Pd/a Crsp Seventeenth Annual Technical Report

Honduras has established itself as the leading producer of pond-raised shrimp in Central America. Although this activity already represents the third staple of the national economy, relatively few economic analyses have been conducted to date. For this study, data on production of farm-raised shrimp were collected from 21 farms. Data are from the year 1997. Information was collected on technical aspects of shrimp culture (stocking densities, feeding rates, FCRs) as well as on financial performance of the farms (production costs, farm revenue) during the considered period. A risk analysis was carried out from the resulting data. Three scenarios were defined according to farm size and a fourth was created to aggregate farms with uncommonly high yields. Scenarios were defined in order to identify possible differences in management strategies. Simulations for this study were run with commercially available risk analysis software. Results indicated that farms of the last scenario have developed a major potential for profit, far greater than that of those farms adopting more conservative approaches. Risk is more associated with low yields than with high production costs. Regardless of size, farms should target a minimum acceptable yield. Annual production of less than 450 kg ha-1 is connected with a large potential for loss. SEVENTEENTH ANNUAL TECHNICAL REPORT 124 by the current size distribution of shrimp farms in Honduras (Table 1). Farms of similar size were substituted for those selected in the original sample that refused to participate. Survey data were entered into spreadsheets, summarized, and cross-tabulated. Figure 1 displays the proportion of shrimp farms in the sample in terms of the number of farms and the production area. Representative enterprise budgets were developed for each farm size group based on the survey data using standard budgeting techniques (Kay and Edwards, 1994). Values used in the enterprise budgets were means for a given parameter for each respective farm size. A fourth enterprise budget was developed for some largeand medium-sized farms having yields greater than 1,250 kg ha-1 yr-1. Stocking and feeding rates and production costs are also higher for this group of farms. These differences in production, management, and cost characteristics made it necessary to develop a separate enterprise budget. To facilitate denomination of scenarios for the risk analysis, the last group of farms are called “intensive,” even though in shrimp aquaculture this term is applied worldwide to units producing more than 4,500 kg ha-1 yr-1 (Fast, 1992). Operations of this type are rare in Latin America since a high capital input is needed in response to constraints in availability of land, water, and cheap labor. Although by definition the term “intensive” is not used correctly in this study, it serves to illustrate differences among scenarios. The risk analysis was conducted as a stochastic simulation using Crystal BallTM software. This is a spreadsheet add-in program that allows the incorporation of uncertainty in risk analysis models. Previous applications of this program to aquaculture situations include the stochastic model of a summer flounder firm developed by Zucker and Anderson (1999). In the simulations, ranges of values that individual variables or parameters may take are defined by probability distributions instead of the single mean values used in standard enterprise budgets. Monte Carlo simulation techniques (using 500 iterations) are used to generate values for individual cost and quantity parameters based on the probability distributions. Results present the entire range of possible outcomes and the likelihood of achieving them. Different distribution forms were selected for different parameters based on availability of data and with input from professionals with long-term experience with the shrimp industry in Honduras. Table 2 summarizes the choice of distributions for each item in the enterprise budgets and the correspondent values selected as distribution parameters. Production area (ha) within each farm size category was defined with a uniform distribution: all values between the minimum and maximum occur with equal likelihood, and minimum and maximum values correspond to the limits for each size range. Normal distributions were used to define shrimp yields and prices. 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 average shrimp size, marketing strategies, and supply-demand interactions in the international market at the moment of harvest. These uncertain variables can be described by a normal distribution. Within each farm size category, the mean and standard deviation values for yields and shrimp prices were used to define the probability distributions. Costs were described by triangular distributions based on the most likely value (which was used in the enterprise budget) and minimum and maximum values determined from the original data for each scenario. In general, minimum values were calculated by assuming that the smallest farm of each size group has the lowest usage rate of a given resource and pays the lowest possible price for it. Likewise, maximum values are obtained under the assumption that the largest farm of each size group will have the highest rate of input usage and will acquire resources at the highest price. Occasionally, minimum values were zero (e.g., fertilizer and electricity costs) since at least one of the farms within each scenario did not report the Figure 1. Distribution of shrimp farms in Honduras by size range: small (10 to 150 ha), medium (150 to 400 ha), and large farms (more than 400 ha). Bar height indicates number of farms and total production area for each size group. Upper portion of the bars represents number and production area of the farms included in the survey. Farm Size Farms Area Number % ha % 10 to 150 ha 44 66 2,710.13 22 150 to 400 ha 17 25 4,605.75 37 > 400 ha 6 9 5,089.28 41 Total 67 12,405.16 Table 1. Size distribution of shrimp farms in Honduras. Source: ANDAH (1997). 0 10 20 30 40 50 10–150 150–400 > 400 Farm size (ha) N u m b er o f fa rm s Sampled farms Farms not in sample 0 1,000 2,000 3,000 4,000 5,000 6,000 10–150 150–400 > 400 Farm size (ha) P ro d u ct io n a re a (h a) Sampled farms Farms not in sample MARKETING AND ECONOMIC ANALYSIS RESEARCH 125 usage of the respective input. Blank spaces in Tables 3 through 6 indicate that no corresponding information was obtained from any of the farms within the respective farm group. An additional cost variable (“others”) was included to account for costs such as security expenses, insurance payments, and estimates of losses by bird depredation and poaching. Most farm managers expressed that expenses of this type can be considerable. The variable (“feed costs”) was divided into two categories (feed quantity and feed price) since a negative correlation was found between farm size and price paid per feed unit. Correlations between variables need to be defined before running the simulations for the risk analysis. Crystal BallTM normally calculates values independently of other values. Therefore, results could be biased if existing dependencies between variables are not accounted for. In addition to feed price (r = -0.65), production area was found to be correlated with six other variables: seed costs (r = 0.94), feed quantity (r = 0.98), full-time labor (r = 0.71), diesel costs (r = 0.98), debt payment (r = 0.65), and infrastructure depreciation (r = 0.75). These costs increased as a function of farm size. Correlation coefficients were calculated from data of the 19 semi-intensive farms and included in the model for each scenario. Other costs such as fertilizer, part-time labor, and electricity were not related to farm size, but varied according to factors such as management strategies, natural fertility of pond water, and electricity availability. Correlation coefficients could not be calculated for the intensive farm scenario due to the low number of observations. Crystal BallTM generated random numbers for each cell independently of the values used for others. 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 trend charts were developed to compare the distribution of outcomes among farm scenarios and to draw overall conclusions from the risk analysis. RESULTS AND DISCUSSION