Impacts of Technology Training Provided by Agricultural Cooperatives on Farmers’ Adoption of Biopesticides in China

Abstract

As pesticide abuse becomes increasingly serious worldwide, it is necessary to pay attention to the biopesticide adoption behavior of agricultural producers. It is worth verifying whether agricultural cooperatives, as training organizations sharing the same social network with farmers, can promote the adoption of biopesticides through their technology diffusion function. Therefore, based on survey data of 837 citrus producers in Sichuan Province, China, the IV-probit regression model and a mediation effects model were used to empirically test the impact of technical training on farmers’ adoption of biopesticides in addition to its mechanism, considering the farmers’ perception of technology as the mediation variable. The results show that (a) participation in technical training can significantly enhance the probability of the adoption of biopesticides; (b) farmers’ perceptions of biopesticides’ economic and health benefits play a partial mediating role in the relationship; and (c) technical training has more significant effects on biopesticides adoption behavior for a household with higher-educated household heads, lower household total income, and smaller household size, relative to their counterparts. This study provides evidence for establishing relevant policy to encourage the full adoption of the technical training function of agricultural cooperatives and popularize the use of biopesticides.

Research on the Impact of Members’ Social Capital within Agricultural Cooperatives on Their Adoption of IPM in China

“…The IPM technology adoption by agricultural cooperative members analyzed in this study is a binary dummy variable. Therefore, following the research by Liu et al [ 21 ], the impact of the social capital within agricultural cooperatives on its members’ IPM technology adoption behavior is analyzed mainly by building a binary Probit model, and the equation is as follows. where is a binary dummy variable indicating whether member has adopted IPM technology, such that a value of 1 means that the member has adopted IPM technology, and 0 otherwise; indicates the valuation of the social capital within agricultural cooperatives owned by the agricultural cooperative member ; and represents other control variables affecting the adoption of IPM technology by agricultural cooperative members, including individual characteristics of members (e.g., gender, age, education level), household characteristics (e.g., land area, number of off-farm labor, annual household income), agricultural cooperative characteristics (e.g., time to create, demonstration level, existing capital), and regional variables.…”

“…As one of the world’s four most cultivated fruits, citrus has a wide planting area. Its green production behavior broadly impacts human health and the environment, especially in mountainous and hilly regions [ 21 ]. According to the National Bureau of Statistics of China [ 26 ], in 2020, China’s citrus planting area reached 2.7 million hectares, and its output was 51,219,000 tons, ranking first in the world in both aspects.…”

Does e-commerce participation affect green agrotechnology adoption among reservoir resettlers? The case of China’s Three Gorges Reservoir area

“…The expected sales price of agricultural products are the keys to promoting the adoption of green agrotechnology among resettlers. The improvement of agrotechnology application capacity through training is also an important factor (Liu et al, 2022).…”

Effects of Market Incentives and Livelihood Dependence on Farmers’ Multi-Stage Pesticide Application Behavior—A Case Study of Four Provinces in China

“…For intensifying agricultural production, the impact of livelihood dependence on farmers’ behavioral choices is generally considered from the perspective of household income and consumption [ 41 , 42 ]. The importance of income will affect the factor input of farmers and their energy input and the higher the proportion of product income, the more farmers need to consider potential market risks, and generally arrange their factor inputs according to the corresponding market requirements [ 43 , 44 ].…”