Md. Akhtaruzzaman Khan
Assistant Professor
Department of Agricultural Finance in Bangladesh Agricultural University, Mymensingh, Bangladesh
Kristin H. Roll
Associate Professor & Co-Supervisor
UMB School of Economics and Business, Norwegian University of Life Sciences, P. Box 5003 N-1432 Ås NORWAY
Atle Guttormsen
Professor & Supervisor
UMB School of Economics and Business, Norwegian University of Life Sciences, P. Box 5003 N-1432 Ås NORWAY
Pangas fish, Production risk, Just-pope, Bangladesh
Animal Health and Management
Pangus, Farming System
Just and pope has proposed two ways of estimation procedure to measure the production risk; maximum likelihood (ML) method and three-step feasible generalized least square (FGLS) methods under heteroskedastic disturbance (Just and Pope 1978; 1979). Since then, most of the empirical studies on production risk have used linear or non-linear FGLS estimation methods of the Just-Pope stochastic production function. For the large sample, both FGLS and ML estimates are consistent and efficient for mean function but FGLS is not efficient for variance function while ML is consistent and efficient (Tveteras 1998; 1999). Moreover, the ML estimator is more efficient and suffers from less bias than FGLS for the small sample. In addition, FGLS is found to seriously play down the risk effects of inputs and provide biased marginal product estimates (Saha et al. 1997). We use Cobb-Douglas specification for both mean and variance function. Initially, we used linear quadratic functional form for mean and risk functions, but most of the square and cross-product terms are not significant. Therefore, we decide to use Cobb-Douglas functional form. The production function is specified with five inputs: labor, fingerlings, feed, capital and pond area/ farm size3. Labor, fingerling and feed are the main variable inputs for pangas fish farming. In addition, capital and farm size (pond area) is considered as a quasi-fixed factor in pangas production process4 According to above discussion, our empirical model production function is as follows. . We also incorporated five socioeconomic variables which may influence the production variability in the production process. These socioeconomic variables are the education of the farmers (year of schooling), experience in fish farming (years), training received from any institution on fish farming (number of days), credit received or not (dummy) and extension service from any government organization or NGO’s (dummy). Although pangas culture is relatively new in Bangladesh, it has flourished within a decade. Therefore, historical data on pangas farm, production, area, growth etc. are not available before 20066 This paper is based on cross-sectional primary data which is collected during June/2010 to August/2010 through the “personal interview” methods. Three stages sampling technique was used to select the samples in the study area. Mymensingh, Bogra, Narshingdi, Sherpur, Jamalpur, Kishorgonj and Tangail district were all appropriate districts with significant Pangas production. In first stage, Mymensingh district was purposively selected because pangas fish farm has expanded tremendously in this district due to suitable agro-climatic conditions (see Figure 1 for the localization of the region). Mymensingh district is situated in the north-central part of Bangladesh and it is only 120 kilometers north from capital city of Dhaka with good communication, favorable resources, good climatic condition, abundant labor and favorable socio-economic condition for fish culture. Total area of Mymensingh district is 4363 square kilometers with a population of 4.49 million where 1.33 million acres is used as cropped land and 76825 acres for fish culture (http://www.bbs.gov.bd/RptZillaProfile.aspx 2007). Mymensingh is the second largest fish-producing district after Chittagong where total inland fish production was . According to Department of Fisheries (DoF) statistics, the total pangas production was only 9.2 thousand metric tonnes in 2006-07 but it grew up to 124.8 thousand metric tonnes in 2009-10 which consists of 9.23% of total aquaculture production and 10.94% of total pond fish production(DoF 2006-07; DoF 2009-10). This paper is based on cross-sectional primary data which is collected during June/2010 to August/2010 through the “personal interview” methods. Three stages sampling technique was used to select the samples in the study area. Mymensingh, Bogra, Narshingdi, Sherpur, Jamalpur, Kishorgonj and Tangail district were all appropriate districts with significant Pangas production. In first stage, Mymensingh district was purposively selected because pangas fish farm has expanded tremendously in this district due to suitable agro-climatic conditions (see Figure 1 for the localization of the region). Mymensingh district is situated in the north-central part of Bangladesh and it is only 120 kilometers north from the capital city of Dhaka with good communication, favorable resources, good climatic condition, abundant labor and favorable socio-economic condition for fish culture. Total area of Mymensingh district is 4363 square kilometers with a population of 4.49 million where 1.33 million acres is used as cropped land and 76825 acres for fish culture (http://www.bbs.gov.bd/RptZillaProfile.aspx 2007). Therefore, we expect that higher education can reduce the output variability for all types of farm. We expect more experienced farmers know more about the culture system and can reduce risk compare to less experienced farmers.
Norwegian University of Life Sciences, NO–1432 Ås, Norway; ISBN 978-82-575-1098-5, ISSN 1503-1667
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