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

Authors:

S. M. M. Samarakoon ,

University of Peradeniya, LK
About S. M. M.
Postgraduate Institute of Agriculture
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L. H. P. Gunaratne,

University of Peradeniya, LK
About L. H. P.
Department of Agricultural Economics and Business Management, Faculty of Agriculture
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H. L. J. Weerahewa

University of Peradeniya, LK
About H. L. J.
Department of Agricultural Economics and Business Management, Faculty of Agriculture
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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.
How to Cite: Samarakoon, S.M.M., Gunaratne, L.H.P. and 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, 21(1), pp.1–16. DOI: http://doi.org/10.4038/sjae.v21i1.4641
Published on 31 Dec 2020.
Peer Reviewed

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