc.contributor.author Castellano, Michael
dc.contributor.author Moore, Kenneth J.
dc.contributor.author VanLoocke, Andy
dc.contributor.author Heaton, Emily
dc.contributor.author Archontoulis, Sotirios
dc.contributor.department Agronomy
dc.contributor.department Iowa Nutrient Research Center
dc.date 2018-04-16T18:09:37.000
dc.date.accessioned 2020-06-29T23:05:29Z
dc.date.available 2020-06-29T23:05:29Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.issued 2018-04-13
dc.description.abstract <p>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 (<em>R</em>2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (<em>R</em>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 (<em>n</em> = 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.</p>
dc.description.comments <p>This article is published as Puntel, Laila Alejandra, John E. Sawyer, Daniel Barker, Peter Thorburn, Michael Castellano, Kenneth James Moore, Andrew Vanloocke, Emily Anne Heaton, and Sotirios Archontoulis. "A systems modeling approach to forecast corn economic optimum nitrogen rate." <em>Frontiers in Plant Science</em> 9 (2018): 436. doi: <a href="https://doi.org/10.3389/fpls.2018.00436">10.3389/fpls.2018.00436</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/agron_pubs/468/
dc.identifier.articleid 1517
dc.identifier.contextkey 11967612
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath agron_pubs/468
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/4829
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/agron_pubs/468/2018_Heaton_SystemsModeling.pdf|||Sat Jan 15 00:24:02 UTC 2022
dc.source.uri 10.3389/fpls.2018.00436
dc.subject.disciplines Agricultural Economics
dc.subject.disciplines Agricultural Science
dc.subject.disciplines Agronomy and Crop Sciences
dc.subject.disciplines Statistical Models
dc.subject.keywords corn
dc.subject.keywords economic optimum N rate
dc.subject.keywords forecast
dc.subject.keywords modeling
dc.subject.keywords APSIM
dc.subject.keywords in-season nitrogen management
dc.subject.keywords nutrient recommendation
dc.title A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate
dc.type article
dc.type.genre article
dspace.entity.type Publication
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