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Technical Efficiency Versus Land-Use Efficiency: A Spatio-Temporal Efficiency Analysis of China's Crop Production

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  • Yin, Fang
  • Sun, Zhanli
  • You, Liangzhi
  • Wei Huang, Vivian

Abstract

Improved land-use efficiency in agricultural production is crucial to meet increasing demand for agricultural commodities using the finite area of arable land worldwide. By applying a spatial autoregressive stochastic frontier methodology to county-level data spanning from 1980 to 2011, we conducted an analysis to investigate changes in both the spatial and temporal dimensions of technical efficiency and land-use efficiency within Chinese crop production. During this period, China achieved a remarkable upsurge in food production, notably within the first three decades of the rural reform that began in 1978. There were substantial transformations in agricultural land use that encompassed changes in cropland areas, shifts in the composition of various crops, alterations in their geographical distributions and enhancements in crop yields. Based on the results of this analysis, land-use efficiency increased slightly from 0.47 to 0.56 in most regions of China during that period and became convergent over time, with spatial gaps narrowing. National technical efficiency increased by 20 % on average, but with substantial regional variations, e.g. lower technical efficiency gains in northeast and northwest China and greater technical efficiency in the north and south. Urbanisation was found to be positively associated with lower technical efficiency, while a greater distance from provincial capitals resulted in higher technical efficiency. Efficient land use can lead to greater agricultural productivity, which, in turn, can boost rural economies and contribute to overall economic growth. These results could help in the design of effective regional policies to optimise land-use efficiency in crop production.

Suggested Citation

  • Yin, Fang & Sun, Zhanli & You, Liangzhi & Wei Huang, Vivian, 2024. "Technical Efficiency Versus Land-Use Efficiency: A Spatio-Temporal Efficiency Analysis of China's Crop Production," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 73(2), pages 1-20.
    • Handle: RePEc:zbw:espost:305178
    DOI: 10.52825/gjae.v73i2.1409
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    References listed on IDEAS

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    1. Fan, Shenggen, 1997. "Production and productivity growth in Chinese agriculture: new measurement and evidence," Food Policy, Elsevier, vol. 22(3), pages 213-228, June.
    2. Tongwei Qiu & S.T. Boris Choy & Biliang Luo, 2022. "Is small beautiful? Links between agricultural mechanization services and the productivity of different-sized farms," Applied Economics, Taylor & Francis Journals, vol. 54(4), pages 430-442, January.
    3. Stijn Reinhard & C. A. Knox Lovell & Geert Thijssen, 2002. "Analysis of Environmental Efficiency Variation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(4), pages 1054-1065.
    4. Tim J. Coelli & D. S. Prasada Rao, 2005. "Total factor productivity growth in agriculture: a Malmquist index analysis of 93 countries, 1980–2000," Agricultural Economics, International Association of Agricultural Economists, vol. 32(s1), pages 115-134, January.
    5. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    6. Chen, Zhuo & Huffman, Wallace E. & Rozelle, Scott, 2009. "Farm technology and technical efficiency: Evidence from four regions in China," China Economic Review, Elsevier, vol. 20(2), pages 153-161, June.
    7. Kutlu, Levent & Tran, Kien C. & Tsionas, Mike G., 2020. "A spatial stochastic frontier model with endogenous frontier and environmental variables," European Journal of Operational Research, Elsevier, vol. 286(1), pages 389-399.
    8. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C., 2016. "A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers," Journal of Econometrics, Elsevier, vol. 190(2), pages 289-300.
    9. Seong-Hoon Cho & Zhuo Chen & Neelam Poudyal, 2010. "Spatial structure of agricultural production in China," Applied Economics, Taylor & Francis Journals, vol. 42(16), pages 2031-2040.
    10. Valerien O. Pede & Francisco J. Areal & Alphonse Singbo & Justin McKinley & Kei Kajisa, 2018. "Spatial dependency and technical efficiency: an application of a Bayesian stochastic frontier model to irrigated and rainfed rice farmers in Bohol, Philippines," Agricultural Economics, International Association of Agricultural Economists, vol. 49(3), pages 301-312, May.
    11. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    12. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    13. Christensen, Laurits R & Jorgenson, Dale W & Lau, Lawrence J, 1973. "Transcendental Logarithmic Production Frontiers," The Review of Economics and Statistics, MIT Press, vol. 55(1), pages 28-45, February.
    14. Ioannis Skevas, 2023. "A novel modeling framework for quantifying spatial spillovers on total factor productivity growth and its components," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(4), pages 1221-1247, August.
    15. Zhang, Bangbang & Li, Xian & Chen, Haibin & Niu, Wenhao & Kong, Xiangbin & Yu, Qiang & Zhao, Minjuan & Xia, Xianli, 2022. "Identifying opportunities to close yield gaps in China by use of certificated cultivars to estimate potential productivity," Land Use Policy, Elsevier, vol. 117(C).
    16. Dubin, Robin A., 1998. "Spatial Autocorrelation: A Primer," Journal of Housing Economics, Elsevier, vol. 7(4), pages 304-327, December.
    17. Viliam Druska & William C. Horrace, 2004. "Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 185-198.
    18. Neumann, Kathleen & Verburg, Peter H. & Stehfest, Elke & Müller, Christoph, 2010. "The yield gap of global grain production: A spatial analysis," Agricultural Systems, Elsevier, vol. 103(5), pages 316-326, June.
    19. Gong, Binlei, 2018. "Agricultural reforms and production in China: Changes in provincial production function and productivity in 1978–2015," Journal of Development Economics, Elsevier, vol. 132(C), pages 18-31.
    20. Yu, Jihai & Lee, Lung-fei, 2010. "Estimation Of Unit Root Spatial Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1332-1362, October.
    21. Julián Ramajo & Geoffrey J. D. Hewings, 2018. "Modelling regional productivity performance across Western Europe," Regional Studies, Taylor & Francis Journals, vol. 52(10), pages 1372-1387, October.
    22. Lei Jiang & Henk Folmer & Minhe Ji & Jianjun Tang, 2017. "Energy efficiency in the Chinese provinces: a fixed effects stochastic frontier spatial Durbin error panel analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(2), pages 301-319, March.
    23. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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