Analysis of low-carbon rice farming behavior and its influencing factors in farmers under the distributed cognition perspective—empirical study based on 2,173 farmers in Jiangxi Province

Abstract

Rice production is the core component of the food security strategy in China, but it is also a major source of methane and nitrous oxide. Promoting low-carbon rice farming (LCRF) to increase the carbon sink, decrease carbon emissions, and achieve low-carbon, high-yield production is an inevitable “win-win” choice for achieving “double carbon” targets and guaranteeing national food security. This study contributes to the advancement of research on farmers’ adoption decisions and fills gaps in LCRF’s technical research on farmers’ decision-making behavior. The results also provide a basis for formulating policies to encourage LCRF and protect cultivated land.This study conducted field research on 2,173 farming households in Jiangxi Province, a traditional agricultural province in China, and examined the effects of personal forces, local forces, and cultural forces on LCRF adoption behavior by introducing the distributed cognition theoretical framework based on a status quo analysis and employing the multivariate ordinal logistic model.The results of the study showed that: 1) the overall acceptance of LCRF behavior is currently not very high. In the 2173 questionnaires, the mean number of LCRF behavior items accepted by farmers was 3.10 items; 153 farmers did not adopt any LCRF behavior, and only 77 farmers adopted all LCRF behaviors. Most farmers (n = 535) adopted three LCRF behaviors. 2) In distributed cognition affecting LCRF behavior, acceptance was primarily affected by cultural forces, followed by local forces, while the effects of personal forces were limited. Therefore, it is recommended that training and promotion should be increased, policy subsidies should be increased, the land market should be improved, and LCRF demonstrations should be carried out to increase the acceptance of LCRF behavior among rice farmers.