Modeling the Impact of Climatological Factors and Technological Revolution on Soybean Yield: Evidence from 13-Major Provinces of China

Int J Environ Res Public Health. 2022 May 7;19(9):5708. doi: 10.3390/ijerph19095708.

Abstract

In recent years, the changing climate has become a major global concern, and it poses a higher threat to the agricultural sector around the world. Consequently, this study examines the impact of changing climate and technological progress on soybean yield in the 13 major provinces of China, and considers the role of agricultural credit, farming size, public investment, and power of agricultural machinery from 2000 to 2020. Fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are applied to assess the long-run effect, while Dumitrescu and Hurlin's (2012) causality test is used to explore the short-run causalities among the studied variables. The results revealed that an increase in the annual mean temperature negatively and significantly affects soybean yield, while precipitation expressively helps augment soybean yield. Furthermore, technological factors such as chemical fertilizers accelerate soybean yield significantly, whereas pesticides negatively influence soybean yield. In addition, farming size, public investment, and power of agricultural machinery contribute remarkably to soybean yield. The causality results endorse that chemical fertilizers, pesticides used, agricultural credit, public investment, and power of agricultural machinery have bidirectional causality links with soybean yield. This study suggests several fruitful policy implications for sustainable soybean production in China.

Keywords: China; climate change; soybean yield; technological progress.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture
  • Carbon Dioxide / analysis
  • China
  • Climate Change
  • Fertilizers*
  • Glycine max
  • Pesticides*

Substances

  • Fertilizers
  • Pesticides
  • Carbon Dioxide

Grants and funding

This research was funded by National Social Science Fund of China (Grant number: 19CSH029).