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Measuring Technical Efficiency of Dairy Farms with Imprecise Data: A Fuzzy Data Envelopment Analysis Approach

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  • Mugera, Amin W.

Abstract

This article integrates fuzzy set theory in Data Envelopment Analysis (DEA) framework to compute technical efficiency scores when input and output data are imprecise. The underlying assumption in convectional DEA is that inputs and outputs data are measured with precision. However, production agriculture takes place in an uncertain environment and, in some situations, input and output data may be imprecise. We present an approach of measuring efficiency when data is known to lie within specified intervals and empirically illustrate this approach using a group of 34 dairy producers in Pennsylvania. Compared to the convectional DEA scores that are point estimates, the computed fuzzy efficiency scores allow the decision maker to trace the performance of a decision-making unit at different possibility levels.

Suggested Citation

  • Mugera, Amin W., 2011. "Measuring Technical Efficiency of Dairy Farms with Imprecise Data: A Fuzzy Data Envelopment Analysis Approach," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103251, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea11:103251
    DOI: 10.22004/ag.econ.103251
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    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
    3. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    4. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    5. Yann Duval & Allen M. Featherstone, 2002. "Interactivity and Soft Computing in Portfolio Management: Should Farmers Own Food and Agribusiness Stocks?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(1), pages 120-133.
    6. Emina Krcmar & G. Cornelis van Kooten, 2008. "Economic Development Prospects of Forest-Dependent Communities: Analyzing Trade-offs Using a Compromise-Fuzzy Programming Framework," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 1103-1117.
    7. Wilson, Paul W, 1993. "Detecting Outliers in Deterministic Nonparametric Frontier Models with Multiple Outputs," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 319-323, July.
    8. Konstantinos Triantis & Olivier Girod, 1998. "A Mathematical Programming Approach for Measuring Technical Efficiency in a Fuzzy Environment," Journal of Productivity Analysis, Springer, vol. 10(1), pages 85-102, July.
    9. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    10. William Cooper & Zhimin Huang & Vedran Lelas & Susan Li & Ole Olesen, 1998. "Chance Constrained Programming Formulations for Stochastic Characterizations of Efficiency and Dominance in DEA," Journal of Productivity Analysis, Springer, vol. 9(1), pages 53-79, January.
    11. Léopold Simar, 2007. "How to improve the performances of DEA/FDH estimators in the presence of noise?," Journal of Productivity Analysis, Springer, vol. 28(3), pages 183-201, December.
    12. A. Hadi‐Vencheh & R. Kazemi Matin, 2011. "An application of IDEA to wheat farming efficiency," Agricultural Economics, International Association of Agricultural Economists, vol. 42(4), pages 487-493, July.
    13. G. Cornelis van Kooten & Emina Krcmar & Erwin H. Bulte, 2001. "Preference Uncertainty in Non-Market Valuation: A Fuzzy Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 487-500.
    14. Kuosmanen, Timo & Post, Thierry & Scholtes, Stefan, 2007. "Non-parametric tests of productive efficiency with errors-in-variables," Journal of Econometrics, Elsevier, vol. 136(1), pages 131-162, January.
    15. W. Cooper & L. Seiford & K. Tone & J. Zhu, 2007. "Some models and measures for evaluating performances with DEA: past accomplishments and future prospects," Journal of Productivity Analysis, Springer, vol. 28(3), pages 151-163, December.
    16. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
    17. 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.
    18. Just, Richard E. & Pope, Rulon D., 2001. "The agricultural producer: Theory and statistical measurement," Handbook of Agricultural Economics, in: B. L. Gardner & G. C. Rausser (ed.), Handbook of Agricultural Economics, edition 1, volume 1, chapter 12, pages 629-741, Elsevier.
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    Cited by:

    1. Fabio A. Madau & Roberto Furesi & Pietro Pulina, 2017. "Technical efficiency and total factor productivity changes in European dairy farm sectors," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 5(1), pages 1-14, December.
    2. Sebastián Lozano & Belarmino Adenso-Díaz, 2021. "A DEA approach for merging dairy farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(6), pages 209-219.
    3. Zoran Ciric P & Dragan Stojic & Otilija Sedlak & Aleksandra Marcikic Horvat & Zana Kleut, 2019. "Innovation Model of Agricultural Technologies Based on Intuitionistic Fuzzy Sets," Sustainability, MDPI, vol. 11(19), pages 1-12, October.
    4. Ahmad Hosseinzadeh & Russell Smyth & Abbas Valadkhani & Amir Moradi, 2018. "What determines the efficiency of Australian mining companies?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 62(1), pages 121-138, January.
    5. Mostafa Mardani Najafabadi & Hanieh Kazmi & Somayeh Shirzadi Laskookalayeh & Abas Abdeshahi, 2023. "Investigating the ability of fuzzy and robust DEA models to apply uncertainty conditions: an application for date palm producers," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 776-801, June.
    6. Peggy Schrobback & Sean Pascoe & Louisa Coglan, 2014. "Shape Up or Ship Out: Can We Enhance Productivity in Coastal Aquaculture to Compete with Other Uses?," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-25, December.
    7. Stefanos A. Nastis & Thomas Bournaris & Dimitrios Karpouzos, 2019. "Fuzzy data envelopment analysis of organic farms," Operational Research, Springer, vol. 19(2), pages 571-584, June.
    8. Ebubekir Karabacak & Hüseyin Ali Kutlu, 2024. "Evaluating the Efficiencies of Logistics Centers with Fuzzy Logic: The Case of Turkey," Sustainability, MDPI, vol. 16(1), pages 1-25, January.

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    More about this item

    Keywords

    Farm Management; Production Economics; Productivity Analysis; Research Methods/ Statistical Methods; Risk and Uncertainty;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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