Water availability, root depths and 2017 crop yields

Archontoulis, Sotirios; Licht, Mark A.; Castellano, Mike; Ordóñez, Raziel A.; Iqbal, Javed; Martinez-Feria, Rafael A.; Edmonds, Pat; Wright, Emily E.; Baum, Mitch; Kessler, Ashlyn; Huber, Isaiah; Sassman, Aaron M.; Liebman, Matt; Helmers, Matt

doi:10.31274/icm-180809-281

Cited by 2 publications

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“…This was particularly noticeable in years 2016 and 2017 in central Iowa in which crops did not receive precipitation for almost 30 days during critical growth periods ( Supplemental Table S3) and still produced high yields. Previous simulation analysis indicated that the water table provided a yield benefit of 26% in maize and 17% in soybean (see details in Archontoulis et al, 2017). Rizzo et al (2018) reached a similar conclusion about the contribution of water table to yield and yield stability across the Corn Belt.…”

Section: Ten Lessons Learned By Performing In-season Yield and Soil Nmentioning

confidence: 56%

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

et al. 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 four 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] of ∼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 50-64%. Model initial conditions and management information accounted for Abbreviations: APSIM, Agricultural Production Systems sIMulator; RRMSE, relative root mean square error.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. one-fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R 2 = 0.88), root depth (R 2 = 0.83), biomass production (R 2 = 0.93), grain yield (R 2 = 0.90), plant N uptake (R 2 = 0.87), soil moisture (R 2 = 0.42), soil temperature (R 2 = 0.93), soil nitrate (R 2 = 0.77), and water table depth (R 2 = 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. Neil Huth from CSIRO for their support with the APSIM model, Iowa State University students () for assistance with data collection and managing the field experiments. We also thank the APSIM Initiative for making the software publicly available and for ensuring software quality. ORCIDSotirios V. Archontoulis https://orcid.org/0000-0001-7595-8107 Mark A. Licht https://orcid.org/0000-0001-6640-7856 Kendall R. Lamkey

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Section: Ten Lessons Learned By Performing In-season Yield and Soil Nmentioning

confidence: 56%