Analysis of farmers’ willingness to adopt genetically modified insect-resistant rice in China

Purpose - Genetically modified (GM) crops, particularly GM grain crops, have been controversial since their commercialization in 1996. However, only a few studies have investigated farmers’ attitudes toward adopting GM grain crops in China. The purpose of this paper is to explore farmers’ willingness to adopt GM insect-resistant rice prior to its commercial release in China and determines the factors that affect farmers’ prospective adoption decisions. Design/methodology/approach - The data are collected using a questionnaire. Descriptive statistics are used to analyze the farmers’ potential willingness to adopt GM rice and level of awareness of GM rice and socioeconomic characteristics. Ordered and binary probit models are applied to identify the key factors that affect the farmers’ decision to adopt GM insect-resistant rice. Findings - Descriptive statistics show that most farmers have little knowledge of GM rice, approximate 35.5 percent of farmers could plant GM rice, and over half of the respondents are uncertain whether or not they will adopt the new crops. The results of econometric analyses show that increasing output and income, and simplicity in crop management, have positive effects on prospective adoption, whereas the high-seed price of GM rice has a significantly negative effect. Health implications also have a significantly positive effect on the farmers’ decision to adopt GM grain crops. A comparative analysis of ordered and binary probit models demonstrates that farmers are more deliberate in their decisions when they have fewer choices. Aside from the above-mentioned variables, the following factors are also statistically significant in the probit model: government technicians’ recommendations, neighbors’ attitudes, level of environmental risks, and the farmer’s age. Originality/value - Information on the major risks and benefits of GM rice was provided to the farmers in the questionnaire. The farmers were then asked to choose from the three ordered alternative answers, namely, “accept,” “uncertain,” and “reject”. Both ordered and binary probit models were applied to comparatively analyze the collected data. This study is one of a handful of studies that employ these econometric models to identify and explain the underlying factors that affect farmers’ decisions. The relevant findings have important implications for future agricultural policy in China.