ReviewProduction risk and technical efficiency of fish farms in Ghana
Introduction
The government, research institutions, the private sector and other major stakeholders under Ghana's fishery sector development goal emphasized the development of inland fisheries and fish farming in particular with the goal of achieving sustainable yields and to compliment the annual domestic supply (mainly from traditional marine fisheries) of about 420,000 metric tons (MT), which is around 52% less of what the country demands (Frimpong and Adwani, 2015). In view of this, policy interventions by government directed banks to enhance finance for fish farming at subsidized interest rates in the 1980s (Wijkstrom and Vincke, 1991). This initiative motivated the young and old; and male and female farmers to consider fish farming either as a full-time occupation or as a part-time business. Majority of these farmers entered the industry with little or no technical knowledge and experience and as such relied on government extension agents or family members with formal fish farming education (FFFE) for technical advice. Formal fish farming education has recently been made easily accessible at government Universities and some private commercial farms. Predominantly, the earthen and the cage pond system have been practiced by fish farmers in the country with the cage ponds limited to lake and river bodies. These farms have diverse sizes depending on whether it is a subsistent or commercially operated. Race and Pen culture systems are still not properly developed in Ghana. Tilapia and catfish are the two predominant species in extensive cultivation. Tilapia (mainly Oreochromis niloticus) constitutes nearly 88% of the total fish farm production, whilst cultivation of catfish (Clarias gariepinus and Heterobranchus longifilis) takes about 10%, with the remaining 2% accounting for other species such as Heterotis niloticus (Onumah et al., 2010).
However, after about three decades of introduction, the fish farming industry has been marked by low production. In 2016, it was estimated that total annual production stood at about 52,000 MT (MT). Studies have been undertaken to assess the possible constraints that has led to the low production by a number of authors and organizations (Onumah et al., 2010; Seini et al., 2002; Wijkstrom and Vincke, 1991; and Frimpong and Adwani, 2015). Their findings include among others; undeveloped factor markets, limited funding for pond production, wrong pond siting, and lack of enough technical personnel. In addition to these constraints, Villano and Fleming (2005) note that technical inefficiency and production risk are very often associated with farming practices including fish farming in developing countries. Up to date, no comprehensive study has been undertaken to investigate technical inefficiency and production risk involved with the fish farming industry in the country. A number of factors make the production of pond fish risky over time in Ghana. These include; fish diseases, unreliable rainfall, extreme weather conditions and poor management practices. Onumah et al. (2010) consider the conventional technical efficiency estimation technique to assess the performance of fish farms in Ghana but this paper fails to account for production risk and time varying technical efficiency effect. Thus, assessing the influence of production efficiency and risk on the performance of fish farms in Ghana whilst accounting for the effect of time is worth studying.
The use of the stochastic production function proposed by Aigner et al. (1977) and Meeusen and van den Broeck (1977) can be seen as more realistic for predicting technical efficiency than the deterministic frontiers pioneered by Farrell (1957) and Aigner and Chu (1968). However, a significant aspect of production which has not been adequately accounted for in the stochastic frontier analysis is production risk. This makes the extent of technical efficiency considerably compromised in studies of farm performance in risky production environments. Risk plays a vital role both in input decision and production of outputs to achieve efficiency.
Work done by Just and Pope (1978) paved the way for understanding production under risk. Their study consider production risk measured by the variance of output, whilst focusing on a specification that allows inputs to be either risk-increasing or risk-decreasing as noted also by Kumbhakar and Tveteras (2002). Against this backdrop, this paper seeks to analyze production risk, technical efficiencies and its determinants to derive stakeholder conclusion for policy interventions.
Section snippets
Theoretical concepts
The stochastic frontier model specified by Battese and Coelli (1995) in accordance with the original models of Aigner et al. (1977) and Meeusen and van den Broeck (1977) has the form:where Yi is the output of the ith farm, xi is corresponding matrix of inputs, β is a vector of parameters to be estimated and εi is the error term that is composed of two independent elements vi and ui such that εi ≡ νi − ui. The νi(s) are symmetric noise error terms assumed to be identically and
Hypotheses test
The null hypothesis that the coefficient of the second – order variables in the translog model are zero, meaning the Cobb-Douglas model is suitable for the data is rejected (Table 2). This implies that the translog model is more suitable to derive conclusions in the data. The second hypothesis which specifies that the production risk in inputs is absent from the production process is rejected. This implies that production risk associated with input choice is present in the data. The Third
Conclusions
This paper considers the stochastic frontier model, whilst incorporating the flexible risk properties to analyze production risk, technical efficiency and its determinants of fish farms in four regions of Ghana including Greater Accra, Volta, Western and Ashanti regions. The paper employs a two year panel data of 320 farms making a total of 640 observation through a random selection. With respects to model fitness, the study demonstrate that the translog production function which allows for
Acknowledgements
This research is a sponsored project of the Office of Research, Innovation and Development (ORID), University of Ghana research fund (6th Call), +233-303-930-436. ORID supervised the research in the study design, data collection and analysis through progress reports. However, ORID had no role in the preparation of the manuscript and decision to publish.
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