Smallholders' resilience-building adaptation and its influencing factors in rainfed agricultural areas in China: based on random forest model

Abstract

In recent years, extreme events and risks have increased under the background of global warming, which influenced agricultural production significantly. Adaptation has been considered as a key pathway to enhancing smallholders' climate resilience. We selected a total of 903 smallholders using the multi-stage random sampling technique in rainfed areas in China, and then collected the survey data through the structured questionnaire and focus group discussion. Three resilience-building adaptation indices (RBAS-A, RBAS-D and RBAS-I) were constructed by the entropy method, whose mean values were 0.378, 0.336, and 0.602, respectively. Furtherly, the random forest model was used to explore influencing factors of climate-resilient adaptation strategies. The results revealed that education level of household head, family size, farmland size, access to information by mass media and kith and kin, perception of temperature change in summer and winter, and perception of crop yield change were the significant factors influencing smallholders' alteration strategies. Meanwhile, age and education level of household heads, off-farm income, farmland size, mass media, and perception of winter temperature changes had significant effects on the diversification strategies. Moreover, demographic characteristics, socioeconomic characteristics, information access and climate change perceptions, and impacts had significant impacts on intensification strategies. Importantly, we found that there was a certain threshold for the impact of several factors on the constructed composite indices. And the impacts of the perceptions of temperature on alteration and intensification strategies showed a V-shape. Finally, we proposed targeted suggestions for improving smallholders' climate-resilient adaptation in the rainfed agricultural areas in China.

Keywords: China; Rainfed agricultural areas; Random forest model; Resilience-building adaptation; Smallholders.