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Effects of Aging on Labor-Intensive Crop Production from the Perspectives of Landform and Life Cycle Labor Supply: Evidence from Chinese Apple Growers

Author

Listed:
  • Pingping Fang

    (Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China)

  • Yiwen Wang

    (College of Economics and Management, China Center for Food Security Studies, Nanjing Agricultural University, Nanjing 210095, China)

  • David Abler

    (Department of Agricultural Economics, Sociology, and Education, Penn State University, University Park, PA 16802, USA)

  • Guanghua Lin

    (College of Economics and Management, China Center for Food Security Studies, Nanjing Agricultural University, Nanjing 210095, China)

Abstract

The aging of the agricultural labor force is an irreversible trend that has become an important issue in China’s economic transformation. Previous studies on the effects of an aging population in developing countries on agriculture mainly focused on food crops, and the conclusions were mixed. Using data for apple growers in Shaanxi Province, China, we used ordinary least squares (OLS), stochastic frontier production function (SFA), and truncated regression to investigate how rural aging affects apple production under different landform conditions. We provided evidence that (i) aging leads apple growers to use hired labor to replace family labor in the flatlands, but not in mountainous and hilly areas, due to landform constraints on the factor substitution; (ii) aging has no significant impact on mechanical inputs in either the plains or the mountains, indicating that machinery cannot effectively replace the labor force; (iii) limited by a shortage of labor quantity and quality, apple growers respond to aging by reducing agricultural inputs in mountainous and hilly areas; (iv) changes in input structure cause aging to have little influence on yield and technical efficiency in flatlands, while aging significantly reduces yield in mountainous and hilly areas; (v) there is a nonlinear relationship between aging and technical efficiency and yield; and (vi) because the overall mechanization level of China’s apple industry is low, mechanical substitution for labor is not common in apple production.

Suggested Citation

  • Pingping Fang & Yiwen Wang & David Abler & Guanghua Lin, 2023. "Effects of Aging on Labor-Intensive Crop Production from the Perspectives of Landform and Life Cycle Labor Supply: Evidence from Chinese Apple Growers," Agriculture, MDPI, vol. 13(8), pages 1-19, July.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1523-:d:1207269
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    References listed on IDEAS

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    1. Jun Ho Seok & Hanpil Moon & GwanSeon Kim & Michael R. Reed, 2018. "Is Aging the Important Factor for Sustainable Agricultural Development in Korea? Evidence from the Relationship between Aging and Farm Technical Efficiency," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    2. 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.
    3. Liu, Zinan & Zhuang, Juzhong, 2000. "Determinants of Technical Efficiency in Post-Collective Chinese Agriculture: Evidence from Farm-Level Data," Journal of Comparative Economics, Elsevier, vol. 28(3), pages 545-564, September.
    4. Börsch-Supan, Axel & Weiss, Matthias, 2016. "Productivity and age: Evidence from work teams at the assembly line," The Journal of the Economics of Ageing, Elsevier, vol. 7(C), pages 30-42.
    5. Pingping Fang & David Abler & Guanghua Lin & Ali Sher & Quan Quan, 2021. "Substituting Organic Fertilizer for Chemical Fertilizer: Evidence from Apple Growers in China," Land, MDPI, vol. 10(8), pages 1-24, August.
    6. Chowdhury, Hedayet & Zelenyuk, Valentin, 2016. "Performance of hospital services in Ontario: DEA with truncated regression approach," Omega, Elsevier, vol. 63(C), pages 111-122.
    7. Qinghua Liu & C. Richard Shumway, 2006. "Geographic aggregation and induced innovation in American agriculture," Applied Economics, Taylor & Francis Journals, vol. 38(6), pages 671-682.
    8. Abdulai, Awudu & Eberlin, Richard, 2001. "Technical efficiency during economic reform in Nicaragua: evidence from farm household survey data," Economic Systems, Elsevier, vol. 25(2), pages 113-125, June.
    9. Liao, Liuwen & Long, Hualou & Gao, Xiaolu & Ma, Enpu, 2019. "Effects of land use transitions and rural aging on agricultural production in China’s farming area: A perspective from changing labor employing quantity in the planting industry," Land Use Policy, Elsevier, vol. 88(C).
    10. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    11. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    12. Guancheng Guo & Qiyu Wen & Jingjuan Zhu, 2015. "The Impact of Aging Agricultural Labor Population on Farmland Output: From the Perspective of Farmer Preferences," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-7, October.
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