Technical Efficiency of Milkfish Pond Farming Using Traditional and Semi-Intensive Technology in West Jawa
DOI:
https://doi.org/10.30736/grouper.v17i1.402Keywords:
DEA, Milkfish, Technical efficiency , tobitAbstract
Efforts to increase milkfish production through efficient resource use are very important to study to provide alternatives for farmers in managing a milkfish pond business. It aims to increase production by using existing inputs so as to reduce farming costs and increase farmers' income. Specifically, the objectives of this study are (1) to describe the performance of the milkfish pond cultivation in both traditional and semi-technical production technologies, (2) to analyze the technical efficiency of the milkfish pond cultivation on both production technologies, and (3) identify the factors which affects the technical efficiency of the milkfish pond cultivation. This study used 2013 Agricultural Census data with a total of 1015 respondents of milkfish farmers in West Java Province. Data analysis was performed using descriptive and quantitative analysis. Descriptive statistical analysis was used to describe the characteristics of the respondents' activity, the socio-economic conditions of farmers, the use of production inputs from the milkfish pond cultivation. Quantitative analysis is used to analyze technical efficiency using the Data Envelopment Analysis (DEA) approach and to identify socio-economic factors that affect technical efficiency using the Tobit regression. The results showed that the milkfish pond cultivation in West Java Province in general is still cultivated traditionally (56 percent). The results also showed that the technical efficiency value of milkfish farmers with efficient traditional technology was 38 percent of the total farmers who cultivated traditionally with an average technical efficiency value of 0.7, while the milkfish farmers using semi-intensive technology were 22 percent of the 449 farmers. which uses semi-intensive technology with an average technical efficiency value of 0.6. The observed inefficiency may be attributable to suboptimal input allocation, although further analysis is required to establish a causal relationship.
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