IDEAS home Printed from https://ideas.repec.org/a/ags/saeasj/359037.html
   My bibliography  Save this article

Determinants of Coconut Production in Large Scale Coconut Plantations in Sri Lanka: A Quantile Regression Approach

Author

Listed:
  • Samarakoon, S.M.M.
  • Gunaratne, L.H.P.
  • Weerahewa, H.L.J.

Abstract

Large variability in yields and input usages have been evident in coconut plantations of Sri Lanka. The studies on the determinants of productivity of coconut lands mainly adopted Ordinary Least Square estimation which only provides overall effects at the mean. This study examines the determinants of land productivity in different land classes of coconut plantations using a Quantile Regression approach which allows the computation of the effect of each determinant in each quantile. Production functions of coconut were specified treating coconut yields as the dependent variable and bearing coconut palms, labor, fertilizer, agrochemicals, machinery usage, and rainfall as the independent variables in Cobb-Douglas form. Annual data from nine estates belong to Kurunegala Plantations Ltd. of Sri Lanka from 2000 to 2018 were used for the analysis. The results indicate that on average, fertilizer usage, agrochemical usage, number of bearing palms and rainfall have positive and significant effects on coconut production. It was found that OLS estimates underestimate and overestimate the input use efficiency at upper and lower quantiles respectively. Rainfall was found to be a significant factor in determining the coconut yield in each quantile except the 90th quantile indicating that investments in irrigation which facilitates soil moisture improvement during dry periods would be important in improving the production. The application of fertilizer and other chemicals to the coconut lands in between the 60th and the 90th quantiles would be more effective. In contrast QR provided meaningful information at different segments in the production that enables to design appropriate structural policies steering the optimal use of inputs in coconut plantations.

Suggested Citation

  • Samarakoon, S.M.M. & Gunaratne, L.H.P. & Weerahewa, H.L.J., 2020. "Determinants of Coconut Production in Large Scale Coconut Plantations in Sri Lanka: A Quantile Regression Approach," Sri Lankan Journal of Agricultural Economics, Sri Lanka Agricultural Economics Association (SAEA), vol. 21(01), December.
  • Handle: RePEc:ags:saeasj:359037
    DOI: 10.22004/ag.econ.359037
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/359037/files/Determinants%20of%20Coconut.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.359037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cristina Bernini & Marzia Freo & Attilio Gardini, 2004. "Quantile estimation of frontier production function," Empirical Economics, Springer, vol. 29(2), pages 373-381, May.
    2. Harsha Aturupane & Anil B. Deolalikar & Dileni Gunewardena, 2008. "The Determinants of Child Weight and Height in Sri Lanka: A Quantile Regression Approach," WIDER Working Paper Series RP2008-53, World Institute for Development Economic Research (UNU-WIDER).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    2. Tsionas, Mike G. & Assaf, A. George & Andrikopoulos, Athanasios, 2020. "Quantile stochastic frontier models with endogeneity," Economics Letters, Elsevier, vol. 188(C).
    3. Galina Besstremyannaya & Richard Dasher & Sergei Golovan, 2022. "Quantifying heterogeneity in the relationship between R&D intensity and growth at innovative Japanese firms: A quantile regression approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 27-45.
    4. Montresor, Sandro & Vezzani, Antonio, 2015. "The production function of top R&D investors: Accounting for size and sector heterogeneity with quantile estimations," Research Policy, Elsevier, vol. 44(2), pages 381-393.
    5. Mamatzakis, E & Koutsomanoli-Filippaki, Anastasia & Pasiouras, Fotios, 2012. "A quantile regression approach to bank efficiency measurement," MPRA Paper 51879, University Library of Munich, Germany.
    6. Monje, Juan Cabas & Sidhoum, Amer Ait & Gil, Jose M., 2021. "Investigating Technical Efficiency of Spanish Pig Farming: A Quantile Regression Approach," 2021 Conference, August 17-31, 2021, Virtual 315196, International Association of Agricultural Economists.
    7. Jeyapraba Suresh, 2023. "Poverty is Lack of Capabilities: A Literature Review," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(3), pages 462-476, March.
    8. Mesbah Fathy Sharaf & Ahmed Shoukry Rashad & Elhussien Ibrahim Mansour, 2019. "Son Preference and Child Under nutrition in the Arab Countries: Is There a Gender Bias against Girls?," Middle East Development Journal, Taylor & Francis Journals, vol. 11(2), pages 199-219, July.
    9. Hung-pin Lai & Cliff J. Huang & Tsu-Tan Fu, 2020. "Estimation of the production profile and metafrontier technology gap: a quantile approach," Empirical Economics, Springer, vol. 58(6), pages 2709-2731, June.
    10. Divya Balasubramaniam & Santanu Chatterjee & David B. Mustard, 2020. "Public Versus Private Investment in Determining Child Health Outcomes: Evidence from India," Arthaniti: Journal of Economic Theory and Practice, , vol. 19(1), pages 28-60, June.
    11. Katsushi Imai & Samuel Kobina Annim & Raghav Gaiha & Veena S. Kulkarni, 2012. "Does Women’s Empowerment Reduce Prevalence of Stunted and Underweight Children in Rural India?," Economics Discussion Paper Series 1209, Economics, The University of Manchester.
    12. Mohammed, Sadick & Abdulai, Awudu, 2021. "Extension Participation and Improved Technology Adoption: Impact on Efficiency and Welfare of Farmers in Ghana," 2021 Conference, August 17-31, 2021, Virtual 315362, International Association of Agricultural Economists.
    13. Balcázar, Carlos Felipe, 2015. "Lower bounds on inequality of opportunity and measurement error," Economics Letters, Elsevier, vol. 137(C), pages 102-105.
    14. Katsushi S. Imai & Samuel Kobina Annim & Veena S. Kulkarni & Raghav Gaiha, 2012. "Nutrition, Activity Intensity and Wage Linkages: Evidence from India," Discussion Paper Series DP2012-10, Research Institute for Economics & Business Administration, Kobe University, revised May 2014.
    15. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, New Economic School (NES).
    16. E. Fusco & R. Benedetti & F. Vidoli, 2023. "Stochastic frontier estimation through parametric modelling of quantile regression coefficients," Empirical Economics, Springer, vol. 64(2), pages 869-896, February.
    17. Bernstein, David H. & Parmeter, Christopher F. & Tsionas, Mike G., 2023. "On the performance of the United States nuclear power sector: A Bayesian approach," Energy Economics, Elsevier, vol. 125(C).
    18. Cristina Bernini & Andrea Guizzardi, 2010. "Internal and Locational Factors Affecting Hotel Industry Efficiency: Evidence from Italian Business Corporations," Tourism Economics, , vol. 16(4), pages 883-913, December.
    19. Fan, Shenggen & Brzeska, Joanna, 2011. "The nexus between agriculture and nutrition: Do growth patterns and conditional factors matter?," 2020 conference briefs 1, International Food Policy Research Institute (IFPRI).
    20. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.

    More about this item

    Keywords

    ;
    ;

    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:saeasj:359037. See general information about how to correct material in RePEc.

    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 bibliographic 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.

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

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.