Impact factors and production performance of adoption of green control technology: An empirical analysis based on the survey data of rice farmers in Sichuan Province

The application and promotion of crop green control technology addresses the urgent need for green agricultural development. However, existing researches on the adoption of green control technologies suffer from a lack of comprehensive analysis of the influencing factors and methods of production performance. A quantitative analysis of impact factors and production performance of the adoption of green control technology by rice farmers in Sichuan Province could provide policy directions to promote green control technologies and enrich our understanding of their application and promotion. Using data from a survey of rice farmers in Sichuan Province, a Logit model identified the main factors affecting adoption of green control technologies and estimated the propensity scores of production performance. DEA-PSM model analyzed the effects on production performance of the adoption of green control technology by farmers. The results showed that the male gender, higher education levels, greater planting area, cooperative membership, proximity to urban areas, green control technology training, ability to obtain a market premium for safe agricultural products produced by green control technology, consideration of the weak quality of agricultural ecological environments, and willingness to reduce pesticide use were associated with a greater likelihood of adopting green control technology. Rice production performance of the surveyed farmers was 0.103-1.000, and the average performance was 0.471. Without changing the technological level and input scale, there was still room for a 52.9% improvement in performance. Production performance of most farmers was between 0.4 and 0.6, and production efficiency was generally not high. The average rice production performance in Qionglai, Xuanhan, and Luxian counties was 0.558, 0.379, and 0.467 respectively, indicating significant differences among regions. The nearest-neighbor, radius, and kernel matching methods were applied to measure the effects of adoption of green control technology on rice production. The results showed that whether farmers adopting green control technology had little difference on production performance and the effect of farmer adoption of green control technology on rice production performance was not significant. Application and promotion of green control technology will therefore require the creation of a supportive external environment that empowers adoption of green control technology and focuses on reducing costs and increasing incomes.