Towards more efficient low-carbon agricultural technology extension in China: identifying lead smallholder farmers and their behavioral determinants.

Abstract

In the transition to low-carbon agriculture, smallholder farmers face more constraints. Identifying lead smallholder farmers and leveraging their peer effects can accelerate low-carbon agricultural technology extension among smallholder farmers. Based on survey data from 643 rice farmers in Zhejiang Province, China, this study constructs a finite mixture model (FMM) to identify lead smallholder farmers and then uses a quantile regression model (QRM) to explore their behavioral determinants. The main conclusions are as follows. First, despite the homogeneity in the production mode and resource constraints, lead smallholder farmers are younger and more open to risk, and they have higher educational levels and more family laborers. Second, a higher use efficiency of heterogeneous information is the key to differentiating lead smallholder farmers from other smallholder farmers. Third, green agricultural producer services can effectively alleviate resource constraints and contribute to the low-carbon transition of all smallholder farmers. These results can help redesign targeted extension policies to incentivize lead smallholder farmers.