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Comprehensive Evaluation of Agricultural Modernization Levels

Author

Listed:
  • Zhixin Zhang

    (School of Economics, Shandong University of Technology, Zibo 255000, China)

  • Yingjie Li

    (School of Economics, Shandong University of Technology, Zibo 255000, China)

  • Ehsan Elahi

    (School of Economics, Shandong University of Technology, Zibo 255000, China)

  • Yameng Wang

    (School of Economics, Shandong University of Technology, Zibo 255000, China)

Abstract

Agricultural modernization is the most important task of modernization construction. This study used the multi-index comprehensive measurement method to estimate the agricultural modernization level of Shandong Province from 2010 to 2019. Regional differences were analyzed using the ESDA method, and the main obstacles were diagnosed using the obstacle degree model. This paper constructs an index system, including an agricultural production system, a management system, an industrial system, output benefits, rural social development, and agricultural sustainable development. The results showed that the level of agricultural modernization in Shandong Province was divided into three stages: decelerating ascent (2010–2013), accelerating ascent (2014–2016), and high-level fluctuation (2017–2019). Results found that the scores of the agricultural production system, rural development level, and agricultural industrial system peaked in 2017.The regional difference in the agricultural modernization level of Shandong Province is high in the north and low in the south of China. The main obstacles in the rising stage of agricultural modernization in Shandong Province are the proportion of the added value of agriculture, forestry, fishery, and animal husbandry service; the amount of fertilizer and pesticide used per land area; the total power of units farmland machinery; the high-level fluctuation order are shelterbelt construction rate; the proportion of livestock production value; rural per capita electricity consumption; and rural employment rate. The average arable land per household and the employment rate of the rural population are the key obstacles to high levels of agricultural modernization. Therefore, optimizing industrial structure, improving the input of productive factors, cultivating a high-quality rural labor force, improving the efficiency of agricultural funds use, and awareness of the sustainable development of agriculture are all suggested.

Suggested Citation

  • Zhixin Zhang & Yingjie Li & Ehsan Elahi & Yameng Wang, 2022. "Comprehensive Evaluation of Agricultural Modernization Levels," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5069-:d:800190
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    References listed on IDEAS

    as
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    2. Elahi, Ehsan & Weijun, Cui & Zhang, Huiming & Nazeer, Majid, 2019. "Agricultural intensification and damages to human health in relation to agrochemicals: Application of artificial intelligence," Land Use Policy, Elsevier, vol. 83(C), pages 461-474.
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    6. Elahi, Ehsan & Weijun, Cui & Jha, Sunil Kumar & Zhang, Huiming, 2019. "Estimation of realistic renewable and non-renewable energy use targets for livestock production systems utilising an artificial neural network method: A step towards livestock sustainability," Energy, Elsevier, vol. 183(C), pages 191-204.
    7. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
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    2. Mengyao Xia & Di Zeng & Qi Huang & Xinjian Chen, 2022. "Coupling Coordination and Spatiotemporal Dynamic Evolution between Agricultural Carbon Emissions and Agricultural Modernization in China 2010–2020," Agriculture, MDPI, vol. 12(11), pages 1-19, October.
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    5. Feixiao Wang & Yaoqun Xu, 2022. "Evolutionary Game Analysis of the Quality of Agricultural Products in Supply Chain," Agriculture, MDPI, vol. 12(10), pages 1-16, September.
    6. Shuya Wang & Xinjia Tian & Hui Wang & Chang Liu & Zhilin Wang & Qiuhua Song, 2023. "Forecasting and Coupled Coordination Analysis of Supply and Demand for Sustainable Talent in Chinese Agriculture," Sustainability, MDPI, vol. 15(9), pages 1-18, April.

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