Knowledge and Farmers' Adoption of Green Production Technologies: An Empirical Study on IPM Adoption Intention in Major Indica-Rice-Producing Areas in the Anhui Province of China

Int J Environ Res Public Health. 2022 Nov 1;19(21):14292. doi: 10.3390/ijerph192114292.

Abstract

As a comprehensive technology with social, economic, and ecological benefits, integrated pest management (IPM) is crucial in fundamentally alleviating the environmental pollution caused by traditional high-input agriculture. Based on the random-sampled data of 981 farmer households in major Indica-rice-producing areas in Anhui Province, this study analyzes the impact of agricultural production knowledge on farmers' willingness to adopt IPM technology through logit models, considering integrated knowledge and categorized knowledge. The results indicate that integrated agricultural production knowledge significantly increases farmers' willingness to adopt IPM technology. However, pest-management knowledge was the only one out of four specific disciplines that significantly individually affect farmers' adoption intention. The more knowledge farmers acquire about pest management, the higher intention they have to adopt IPM. Some demographic and household characteristics also significantly influence their willingness. Based on these results, we suggest that increasing farmers' agricultural production knowledge, especially knowledge about pest management, is essential in promoting IPM technology. Besides this, IPM technology should be promoted purposely and consciously, combined with farmers' individual and family characteristics.

Keywords: IPM technology; agro-environment knowledge; cultivation technology knowledge; nutrient management knowledge; pest control; pest management knowledge; production knowledge; rice production.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture / methods
  • Animals
  • China
  • Farmers
  • Humans
  • Intention
  • Moths*
  • Oryza*
  • Pest Control
  • Technology

Grants and funding

This research was funded by the National Natural Science Foundation of China (grant number: 72103081), and China Postdoctoral Science Foundation (grant number: 2020M671391).