IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i11p1899-d969910.html
   My bibliography  Save this article

Differences and Factors of Raw Milk Productivity between China and the United States

Author

Listed:
  • Yuhang Bai

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Kuixing Han

    (College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China)

  • Lichun Xiong

    (College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China
    Zhejiang Province Key Cultivating Think Tank Research Academy for Rural Revitalization of Zhejiang Province, Zhejiang A&F University, Hangzhou 311300, China
    Institute of Ecological Civilization, Zhejiang A&F University, Hangzhou 311300, China)

  • Yifei Li

    (Business School, Zhengzhou University, Zhengzhou 450001, China)

  • Rundong Liao

    (School of Digital Commerce and Trade, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China)

  • Fengting Wang

    (College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China
    Zhejiang Province Key Cultivating Think Tank Research Academy for Rural Revitalization of Zhejiang Province, Zhejiang A&F University, Hangzhou 311300, China
    Institute of Ecological Civilization, Zhejiang A&F University, Hangzhou 311300, China)

Abstract

In order to explore the differences in the productivity level and influencing factors of raw milk between China and the United States, this study uses the stochastic frontier production function and is based on the input and output of factors of raw milk in China and the United States from 2005 to 2020 to measure the impact of factor inputs on raw milk output and the output differences. The results of the study found that: the inefficiency term of raw milk production technology in China is higher than that in the United States; feed costs and fuel power costs have a significant positive role in promoting the growth of raw milk output in China and the United States; health and epidemic prevention costs, as well as maintenance costs, have significant impacts on the output value of raw milk in China, but they have no significant impact on the output value of raw milk in the United States. In terms of the contribution of each input factor, the contribution share of feed costs to the output value of raw milk in China is 52.53% and 25.74%, respectively, compared to the value of raw milk in the United States; The contribution share of technological progress to the output value of raw milk in China is 34.92%, and 53.77%, respectively, compared to U.S. raw milk production value. In order to narrow the productivity gap with the United States dairy industry, China’s dairy industry must pay attention to the moderate-scale breeding of dairy cows; develop an integrated production mode of planting and breeding; promote the development of grain to feed; accelerate the genetic improvement of dairy cattle populations; and learn from the pasture management experiences of foreign countries.

Suggested Citation

  • Yuhang Bai & Kuixing Han & Lichun Xiong & Yifei Li & Rundong Liao & Fengting Wang, 2022. "Differences and Factors of Raw Milk Productivity between China and the United States," Agriculture, MDPI, vol. 12(11), pages 1-13, November.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1899-:d:969910
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/11/1899/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/11/1899/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maslak, Nataliia & Lei, Zhang & Xu, Lu, 2020. "Analysis of agricultural trade in China based on the theory of factor endowment," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 6(1), March.
    2. Yuhang Bai & Li Li & Fengting Wang & Lizhong Zhang & Lichun Xiong, 2022. "Impact of Dairy Imports on Raw Milk Production Technology Progress in China," IJERPH, MDPI, vol. 19(5), pages 1-17, March.
    3. Munir Ahmad & Boris E. Bravo-Ureta, 1995. "An Econometric Decomposition of Dairy Output Growth," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(4), pages 914-921.
    4. Kompas, Tom & Che, Tuong Nhu, 2004. "Productivity in the Australian Dairy Industry," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 12.
    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. Ding, Huiping & Fu, Yanan & Zheng, Lucy & Yan, Zhu, 2019. "Determinants of the competitive advantage of dairy supply chains: Evidence from the Chinese dairy industry," International Journal of Production Economics, Elsevier, vol. 209(C), pages 360-373.
    7. Lukáš Čechura & Zdeňka Žáková Kroupová & Irena Benešová, 2021. "Productivity and Efficiency in European Milk Production: Can We Observe the Effects of Abolishing Milk Quotas?," Agriculture, MDPI, vol. 11(9), pages 1-21, August.
    8. repec:ags:auagre:126559 is not listed on IDEAS
    9. Derek Headey & Mohammad Alauddin & D.S. Prasada Rao, 2010. "Explaining agricultural productivity growth: an international perspective," Agricultural Economics, International Association of Agricultural Economists, vol. 41(1), pages 1-14, January.
    10. Goksel Armagan & Suleyman Nizam, 2012. "Productivity and efficiency scores of dairy farms: the case of Turkey," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(1), pages 351-358, January.
    11. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    12. 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.
    13. Njuki, Eric, 2022. "Sources, Trends, and Drivers of U.S. Dairy Productivity and Efficiency," Economic Research Report 320329, United States Department of Agriculture, Economic Research Service.
    14. Carol Newman & Alan Matthews, 2006. "The productivity performance of Irish dairy farms 1984–2000: a multiple output distance function approach," Journal of Productivity Analysis, Springer, vol. 26(2), pages 191-205, October.
    15. Moreira Lopez, Victor H. & Bravo-Ureta, Boris E. & Arzubi, Amilcar & Schilder, Ernesto, 2006. "Multi-output Technical Efficiency for Argentinean Dairy Farms Using Stochastic Production and Stochastic Distance Frontiers with Unbalanced Panel Data," Economi­a Agraria (Revista Economia Agraria), Agrarian Economist Association (AEA), Chile, vol. 10, pages 1-10.
    16. A. M. Theodoridis & A. Psychoudakis, 2008. "Efficiency Measurement in Greek Dairy Farms: Stochastic Frontier vs. Data Envelopment Analysis," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 1(2), pages 53-67, December.
    17. Hussain, Muzzammil & Ye, Zhiwei & Bashir, Adnan & Chaudhry, Naveed Iqbal & Zhao, Yingjun, 2021. "A nexus of natural resource rents, institutional quality, human capital, and financial development in resource-rich high-income economies," Resources Policy, Elsevier, vol. 74(C).
    18. Peter J. Klenow & Andrés Rodríguez-Clare, 1997. "The Neoclassical Revival in Growth Economics: Has It Gone Too Far?," NBER Chapters, in: NBER Macroeconomics Annual 1997, Volume 12, pages 73-114, National Bureau of Economic Research, Inc.
    19. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chenyang Liu & Xinyao Wang & Ziming Bai & Hongye Wang & Cuixia Li, 2023. "Does Digital Technology Application Promote Carbon Emission Efficiency in Dairy Farms? Evidence from China," Agriculture, MDPI, vol. 13(4), pages 1-23, April.

    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. Arazmuradov, Annageldy & Martini, Gianmaria & Scotti, Davide, 2014. "Determinants of total factor productivity in former Soviet Union economies: A stochastic frontier approach," Economic Systems, Elsevier, vol. 38(1), pages 115-135.
    2. Gong, Binlei, 2020. "Agricultural productivity convergence in China," China Economic Review, Elsevier, vol. 60(C).
    3. Lundgren, Tommy & Marklund, Per-Olov & Zhang, Shanshan, 2016. "Industrial energy demand and energy efficiency – Evidence from Sweden," Resource and Energy Economics, Elsevier, vol. 43(C), pages 130-152.
    4. Gangopadhyay, Partha & Jain, Siddharth & Bakry, Walid, 2022. "In search of a rational foundation for the massive IT boom in the Australian banking industry: Can the IT boom really drive relationship banking?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    5. repec:use:tkiwps:3232 is not listed on IDEAS
    6. Cazals Catherine & Dudley Paul & Florens Jean-Pierre & Jones Michael, 2011. "The Effect of Unobserved Heterogeneity in Stochastic Frontier Estimation: Comparison of Cross Section and Panel with Simulated Data for the Postal Sector," Review of Network Economics, De Gruyter, vol. 10(3), pages 1-22, September.
    7. Baños, José F. & Rodríguez-Álvarez, Ana & Suárez, Patricia, 2016. "Matching frontiers: A random parameter model approach," Efficiency Series Papers 2016/07, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    8. Karim L. Anaya & Michael G. Pollitt, 2014. "Does Weather Have an Impact on Electricity Distribution Efficiency? Evidence from South America," Working Papers EPRG 1404, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    9. Farsi, Mehdi & Filippini, Massimo, 2009. "An analysis of cost efficiency in Swiss multi-utilities," Energy Economics, Elsevier, vol. 31(2), pages 306-315, March.
    10. Christopoulos, Dimitris K. & McAdam, Peter, 2019. "Efficiency, Inefficiency, And The Mena Frontier," Macroeconomic Dynamics, Cambridge University Press, vol. 23(2), pages 489-521, March.
    11. Keller, Michael, 2020. "Wasted windfalls: Inefficiencies in health care spending in oil rich countries," Resources Policy, Elsevier, vol. 66(C).
    12. Federico Belotti & Giuseppe Ilardi & Andrea Piano Mortari, 2019. "Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency," CEIS Research Paper 459, Tor Vergata University, CEIS, revised 30 May 2019.
    13. Rendao Ye & Yue Qi & Wenyan Zhu, 2023. "Impact of Agricultural Industrial Agglomeration on Agricultural Environmental Efficiency in China: A Spatial Econometric Analysis," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    14. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    15. Cristian Barra & Nazzareno Ruggiero, 2022. "How do dimensions of institutional quality improve Italian regional innovation system efficiency? The Knowledge production function using SFA," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 591-642, April.
    16. Dorn, Florian, 2023. "Elections and Government Efficiency," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277700, Verein für Socialpolitik / German Economic Association.
    17. Lingran Yuan & Shurui Zhang & Shuo Wang & Zesen Qian & Binlei Gong, 2021. "World agricultural convergence," Journal of Productivity Analysis, Springer, vol. 55(2), pages 135-153, April.
    18. Afrifa, Godfred Adjapong & Tingbani, Ishmael & Adesina, Oluseyi Oluseun, 2022. "Stochastic frontier modelling of working capital efficiency across Europe," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    19. Yuda, Michio, 2016. "Inefficiencies in the Japanese National Health Insurance system: A stochastic frontier approach," Journal of Asian Economics, Elsevier, vol. 42(C), pages 65-77.
    20. Russ Kashian & Nicholas Lovett & Yuhan Xue, 2020. "Has the affordable care act affected health care efficiency?," Journal of Regulatory Economics, Springer, vol. 58(2), pages 193-233, December.
    21. Antonio Alvarez & Carlos Arias, 2014. "A selection of relevant issues in applied stochastic frontier analysis," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 3-11.

    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:jagris:v:12:y:2022:i:11:p:1899-:d:969910. 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.