IDEAS home Printed from https://ideas.repec.org/a/kap/jproda/v49y2018i2d10.1007_s11123-018-0525-y.html
   My bibliography  Save this article

Application of a bias-corrected meta-frontier approach and an endogenous switching regression to analyze the technical efficiency of conservation tillage for wheat in South Asia

Author

Listed:
  • Sreejith Aravindakshan

    () (Wageningen University
    International Maize and Wheat Improvement Center (CIMMYT))

  • Frederick Rossi

    (International Maize and Wheat Improvement Center (CIMMYT)
    University of Florida)

  • T. S. Amjath-Babu

    (Leibniz Centre for Agricultural Landscape Research (ZALF))

  • Prakashan Chellattan Veettil

    (International Rice Research Institute (IRRI))

  • Timothy J. Krupnik

    (International Maize and Wheat Improvement Center (CIMMYT))

Abstract

Abstract Conservation tillage (CT) options are among the most rapidly spreading land preparation and crop establishment techniques globally. In South Asia, CT has spread dramatically over the last decade, a result of strong policy support and increasing availability of appropriate machinery. Although many studies have analyzed the yield and profitability of CT systems, the technical efficiency impacts accrued by farmers utilizing CT have received considerably less attention. Employing a DEA framework, we isolated bias-corrected meta-frontier technical efficiencies and meta-technology ratios of three CT options adopted by wheat farmers in Bangladesh, including bed planting (BP), power tiller operated seeding (PTOS), and strip tillage (ST), compared to a control group of farmers practicing traditional tillage (TT). Endogenous switching regression was subsequently employed to overcome potential self-selection bias in the choice of CT, in order to robustly estimate efficiency factors. Among the tillage options studied, PTOS was the most technically efficient, with an average meta-technology ratio of 0.90, followed by BP (0.88), ST (0.83), and TT (0.67). The average predicted meta-frontier technical efficiency for the CT non-adopters under a counterfactual scenario (0.80) was significantly greater (P = 0.00) than current TE scores (0.65), indicating the potential for sizeable profitability increases with CT adoption. Conversely, the counterfactual TE of non-adopters was 23% greater than their DEA efficiency, also indicating efficiency gains from CT adoption. Our results provide backing for agricultural development programs in South Asia that aim to increase smallholder farmers’ income through the application of CT as a pathway towards poverty reduction.

Suggested Citation

  • Sreejith Aravindakshan & Frederick Rossi & T. S. Amjath-Babu & Prakashan Chellattan Veettil & Timothy J. Krupnik, 2018. "Application of a bias-corrected meta-frontier approach and an endogenous switching regression to analyze the technical efficiency of conservation tillage for wheat in South Asia," Journal of Productivity Analysis, Springer, vol. 49(2), pages 153-171, June.
  • Handle: RePEc:kap:jproda:v:49:y:2018:i:2:d:10.1007_s11123-018-0525-y
    DOI: 10.1007/s11123-018-0525-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11123-018-0525-y
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    2. 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.
    3. Esmeralda Ramalho & Joaquim Ramalho & Pedro Henriques, 2010. "Fractional regression models for second stage DEA efficiency analyses," Journal of Productivity Analysis, Springer, vol. 34(3), pages 239-255, December.
    4. Boris Bravo-Ureta & William Greene & Daniel Solís, 2012. "Technical efficiency analysis correcting for biases from observed and unobserved variables: an application to a natural resource management project," Empirical Economics, Springer, vol. 43(1), pages 55-72, August.
    5. Leopold Simar & Valentin Zelenyuk, 2006. "On Testing Equality of Distributions of Technical Efficiency Scores," Econometric Reviews, Taylor & Francis Journals, vol. 25(4), pages 497-522.
    6. Sanzidur Rahman & Aree Wiboonpongse & Songsak Sriboonchitta & Yaovarate Chaovanapoonphol, 2009. "Production Efficiency of Jasmine Rice Producers in Northern and North‐eastern Thailand," Journal of Agricultural Economics, Wiley Blackwell, vol. 60(2), pages 419-435, June.
    7. Krishna, Vijesh V. & Aravindakshan, Sreejith & Chowdhury, Apurba & Rudra, Bankim, 2012. "Farmer Access and Differential Impacts of Zero Tillage Technology in the Subsistence Wheat Farming Systems of West Bengal, India," Socioeconomics Program Working Papers 147204, CIMMYT: International Maize and Wheat Improvement Center.
    8. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    9. Daniel Solís & Boris E. Bravo-Ureta & Ricardo E. Quiroga, 2007. "Soil conservation and technical efficiency among hillside farmers in Central America: a switching regression model ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(4), pages 491-510, December.
    10. González-Flores, Mario & Bravo-Ureta, Boris E. & Solís, Daniel & Winters, Paul, 2014. "The impact of high value markets on smallholder productivity in the Ecuadorean Sierra: A Stochastic Production Frontier approach correcting for selectivity bias," Food Policy, Elsevier, vol. 44(C), pages 237-247.
    11. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    12. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    13. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    14. O. B. Olesen & N. C. Petersen, 1995. "Chance Constrained Efficiency Evaluation," Management Science, INFORMS, vol. 41(3), pages 442-457, March.
    15. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
    16. Léopold Simar & Paul Wilson, 2011. "Two-stage DEA: caveat emptor," Journal of Productivity Analysis, Springer, vol. 36(2), pages 205-218, October.
    17. Lokshin, Michael & Sajaia, Zurab, 2004. "Maximum likelihood estimation of endogenous switching regression models," Stata Journal, StataCorp LP, vol. 4(3), pages 1-8.
    18. Arega Alene & V. Manyong, 2007. "The effects of education on agricultural productivity under traditional and improved technology in northern Nigeria: an endogenous switching regression analysis," Empirical Economics, Springer, vol. 32(1), pages 141-159, April.
    19. Simar, L., 1991. "Estimating efficiencies from frontier models with panel data: a comparison of parametric, non-parametric and semi-parametric methods with boot strapping," CORE Discussion Papers 1991026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    20. Li, Qi & Maasoumi, Esfandiar & Racine, Jeffrey S., 2009. "A nonparametric test for equality of distributions with mixed categorical and continuous data," Journal of Econometrics, Elsevier, vol. 148(2), pages 186-200, February.
    21. 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.
    22. Wadud, Md. Abdul., 2003. "Technical, Allocative, and economic efficiency of farms in Bangladesh: a stochastic frontier and DEA approach," Journal of Developing Areas, Tennessee State University, College of Business, vol. 37(1), pages 109-126, September.
    23. Ralph E. Heimlich, 1985. "Landownership and the Adoption of Minimum Tillage: Comment," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 67(3), pages 679-681.
    24. Jeffrey M. Perloff & Lori Lynch & Susan M. Gabbard, 1998. "Migration of Seasonal Agricultural Workers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 154-164.
    25. Renato Villano & Boris Bravo-Ureta & Daniel Solís & Euan Fleming, 2015. "Modern Rice Technologies and Productivity in the Philippines: Disentangling Technology from Managerial Gaps," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(1), pages 129-154, February.
    26. Asad Qureshi & Zia Ahmad & Timothy Krupnik, 2015. "Moving from Resource Development to Resource Management: Problems, Prospects and Policy Recommendations for Sustainable Groundwater Management in Bangladesh," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(12), pages 4269-4283, September.
    27. 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.
    28. repec:zwi:journl:v:43:y:2012:i:1:p:55-72 is not listed on IDEAS
    29. Wollni, Meike & Brümmer, Bernhard, 2012. "Productive efficiency of specialty and conventional coffee farmers in Costa Rica: Accounting for technological heterogeneity and self-selection," Food Policy, Elsevier, vol. 37(1), pages 67-76.
    30. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    31. Aravindakshan, Sreejith & Rossi, Frederick J. & Krupnik, Timothy J., 2015. "What does benchmarking of wheat farmers practicing conservation tillage in the eastern Indo-Gangetic Plains tell us about energy use efficiency? An application of slack-based data envelopment analysis," Energy, Elsevier, vol. 90(P1), pages 483-493.
    32. Erenstein, Olaf & Thorpe, William, 2011. "Livelihoods and agro-ecological gradients: A meso-level analysis in the Indo-Gangetic Plains, India," Agricultural Systems, Elsevier, vol. 104(1), pages 42-53, January.
    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. Jung Ho Park & Kwangsoo Shin, 2018. "Efficiency of Government-Sponsored R&D Projects: A Metafrontier DEA Approach," Sustainability, MDPI, Open Access Journal, vol. 10(7), pages 1-17, July.
    2. T. O Ojo & L.J. S Baiyegunhi & A. O Salami, 2019. "Impact of Credit Demand on the Productivity of Rice Farmers in South West Nigeria," Journal of Economics and Behavioral Studies, AMH International, vol. 11(1), pages 166-180.
    3. Aravindakshan, Sreejith & Krupnik, Timothy J. & Groot, Jeroen C.J. & Speelman, Erika N. & Amjath- Babu, T.S. & Tittonell, Pablo, 2020. "Multi-level socioecological drivers of agrarian change: Longitudinal evidence from mixed rice-livestock-aquaculture farming systems of Bangladesh," Agricultural Systems, Elsevier, vol. 177(C).

    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:kap:jproda:v:49:y:2018:i:2:d:10.1007_s11123-018-0525-y. 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: (Sonal Shukla) or (Springer Nature Abstracting and Indexing). General contact details of provider: http://www.springer.com .

    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.