Collaborating Institution
Escuela Agrícola Panamericana, Zamorano, Honduras
     Freddy Arias
     Dan Meyer

Universidad de Centroamerica, Nicaragua
     Agnes Saborio

University of Arkansas at Pine Bluff
     Carole R. Engle

Objectives
1) Estimate marketing costs associated with transportation and storage for both live and processed fish in Honduras and Nicaragua.

2) Identify specific target markets that will maximize profits for small and medium-scale tilapia farmers in Honduras and in Nicaragua.

Significance
Identification of market segments that are most likely to purchase farm-raised tilapia does not mean that it is economically feasible for a tilapia farmer to sell his/her product to that market segment. In many countries, storage problems, transportation costs, and risks associated with marketing a highly perishable product such as tilapia prevent the development of viable domestic markets. This study will address whether the prices that market outlets are willing to pay for various sizes and product forms of tilapia by the vendors most likely to increase sales of tilapia are adequate to cover storage and transportation costs and still provide a profit for tilapia growers. The analysis will incorporate risk elements to evaluate profit-maximizing marketing strategies under conditions of risk aversion as well as for risk-tolerant growers.

Linear programming (LP) refers to the computational procedure used in the allocation of limited resources to maximize profit or minimize costs of producing a specific commodity (Shang, 1990). It has been widely used as a tool for solving resource allocation problems. While LP analysis has been used most commonly to select the best combination of inputs to maximize profits or to formulate least-cost feed rations, it can also be used to evaluate allocation of output to supply geographic markets. An innovative use of this technique is to select, from the perspective of the farmer, the most profitable geographic and demographic markets

The risk inherent in the parameters of a model can be depicted through different risk programming techniques. The intention of the risk model is to adequately represent the decision-maker's response to parameter risk (McCarl and Spreen, 1994). The ultimate goal is to generate a robust solution that yields satisfactory results across the distribution of parameter values.

LP models have been used to schedule harvesting and stocking dates for shrimp aquaculture in several Latin American countries (Perez, 1986 in Panama; Dunning, 1989 in Ecuador; Stanley, 1993 in Honduras). Stanley (1993) concluded that, assuming a constant survival rate (70%) across stocking densities and months of the year, farm managers should use the highest stocking densities considered in the model and extend the duration of the grow-out cycles. Hatch et al. (1987) demonstrated the usefulness of explicitly considering risk in the formulation of optimal farm plans for shrimp culture in Panama. Valderrama (2000) used risk programming techniques to outline optimal plans of activities for shrimp farms in Honduras with data reflecting the impact of Taura Syndrome Virus and White Spot Syndrome Virus and evaluated various management plans related to the onset of these viral epizootics.

Mathematical programming analysis has been used to evaluate a variety of management issues on catfish farms. This technique was used to compare aeration strategies on catfish farms (Engle and Hatch 1988). Engle and Pounds (1994) later developed a two-year model to estimate costs and benefits associated with single- and multiple-batch production systems of catfish in such a model. Risk factors, various stocking densities, and marketing strategies were modeled to determine that farmers use multiple-batch strategies, even though profits are lower than with single-batch strategies, due to off-flavor and cash flow reasons. Engle et al. (1995) extended this analysis to estimate off-flavor costs on catfish farms by including farm-level off-flavor data sets. Hatch and Atwood (1988) used risk programming to analyze catfish production.

This study proposes a more innovative use of LP techniques to identify optimal marketing strategies for varying tilapia farm sizes and farm locations. No LP analyses of tilapia production in Latin America have been identified in the literature.

Quantified Anticipated Benefits
The primary direct beneficiaries of this study will be tilapia growers in Honduras and in Nicaragua. The discrete choice study described above will identify those market outlets likeliest to purchase farm-raised tilapia, but growers must be able to sell tilapia at prices that cover both production and marketing costs as well as provide a profit. Some types of market outlets may entail greater marketing or transportation costs than others. With sound guidance on the types of market outlets with the most favorable marketing margins from the perspective of the grower, the grower can make better decisions as to the best marketing strategy for his/her farm operation.

Honduras and the region will benefit secondarily from this project through the enhanced analytical ability of the Honduran student to be funded 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 increased market opportunities for small and medium-scale tilapia growers will result in greater impacts from the PD/A CRSP production optimization research.

Research Design
Location of Work: The majority of this work will be done at the University of Arkansas at Pine Bluff. However, additional data on storage, transportation, and other marketing costs will be collected in Honduras and in Nicaragua.

Methods: Mathematical programming models will be developed with profit-maximizing objective functions using standard techniques (Hazell and Norton, 1986; McCarl and Spreen, 1994). The models will be based on tilapia production technologies and production costs for Honduras as described in Green and Engle (2000). Different levels of tilapia production technologies will be modeled as separate activities within the model along with their associated costs and input requirements. Separate models will be developed for Honduras and for Nicaragua.

Alternative target markets will be specified as marketing activities. Appropriate transfer and balance rows will be created to link the production and marketing activities. Target markets will be defined based on results from the discrete choice study. These will include geographic markets such as the urban centers of Tegucigalpa and San Pedro Sula in Honduras and Managua in Nicaragua as well as smaller towns such as those included in the surveys described in the first study. The geographic markets modeled will be segmented into market channel categories of restaurants, supermarkets, and open-air fish markets. Each of these will be further subdivided into categories and classifications developed with results from the previously described study. These categories and classifications will be based on those independent variables found to be significant in the discrete choice analyses. They may include categories such as the size of the supermarket or restaurant, whether it is a chain or an independent, the type of clientele served, cuisine types, etc. The product form, product size, volume requirements, delivery quotas required, and other key factors as determined from the survey data will be specified as constraint equations in the model with appropriate technical coefficients developed from the survey data.

Additional data will be collected in Honduras and Nicaragua on storage costs, transportation costs, ice, labor, and other marketing costs. Direct personal interviews will be conducted with wholesalers identified in the survey data as principal suppliers of fish and seafood. Costs associated with the storage and transportation of a wide variety of volumes of fish, both live and on ice, will be determined. These marketing costs will be entered into the model as marketing cost constraint equations across the different marketing strategies and production activities.

The model will be solved for a variety of farm sizes to determine which marketing strategies maximize profits. Other versions of the model will simulate farms located in different regions of the country. Once the base solutions have been identified for the various farm scenarios identified, risk constraints will be added to the model using Target MOTAD techniques (Hazell and Norton, 1986; McCarl and Spreen, 1994). The primary source of risk to be analyzed in this study will be marketing risks associated with storage problems, transportation problems, power outages, and price risk.

Regional Integration
The Regional Plan for Central America 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
7/1/01 Initiate project
8/31/01 Collect storage, transportation, and marketing cost data in Honduras and Nicaragua
12/31/01 Have prototype model developed for validation.
6/30/02 Have completed models and analyses for Honduran tilapia farms.
12/31/02 Have completed models and analyses for Nicaraguan tilapia farms.
4/30/03 Submit final report and have completed manuscripts and articles on the project.

Literature Cited
Dunning, R.D., 1989. Economic optimization of shrimp culture in Ecuador. MS thesis. Auburn University, Alabama.

Engle, C.R. and L.U. Hatch, 1988. Economic assessment of alternative aeration strategies. Journal of the World Aquaculture Society 19:85­96.

Engle, C.R. and G. Pounds, 1994. Trade-offs between single- and multiple-batch production of channel catfish, Ictalurus punctatus: An economics perspective. Journal of Applied Aquaculture, 3:311­332.

Engle, C.R., G.L. Pounds, and M. van der Ploeg, 1995. The cost of off-flavor. Journal of the World Aquaculture Society 26(3):297­306.

Green, B.W. and C.R. Engle, 2000. Commercial tilapia aquaculture in Honduras. In: B.A. Costa-Pierce and J.E. Rakocy (Editors), Tilapia Aquaculture in the Americas, Vol. 2. The World Aquaculture Society, Baton Rouge, Louisiana, pp. 151­170.

Hatch, U. and J. Atwood, 1988. A risk programming model for farm-raised catfish. Aquaculture, 70: 219­230.

Hatch, U., S. Sindelar, D. Rouse, and H. Perez, 1987. Demonstrating the use of risk programming for aquacultural farm management: the case of penaeid shrimp in Panama. Journal of the World Aquaculture Society, 18:260­269.

Hazell, P.B.R. and R.D. Norton, 1986. Mathematical Programming for Economic Analysis in Agriculture. MacMillan Publishing Company, New York.

McCarl, B.A. and T.H. Spreen, 1994. Applied mathematical programming using algebraic systems. Texas A&M University, College Station, Texas.

Perez, H.A. ,1986. Use of Linear Programming in the Panamanian shrimp industry. Master's thesis. Auburn University, Alabama.

Stanley, D.L. 1993. Optimización económica y social de la maricultura Hondureña. In: Unknown editor, Memorias del Segundo Simposio Centroamericano Sobre Camarón cultivado. Federación de Productores y Agroexportadores de Honduras and Asociación Nacional de Acuicultores de Honduras, Tegucigalpa, Honduras, pp. 94­118.

Valderrama, D., 2000. Economic analysis of shrimp farming in Honduras. MS thesis. University of Arkansas at Pine Bluff.