Recent information released through the mass media related to contamination of imported milk powder with hazardous components has made a considerable effect on the preference of milk powder brands among the consumers in Sri Lanka. This study was focused to investigate: (1) the factors influencing consumer brand preference for local and imported milk powder brands, (2) whether these identified factors have significant influences on consumer preference in milk powder brands. A questionnaire survey was conducted by means of face to face interview to gather primary data from a sample of 250 respondents covering five Divisional Secretariats in Kegalle District. Data were analyzed by using confirmatory factor analysis in AMOS in SPSS. The study shows that trust on the brand, product factors and brand loyalty are the main factors that significant and highly influence consumer brand preference for a particular milk powder brand. Findings of this study are important to milk brand producers, investors, policymakers, marketers, relevant enterprises and government to implement necessary product improvements and quality enhancement in the milk powder industry.
The purpose of this study was to assess farmers’ perception and willingness to pay for pesticides concerning quality & efficacy, and exploring the socio-demographic factors that influence the decision to pay for pesticides. A sample of 141 farmers in Hambanthota and Dambulla regions was selected and information were collected by using a structured questionnaire. An econometric model called “Binary Logistic Regression” was carried out using six explanatory variables after screening out of twelve variables in the Chi-Square analysis to identify factors highly likely to affect farmers’ perception and willingness to pay. The results revealed that four variables namely; age, average monthly income, pest intensity and action have a significant relationship with farmers’ perception and willingness to pay for pesticides concerning quality and efficacy. Average monthly income and action have a positive impact on perception and willingness to pay while age and pest intensity have a negative impact. DOI: http://dx.doi.org/10.4038/jas.v8i3.6092 The Journal of Agricultural Sciences, 2013, vol.8, no3 p. 153-160
The objectives of this study are (i) to develop the Recommended Technology Adoption Index (RTAI), and (ii) to determine the relationship between socioeconomic characteristics of the farmers which affect the technology adoption level of Sugarcane (Saccharum officinarum). The average technology adoption index of study area was 0.72 with minimum 0.46 and maximum of 0.89 and the correlation between index and yield was 62.26%. Multiple Linear Regression technique was used to determine the relationship between RTAI and socioeconomic characteristics. The statistical outcome shows that monthly income, education, being a member of social association and farmers' visit of extension office, have a significant impact on recommended technology adaptation. Increment of access for loans and strengthening the extension service is suggested to enhance the technology adoption level of sugarcane cultivation.
Maximum entropy (MaxEnt) modeling is extensively tested high performing quantitative modeling technique which has great potential for identifying best ecological requirement of species based on "presence only data" together with environmental variables. This study was aim to model the high-potential areas for pineapple cultivation in Sri Lanka using MaxEnt model. Total of 215 locations of pineapple cultivation covering whole Sri Lanka and several environmental covariates namely monthly rainfall, monthly mean temperature, Digital Elevation Model (DEM), slope, slope aspect, Normalized Difference Vegetation Index (NDVI) were used as model drivers. The resulting model was validated by using area under the receiver operator characteristic curve analysis. In addition to mapping, a questionnaire survey was conducted with a sample of 60 farmers in four divisional secretariat divisions of Gampaha and Kurunegala districts to explore prevailing conditions and constraints for pineapple cultivation. Highly significant constraints were identified using Wilcoxon signed rank test. Probability prediction map developed by MaxEnt with high predictive power (AUC = 0.913) indicated that some parts of Ampara, Monaragala, Puttalam, Colombo, Kaluthara, Kegalle, Badulla districts as high-potential areas in addition to traditionally pineapple grown districts which are Gampaha and Kurunegala. Wilcoxon signed rank test proved that high cost of inputs, high price of mulching materials, high cost and shortage of labour, high investment, lack of government subsidy facilities, weed problem, threat of mealy bug attack as highly significant production constraints while lack of guaranteed price as the major marketing constraint for pineapple cultivation (p < 0.05). This information on high-potential areas important for investors as well as entrepreneurs to take information-based decisions and provide decisive guidance for farmers to expand their cultivation.
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