Do shallow water tables contribute to high and stable maize yields in the US Corn Belt?

Rizzo, Gonzalo; Edreira, Juan I. Rattalino; Archontoulis, Sotirios; Yang, Haishun; Grassini, Patricio

doi:10.1016/j.gfs.2018.07.002

Cited by 50 publications

(35 citation statements)

References 43 publications

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“…Soybean crops sowed into soils with water tables deeper than 2 m below the surface had greater dependency on rainfall, especially during the reproductive period of the crop. Rizzo et al (2018) highlighted that the presence of shallow water tables under maize crops are leading to higher and more stable yields in central United States. The results for soybean in central Argentina in this analysis agree with those findings for maize in the United States.…”

Section: Discussionmentioning

confidence: 99%

Sowing date, genotype choice, and water environment control soybean yields in central Argentina

Vitantonio‐Mazzini

Gómez²,

Gambín

et al. 2020

Crop Science

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Soybean [Glycine max (L.) Merr.] is one of the most important crops worldwide, and Argentina is the third largest global grain producer and the worlds´ largest meal exporter. Under the continuous challenge of increasing crop yields, especially in the central temperate region of the country, there is a growing need to optimize management in relation to the environment that each specific farm and paddock presents. Understanding the impact of available technologies and management options can help optimize crop design. Here, we identify and quantify the effect of the most relevant variables affecting soybean yield by analyzing a database that includes 53 field trials with four common commercial genotypes, reporting 50 management and environmental variables. Linear mixed‐effect models revealed that two management decisions (genotype and sowing date selection) and three environmental variables (rainfall during the reproductive crop period from R1 to R7, soil type [Hapludoll vs. Argiudoll], and water table presence above or below 2 m of depth from the surface) helped explain ∼40% of total yield variability, which ranged from 1,675 to 7,226 kg ha−1 and averaged 5,133 kg ha−1. Water table presence generated higher and more stable yields particularly in coarse‐textured Hapludolls and under low‐rainfall conditions. Results highlight specific management and environmental conditions that affect soybean crop yields in the region, pointing to effective pathways toward yield gap reductions.

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Section: Discussionmentioning

confidence: 99%

Sowing date, genotype choice, and water environment control soybean yields in central Argentina

Vitantonio‐Mazzini

Gómez²,

Gambín

et al. 2020

Crop Science

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“…Upward moisture fluxes on the basis of soil moisture gradients have been observed in nature (Scanlon 1992) and corroborated by models (Scanlon and Milly 1994), although these types of assessments have not been done over long periods or large areas. Examples of physical mechanisms that might drive water upward include 1) hydraulic redistribution, which is water movement from deep wet soil to dry shallow soil through the root system in the absence of transpiration demand (Lee et al 2005;Meinzer et al 2004;Neumann and Cardon 2012;Ryel et al 2003;Ryel et al 2002); 2) deep root plant water uptake, where deeper-rooted plants can access deep layer (and therefore higher memory) soil water (Huete et al 2006); and 3) a shallow groundwater table position, which can contribute to the evaporative demand through capillary rise especially during droughts (Gao et al 2017;Rizzo et al 2018).…”

Section: Hypothesized Soil Moisture Reemergence Mechanismsmentioning

confidence: 99%

Potential Reemergence of Seasonal Soil Moisture Anomalies in North America

et al. 2019

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Soil moisture anomalies within the root zone (roughly, soil depths down to ~0.4 m) typically persist only a few months. Consequently, land surface–related climate predictability research has often focused on subseasonal to seasonal time scales. However, in this study of multidecadal in situ datasets and land data assimilation products, we find that root zone soil moisture anomalies can recur several or more seasons after they were initiated, indicating potential interannual predictability. Lead–lag correlations show that this recurrence often happens during one fixed season and also seems related to the greater memory of soil moisture anomalies within the layer beneath the root zone, with memory on the order of several months to over a year. That is, in some seasons, notably spring and summer when the vertical soil water potential gradient reverses sign throughout much of North America, deeper soil moisture anomalies appear to return to the surface, thereby restoring an earlier root zone anomaly that had decayed. We call this process “reemergence,” in analogy with a similar seasonally varying process (with different underlying physics) providing winter-to-winter memory to the extratropical ocean surface layer. Pronounced spatial and seasonal dependence of soil moisture reemergence is found that is frequently, but not always, robust across datasets. Also, some of its aspects appear sensitive to spatial and temporal sampling, especially within the shorter available in situ datasets, and to precipitation variability. Like its namesake, soil moisture reemergence may enhance interannual-to-decadal variability, notably of droughts. Its detailed physics and role within the climate system, however, remain to be understood.

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“…Others have shown that inclusion of waterlogging routines improved the overall model capability to predict production and environmental outcomes (Ebrahimi-Mollabashi et al, 2019). Excessive moisture is most damaging in poorly drained soils with shallow water tables, which are present in a large portion of cropland in the US Midwest (Fan et al, 2013;Rizzo et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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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Do shallow water tables contribute to high and stable maize yields in the US Corn Belt?

Cited by 50 publications

References 43 publications

Sowing date, genotype choice, and water environment control soybean yields in central Argentina

Sowing date, genotype choice, and water environment control soybean yields in central Argentina

Potential Reemergence of Seasonal Soil Moisture Anomalies in North America

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

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