Field and model assessments of irrigated soybean responses to increased air temperature

Sima, Matthew; Fang, Qiannan; Burkey, Kent O.; Ray, Samuel J.; Pursley, Walter A.; Kersebaum, Kurt Christian; Boote, Kenneth J.; Malone, Robert W.

doi:10.1002/agj2.20394

Cited by 7 publications

(17 citation statements)

References 43 publications

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“…RCP = Representative Concentration Pathways, RE = CO 2 factor to leaf quantum efficiency, QE = quantum efficiency T A B L E 1 Calibrated soybean cultivar parameters in CROPGRO-Soybean. (Sima et al, 2020) HERMES (20%), with an ensemble reduction of 13% that was close to observed 11% decrease. CROPGRO simulated maturity dates were closer to experimental results than that of HERMES.…”

Section: Model Overviewsupporting

confidence: 76%

“…Although the CROPGRO and HERMES modules use different CO 2 assimilation algorithms and temperature response functions (Figure 1; Sima et al, 2020), both produced similar responses of soybean yield to climate change scenarios under RCP4.5 and RCP8.5 (Figures 3 and 4), which was consistent with previous results on evaluating several CO 2 assimilation algorithms with FACE experiments (Nendel et al, 2009). Under rainfed conditions, however, CROPGRO simulated greater climate change effects on seed yield and biomass than HERMES under both RCP4.5 (50 vs. 26%) and RCP8.5 (55 vs. 22%), indicating greater sensitivity of CROPGRO to 4).…”

Section: Discussionmentioning

confidence: 99%

“…The Shuttleworth-Wallace equation is used to estimate potential evapotranspiration considering crop residue and partial canopy cover effects (Ma et al, 2012). The model contains multiple crop growth modules including the DSSAT suite of crop modules (CROPGRO and CERES) and the HERMES module (Ma, Hoogenboom, Ahuja, Nielsen, & Ascough, 2005;Sima et al, 2020). The CROPGRO has detailed simulation of soybean phenology, yield components, N 2 fixation, and environmental stresses (Boote, Jones, & Hoogenboom, 1998;Jones et al, 2003) and has been evaluated under elevated CO 2 conditions (Alagarswamy, Boote, Allen, & Jones, 2006).…”

Section: Model Overviewmentioning

confidence: 99%

“…Across the 4 yr and two treatments, simulation error (root mean square error, RMSE) for seed yield was 971 kg ha −1 by CROPGRO and 814 kg ha −1 by HERMES, and that for biomass was 1,609 kg ha −1 for CROPGRO and 1,631 kg ha −1 for HERMES. Other details and statistics on model calibration are available in Sima et al (2020). 3) for two commonly studied Representative Concentration Pathways (RCP) (RCP4.5 and RCP8.5) by the end of Year 2100 (Brekke, Thrasher, Maurer, & Pruittt, 2013;Pierce, Cayan, Maurer, Abatzoglou, & Hegewisch, 2015).…”

Section: Model Overviewmentioning

confidence: 99%

“…However, simulated reduction in biomass was lower by CROPGRO (7%) than by F I G U R E 1 Temperature and CO 2 effect (538 ppm for RCP4.5 and 936 ppm for RCP8.5) on leaf photosynthesis, respiration, and growth phenology in (a) CROPGRO and (b) HERMES: (a-1) temperature effects on A max (RE CO2 ) and QE (QE CO2 ); (a-2) TEMPMX effect on A max ; (a-3) overall temperature and CO 2 effect on A; (a-4) temperature effect on respiration; (a-5) temperature effect on phenology. (b-1) temperature effect on K o /K c and V cmax ; (b-2) temperature and CO 2 effect on C i /C o ; (b-3) overall temperature and CO 2 effect on A max ; (b-4) temperature effect on respiration; (b-5) temperature effect on phenology (Sima et al, 2020). RCP = Representative Concentration Pathways, RE = CO 2 factor to leaf quantum efficiency, QE = quantum efficiency T A B L E 1 Calibrated soybean cultivar parameters in CROPGRO-Soybean.…”

Section: Model Overviewmentioning

confidence: 99%

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Simulated climate change effects on soybean production using two crop modules in RZWQM2

Ma¹,

Fang

Sima

et al. 2021

Agronomy Journal

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The ability to predict climate change effects on crop yield through field experiments and crop modeling is essential for developing mitigation strategies. The objective of this study was to compare two different crop modules (CROPGRO and HERMES) in the Root Zone Water Quality Model 2 (RZWQM2) for predicting climate change effects on soybean [Glycine max (L.) Merr.] production. The modules were previously calibrated for measured temperature responses using data from a 4-yr open-top chamber experiment (2015)(2016)(2017)(2018) in North Carolina. Both crop modules simulated similar climate change effects in terms of yield and biomass by the end of Year 2100 (2083-2099) using 40 general circulation model (GCM) projections and two Representative Concentration Pathways (RCP4.5 and RCP8.5), compared with the simulations using current baseline (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018). For both modules, much greater reductions in biomass and seed yield were simulated under RCP8.5 than under RCP4.5 due to higher air temperature. In addition, both modules predicted lower variability of biomass and seed yield across these GCMs under irrigated than under rainfed conditions. CROPGRO predicted a greater positive climate change effect in response to the projected higher precipitation and increased atmospheric CO 2 (compared with baseline conditions) than HERMES. Soybean production will likely benefit more from the projected high precipitation and elevated CO 2 under rainfed conditions than under irrigated conditions. Due to much higher simulated yield under irrigation than under rainfed conditions, supplementary irrigation may be an effective mitigation strategy to maintain soybean yield; however, adjusting sowing dates appear to have little effect on soybean production. INTRODUCTIONCrop models have been used for more than two decades to simulate climate change effects on soybean [Glycine max (L.

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Section: Model Overviewsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Section: Model Overviewmentioning

confidence: 99%

Section: Model Overviewmentioning

confidence: 99%

Section: Model Overviewmentioning

confidence: 99%

See 3 more Smart Citations

Simulated climate change effects on soybean production using two crop modules in RZWQM2

Ma¹,

Fang

Sima

et al. 2021

Agronomy Journal

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Modulating soybean yield responses to climate warming: The role of E3 and E4 loci in growth period adaptation

Kumagai,

Nakano,

Matsuo

et al. 2024

Crop Science

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Crop models have suggested high sensitivity of soybean [Glycine max (L.) Merr.] to day length and the resultant extension of the growth period as a concept to overcome yield loss due to climate warming. However, experimental validation of this concept is incomplete. The E3 and E4 loci encode phytochrome A isoforms and enhance sensitivity to day length. Our previous study showed that the dominant E4 allele can mitigate yield loss due to warming. In this study, we similarly tested the hypothesis that the dominant E3 allele can reduce yield loss caused by warming. One of the leading Japanese cultivars Enrei (e3/E4) and near‐isogenic lines (NIL‐e3e4, NIL‐E3e4, and NIL‐E3E4) in the Enrei genetic background were grown at near ambient temperature or either 2.0°C or 4.7°C above ambient temperature in temperature‐gradient chambers (TGCs) in a cool region in Japan. The number of days from sowing to beginning of flowering (R1) decreased with increasing temperature, regardless of genotype. However, increasing temperature extended the period from R1 to beginning of pod filling (R3) and increased pod number per plant and seed yield per plant in all genotypes except NIL‐e3e4. Combined data from the TGC experiment and from field trials across three different latitudes showed that the decreased developmental rate during the R1–R3 period can be attributed to longer days, rather than by supra‐optimal temperatures. We conclude that E3, in addition to E4, could potentially increase seed yield in cool regions under future warming.

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Genetic progress battles climate variability: drivers of soybean yield gains in China from 2006 to 2020

Zhang

Zheng

et al. 2023

Agron. Sustain. Dev.

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Field and model assessments of irrigated soybean responses to increased air temperature

Cited by 7 publications

References 43 publications

Simulated climate change effects on soybean production using two crop modules in RZWQM2

Simulated climate change effects on soybean production using two crop modules in RZWQM2

Modulating soybean yield responses to climate warming: The role of E3 and E4 loci in growth period adaptation

Genetic progress battles climate variability: drivers of soybean yield gains in China from 2006 to 2020

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