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Economic and Risk Analysis of Tilapia Production in Kenya 10MEAR4

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Economic and Risk Analysis of Tilapia Production in Kenya

Marketing and Economics Analysis Research 4 (10MEAR4)/Study/Kenya

Collaborating Institution
Moi University, Kenya
      Mucai Muchiri

University of Arkansas at Pine Bluff
      Carole R. Engle

Objectives
1) Estimate the expected net returns, breakeven cost, and breakeven yield of tilapia farming in Kenya.

2) Estimate the likelihood of achieving profit (positive net returns) and the distribution of outcomes for total revenue, total costs, breakeven yield and breakeven price for varying farm scenarios.

Significance
Further growth and development of the tilapia industry in Kenya will depend upon its profitability. Estimates of net returns are essential for both the prospective producer and the lender to understand whether or not the proposed enterprise is expected to be profitable. Moreover, the level of profitability is important for comparison with other possible alternative enterprises. Enterprise budgets can be used to identify the overall expected level of returns and the level of costs that will be required. Enterprise budgets provide a means for analyzing a potential enterprise before resources are committed to it.

Enterprise budgets are based on single, usually mean, values for prices, costs, and quantity values included in the budget. However, market, financial, and production risks result in fluctuating prices, costs, and yields. An individual who is making management decisions on a fish farm will be faced with a variety of risks. Information on the varying likelihoods or probabilities of losses that would result from different management options would help farmers and planners make better decisions. Those individuals who are extremely risk averse would select those management options that are less likely to result in losses whereas more risk-tolerant individuals might opt for riskier options that have a chance of producing high levels of profits. In either case, information on the riskiness of management options would provide guidance for decision makers.

Quantified Anticipated Benefits
The primary direct beneficiaries of this study will be tilapia growers and agricultural lenders in Kenya. Enterprise budgets form the foundation of a business plan. Detailed enterprise budgets that address a variety of farm situations will provide a broad overview of profitability of tilapia production under a variety of different scenarios. The sensitivity analyses will further shed light on how robust are the estimates of net returns and what factors are most likely to affect the profitability of tilapia production. The risk analysis will provide further evidence of the confidence with which growers and lenders will be able to have in this type of production enterprise.

Secondary beneficiaries will include the university and extension personnel. All aquaculture programs rely upon enterprise budgets as the basis for discussions of the profitability of aquaculture enterprises. These budgets would be expected to be used in the classroom and by extension personnel as they work with prospective new tilapia growers.

The Moi University student will benefit from this project by using it as the basis for his/her thesis project. The student will learn the basic skills needed to develop enterprise budget and risk analyses.

Kenya and the region will benefit from the availability of fundamental enterprise budgets as well as the increased human capital that will result from this project. The region will also benefit from the economic growth anticipated from enhanced market development and profitability of tilapia farms. The PD/A CRSP will be a secondary beneficiary because a more complete foundation of economic information on the profitability and risks of tilapia production in Kenya will assist lending institutions to make better decisions on loan applications. This should result in improved success rates of aquaculture loans and this should result in growth of the tilapia industry in Kenya.

Research Design
Location of Work: The majority of this work will be done at Moi University in Kenya. There will be close communication with the cooperators at UAPB.

Methods: Economic engineering techniques will be used to develop a set of enterprise budgets for tilapia production in Kenya. Different scales of production will be defined based on the ranges of sizes of tilapia farms in Kenya. Separate budgets will be developed based on each scale of production. Typical yields and fish prices will be used to calculate gross revenue. Typical quantities of fixed and variable resources used in the production of tilapia will be specified. The budgeted values will be used to calculate net returns to operator's labor, management, and risk. Breakeven prices and costs above variable and total costs will be calculated.

The profitability of tilapia farming under conditions of risk and uncertainty will also be evaluated. The risk analysis will be conducted as a stochastic simulation using Crystal BallTM software, a spreadsheet add-in program that allows the incorporation of uncertainty in risk analysis models (Decisioneering, 1996). This program has been used previously for bio-economic modeling of aquaculture firms (Zucker and Anderson, 1999) and to analyze the profitability of shrimp farming in Honduras under conditions of risk and uncertainty (Valderrama and Engle, 2001). In the simulations, ranges of values that random variables such as yield and prices may take are defined by probability distributions instead of the sample averages used in standard enterprise budgets. Monte Carlo simulation techniques (500 iterations per simulation) will be used to generate values for individual cost and quantity items based on the probability distributions. Results presented will include the entire range of possible outcomes for parameters such as gross receipts, total costs, and net returns, as well as their associated probability.

Normal distributions will be used to define tilapia yield and price. These parameters are highly variable and influenced by many factors. Yield is determined by stocking densities, feeding rates, cycle length, and overall survival, but is also influenced by weather patterns that fluctuate randomly. Farm prices depend on marketing strategies and supply-demand interactions. As with many biological processes, these uncertain variables can be described best by a normal distribution (Zar, 1999).

Production costs will be described by triangular distributions based on the most likely value (means included in the enterprise budgets) and minimum and maximum values determined from the original data for each scenario. Triangular distributions are considered to provide the best representation of estimates when only a small number of data can be obtained (Taha, 1988).

The likelihood of achieving profit (positive net returns) and the distribution of outcomes for total revenue, total costs, breakeven yield, and breakeven price were calculated for each farm scenario. Overlay and bar charts will be developed to compare the distribution of outcomes among farm scenarios and to draw inferences from the risk analysis.

Regional Integration
The Regional Plan for Africa refers specifically to planning and implementing economics and marketing research activities in the region. Research needs for tilapia culture specifically refer to economic analysis and market development.

Schedule
10/1/01 Initiate project. Conduct initial planning meetings with project participants. Select the student.
11-12/01 Collect secondary information on prices of tilapia, costs of inputs, and farm yields of tilapia.
3/30/01 Have prototype spreadsheet model developed and ready for validation.
6/30/02 Have enterprise budget models completed.
9/30/02 Complete collection of primary data necessary from tilapia farms.
12/30/02 Finalize enterprise budgets.
3/30/02 Complete risk analyses.
4/30/02 Submit final report and have a manuscript prepared from this project.

Literature Cited
Decisioneering, 1996. Crystal Ball Version 4.0 User Manual. Decisioneering, Denver, Colorado.

Taha, H.A., 1988. Simulation Modeling and SIMNET. Prentice Hall, Englewood Cliffs, New Jersey.

Valderrama, D. and C.R. Engle, 2001. Risk analysis of shrimp farming in Honduras. Aquaculture Economics and Management, 5(1/2):49­68.

Zar, J.H., 1999. Biostatistical Analysis, Fourth Edition. Prentice Hall, Upper Saddle River, New Jersey.

Zucker, D.A. and J.L. Anderson, 1999. A dynamic, stochastic model of a land-based summer flounder Paralichtys dentatus aquaculture firm. Journal of the World Aquaculture Society, 30:219­235.

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