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Efficiency Analysis of Scientific and Technological Innovation in Grain Production Based on Improved Grey Incidence Analysis

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  • Shuhua Zhang

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
    School of Computer Science and Informatics, Institute of Artificial Intelligence, De Montfort University, The Gateway, Leicester LE1 9BH, UK)

  • Bingjun Li

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Yingjie Yang

    (School of Computer Science and Informatics, Institute of Artificial Intelligence, De Montfort University, The Gateway, Leicester LE1 9BH, UK)

Abstract

Analyzing and evaluating the efficiency of scientific and technological innovation in grain production is conducive to the rational allocation of resources, promoting the development of scientific and technological innovation in grain production and providing guarantee for grain security. By refining the elements of grain production and scientific and technological innovation, an evaluation system of scientific and technological innovation in grain production is constructed. Firstly, combining linear programming together with the traditional grey synthetic incidence analysis model, a incidence analysis of the scientific and technological innovation indicators of grain production is carried out, and the key and secondary indexes affecting grain outputs are screened by an improved grey incidence analysis model. Secondly, based on DEA-Malmquist index model and taking the grain production process as the research object, the scientific and technological achievement transformation indicators are divided into pre-production, in-production and post-production indicators. The key indicators and secondary indicators of scientific and technological innovation of grain production in various cities of Henan Province from 2010 to 2019 are used to analyze the efficiency of scientific and technological innovation in each stage of grain production. The results show that: (1) The type of basic ability of scientific and technological innovation indicators and the transformation ability of scientific and technological innovation achievements are the major indicators influencing grain outputs, and the investment of basic resources of scientific and technological innovation and the transformation of scientific and technological innovation achievements are the most important to improve grain outputs. (2) In addition, the study reveals that the secondary indicators of the technological innovation efficiency of grain production based on the DEA-Malmquist index model are more efficient than the key indicators in the pre-production, in-production and post-production stages. And there are gaps in the scientific and technological innovation performance of grain production among cities in Henan Province, and the index of technological progress is the leading factor for the gap.

Suggested Citation

  • Shuhua Zhang & Bingjun Li & Yingjie Yang, 2021. "Efficiency Analysis of Scientific and Technological Innovation in Grain Production Based on Improved Grey Incidence Analysis," Agriculture, MDPI, vol. 11(12), pages 1-21, December.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1241-:d:697876
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    References listed on IDEAS

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    1. Alexis Dinno, 2009. "Implementing Horn’s parallel analysis for principal component analysis and factor analysis," Stata Journal, StataCorp LLC, vol. 9(2), pages 291-298, June.
    2. Wei Wang & Liwei Guo, 2021. "Sources of production growth in Chinese agriculture:empirical evidence from penal data results 2001–2018," Applied Economics, Taylor & Francis Journals, vol. 53(44), pages 5135-5157, September.
    3. Shokhrukh-Mirzo Jalilov & Mohammed Mainuddin & Md. Maniruzzaman & Md. Mahbubul Alam & Md. Towfiqul Islam & Md. Jahangir Kabir, 2019. "Efficiency in the Rice Farming: Evidence from Northwest Bangladesh," Agriculture, MDPI, vol. 9(11), pages 1-14, November.
    4. Khan Claudette Mengui & Saera Oh & Sang Hyeon Lee, 2019. "The Technical Efficiency of Smallholder Irish Potato Producers in Santa Subdivision, Cameroon," Agriculture, MDPI, vol. 9(12), pages 1-13, December.
    5. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers," Economic Journal, Royal Economic Society, vol. 92(365), pages 73-86, March.
    6. Derek Headey & Mohammad Alauddin & D.S. Prasada Rao, 2010. "Explaining agricultural productivity growth: an international perspective," Agricultural Economics, International Association of Agricultural Economists, vol. 41(1), pages 1-14, January.
    7. Zhihai Yang & Dong Wang & Tianyi Du & Anlu Zhang & Yixiao Zhou, 2018. "Total-Factor Energy Efficiency in China’s Agricultural Sector: Trends, Disparities and Potentials," Energies, MDPI, vol. 11(4), pages 1-16, April.
    8. Meimei Chen & Libang Ma & Xinglong Che & Haojian Dou, 2020. "Identification of Transformation Stages and Evolution of Agricultural Development Types Based on Total Factor Productivity Analysis: A Case Study of Gansu Province, China," Agriculture, MDPI, vol. 10(8), pages 1-19, August.
    9. Unknown, 2005. "Agriculture In Transition," Economics of Agriculture, Institute of Agricultural Economics, vol. 52(01).
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