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Impact of Economic Development Level and Agricultural Water Use on Agricultural Production Scale in China

Author

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  • Jiaxing Pang

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
    Institute of County Economic Development, Lanzhou University, Lanzhou 730000, China)

  • Ningfei Wang

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China)

  • Xue Li

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China)

  • Xiang Li

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China)

  • Huiyu Wang

    (College of Geology and Jewelry, Lanzhou Resources and Environment Voc-Tech College, Lanzhou 730000, China)

  • Xingpeng Chen

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
    Institute of County Economic Development, Lanzhou University, Lanzhou 730000, China)

Abstract

The change of agricultural production scale is directly related to food security and the stable development of social economy. Particularly, the influence of economic development level and agricultural water use on agricultural production scale cannot be ignored. Therefore, this paper uses the fully modified ordinary least squares ( FMOLS ) and the Dumitrescu–Hurlin panel causality test models to discuss the effects of the level of economic development, agricultural water use, the level of urbanization, and the market price of agricultural products on the scale of agricultural production in China. The analysis results indicated that agricultural water use, the level of urbanization, and the market price of agricultural products promoted an increase of the scale of agricultural production at the total sample level; a 1% increase for these three variables will result in an increase of the scale of agricultural production of 0.634%, 0.377%, and 0.292%, respectively. The influence of economic development level on agricultural production scale accords with Kuznets curve. However, at the regional level, the influence of each variable on the eastern region is consistent with the trend of the total sample. In the central region, the impact of economic development on agricultural production scale shows a U-shaped curve, and the improvement of urbanization level inhibits the expansion of agricultural production scale. In the western region, all variables failed to pass the significance test. The results of the FMOLS model were validated by the fixed effects model. The results of causality tests showed that bidirectional causality existed between the scale of agricultural production and the level of economic development, the scale of agricultural production and agricultural water use, the level of economic development and the market price of agricultural products, and the level of urbanization and the market price of agricultural products. In different regions, there were differences in causality between variables. Therefore, based on the empirical results, we put forward some policy suggestions to maintain the scale of agricultural production.

Suggested Citation

  • Jiaxing Pang & Ningfei Wang & Xue Li & Xiang Li & Huiyu Wang & Xingpeng Chen, 2021. "Impact of Economic Development Level and Agricultural Water Use on Agricultural Production Scale in China," IJERPH, MDPI, vol. 18(17), pages 1-12, August.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9085-:d:624135
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    References listed on IDEAS

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    1. Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2012. "Testing for Granger non-causality in heterogeneous panels," Economic Modelling, Elsevier, vol. 29(4), pages 1450-1460.
    2. Dionysis Latinopoulos, 2009. "Multicriteria decision-making for efficient water and land resources allocation in irrigated agriculture," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 11(2), pages 329-343, April.
    3. Xu, Dingde & Deng, Xin & Huang, Kai & Liu, Yi & Yong, Zhuolin & Liu, Shaoquan, 2019. "Relationships between labor migration and cropland abandonment in rural China from the perspective of village types," Land Use Policy, Elsevier, vol. 88(C).
    4. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    5. Luciano Lopez & Sylvain Weber, 2017. "Testing for Granger causality in panel data," Stata Journal, StataCorp LP, vol. 17(4), pages 972-984, December.
    6. Flückiger, Matthias & Ludwig, Markus, 2018. "Geography, human capital and urbanization: A regional analysis," Economics Letters, Elsevier, vol. 168(C), pages 10-14.
    7. Xinhai Lu & Yanwei Zhang & Handong Tang, 2021. "Modeling and Simulation of Dissemination of Cultivated Land Protection Policies in China," Land, MDPI, vol. 10(2), pages 1-21, February.
    8. Lu, Hua & Xie, Hualin & Lv, Tiangui & Yao, Guanrong, 2019. "Determinants of cultivated land recuperation in ecologically damaged areas in China," Land Use Policy, Elsevier, vol. 81(C), pages 160-166.
    9. Peter Pedroni, 2001. "Purchasing Power Parity Tests In Cointegrated Panels," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 727-731, November.
    10. Jiuliang Xu & Zhihua Zhang & Xian Zhang & Muhammad Ishfaq & Jiahui Zhong & Wei Li & Fusuo Zhang & Xuexian Li, 2020. "Green Food Development in China: Experiences and Challenges," Agriculture, MDPI, vol. 10(12), pages 1-15, December.
    11. Peter C. B. Phillips & Bruce E. Hansen, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(1), pages 99-125.
    12. Kuang, Bing & Lu, Xinhai & Zhou, Min & Chen, Danling, 2020. "Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    13. Shilong Piao & Philippe Ciais & Yao Huang & Zehao Shen & Shushi Peng & Junsheng Li & Liping Zhou & Hongyan Liu & Yuecun Ma & Yihui Ding & Pierre Friedlingstein & Chunzhen Liu & Kun Tan & Yongqiang Yu , 2010. "The impacts of climate change on water resources and agriculture in China," Nature, Nature, vol. 467(7311), pages 43-51, September.
    14. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    15. Xu, Weiyi & Jin, Xiaobin & Liu, Jing & Zhou, Yinkang, 2021. "Analysis of influencing factors of cultivated land fragmentation based on hierarchical linear model: A case study of Jiangsu Province, China," Land Use Policy, Elsevier, vol. 101(C).
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