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Evaluating the Effectiveness of Different Demonstration Models on Agricultural Climate-Smart Technology Adoption: Evidence from China’s Cotton Farmers

Author

Listed:
  • Lu Cai

    (Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    Xinjiang Key Laboratory for Crop Gene Editing and Germplasm Innovation, Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, China)

  • Zhenggui Zhang

    (Xinjiang Key Laboratory for Crop Gene Editing and Germplasm Innovation, Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, China
    State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang 455000, China)

  • Shaohua Mao

    (Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    Xinjiang Key Laboratory for Crop Gene Editing and Germplasm Innovation, Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, China)

  • Jamshed Azimov

    (Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    Xinjiang Key Laboratory for Crop Gene Editing and Germplasm Innovation, Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, China)

  • Nilupaier Yusufujiang

    (Xinjiang Key Laboratory for Crop Gene Editing and Germplasm Innovation, Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, China
    College of Agriculture, Xinjiang Agricultural University, Urumqi 830052, China)

  • Yaopeng Zhang

    (Xinjiang Key Laboratory for Crop Gene Editing and Germplasm Innovation, Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, China)

  • Rusheng Bi

    (Xinjiang Key Laboratory for Crop Gene Editing and Germplasm Innovation, Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, China)

  • Lin Wang

    (Xinjiang Key Laboratory for Crop Gene Editing and Germplasm Innovation, Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, China)

  • Zhanbiao Wang

    (Xinjiang Key Laboratory for Crop Gene Editing and Germplasm Innovation, Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, China
    State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang 455000, China)

  • Lei Gao

    (Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    Xinjiang Key Laboratory for Crop Gene Editing and Germplasm Innovation, Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, China
    State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang 455000, China)

Abstract

Amid escalating global climate challenges and the accelerating transition toward low-carbon agriculture, the effective diffusion of climate-smart technologies (CSTs) has become a critical pathway to achieving sustainable agricultural development. This study focuses on cotton farmers in Xinjiang and draws on micro-level survey data from 504 respondents to construct an analytical framework of “demonstration–cognition–adoption”. It systematically examines the impact pathways and mediating mechanisms of different demonstration models. The findings reveal that technology demonstration indirectly influences farmers’ adoption of CSTs by shaping their cognitive perceptions, with perceived operational utility emerging as the most critical mediating mechanism in the entire technology dissemination chain. Among current extension models, government-led demonstrations play a central role, while the effectiveness of enterprise-led demonstrations hinges on brand credibility and service quality. Moreover, the ease of operation of a technology outweighs its economic returns in determining adoption outcomes, and farmers exhibit significant heterogeneity in their responses to different demonstration types. Based on these insights, the study recommends the development of a stratified and differentiated dissemination strategy, the strengthening of government-led demonstration functions, the promotion of standardized enterprise participation, and the enhancement of both farmers’ cognitive understanding and technology fit to enable broader and higher-quality adoption of climate-smart technologies.

Suggested Citation

  • Lu Cai & Zhenggui Zhang & Shaohua Mao & Jamshed Azimov & Nilupaier Yusufujiang & Yaopeng Zhang & Rusheng Bi & Lin Wang & Zhanbiao Wang & Lei Gao, 2025. "Evaluating the Effectiveness of Different Demonstration Models on Agricultural Climate-Smart Technology Adoption: Evidence from China’s Cotton Farmers," Sustainability, MDPI, vol. 17(16), pages 1-28, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7367-:d:1724684
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    References listed on IDEAS

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    1. Hassen, Sied, 2018. "The effect of farmyard manure on the continued and discontinued use of inorganic fertilizer in Ethiopia: An ordered probit analysis," Land Use Policy, Elsevier, vol. 72(C), pages 523-532.
    2. Lantong Shao & Jiaqin Gong & Wenqing Fan & Zongyi Zhang & Meng Zhang, 2022. "Cost Comparison between Digital Management and Traditional Management of Cotton Fields—Evidence from Cotton Fields in Xinjiang, China," Agriculture, MDPI, vol. 12(8), pages 1-18, July.
    3. Athanasios Balafoutis & Bert Beck & Spyros Fountas & Jurgen Vangeyte & Tamme Van der Wal & Iria Soto & Manuel Gómez-Barbero & Andrew Barnes & Vera Eory, 2017. "Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics," Sustainability, MDPI, vol. 9(8), pages 1-28, July.
    4. Tan, Mingdong & Cui, Ningbo & Jiang, Shouzheng & Xing, Liwen & Wen, Shenglin & Liu, Quanshan & Li, Weikang & Yan, Siwei & Wang, Yaosheng & Jin, Haochen & Wang, Zhihui, 2025. "Effect of practicing water-saving irrigation on greenhouse gas emissions and crop productivity: A global meta-analysis," Agricultural Water Management, Elsevier, vol. 308(C).
    5. Ngoma, Hambulo & Marenya, Paswel & Tufa, Adane & Alene, Arega & Matin, Md Abdul & Thierfelder, Christian & Chikoye, David, 2024. "Too fast or too slow: The speed and persistence of adoption of conservation agriculture in southern Africa," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    6. Bell, Andrew Reid & Engelbert, Mark, 2025. "When does new information encourage adoption, and where can we observe it: A synthesis of 3ie’s thematic window on agricultural innovation," World Development Perspectives, Elsevier, vol. 37(C).
    7. Mao, Hui & Sun, Zhenkai & Chai, Anyuan & Fang, Lan & Shi, Chaoqian, 2025. "Extreme Weather, agricultural insurance and farmer's climate adaptation technologies adoption in China," Ecological Economics, Elsevier, vol. 228(C).
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