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Identifying and prioritizing opportunities for improving efficiency on the farm: holistic metrics and benchmarking with Data Envelopment Analysis

Author

Listed:
  • Soteriades, A.D.
  • Rowland, K.
  • Roberts, D.J.
  • Stott, A.W.

Abstract

Efficiency benchmarking is a well-established way of measuring and improving farm performance. An increasingly popular efficiency benchmarking tool within agricultural research is Data Envelopment Analysis (DEA). However, the literature currently lacks sufficient demonstration of how DEA could be tuned to the needs of the farm advisor/extension officer, rather than of the researcher. Also, the literature is flooded with DEA terminology that may discourage the non-academic practitioner from adopting DEA. This paper aims at making DEA more accessible to farm consultants/extension officers by explaining the method step-by-step, visually and with minimal use of specialised terminology and mathematics. Then, DEA’s potential for identifying cost-reducing and profit-making opportunities for farmers is demonstrated with a series of examples drawn from commercial UK dairy farm data. Finally, three DEA methods for studying efficiency change and trends over time are also presented. Main challenges are discussed (e.g. data availability), as well as ideas for extending DEA’s applicability in the agricultural industry, such as the use of carbon footprints and other farm sustainability indicators in DEA analyses.

Suggested Citation

  • Soteriades, A.D. & Rowland, K. & Roberts, D.J. & Stott, A.W., 2018. "Identifying and prioritizing opportunities for improving efficiency on the farm: holistic metrics and benchmarking with Data Envelopment Analysis," International Journal of Agricultural Management, Institute of Agricultural Management, vol. 7(1), July.
    • Handle: RePEc:ags:ijameu:292483
    DOI: 10.22004/ag.econ.292483
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    Cited by:

    1. Hamsa, K.R. & Umesh, K.B., 2021. "Technical Efficiency in Finger Millet and Paddy Crop in Southern Karnataka: Application of Data Envelop Analysis and Stochastic Frontier Approach," 2021 Conference, August 17-31, 2021, Virtual 315862, International Association of Agricultural Economists.

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