Browsing by Author "Rathnayake, R.M.S.D."

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    Analysis of technical efficiency of pepper growers in Kandy district
    (Uva Wellassa University of Sri Lanka, 2015) Jayalath, J.K.S.; Rathnayake, R.M.S.D.; Seneviratne, M.A.P.K.
    Pepper is the second most important perennial spice crop, next to Cinnamon, in Sri Lankan economy, and the most important perennial spice for domestic consumption. The pepper growers have faced severe problems during the last decades. Some of these problems are prolonged fall in the production and productivity of pepper. Agricultural productivity refers to the output produced by a given level of input(s) in the agricultural sector of a given economy (Fulginiti and Perrin 1998). Technical efficiency reflects the ability of producers to maximize output for a given set of resource inputs (Chirwa 2003).The Department of Export Agriculture in Sri Lanka with the mandate of perennial spices launched several programmes to develop this sector, including subsidy schemes for new planting, replanting and infilling, fertilizer subsidy schemes and extension services. Despite such efforts, the performances of spice based agro-forestry systems are not satisfactory. The average yield of pepper is 350-500 kg per hectare, but target yield is 1000 kg per hectare (Department of Export Agriculture in Sri Lanka 2002). Farmers have less information on efficiency. In order to realize increased production and efficiency, farmers in Sri Lanka need to efficiently utilize the limited resources accessed for farm income generation. This research determined efficiency levels of pepper farmers and identified socio economic factors affecting efficiency levels. Methodology The study was conducted in Kandy District in 2014. Kandy district comprises with 6,982.8 ha of cultivated land of pepper. The total sample size was hundred (100) respondents from six selected extension office ranges. Multi Stage sampling technique was used. Primary sample data was collected from farmers using a survey method involving a structured questionnaire which was administered to the selected pepper producing farmers in Kandy District. The analysis of Cobb-Douglas frontier production function was tested by ordinary least square (OLS) and maximum likelihood estimation (MLE). STATA version 11 was used for the summary statistics and estimate coefficient of stochastic frontier and measure technical efficiencies.
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    Consumer Attitude on Fresh and Processed Meat Quality; A Case Study from Badulla District
    (Uva Wellassa University of Sri Lanka, 2013) Karunasena, K.T.S.; Ranasinghe, M.K.; Rathnayake, R.M.S.D.
    Worldwide, food safety crisis debates have come forward as an important aspect especially in meat industry. Hence, producers, distributors, marketing staff and policy makers should have higher consideration towards meat quality. Thus, this study was conducted with an aim of identifying the factors influenced on consumer attitude towards meat quality with special reference to Badulla district. Two hundred consumers were randomly selected and interviewed using a pre-tested structured questionnaire. The primary data were analyzed using the Microsoft Office Excel (2007) and Minitab14 software. The Regression model was developed to determine the socio-demographic factors that influence for consumer attitude toward meat quality as, Consumer attitude on meat quality (AI) = f (Age + Religion + Income + Education + Nutritional purpose + Taste purpose + Gender+ Government occupation + Private occupation + Market type). All respondents consumed at least one kind of meat product and 1% of the respondents did not consume any type of meat. Ninety percent of the respondents had established their meat consumption pattern at their child age. The religious believes (55%), economic concerns (16%) and antipathy for killing animals (17%) were the most popular reasons for not being meat consumers. The “meat colour” (80%) was the most concerning factor at the time of purchasing and 20.5% of respondents are concerned of “quality standards” as first. The cleanliness and freshness (30%), tenderness (2.5%), juiciness (1.5%) and marbling (1.2%) were other most concerned quality parameter at the time of purchasing. The income, education level, gender and religion have significantly affected the consumer attitude towards meat quality. There were no significant correlation between the attitude index and age, purpose of meat consumption, occupation and market type. The results showed a positive coefficient of the gender and income level with attitude index.
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    Production characteristics and technical efficiency of buffalo farmers in Thanamalwila veterinary division
    (Uva Wellassa University of Sri Lanka, 2015) Malcolm, M.B.J.G.R.; Samaraweera, A.M.; Rathnayake, R.M.S.D.
    At present milk production from large ruminants only meet 17% of the countries requirements (Ministry of livestock and rural community development, 2012). According to Department of animal production and health (2012) Thanamalwila Veterinary division (VD) in Moneragala district has the highest buffalo population in Uva province which is well established over cattle rearing and plays an important role in income generation of rural farm households. Therefore, this study was conducted to identify the important socio-economic determinants of milk production and to estimate the technical efficiency of dairy production in Thanamalwila VD. Materials and methods Study was conducted in Thanamalwila VD. Fifty buffalo farmers were selected using multi stage sampling technique. Random numbers of buffalo farms were selected from each Grama Niladhari division to field survey based on buffalo farm population. Rearing buffalo as primary and secondary income source was the selected criteria for buffalo farmers. Primary data were collected using pre tested structured questionnaire and following models were used in the analysis of stochastic production function and inefficiency model. Then, data were analyzed using Minitab 14 and STATA 11 software packages. Model 1: Cobb-Douglas model = + + + + + + + + + ( − ) Where “ln” denotes logarithms to base e, while, Yi= Milk yield (L animal-1 day-1), X1= Breed, X2= Average birth weight (kg), X3= Condition of the shed , X4= Grazing duration (hours day-1), X5= Labor allocation (hour animal-1 day-1), X6= Frequency of water given (number of times per day), X7= Cost of buffalo farming (LKR per month), X8= Value of feed, Vi= Random variable, Ui= Non negative random variables. Model 2: The inefficiency model specification (Battese and Coelli, 1995), = + + + + + Where, Z1 = Age of the farmer (Year), Z2 = Education level (Year), Z3 = Monthly income level (LKR), Z4 = Experience of the farmer (Year), Wi = Unobservable random variables Result and discussion All the buffalo farmers in the sample were male and majority was belonged to 21-30 age category (30%) and a high proportion (62%) of buffalo farmers had education up to grade 10. Only 6% of respondents had the education level beyond GCE ordinary level. Most of the villages (98%) reared both local as well as exotic river type buffalo breeds and the preferable breed combination was local buffalo and Murrah or Niliravi cross breds. Only 2% of farmers reared solely local buffaloes. Herd size ranged between 2-185 animals and majority of respondents (38%) had a herd size of 21-40. Moreover, the predominant management system (94%) was the extensive management system. Interestingly, one farmer (2%) has practiced the intensive management system. Moreover, 6% of farmers practiced artificial inseminations (AI) in their breeding program. Feed availability, water availability, changes in rainfall pattern, and land availability were the most serious constraints faced by respondents. Elephant attack and illegal smuggling were also critical problems in buffalo farming in the area. The maximum likelihood estimates (MLE) of the parameters of stochastic frontier production function are present in Table 01. The OLS function provided the estimates of the average production function while MLE model provided the estimates of stochastic production frontier. The MLE coefficient for breed, allocation of labour hours day-1 animal-1 and average birth weight shows a positive and significant contribution to determine the output of stochastic production function. Therefore, by improving these aspects the farmer can enhance the milk output by the given MLE. Table 01: Estimates of stochastic production function Variable Coefficient Standard error p value OLS MLE OLS MLE OLS MLE Breed 0.4768** 0.5830*** 0.1834 0.1428 0.013 0.000 Birth weight 0.5367 0.6169** 0.3802 0.2856 0.166 0.031 Shed condition -0.1626 -0.2230 0.1798 0.1375 0.371 0.105 Grazing duration -0.0047 -0.0833 0.2994 0.2237 0.988 0.709 Labour hours 0.0941 * 0.2054 *** 0.0528 0.0564 0.082 0.000 Frequency of water 0.0086 0.1461 0.1152 0.1183 0.941 0.217 supply Cost of buffalo farming 0.0341 0.0187 0.0275 0.0217 0.223 0.391 Feeding method 0.0954 0.0570 0.2038 0.1674 0.642 0.733 Constant -1.1127 -0.7375 1.5874 1.1581 0.487 0.524 OLS= Ordinary Least Square estimation, MLE= maximum Likelihood estimation,*Significant at 10%, **Significant at 5%, ***Significant at 1% Estimated variables of the inefficiency model are represented in Table 02. Monthly income was the only significant variable of inefficiency model in this study. Therefore, farmers with higher monthly income have the capacity to increase the efficiency of milk production. Moreover, farmers had tendency to invest their money on livestock than cash crop cultivation because they considered cash crop cultivation as relatively risky business due to dry climatic condition in the area. Table 02: Technical inefficiency estimates- buffalo farming Variable Coefficient Standard error p-value Age 0.0459 0.039481 0.243 Education level 0.1189 0.615439 0.846 Monthly income -0.0005* 0.000026 0.052 Experience -0.1057 0.085551 0.217 Contact times of VS/LDI -0.3871 1.024504 0.709 *Significant at 10% **Significant at 5% ***Significant at 1% Moreover, mean technical efficiency for buffalo farmers in Thanamalwila VD is 86.83, which indicates that the output could be increased by 13.7%, if all farmers achieved the TE level of the best farmer. Conclusion Coefficients for breed, feed, average birth weight, and level of labor power allocation on dairy industry have significant impact on milk production of buffalo farms in Thanamalwila VD. Moreover, by reducing the technical inefficiency by 13.7% the farmers can increase the milk yield without increasing the level of inputs.