IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i17p7540-d1468000.html
   My bibliography  Save this article

Impact of Operating Scale on Factor Inputs in Grassland Animal Husbandry—Intermediary Effects Based on Market Risk

Author

Listed:
  • Chen Xue

    (College of Economics and Management, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Fulin Du

    (College of Economics and Management, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Mei Yong

    (College of Economics and Management, Inner Mongolia Agricultural University, Hohhot 010018, China)

Abstract

The Chinese government has made the realization of sustainable development in grassland animal husbandry an important policy objective, and achieving a reasonable input of production factors is the key to realizing that goal. Based on the assumption of “rational economic man”, this study measures the economically optimal inputs and actual input bias of production factors, and constructs an econometric model focusing on analyzing the impact of operation scale on the factor input bias. The results indicate that herdsmen deviate from the economically optimal production input levels in forage, labor, and machinery, with the degree of bias decreasing as the livestock size or pasture size expands. Furthermore, it is established that market risk plays a role in mediating the impact of operation scale on the bias of variable production factors. Overall, large-scale herding households have a smaller bias in factor inputs, and should be promoted to operate on an appropriate scale, while paying attention to the prevention of market risk and the enhancement of information symmetry between herders and factor markets.

Suggested Citation

  • Chen Xue & Fulin Du & Mei Yong, 2024. "Impact of Operating Scale on Factor Inputs in Grassland Animal Husbandry—Intermediary Effects Based on Market Risk," Sustainability, MDPI, vol. 16(17), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7540-:d:1468000
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/17/7540/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/17/7540/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Xiaobing & Yamauchi, Futoshi & Huang, Jikun & Rozelle, Scott, 2020. "What constrains mechanization in Chinese agriculture? Role of farm size and fragmentation," China Economic Review, Elsevier, vol. 62(C).
    2. Kumbhakar, Subal C. & Wang, Hung-Jen, 2006. "Estimation of technical and allocative inefficiency: A primal system approach," Journal of Econometrics, Elsevier, vol. 134(2), pages 419-440, October.
    3. Tsionas, Efthymios & Kumbhakar, Subal, 2006. "Estimation of Technical and Allocative Inefficiencies in a Cost System: An Exact Maximum Likelihood Approach," MPRA Paper 20173, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaoheng Zhang & Xiaohua Yu & Xu Tian & Xianhui Geng & Yingheng Zhou, 2019. "Farm size, inefficiency, and rice production cost in China," Journal of Productivity Analysis, Springer, vol. 52(1), pages 57-68, December.
    2. Jean-Philippe Boussemart & Herv頌eleu & Vivian Valdmanis, 2015. "A two-stage translog marginal cost pricing approach for Floridian hospital outputs," Applied Economics, Taylor & Francis Journals, vol. 47(38), pages 4116-4127, August.
    3. repec:ags:aaea22:335541 is not listed on IDEAS
    4. Dibyendu Maiti & Chiranjib Neogi, 2024. "Endogeneity-corrected stochastic frontier with market imperfections," Empirical Economics, Springer, vol. 67(3), pages 1149-1183, September.
    5. Ouyang, Xiaoling & Wei, Xiaoyun & Sun, Chuanwang & Du, Gang, 2018. "Impact of factor price distortions on energy efficiency: Evidence from provincial-level panel data in China," Energy Policy, Elsevier, vol. 118(C), pages 573-583.
    6. Kumbhakar, Subal C. & Tsionas, Mike G., 2020. "On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model: Some new formulations and generalizations," European Journal of Operational Research, Elsevier, vol. 287(2), pages 762-775.
    7. Li, Linfei & Khan, Sufyan Ullah & Guo, Chenhao & Huang, Yanfen & Xia, Xianli, 2022. "Non-agricultural labor transfer, factor allocation and farmland yield: Evidence from the part-time peasants in Loess Plateau region of Northwest China," Land Use Policy, Elsevier, vol. 120(C).
    8. Selorm Yaotse Dorvlo & Elizabeth Mkandawire & Katy Roelich & Charles Blessings Jumbe, 2023. "Pathways and Interactions for Integrating Mechanisation into Sustainable Agricultural Production: The Case of Rice Production in Asutsuare, Ghana," Sustainability, MDPI, vol. 15(22), pages 1-17, November.
    9. Tung Liu, 2020. "Measuring Technical, Allocative inefficiency, and Cost Inefficiency by Applying Duality Theory," Working Papers 202001, Ball State University, Department of Economics, revised Jun 2020.
    10. Wang, Hung-Jen, 2006. "Stochastic frontier models," MPRA Paper 31079, University Library of Munich, Germany.
    11. Yuanying Chi & Wenbing Zhou & Zhenyu Wang & Yu Hu & Xiao Han, 2021. "The Influence Paths of Agricultural Mechanization on Green Agricultural Development," Sustainability, MDPI, vol. 13(23), pages 1-16, November.
    12. Yuewen Huo & Songlin Ye & Zhou Wu & Fusuo Zhang & Guohua Mi, 2022. "Barriers to the Development of Agricultural Mechanization in the North and Northeast China Plains: A Farmer Survey," Agriculture, MDPI, vol. 12(2), pages 1-14, February.
    13. Tommy Lundgren & Mattias Vesterberg, 2024. "Efficiency in electricity distribution in Sweden and the effects of small-scale generation, electric vehicles and dynamic tariffs," Journal of Productivity Analysis, Springer, vol. 62(2), pages 121-137, October.
    14. Xiuhao Quan & Reiner Doluschitz, 2021. "Factors Influencing the Adoption of Agricultural Machinery by Chinese Maize Farmers," Agriculture, MDPI, vol. 11(11), pages 1-11, November.
    15. Yishao Shi & Qianqian Yang & Liangliang Zhou & Shouzheng Shi, 2022. "Can Moderate Agricultural Scale Operations Be Developed against the Background of Plot Fragmentation and Land Dispersion? Evidence from the Suburbs of Shanghai," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    16. Kumbhakar, Subal C., 2011. "Estimation of production technology when the objective is to maximize return to the outlay," European Journal of Operational Research, Elsevier, vol. 208(2), pages 170-176, January.
    17. Ke, Xinli & Chen, Jing & Zuo, Chengchao & Wang, Xiaoqian, 2024. "The cropland intensive utilisation transition in China: An induced factor substitution perspective," Land Use Policy, Elsevier, vol. 141(C).
    18. Xiaoxuan Chen & Tongshan Liu, 2023. "Can Agricultural Socialized Services Promote the Reduction in Chemical Fertilizer? Analysis Based on the Moderating Effect of Farm Size," IJERPH, MDPI, vol. 20(3), pages 1-16, January.
    19. Fabrizio Erbetta & Carmelo Petraglia, 2011. "Drivers of Regional Efficiency Differentials in Italy: Technical Inefficiency or Allocative Distortions?," Growth and Change, Wiley Blackwell, vol. 42(3), pages 351-375, September.
    20. Lai, Hung-pin & Kumbhakar, Subal C., 2019. "Technical and allocative efficiency in a panel stochastic production frontier system model," European Journal of Operational Research, Elsevier, vol. 278(1), pages 255-265.
    21. Tang, Jianjun & Folmer, Henk & Xue, Jianhong, 2015. "Technical and allocative efficiency of irrigation water use in the Guanzhong Plain, China," Food Policy, Elsevier, vol. 50(C), pages 43-52.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7540-:d:1468000. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.