SRI LANKAN JOURNAL OF AGRICULTURAL ECONOMICS
Volume 21 - Issue 1 - 2020
Author - S. M. M. Samarakoon , L. H. P. Gunaratne, H. L. J. Weerahewa
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.
S. M. M. Samarakoon , L. H. P. Gunaratne, H. L. J. Weerahewa
Author - K. R. H. M. Ranjan , J. C. Edirisinghe
Sri Lankan tea industry is confronting the danger of losing key markets on the grounds of exceeding the Maximum Residual Levels (MRLs) in chemicals permitted by the European Union. This study assesses the impact of Non-tariff measures on Sri Lankan tea exports because of MRLs stipulated for the pesticide, Endosulfan. This study employs a Bayesian version of the Gravity equation using a Multi-level Mixed Model, because of the additional advantages that Bayesian analysis provides. Panel data from 2003 to 2017 for fourteen prime destinations of Ceylon tea were considered for this study. The estimated coefficient of MLR for Endosulfan suggests that a 1% increase in the regulatory stringency on Endosulfan (tighter restrictions on pesticide) can result in a 0.67% (approximately US$ 8,907,708.15 in 2020) decrease and a 1% increase in the tariff rate is prompt to a 0.03% (approximately US$ 398, 852.60 in 2020) decline in the value of tea exports. Compared to the tariff, the MRL is associated with a higher impact on trade. Therefore, the negative impact of MRLs is found to outweigh the impact of import tariffs, highlighting the greater role that non-tariff measures play.
K. R. H. M. Ranjan , J. C. Edirisinghe
Authors - W. C. S. M. Abeysekara , M. Siriwardana, S. Meng
This study developed a set of composite indices to analyse the vulnerability to climate change of the agricultural sector in Sri Lanka using the data between 2001 and 2018. The aim was to identify the level of vulnerability of the agricultural sector to climate risks at the country level, as a tool to better understand the variability and magnitude of impacts and adaptive capacities required to overcome the risks due to climate change. To calculate the indices, environmental and socio-economic indicators representing the conceptual components of vulnerability, namely, exposure, sensitivity, and adaptive capacity were selected based on previous studies. Secondary data were collected for the selected indicators and normalised considering the indicator’s functional relationship to vulnerability. Normalised data were then weighted and aggregated using two weighting methods and two aggregation methods to calculate four vulnerability indices, in order to minimize the impact of the known limitations of the methodological approaches to create composite indices. The values for composite indices were standardised to the range 0-1 and divided into five levels of vulnerability based on equal intervals, which revealed a moderate level of agricultural vulnerability to climate change over the eighteen-year study period. The multidimensional assessment, further, revealed the upward trend of vulnerability due to the increased sensitivity of the system to climate change. Even though the adaptive capacity of the country has been strengthened in the recent past, it has a critical role to play in mitigating vulnerability. The study also suggests methods for predicting future vulnerability by replicating the calculations.
W. C. S. M. Abeysekara , M. Siriwardana, S. Meng
Authors - V. Yogarajah , S. A. Weerasooriya
Climate-Smart Agriculture (CSA) technologies have been introduced to mitigate adverse climate change impacts. However, the adaptation of CSA technologies by farmers is relatively low in the dry zone of Sri Lanka. This study focuses on how farmers’ adaptation behavior for CSA technologies varies with household characteristics, economic status, and farm characteristics. Data on 94 farmers were collected using a pre-tested questionnaire. A score was developed for each farmer on the adaptation of CSA technologies under 4 broad categories namely: water and energy-smart, nutrient and soil-smart, carbon and weather-smart, and knowledge and yield-smart. A generalized linear model (GLM) was employed to identify the factors that influence farmers’ adaptation and the level of adaptation. According to GLM results, income increases the adaptation of water and energy-smart technologies, while land ownership and managing livestock compared to labour decrease the adaptation. Adaptation of nutrient and soil-smart technologies were positively influenced by experience, and training while negatively influenced by labor use and managing livestock compared to crops. Adaptation of carbon and weather-smart technologies were positively influenced by income, cost of transport, higher education levels, managing both crop and livestock compared to only crop, and training while negatively influenced by the gender of the household head and household size, source of income, and land ownership. Knowledge and yield-smart technologies were positively influenced by income, level of education, and training while negatively influenced by only managing livestock compared to crops. Overall, the results suggest the importance of providing training and facilitating knowledge dissemination in addition to understanding the subtle issues that act as barriers to the adaptation of CSA technologies in dry zone farming.
V. Yogarajah , S. A. Weerasooriya