Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

By Sotirios V. Archontoulis, Michael J. Castellano, M. Licht, V. Nichols, Mitch Baum, I. Huber, Rafael A. Martinez-Feria, L. Puntel, Raziel A. Ordóñez, J. Iqbal and 15 more. 2020

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We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we 1) describe the methodology used to perform forecasts; 2) evaluate model prediction accuracy against data collected from 10 locations over 4-years; and 3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end-of-season yields (relative root mean square error, RRMSE ~ 20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50 to 64%. Model initial conditions and management information accounted for 1⁄4 of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2=0.88), root depth (R2=0.83), biomass production (R2=0.93), grain yield (R2=0.90), plant N uptake (R2=0.87), soil moisture (R2=0.42), soil temperature (R2=0.93), soil nitrate (R2=0.77), and water table depth (R2=0.41). We concluded that model set-up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment. Disciplines Agriculture | Agronomy and Crop Sciences | Bioresource and Agricultural Engineering | Soil Science | Statistical Models Comments This is a manuscript of an article published as Archontoulis, Sotirios V., Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez‐Feria et al. "Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt." Crop Science (2020). doi: 10.1002/csc2.20039. Authors Sotirios V. Archontoulis, Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez-Feria, Laila Puntel, Raziel A. Ordonez, Javed Iqbal, Emily E. Wright, Ranae N. Dietzel, Matthew Helmers, Andy VanLoocke, Matt Liebman, Jerry L. Hatfield, Daryl Herzmann, S. Carolina Córdova, Patrick Edmonds, Kaitlin Togliatti, Ashlyn Kessler, Gerasimos Danalatos, Heather Pasley, Carl Pederson, and Kendall R. Lamkey This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/agron_pubs/623 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:

Subfields: Agronomy and Crop Science, Ecology, Evolution, Behavior and Systematics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/11c977faa82416c10681c986cbb9b44e5c345358

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In Agronomy and Crop Science

  1. Jerry L. Hatfield
  2. Sergey Shabala
  3. Sotirios V. Archontoulis

In Ecology, Evolution, Behavior and Systematics

  1. Jerry L. Hatfield
  2. Holger Meinke
  3. Sergey Shabala
  4. Sotirios V. Archontoulis

In Global and Planetary Change

  1. Jerry L. Hatfield
  2. Holger Meinke
  3. Sergey Shabala
  4. Sotirios V. Archontoulis

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