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Influential factors of farmers' demands for agricultural science and technology in China

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  • Hua, Ye

Abstract

Agricultural science and technology (AST) is of great significance to the development of rural China. In order to boost farmers' demands for AST, possible influential factors affecting the demands are analyzed by this study. With regard to multiple types of AST for Chinese main agricultural industries-breeding and planting, the study discusses farmer families' demands instead of farmer individual demands. Using linear regression analysis, variables of the possible influential factors are categorized in four groups: family characteristics, farmers' operational status, farmers' attitudes toward AST and government service. Based on 3669 valid questionnaires collected from 15 towns of Anhui Province in China, the paper does the factor analysis and reliability test as well as examines the significance of possible influential factors, indicating that labor saving in farmers' attitudes group is the most important factor for planting industrial AST, and showing that capital support in government service group has the most crucial effect for breeding industrial AST. Hence, differences do exist among diverse factors, and the most significant factors affecting the demands for breeding and planting industrial AST are disparate. According to these results, suggestions concerning the most significant factors are specifically given to the Chinese government on how to expand the demands for AST and how to stimulate rural development.

Suggested Citation

  • Hua, Ye, 2015. "Influential factors of farmers' demands for agricultural science and technology in China," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 249-254.
  • Handle: RePEc:eee:tefoso:v:100:y:2015:i:c:p:249-254
    DOI: 10.1016/j.techfore.2015.07.008
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    References listed on IDEAS

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    Cited by:

    1. Yan, Yan & Guan, JianCheng, 2018. "Social capital, exploitative and exploratory innovations: The mediating roles of ego-network dynamics," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 244-258.
    2. Kung, Chih-Chun & Cao, Xiaoyong & Choi, Yongrok & Kung, Shan-Shan, 2019. "A stochastic analysis of cropland utilization and resource allocation under climate change," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    3. Yury Dranev & Maxim Kotsemir & Boris Syomin, 2018. "Diversity of research publications: relation to agricultural productivity and possible implications for STI policy," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1565-1587, September.

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