Modelling soybean yield responses to seeding date under projected climate change scenarios

Jing, Qi; Huffman, Ted; Shang, Jiali; Liu, Jiangui; Pattey, E.; Morrison, Malcolm J.; Jégo, Guillaume; Qian, Budong

doi:10.1139/cjps-2017-0065

Cited by 13 publications

(16 citation statements)

References 52 publications

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“…SD shifts has been proven to be one of the main anthropogenic management measures affecting crop phenological changes 44 , 45 . Any SD shift will affect the time and length of soybean development to a certain extent 22 , 46 , and shifting SD within an optimum planting range in different regions could mitigate the adverse impacts of climate conditions on soybean yield 47 . The results showed that, in the context of climate change, the delay in SD was accelerated by the increase in average temperature in three soybean cultivation zones.…”

Section: Discussionmentioning

confidence: 99%

“…This may due to the climate warming within the range suitable for soybean growth, which is likely to increase soybean production. Therefore, new cultivars with longer vegetative, reproductive, and seed filling periods will be favorable to capitalize on the extra heat in northern soybean zone 46 . By contrast, key growth periods of soybean were extended at most stations in Huang-Huai-Hai soybean zone and southern soybean zone, which may increase the heat waves during the growing periods in low-latitude regions.…”

Section: Discussionmentioning

confidence: 99%

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Shortened key growth periods of soybean observed in China under climate change

Tan

Liu

Dai

et al. 2021

Sci Rep

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Phenology is an important indicator of global climate change. Revealing the spatiotemporal characteristics of crop phenology is vital for ameliorating the adverse effects of climate change and guiding regional agricultural production. This study evaluated the spatiotemporal variability of soybean’s phenological stages and key growth periods, and assessed their sensitivity to key climatic factors, utilizing a long-term dataset (1992–2018) of soybean phenology and associated meteorological data collected at 51 stations across China. The results showed that (1) during the soybean growing seasons from 1992 to 2018, the average temperature (0.34 ± 0.09 ℃ decade−1) and cumulative precipitation (6.66 ± 0.93 mm decade−1) increased, but cumulative sunshine hours (− 33.98 ± 1.05 h decade−1) decreased. (2) On a national scale, dates of sowing, emergence, trifoliate, anthesis, and podding of soybean were delayed, while the maturity date showed an advancing trend. The vegetative growth period (− 0.52 ± 0.24 days decade−1) and whole growth period (− 1.32 ± 0.30 days decade−1) of soybean were shortened, but the reproductive growth period (0.05 ± 0.26 days decade−1) was slightly extended. Trends in soybean phenological stages and key growth periods diverged in regions. Soybean phenological stages were delayed in Huang-Huai-Hai soybean zone, whereas advanced in southern soybean zone. Moreover, the key growth periods were greatly shortened in northern soybean zone. (3) In general, the sensitivity of soybean key growth periods to temperature was negative, whereas those to precipitation and sunshine hours differed among regions. In particular, most phenological stages were negatively sensitive to sunshine hours. Our results will provide scientific support for decision-making in agricultural production practices.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Shortened key growth periods of soybean observed in China under climate change

Tan

Liu

Dai

et al. 2021

Sci Rep

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“…Jing et al. (2017) showed that CROPGRO simulated a 14% increase in soybean yield in the near future (2041–2070) but a decrease in distant future (2071–2100), whereas the STICS model estimated a decrease in both near and distant future under RCP8.5. Wolf (2002a, 2002b) compared CROPGRO and SOYBEANW models for simulated climate change effects after calibration and found that the models responded differently to irrigation management depending on locations.…”

Section: Introductionmentioning

confidence: 99%

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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“…Jing et al. (2017) found that the CROPGRO model simulated a seed yield increase of 14% in the near future (2041–2070) and a decrease in the distant future (2071–2100) under RCP8.5 in eastern Canada, whereas the STICS model projected a yield decrease in both near and distant future.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Correctly quantifying elevated air temperature effects on crop yield through field experiments and crop modeling is essential for developing adaptation strategies under climate change. In this study, the effects of elevated air temperature on soybean [Glycine max (L.) Merr.] production were investigated using a four-year open top chamber experiment (2015-2018) in North Carolina, USA. The experimental results showed that an increase of average air temperature during growing seasons by 3.4 • C to 25.7 • C decreased seed yield by 22%, final biomass by 11%, and harvest index by 12%, but did not significantly affect maturity date. The reduction in seed yield was more associated with reduction in seed number (19%) than in seed weight (2%). Two soybean growth models (CROPGRO and HER-MES) in the Root Zone Water Quality Model (RZWQM) were evaluated against measured temperature responses from the four-year experiment. Both crop models simulated lower reduction in seed yield (15% for CROPGRO and 17% for HER-MES), but simulated reduction in biomass was lower by CROPGRO (7%) and higher by HERMES (20%) than that measured in the chambers. Between the two models, HERMES predicted slightly better responses of seed yield and biomass to temperature across the four years than CROPGRO, whereas the opposite was true for maturity date and harvest index. However, HERMES simulated earlier maturity dates for heated treatment than observed, suggesting that improvements are needed in the model for soybean phenology response to temperature increase.

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Modelling soybean yield responses to seeding date under projected climate change scenarios

Cited by 13 publications

References 52 publications

Shortened key growth periods of soybean observed in China under climate change

Shortened key growth periods of soybean observed in China under climate change

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

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

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