Soybean nitrogen fixation dynamics in Iowa, USA

Córdova, S. Carolina; Castellano, Michael J.; Dietzel, Ranae; Licht, Mark A.; Togliatti, Kaitlin; Martinez-Feria, Rafael A.; Archontoulis, Sotirios

doi:10.1016/j.fcr.2019.03.018

Cited by 58 publications

(81 citation statements)

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“…In brief, in the maize crop model, we increased the radiation use efficiency parameter (Soufizadeh et al, 2018), decreased the root front velocity (Ordóñez, Castellano, Hatfield, Helmers et al, 2018), decreased the leaf appearance rates, and decreased the critical maize grain N concentration (Ciampitti & Vyn, 2012). In the soybean model, we increased the node senescence parameter to slow down senescence (Archontoulis et al, 2014b;Wu et al, 2019), decreased the potential fixation rate (Córdova et al, 2019), decreased the root front velocity (Ordóñez, Castellano, Hatfield, Helmers et al, 2018), decreased the critical grain N concentration (Balboa et al, 2018) and the pod N concentration. In addition, we decreased the fraction of dry matter allocated to pods at the early reproductive stages and decreased stem and leaf N concentrations at late reproductive stages.…”

Section: Apsim Model Set Up and Calibrationmentioning

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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Section: Apsim Model Set Up and Calibrationmentioning

confidence: 99%

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

et al. 2020

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“…Although our single N fertilizer rate (168 kg N ha −1 ) did not allow us to explore the effect of N fertilizer on maize root growth, previous studies demonstrated that N fertilizer can affect R:S ratios (Amos and Walters, 2006;Bolinder et al, 2007). In the case of soybean, soil N availability may affect root growth and fixation but this biological process does not produce dramatic impact on final seed yields (La Menza et al, 2017;Córdova et al, 2019).…”

Section: Root To Shoot Ratiomentioning

confidence: 99%

Root to shoot and carbon to nitrogen ratios of maize and soybean crops in the US Midwest

Ordóñez

Archontoulis

Martinez-Feria

et al. 2020

European Journal of Agronomy

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Root traits are important to crop functioning, yet there is little information about how root traits vary with shoot traits. Using a standardized protocol, we collected 160 soil cores (0−210 cm) across 10 locations, three years and multiple cropping systems (crops x management practices) in Iowa, USA. Maximum root biomass ranged from 1.2 to 2.8 Mg ha−1 in maize and 0.86 to 1.93 Mg ha−1 in soybean. The root:shoot (R:S) ratio ranged from 0.04 to 0.13 in maize and 0.09 to 0.26 in soybean. Maize produced 27 % more root biomass, 20 % longer roots, with 35 % higher carbon to nitrogen (C:N) ratio than soybean. In contrast, soybean had a 47 % greater R:S ratio than maize. The maize R:S ratio values were substantially lower than literature values, possibly due to differences in measurement methodologies, genotypes, and environment. In particular, we sampled at plant maturity rather than crop harvest to minimize the effect of senescence on measurements of shoots and roots. Maximum shoot biomass explained 70 % of the variation in root biomass, and the R:S ratio was positively correlated with the root C:N measured in both crops. Easily-measured environmental variables including temperature and precipitation were weakly associated with root traits. These results begin to fill an important knowledge gap that will enable better estimates of belowground net primary productivity and soil organic matter dynamics. Ultimately, the ability to explain variation in root mass production can be used to improve C and N budgets and modeling studies from crop to regional scales.

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“…For instance, few studies found a negative correlation between soybean yield and increasing late-summer temperatures for the interval between late July and early August (Willhem & TA B L E 4 The economic return to N fertilizer applied to soybean at different rates by year. Soybean yield, yield difference, and potential net return to N cost are data means (n = 12 ± standard error) Wortmann Yamoah, Walters, Shapiro, Francis, & Hayes, 2000), which could have been around R4 stage and the beginning of R5 (Córdova et al, 2019), thus, significantly impacting soybean production during early stages prior to pod filling (Willhem & Wortmann, 2004). Moreover, optimal soil temperature for soybean BNF and nodule formation ranges between 20 and 25 • C (Lindemann & Ham, 1979;Purcell, Serraj, Sinclair, & De, 2004).…”

Section: Discussionmentioning

confidence: 99%

Soybean profitability and yield component response to nitrogen fertilizer in Iowa

Córdova

Archontoulis

Licht

2020

Agrosystems Geosci & Env

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Nitrogen fertilizer application to soybean [Glycine max (L.) Merr.] in Iowa, USA, has shown inconsistent results. We performed a study in central Iowa (2015 and 2016) to investigate the effect of N fertilizer rate (0, 45, 90, 135 kg N ha −1) and application timing (planting, flowering, pod setting) on soybean yield, yield components, and to calculate the economic net return to N fertilizer. Results showed a positive effect of N fertilizer on soybean yield and yield components both years. Seed and aboveground biomass dry weight were positively correlated to N fertilizer, and both were 17% greater than No-N treatment. Nitrogen fertilizer rate that significantly increased seed and aboveground biomass was 135 kg N ha −1 regardless of application timing (2015), or at planting (2016). Moreover, the same N fertilizer addition applied at planting benefitted seed and aboveground biomass N accumulation only in 2016 (avg. 32.00 and 34.68 g N uptake m −2 , respectively), both 1.5-times higher than No-N treatment. Favorable environmental conditions during 2016 lead to hand-measured yield difference of 22% compared to 2015. Economic net return analysis showed that the additional revenue from increased yield attributed to supplemental N fertilization offset the application cost, resulting in net return gains between US$5.83 to $281.89 ha −1 (all treatments except 45 kg N ha −1 on 2015). This study highlights the importance to parse out soybean yield in its components, and the need to quantify yield gains from N fertilizer additions in economic terms which shed some light on any tradeoffs. Abbreviations: BNF, biological nitrogen fixation; DOY, day of the year; NHI, nitrogen harvest index; TC, total carbon; TN, total nitrogen. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Soybean nitrogen fixation dynamics in Iowa, USA

Cited by 58 publications

References 64 publications

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

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

Root to shoot and carbon to nitrogen ratios of maize and soybean crops in the US Midwest

Soybean profitability and yield component response to nitrogen fertilizer in Iowa

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