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

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 90 th 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 60 th and the 90 th 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.

Estimating Coconut Yield Production using Hyperparameter Tuning of Long Short-Term Memory

“…The yield population density of each area was determined by different factors such as the number of palms dying, and the pests and diseases that are not suitable for coconut production. Samarakoon et al [11] developed a different coconut plantation using Ordinary Least Square (OLR) estimation and Quantile Regression (QR) approaches to estimate the production function in Cobb Douglas functional form. OLR estimation was an appropriate method for conditional mean model estimation, while the QR provides an equally appropriate method for the conditional quantile function.…”

“…), Overall Equation: 𝑦 ̂𝑡+ℎ = (𝑙 𝑡 + ℎ𝑏 𝑡 )𝑠 𝑡+ℎ−𝑚(8)Level Equation:𝑙 𝑡 = 𝛼 𝑦 𝑡 𝑆 𝑡−𝑚 + (1 − 𝛼 )(𝑙 𝑡−1 + 𝑏 𝑡−1 )(9)Trend Equation: 𝑏 𝑡 = 𝛽(𝑙 𝑡 − 𝑙 𝑡−1 ) + (1 − 𝛽) 𝑏 𝑡−1(10) Seasonality Equation: 𝑠 𝑡 = 𝛾 𝑦 𝑡 𝑙 𝑡−1 + 𝑏 𝑡−𝛾) 𝑠 𝑡−𝑚(11) …”