Farm-level adoption of conservation agriculture practices and their role in building climate-resilient and resource-efficient food systems in China
This study emphasizes how conservation agricultural practices assist farm managers to use farm input efficiently. Therefore, the primary objective of current study aimed to identify conservation agriculture practices adopted at the farm level and their role in the optimal use of farm resources. Data collected from 384 farmers were analyzed using descriptive statistics and stochastic frontier analysis. Farmers have adopted diversification, water conservation, and soil conservation strategies to minimize the effects of climate change on their farms. The mean technical efficiency score of farmers was 0.69 indicating that farmers could increase their farm output by 31% at the current input level. The findings also showed that a 1% increase in land and working capital increases output by 0.259% and 0.096%, respectively. Similarly, a 1 unit increase in the adoption of diversified strategies improved farm output by 0.037%. The results showed that age (β= −0.0043), training (β = 0.027), government policies (β = 0.0105), climate change awareness (β = 0.0127), cooperative membership (β = 0.0317), and internet accessibility (β = 0.0238) significantly affected farm efficiency. The outcomes of this study substantially provide the practical guidance for enhancing the farm efficiency. Therefore, public policymakers should promote adoption of digital tools in farming by increasing access and training to mobile-based digital tools.
Authors
- Liyuan Zheng
- Pengqun Gao
- Wei Wang
- Shamsheer Ul Haq
Institutions
- Hohai University (CN)
- Yili Normal University (CN)
- Shanghai Open University (CN)
- Ningbo Polytechnic (CN)
- Applied Science University (BH)
- Nanjing University of Aeronautics and Astronautics (CN)
Publication Details
- Journal
- Cogent Food & Agriculture
- Published
- 2025-12-21
- DOI
- https://doi.org/10.1080/23311932.2025.2602861
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00