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Productive Efficiency of Potato and Melon Growing Farms in Uzbekistan: A Two Stage Double Bootstrap Data Envelopment Analysis

  • Aziz Karimov


    (UNU-WIDER, World Institute for Development Economics Research, Katajanokanlaituri 6B, Helsinki 00160, Finland)

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    This article is one of the first to carry out a non-parametric efficiency analysis of crop production in Uzbekistan. The study applies the bootstrap Data Envelopment Analysis (DEA) to compute bias corrected technical efficiency (TE) scores using a sample of farms located in two regions of Uzbekistan. The study also investigates the determinants of TE in potato and melon production. Results indicate that there is room for efficient use of resources. Farmers are found to be more scale-efficient but not productively efficient. Findings from the second stage DEA model display that soil fertility index, farm size, water availability, crop diversification index, dependency ratio, potential to work in large land area, and longer distance to market contribute positively to production efficiency.

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    Article provided by MDPI, Open Access Journal in its journal Agriculture.

    Volume (Year): 3 (2013)
    Issue (Month): 3 (September)
    Pages: 503-515

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    Handle: RePEc:gam:jagris:v:3:y:2013:i:3:p:503-515:d:28449
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    1. Nabil Amara & Namatié Traoré & Réjean Landry & Robert Remain, 1999. "Technical Efficiency and Farmers' Attitudes toward Technological Innovation: The Case of the Potato Farmers in Quebec," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 47(1), pages 31-43, 03.
    2. 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.
    3. Johan Swinnen & Liesbet Vranken, 2010. "Reforms and agricultural productivity in Central and Eastern Europe and the Former Soviet Republics: 1989–2005," Journal of Productivity Analysis, Springer, vol. 33(3), pages 241-258, June.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    6. Charnes, A. & Cooper, W. W. & Karwan, K. R. & Wallace, W. A., 1979. "A chance-constrained goal programming model to evaluate response resources for marine pollution disasters," Journal of Environmental Economics and Management, Elsevier, vol. 6(3), pages 244-274, September.
    7. Raushan Bokusheva & Heinrich Hockmann & Subal C. Kumbhakar, 2012. "Dynamics of productivity and technical efficiency in Russian agriculture," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 39(4), pages 611-637, September.
    8. Simar, L. & Wilson, P.W., 1998. "A General Methodology for Bootstrapping in Nonparametric Frontier Models," Papers 9811, Catholique de Louvain - Institut de statistique.
    9. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    10. A. Charnes & W. W. Cooper & E. Rhodes, 1981. "Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through," Management Science, INFORMS, vol. 27(6), pages 668-697, June.
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