Historical and projected impacts of climate change and technology on soybean yield in China
Graphical abstract
Introduction
Soybean production is critical to national food security and sustainability in China (Liu et al., 2021a, Liu et al., 2021b). In 2020, China accounted for 7.7% of the world's soybean harvested area but only 5.5% of the world's soybean production; the soybean yield level was relatively low in China, about 70.7% of world's yield level (FAOSTAT, 2019). During the period of 2000–2017, annual domestic consumption of soybean in China increased from 24.6 to 108.7 Mt.; among this, about 90% were relayed on the soybean traded on the international market (FAOSTAT, 2019). The demand of soybean in China is expected to reach 133 Mt. by 2035, which would put more pressure on the domestic soybean production (Liu et al., 2021a, Liu et al., 2021b; Najafi et al., 2018).
Historical crop yields in China showed increasing trends, mostly due to improvements in technology (i.e., cultivar genetics and agricultural management practices) (Burchfield et al., 2020). Meanwhile, warming trends and more frequent extreme weather events during crop growing season which resulted by climate change have reduced crop yields in many regions (Amundson et al., 2015; Guo et al., 2022). Climate variability has been increasing against the background of global warming, and it is pertinent to comprehensively assess the impacts of technology and climate change on crop yields. Such assessment could help the agricultural community to combat climate change in an effective and sustainable manner. The typical methods used in an agricultural impacts assessment include crop models, panel statistical methods, multiple linear regression, etc. A crop model could be used to quantify the impacts of changes in cultivar and agricultural management practices on soybean yield (Zhang et al., 2020b). Crop cultivar parameters in a crop model describe how particular cultivar genetics are able to respond to environmental factors, which could transform complex phenotypes (i.e., yield) into simple genetic traits (Wang et al., 2019). We selected the DSSAT-CROPGRO-Soybean model in this search, which has been proved to perform well in simulating the soybean phenology, development, and yield formation (Timsina et al., 2007; Boote et al., 2017). Many studies had used DSSAT-CROPGRO-Soybean model to simulate soybean cultivars traits based on the genetic parameters (da Silva et al., 2021; Perondi et al., 2022). In order to better understand the changes in crop yields due to climate change and technology changes, some researchers had developed a Bayesian model. Parameters of Bayesian model were allowed to vary for each high-resolution region, represented here with soybean-planting counties in China. Besides, the hierarchical structure of Bayesian model is more flexible than a fixed model and could make the fitting more robust and easier to explain (Rising and Devineni, 2020; Wang et al., 2022). A Bayesian model was used to quantify the changes in global crop yields corresponding to changes in climate and technology (Najafi et al., 2018) and the impacts of climate change and technology on different crop yields in the United States (Rising and Devineni, 2020).
Although historical technological improvements could increase soybean yields, recent soybean yield has remained static, stagnated, or even collapsed in 23% of the global soybean-growing area (Ray et al., 2012). Meanwhile, more and more researches had shown that the negative impact of future climate change on soybean yield (Hampf et al., 2020; Liu et al., 2021a, Liu et al., 2021b). In order to combat climate change in the future, it is important to predict soybean yield under different cases of technology improvement, but it is difficult (Farmer and Lafond, 2016). Due to the lack of future cultivar and agricultural management practices changes data, previous studies had put forward an assumption: projected base on the linear trends of crop historical yield increase (Fargione et al., 2010). Burchfield et al. (2020) applied this assumption to maize and soybean yield prediction in the United States and proved the accuracy of this assumption. So, in this study, we also used the same method to project future soybean yield under technology improvement.
Most of the previous agricultural studies used linear or quadratic regression of year to capture the impacts of technology on crop yields (Gammans et al., 2017; Zhu and Burney, 2021), which assumed the impacts of technology on crop yields were linear or quadratic correlation. But in this study, we used specific management practices and crop cultivar genetic parameters to capture the impacts of technology could better isolated the impacts of different technology factors (Najafi et al., 2018). Meanwhile, the impacts of technology on crop yield were most reported in China's maize, wheat and rice (Qin et al., 2015; Zhao and Yang, 2018b; Xu et al., 2021), but rarely in soybean. Also little researches focused on projecting crop yield under future climate change and technology improvement in China. In this study, we factored in two parts of agricultural technology in historical analysis: agricultural management practices like sowing date, total agricultural machinery, effective irrigation area, pesticide application amount and rural labor force; and cultivar genetic parameters during the vegetative and reproductive stages as well as yield formation for soybean. Based on historical climate data, observed crop data and agricultural technology data from 1990 to 2017, we used Bayesian methods to assess the impacts of climate change and technology improvement on soybean yield in the 1312 soybean-growing counties in China. In addition, we referenced historical linear trends to project soybean yield in future periods in scenarios with the highest, average, and lowest rates of technology improvement.
Section snippets
Research area and data
In this study, the research area consists of 1312 soybean-growing counties in the main soybean production region of China. The soybean planting area in the research area accounts for 88% of the national total soybean planting area (NBSC, 2021). Historical climate data of 1312 soybean-growing counties during 1990 to 2017 were obtained from China Meteorological Data Sharing Service System (http://data.cma.cn//). The historical daily climate data including maximum temperature, minimum temperature,
Historical soybean yield, climate, and technology
From 1990 to 2017, the soybean yield in the study counties of China ranged from 649 to 4521 kg/ha, with an average of 1887 kg/ha (Fig. 1 (a)). Overall, soybean yield was higher in the eastern part than in the western part of the research area. More than 90% of the study counties showed an increasing trend in soybean yield, with an average magnitude of 283 kg/ha/decade (Fig. 1 (b)). During the study period, soybean yield was increasing fastest in the 1990s (with an average magnitude of
Discussion
In this study, we collected specific management practices and crop cultivar genetic parameters data, combined with historical climate and soybean yield data, we assessed the impacts of historical climate change and technology on soybean yield in China using Bayesian method. This method could better isolated the impacts of different technology factors (Gammans et al., 2017; Najafi et al., 2018; Zhu and Burney, 2021). Meanwhile, we referenced historical linear trends to project soybean yield in
Conclusions
From 1990 to 2017, more than 90% of the study counties showed an increase in soybean yield, although the increase slowed down after 2010. On average, changes in agricultural management practices and genetic parameters led to a 16% (ranged from −24% to 50%) and 6% (ranged from −14% to 24%) increase in soybean yield, respectively. By contrast, changes in climatic factors led to a −14% to 18% (with an average of −1%) change in soybean yield. The soybean yield increased caused by genetic parameters
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This study is supported by the National Natural Science Foundation of China (Grant no. 31471408), the National Key Research and Development Program of China (2019YFA0607402) and the 2115 Talent Development Program of China Agricultural University.
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