Distribution characteristics of climate potential productivity of soybean in frigid region and its response to climate change

Gong, Lijuan; Dan, Liu; Jiang, Lili; Li, Xiufen; Jia-jia, LV

doi:10.1007/s11356-021-15879-y

Cited by 14 publications

(7 citation statements)

References 39 publications

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“…In this study, three niche models were applied to simulate the potential distribution of soybean based on the environmental variables in the frigid region. The low to highly suitable areas were concentrated in the plains, which were basically consistent with the soybean production areas in this region (Dong et al, 2017;Gong et al, 2022b). Therefore, as confirmed by the prediction results, the Northeast Plain could be regarded as the main soybean cultivation area.…”

Section: Discussionsupporting

confidence: 72%

Differences between ecological niche models when predicting the potential distribution of soybean

Gong,

Li,

Liu

et al. 2024

Sci. agric. (Piracicaba, Braz.)

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Against the background of a sharp decline in soybean planting, rising imports, and natural disasters in China, finding appropriate distribution areas for soybean has become a matter of urgency so that soybean planting policies can be formulated and soybean food security ensured. Among the numerous ecological niche models, the most suitable one for predicting potential distribution areas of soybean in the frigid region must be identified. Based on 65 soybean occurrence points and nine environmental variables, three ecological niche models, MaxEnt, BIOCLIM, and DOMAIN, were applied to the prediction of potential distribution areas for soybean. According to the analytical comparison, the three models predicted the potential distribution of soybean and, specifically, MaxEnt, stood out above the other two models as regards predicting the soybean distribution (Receiver Operating Characteristic curve, AUC = 0.916, Kappa = 0.685). The potential distribution areas (from low suitability to high suitability) predicted by MaxEnt was the largest and accounted for 59.5 % of the total area. The potential suitable distribution area of soybean was mainly concentrated in relatively flat terrain. The Sanjiang Plain and the Northeast Plain accounted for 9.4 % of the total area in the frigid region and are highly suitability for soybean. At the same time, annual mean temperature, elevation and Apr solar radiation were the key determinants affecting soybeans' habitat.On the whole, the selection of ecological niche models and the prediction of soybean potential distribution can provide an essential reference for soybean planting and planning. Moreover, it would be a reliable example for the subsequent related research on soybean habitats.

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Section: Discussionsupporting

confidence: 72%

Differences between ecological niche models when predicting the potential distribution of soybean

Gong,

Li,

Liu

et al. 2024

Sci. agric. (Piracicaba, Braz.)

Self Cite

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“…The decision to use the Mann-Kendall test and Pearson's correlation coefficient (r) was based on the need for an initial analysis of trends and the correlation between variables, being a preliminary stage for future studies that may employ more sophisticated models [63][64][65][66]. It was also based on their applicability and research into other variables, such as air humidity and soil moisture reserves.…”

Section: Discussionmentioning

confidence: 99%

The Relationship between Climate, Agriculture and Land Cover in Matopiba, Brazil (1985–2020)

Araújo,

Rufino,

Silva

et al. 2024

Sustainability

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Climate change has been at the forefront of discussions in the scientific, economic, political, and public spheres. This study aims to analyze the impacts of climate change in the Matopiba region, assessing its relationship with land cover and land use, soybean crop production and yield, and ocean–atmosphere anomalies from 1985 to 2020. The analysis was conducted in four parts: (1) trends in annual and intra-annual climate changes, (2) the spatiotemporal dynamics of land cover and use, (3) the spatiotemporal dynamics of soybean production and yield, and (4) the relationship between climate change, agricultural practices, land cover and use, and ocean–atmosphere anomalies. Statistical analyses, including Mann–Kendall trend tests and Pearson correlation, were applied to understand these relationships comprehensively. The results indicate significant land cover and use changes over 35 years in Matopiba, with municipalities showing increasing soybean production and yield trends. There is a rising trend in annual and intra-annual maximum temperatures, alongside a decreasing trend in annual precipitation in the region. Intra-annual climate trends provide more specific insights for agricultural calendar planning. No correlation was found between the climate change trends and soybean production and yield in the evaluated data attributed to genetic and technological improvements in the region. The North Atlantic Ocean shows a positive correlation with soybean agricultural variables. Evidence suggests soybean production and yield growth under climate change scenarios. This study highlights soybeans’ adaptation and climate resilience in the Matopiba region, providing valuable insights for regional agricultural development and contributing to further research in environmental, water-related, social, and economic areas of global interest.

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“…Several studies on the effect of climate change on soybean output have also been carried out (Coleman et al 2021 ; Gong et al 2022 ). Durodola and Mourad ( 2020 ) discovered that yearly soybean yields are influenced by the meteorological conditions of the agricultural season while estimating the influences of climate change on soybean output in Nigeria.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to simulation research conducted by Mall et al ( 2004 ), higher temperatures can reduce soybean yields by 10 to 20%. Changes in climatic factors have a variety of effects on soybean climate potential productivity, according to a recent study (Gong et al 2022 ). Weather changes have already affected global food production, according to the study.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards a sustainable food production: modelling the impacts of climate change on maize and soybean production in Ghana

Ntiamoah

Appiah-Otoo

et al. 2022

Environ Sci Pollut Res

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The Ghanaian economy relies heavily on maize and soybean production. The entire maize and soybean production system is low-tech, making it extremely susceptible to environmental factors. As a result, climate change and variability have an influence on agricultural production, such as maize and soybean yields. Therefore, the study’s ultimate purpose was to analyze the influence of CO 2 emissions, precipitation, domestic credit, and fertilizer consumption on maize and soybean productivity in Ghana by utilizing the newly constructed dynamic simulated autoregressive distributed lag (ARDL) model for the period 1990 to 2020. The findings indicated that climate change enhances maize and soybean yields in Ghana in both the short run and long run. Also, the results from the frequency domain causality showed that climate change causes maize and soybean yield in the long-run. These outcomes were robust to the use of the ordinary least squares estimator and the impulse response technique. The findings show that crop and water management strategies, as well as information availability, should be considered in food production to improve resistance to climate change and adverse climatic circumstances.

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Distribution characteristics of climate potential productivity of soybean in frigid region and its response to climate change

Cited by 14 publications

References 39 publications

Differences between ecological niche models when predicting the potential distribution of soybean

Differences between ecological niche models when predicting the potential distribution of soybean

The Relationship between Climate, Agriculture and Land Cover in Matopiba, Brazil (1985–2020)

Towards a sustainable food production: modelling the impacts of climate change on maize and soybean production in Ghana

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