IDEAS home Printed from https://ideas.repec.org/p/ags/eaae11/114763.html
   My bibliography  Save this paper

Comparing productivity growth in conventional and grassland dairy farms

Author

Listed:
  • Kellermann, Magnus
  • Salhofer, Klaus

Abstract

This paper analyzes technical efficiency and productivity growth of dairy farms in southern Germany. We compare the performance of farms operating on permanent grassland and conventional farms using fodder crops from arable land. Using a latent class stochastic frontier model, intensive and extensive production systems are identified for both types of farms. We estimate stochastic output distance functions to represent the production technology. TFP change is calculated and decomposed using a generalized Malmquist productivity index. Our results show that grassland farms can in general keep up with conventional farms. The productivity on intensive (extensive) grassland dairy farms grew by 1.15% (0.93%) per year, compared to 1.19% (intensive) and 1.0% (extensive) on conventional farms.

Suggested Citation

  • Kellermann, Magnus & Salhofer, Klaus, 2011. "Comparing productivity growth in conventional and grassland dairy farms," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114763, European Association of Agricultural Economists.
  • Handle: RePEc:ags:eaae11:114763
    as

    Download full text from publisher

    File URL: http://purl.umn.edu/114763
    Download Restriction: no

    References listed on IDEAS

    as
    1. Tim Coelli & Sergio Perelman, 2000. "Technical efficiency of European railways: a distance function approach," Applied Economics, Taylor & Francis Journals, vol. 32(15), pages 1967-1976.
    2. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    3. Antonio Alvarez & Julio del Corral, 2010. "Identifying different technologies using a latent class model: extensive versus intensive dairy farms," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 37(2), pages 231-250, June.
    4. 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.
    5. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    6. Bernhard Brümmer & Thomas Glauben & Geert Thijssen, 2002. "Decomposition of Productivity Growth Using Distance Functions: The Case of Dairy Farms in Three European Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(3), pages 628-644.
    7. Fried, Harold O. & Lovell, C. A. Knox & Schmidt, Shelton S. (ed.), 2008. "The Measurement of Productive Efficiency and Productivity Growth," OUP Catalogue, Oxford University Press, number 9780195183528.
    8. 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.
    9. Luis Orea & Subal C. Kumbhakar, 2004. "Efficiency measurement using a latent class stochastic frontier model," Empirical Economics, Springer, vol. 29(1), pages 169-183, January.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    productivity; dairy farming; stochastic frontier analysis; Livestock Production/Industries; Productivity Analysis;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ags:eaae11:114763. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: http://edirc.repec.org/data/eaaeeea.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.