SUGARCANE FARM EFFICIENCY IN GORONTALO PROVINCE

Jaka Sumarno, Rahmat Hanif Anasiru, Erna Retnawati

Abstract


Sugarcane is an important commodity in Gorontalo Province. However, the development of sugarcane farming is constrained by limited cultivation land and low productivity of sugarcane. The government continues to improve farm productivity and efficiency through the application of technology, optimization of the use of agricultural tools and machinery, production input assistance, improvement of irrigation networks, and other supporting facilities. Increasing farm efficiency is vital to increase farmers' production and income. This study aimed to determine the technical capability, allocative, and economical of sugarcane farming, and find out the factors that influence the efficiency of sugarcane farming in Gorontalo. The study was conducted in 2017 with a survey method with questionnaires or a structured questionnaire. The research location was determined purposively in one of the sugar cane production centers of Gorontalo Province, namely Boalemo Regency. The selection of respondents was carried out by a simple random sampling technique with a total of 47 sugarcane farmers. The data analysis method uses the Cobb Douglas stochastic frontier production function with the Maximum Likelihood Estimates. Financial analysis of farming was done to determine the feasibility of sugarcane farming. The results showed that the average level of technical, allocative, and economic efficiency of sugarcane farming in Gorontalo were 0.90, 0.52, and 0.53, which means that sugarcane farming was technically efficient, but not yet allocatively and economically efficient. Six out of seven variables that influence the inefficiency of sugarcane farming were farmers' education, farming experience, frequency of counseling, participation in farmer groups, credit access, and technological assistance. The age variable does not significantly affect the technical inefficiency of sugarcane farming.

Keywords  : Maximum Likelihood Estimates, sugarcane national production.


Keywords


Maximum Likelihood Estimates;sugarcane national production.

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DOI: http://dx.doi.org/10.21082/jlittri.v26n1.2020.11-22

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