Effect of Social Factors in Stochastic Frontier Profit of Organic Rice Farming in Boyolali

IGNATIUS SUPRIH SUDRAJAT1; ENDANG SITI RAHAYU2; KUSNANDAR2; SUPRIYADI2
1 University of Sarjanawiyata Tamansiswa Yogyakarta, Faculty of Agriculture, Department of Agribusiness, Umbulharjo, Post Code 55167, Yogyakarta, Indonesia
2 University of Sebelas Maret Surakarta, Graduate School, Department of Agricultural Science, Central Java 57126, Indonesia

Abstract

Sudrajat, I. S., E. S. Rahayu, Kusnandar and Supriyadi, 2017. Effect of social factors in stochastic frontier profi t of organic rice farming in Boyolali. Bulg. J. Agric. Sci., 23 (4): 551–559

This research explored about the profi t of organic rice farming system that infl uenced by social factors with stochastic frontier Cobb-Douglas profi t function and MLE (Maximum Likelihood Estimation). The observation was made at farmer groups namely Pangudi Raharjo and Pangudi Boga in Dlingo Village, Mojosongo, Boyolali, Central Java, Indonesia. Stochastic frontier method is equation model to estimate the parameters of the factors that will affect the level of profi t effi ciency in order to be close to the maximum (frontier). In this model, the factors that affecting the profi tability and the factors causing the profi t ineffi ciency of organic rice farming can be seen. This model was called composed error model because the error term consists of two components, namely the external factor/ errors caused by factors that can’t be controlled by the farmers (V1) and internal/ errors caused by factor that can be controlled by the farmers (U1). Error due to internal factors (U1) is composed of economic and social factors. This study elaborated the effects of social factors on ineffi ciency in the profi t of organic rice farming system. As the result, the most dominant social factor was management which followed by frequency of participation in training, the role of institutions/ associations, frequency of participation in counseling, and the age of the farmers.

Key words: Boyolali; management; maximum likelihood estimation; organic farming; profi t function

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