Economic Risk Assessment of a Semi

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ECONOMIC RISK ASSESSMENT OF A SEMI-INTENSIVE SHRIMP FARM IN SINALOA,MEXICOEdgar Sanchez-Zazueta a & Francisco Javier Martinez-Cordero aa Centro de Investigacion en Alimentacion y Desarrollo (CIAD), A.C.Unidad Mazatlan, Laboratorio de Economia Acuicola, Mazatlan,Sinaloa, Mexico

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To cite this article: Edgar Sanchez-Zazueta & Francisco Javier Martinez-Cordero (2009): ECONOMICRISK ASSESSMENT OF A SEMI-INTENSIVE SHRIMP FARM IN SINALOA, MEXICO, Aquaculture Economics &Management, 13:4, 312-327

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ECONOMIC RISK ASSESSMENT OF A SEMI-INTENSIVESHRIMP FARM IN SINALOA, MEXICO

Edgar Sanchez-Zazueta and Francisco Javier Martinez-Cordero

Centro de Investigacion en Alimentacion y Desarrollo (CIAD), A.C. Unidad Mazatlan,Laboratorio de Economia Acuicola, Mazatlan, Sinaloa, Mexico

& In Mexico shrimp farming is the most important aquaculture activity. However, its sustainabledevelopment has been threatened in recent years by the economic risk associated with low yieldscaused by outbreaks of viral diseases. A stochastic bioeconomic model was developed to analyzethe economics of farm management adjustments as a response to disease risks, using pond-level datafrom a farm operating in the State of Sinaloa, Mexico, during the period 2001–2005. The data baseanalyzed included different combinations of stocking density (in the range 6–30 PL=m2) andculture time (from 12 to 31 weeks), which allows for wider application of the simulation results, evenat the industry level. Results from this study indicate that operating costs would increase by 33% ifthe farmer would choose to market product directly. Scenarios with lower stocking densities andintermediate culture times generated the highest probabilities 6–9 PL=m2 16–19 weeks(76%=100%=70%), and 10–14 PL=m2 20–24 weeks (72%=99%) of achieving superior economicperformance, as demonstrated by achieving the target reference point of 35% operating profit marginratio. The study reinforces the value of the current trends in Sinaloa to reduce stocking density as agood management practice to decrease the impact of diseases. This study also provides importantadditional knowledge on the specific economic results and risks associated with the combinationof these two management variables at different levels.

Keywords diseases, economic risk analysis, shrimp farming

INTRODUCTION

Shrimp farming is the most important aquaculture activity in Mexico.According to 2003 statistics, farm-raised shrimp represented 50% of thetotal national production of fisheries and aquaculture, and 58% of the totalvalue of aquaculture. Ninety-nine percent of the total production area(65,085 hectares) was operated under semi-intensive production systems(SAGARPA=CONAPESCA, 2003). The States of Sinaloa and Sonora are

Address correspondence to Edgar Sanchez-Zazueta, CIAD, Laboratorio de Economia Acuicola,Sabalo Cerritos S/N, Estero del Yugo, A.P. 711, Mazatlan, Sinaloa, Mexico. E-mail: [email protected]

Aquaculture Economics & Management, 13:312–327, 2009Copyright # 2009 IAAEMISSN: 1365-7305DOI: 10.1080/13657300903351685

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the principal regions in Mexico that produce shrimp from aquaculture.Historical production levels of 34,239 and 66,030 tons in 37,653 and18,771 ha, respectively, were recorded in 2006 (CESASIN, 2006 andCOSAES, 2006).

Since 1995, shrimp production in Mexico has been threatened bythe effect of viral diseases, including taura syndrome virus (TSV) andwhite spot syndrome virus (WSSV). These diseases negatively affect theoperation and finances of shrimp farmers (Zarain-Herzberg &Ascencio-Valle, 2003; Galavı́z-Silva et al., 2004; Martinez-Cordero & Leung,2004; Wurmann et al., 2004; Lyle-Fritch et al., 2006; Peinado-Guevara &Lopez-Meyer, 2006).

The incidence of shrimp aquaculture production losses vary by regionand by year. Sinaloa is the region with the highest density of farms inMexico (Peinado-Guevara & Lopez-Meyer, 2006). Water sources for shrimpfarms in Sinaloa are predominantly creeks connected to coastal lagoons(Lyle-Fritch et al., 2006). Most of the farms share water bodies anddischarge effluents into the same creek or coastal lagoon that serves asthe water source. Shang et al. (1998) pointed out that farm performanceis generally improved (higher profit and lower losses due to diseases) whenfarms use separate intake and drainage canals, and where the number offarms discharging effluents from shrimp ponds into supply canals is small.This is the main reason for the water quality problems and lower produc-tion levels in Sinaloa as compared with Sonora.

Farms in different states have made different adjustments as a responseto diseases. Zarain-Herzberg and Ascencio-Valle (2000) documented aswitch in farm production practices for the period 1995–1998 in a sampleof shrimp farms affected by TSV in the State of Sinaloa. Farms shifted fromwhite shrimp Litopenaeus vannamei (100% in 1995 to 30% in 1998) to blueshrimp Litopenaeus stylirostris, resulting in a decrease in the prevalence ofTSV. Production stabilized nationally in 1998, following decreases of 37%in 1996 due to this disease.

In 1999 the first cases of WSSV were diagnosed in Mexico (Galavı́z-Silvaet al., 2004). Since L. vannamei has a higher resistance to WSSV thanL. stylirostris (Morales-Covarrubias et al., 2004), the industry shifted backto L. vannamei in 2000. Lyle-Fritch et al. (2006) presented results of a2001 survey that characterized shrimp farming in Sinaloa. Ninety-onepercent of the farms sampled reared L. vannamei post-larvae and the mostfrequent disease was WSSV. According to these authors, mortality fluctu-ated from 8% to 100%. Higher mortality rates (80%) in ponds of a semi-intensive commercial shrimp farm in Sinaloa in the year 2001 werealso caused by WSSV (Peinado-Guevara & Lopez-Meyer, 2006). L. vannameiremained as the main species cultured in Sinaloa during the period2000–2005, despite continued high mortality rates.

Economic Risk of Shrimp Farm in Sinaloa, Mexico 313

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In addition, Galaviz-Silva et al. (2004) demonstrated that WSSV ispresent in wild populations of crustaceans (L. vannamei, L. stylirostris)and crabs (C. arcuatus) in Sinaloa. This presence represents a latent riskfor the spread of disease into shrimp ponds (68% of the 103 farms sampledwere diagnosed positive to WSSV).

There are few reports on the economic results of adjustments to diseaseoutbreaks. Martinez-Cordero and Leung (2004) evaluated efficiency andproductivity by considering the emission of pollutants for a group ofsemi-intensive shrimp farms in the State of Sonora (Mexico) during theperiods of 1994 and 1996–1998. Farms reared white shrimp in 1994 butswitched to blue shrimp in 1996–1998 due to TSV outbreaks. The changein species represented a technological change and the production systemalso became input-intensive (feed, post-larvae, water exchange, labor).Yields increased but inefficiency levels increased as well. The output=inputratio was smaller after the introduction of the new technology.

On the other hand, Valderrama and Engle (2001) found that manyfarm managers in Honduras, after TSV outbreaks, opted for increasingstocking densities (L. vannamei) from 8–10 to 15–20 PL=m2 to sustain pro-duction levels. The authors developed profit-maximizing and risk program-ming models to analyze different management decisions, finding that incomewas maximized with low risk levels by stocking ponds in specific months ofthe year and selecting intermediate stocking densities (12–15 PL=m2), longgrow-out cycles (19–21 weeks) and low water-exchange rates, in the pre-sence of TSV and WSSV.

In addition to the low yields caused by disease, another negative issuethat farmers face is low prices as a result of an increasing world supply.Production of farm-raised L. vannamei increased from 0.14 million tonsin 2000 to 1.38 million in 2004. Consequently, the mean price per ton fellapproximately 44% in the same period (FAO’s FIGIS, October 2006).

Producers in Sinaloa currently improve profitability and diminish riskof heavy losses from diseases by adjusting four management variables: (a)diminishing water exchange rate, (b) scheduling production cycles to coin-cide with the most favorable environmental conditions (temperature), (c)reducing stocking density and (d) closely monitoring the main biologicaland marketing variables throughout the rearing period, to adjust the cul-ture length. However, there have been few studies in Mexico that evaluatein detail the economic results of recently adopted production strategies tocounteract low survival rates resulting from WSSV outbreaks. Decisions onstocking density and culture time may vary even among ponds within aproduction unit (farm). Therefore, this study carries out an economicassessment of a shrimp farm operation in the State of Sinaloa, during theperiod 2001–2005 that is focused on stocking density and culture timeunder risk scenarios.

314 E. Sanchez-Zazueta & F. J. Martinez-Cordero

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METHODS

Primary data from individual ponds for the period 2001–2005 wereobtained from a commercial semi-intensive shrimp farm in the State ofSinaloa, Mexico. A bioeconomic model was developed to evaluateeconomic results of joint production strategies related to stocking densityand culture time. This farm has more than 15 years of operation. Itsproduction practices are generally typical of semi-intensive shrimp culturein Sinaloa. However, the farm is exceptional in that it has thoroughly testedand recorded many adjustments to original practices. The farm has incor-porated many good management practices (GMPs) since 2000 to try toavoid disease problems and their consequent low yields. These include:preparation and disinfection of pond soils, water filtration, fertilizationand quality control, feed inventory management, use of feeding trays, useof hatchery post-larvae (PL 12), 15 to 21 days of stocking post-larvae inrecirculation raceways, disinfection of vehicle tires and equipment, anddividing the farming area into sections. The application of GMPs is general-ized in the State’s industry.

Production area in the farm increased from 139 ha in 2001–2003 to215 ha in 2004 and 307 ha in 2005. Most farms used two production cyclesa year until 2001. Beginning in 2002, the farm that is the subject of thisstudy switched to a single production cycle.

Ponds analyzed were only those that completed a culture cycle, elimi-nating those that required emergency harvest or for which available infor-mation was incomplete. The number of ponds and total area (ha) analyzedfor the period 2001–2005 are described in Table 1. Individual pond areavaried from 1 to 13 ha. Ponds with similar stocking dates (�1 day), stockingdensity (�3 PL=m2) and culture time (�18 days harvest date) were groupedtogether in the same category within each year. Table 2 shows mean valuesfor stocking density and culture time in the 28 categories developed.

These categories were subsequently aggregated into combinations offive stocking densities: (1) low (6–9 PL=m2); (2) medium-low (10–14 PL=m2);(3) medium (15–19 PL=m2); (4) medium-high (20–24 PL=m2); and (5) high(25–31 PL=m2) and four culture times (12–15, 16–19, 20–24, 25–31 weeks),

TABLE 1 Ponds and Surface Analyzed in the 2001–2005 Period

Year Ponds Surface (ha)

2001 23 442002 41 1282003 34 1062004 45 1892005 36 172


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resulting in 20 possible production scenarios (Table 3). Since not allpossible combinations were carried out by the farm, a final number of13 production scenarios were analyzed for this matrix.

The production scenarios were evaluated by means of productionand economic performance indicators. These included mean individual

TABLE 2 Production Categories Observed in the 2001–2005 Period

Year Ponds Surface (ha) Stocking Density (PL=m2) Culture Time (Weeks)

2001 7 7 28 153 7 11 16

13 30 10 212002 7 7 30 25

6 28 20 313 15 20 285 25 14 25

20 53 14 192003 6 6 26 28

9 21 18 193 15 17 209 25 15 203 15 13 214 20 11 12

2004 7 7 30 174 18 22 223 15 16 186 30 15 18

11 30 12 178 18 10 146 70 7 16

2005 4 4 22 213 3 21 207 35 9 213 33 7 203 39 7 177 37 6 229 21 6 18

TABLE 3 Categories Grouped into Production Scenarios According toStocking Density and Culture Time

Culture Time (Weeks)

Stocking Density (PL=m2) 12–15 16–19 20–24 25–31

6–9 3 310–14 2 3 2 115–19 3 220–24 3 225–30 1 1 2


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harvesting weight (g), survival rate (%), feed conversion ratio (FCR) andyield (kg=ha). Economic performance indicators considered were total cost(TC), profit (p), breakeven yield (BEY) and operating profit margin ratio(OPMR), defined as follows:

TC ¼X

px1 þ px2 þ px3 . . .pxn ð1Þ

P: priceX: input

p ¼ gross revenue � total cost ð2Þ

BEY ¼ total cost/unitary output value ð3Þ

OPMR ¼ profit/gross revenue � 100 ð4Þ

Total cost included production, harvesting and processing, and market-ing costs.1 Production costs were calculated per unit area (ha) and includedfeed, seed, fertilizer, labor and fuel costs. The farm registered the last two asthe total labor and fuel cost for a full production cycle. Consequently, inorder to obtain the cost per pond according to the number of days a parti-cular pond was operated and its surface area, the total labor and fuel expen-diture per cycle were divided by the area of the operation and the cyclelength, and multiplied by the culture time (days) and surface area of a spe-cific pond. Harvesting, processing and marketing costs are those related tolabor (three-day’s wage on harvest activities per hectare was included), chil-ling, packaging, processing (frozen head-on) and storage. For the analysis,marketing costs and sales price were held constant at 2005 values. Gross rev-enue was obtained by multiplying selling price times biomass, taking intoaccount the different prices for different sizes at harvest.

Operating profit margin ratio is expressed as a percentage andrepresents the share of gross revenue that can be considered profit whendiscounting total cost. This indicator was used by Valderrama and Engle(2001) to compare profitability among different shrimp farm sizes.

Scenarios were analyzed initially by two-way analysis of variance ANOVA(logarithm transformation to correct for non-normality was applied), todetermine the joint effect of stocking density and culture time oneconomic and production performance indicators. Subsequently a statisti-cal evaluation of all ponds (n¼ 179) showed significant correlations(Spearman, r¼ 0.81) between stocking density and total cost; thus aone-way ANOVA (parametric and non-parametric Kruskal–Wallis) was per-formed that focused on stocking density. Post-tests (Holm-Sidak or Dunn)were carried out on mean values of economic and production performance


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indicators. Differences among scenarios were considered significant atlevels of p< 0.05.

Reference points (Seijo, 2004) were set to evaluate and select produc-tion scenarios according to minimum or maximum levels of profit allowedor expected by the farm owner. Values of 27% and 35% in operating profitmargin ratio were established as the limit reference point and target refer-ence point, respectively, meaning that the farmer seeks to obtain at least 27cents of profit per dollar of income and similarly expects profits equal to orabove of 35 cents per dollar of income. Cost structure and sensitivityanalyses were also conducted for the best scenarios.

The economic risk associated with results observed in the best scenar-ios, was evaluated with Monte Carlo simulation (10,000 iterations wereperformed with a risk analysis and simulation program, @Risk 4.5TM, add-infor electronic spreadsheet, ExcelTM 2003) (Palisade Corporation, 2004) foreach one of the categories integrating these production scenarios. Prob-abilities of achieving the target reference point for operating profit marginratio were calculated. Probability distributions for yield, selling price, andtotal cost were included in this final evaluation.

Yield (kg=ha) was modeled by fitting continuous probability distribu-tions (i.e., normal, logistic, extreme value, triangular, beta general) tothe data (Kazmierczak & Soto, 2001). Probability distribution functionswere truncated according to the minimum and maximum values observed.Selling price variation according to shrimp size was included in the modelby means of discrete distribution functions. A single, individual size (andcorrespondent price) per pond was used, based on the mean weight at har-vest. However, categories (made out of aggregated ponds with similar stock-ing density and culture time) do take into account size distributions, andsimulate different selling prices. Total costs ($=ha) were represented bydifferent continuous probability distributions. Since yield affected feedingcost (Spearman, r¼ 0.71), a correlation matrix was inserted. Marketingcosts were calculated based on yield. Labor and fuel inputs were insertedas discrete distribution functions.

RESULTS

Two-way ANOVA results showed that extending culture time periodsbeyond 25 weeks resulted in significantly (p< 0.05) lower survival levels(mean¼ 20%), higher feed conversion ratios (mean¼ 2.9) and conse-quently lower profits (mean $1,050=ha) and operating profit margin ratio(mean¼ 1.3%). Stocking density was a critical factor (p< 0.05) of the totalcost level. In terms of operating profit margin ratio there were no statisticaldifferences (p< 0.05) among different stocking densities.


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Production and economic performance indicators for the five stockinggroups with different culture times are shown on Tables 4 and 5. Themost common size of shrimp harvested was in the >20 g category. Survivalwas significantly higher (p< 0.05) with the combination of stocking25–30 PL=m2 for 12–15 weeks and 10–14 PL=m2 for 20–24 weeks. Feed con-version ratio was lowest for the 25–30 PL=m2 for 12–15 weeks, 6–9 PL=m2

TABLE 5 Economic Results for Stocking Density-Culture Time Scenarios

Density PL=m2

CultureTime

(Weeks)

OperatingProfit Margin

Ratioa %

Profit($USD=ha)

Total Cost($USD=ha)

Break EvenYield (Kg=ha)

Low 6–9 16–19 40a� 10 1,650.3bc� 692.6 2,285.4a� 343.3 353.5a� 61.820–24 34ab� 19 1,611.1bc� 940.6 2,577.6a� 434.3 379.9ab� 60.9

Medium low 10–14 12–15 26ac� 19 1,546.9bc� 1,224.8 3,081.9ab� 766.3 585.9cd� 58.516–19 37ab� 7 2,298.1b� 798.9 3,793.1b� 404.3 615.4c� 73.120–24 41a� 6 3,765.8a� 980.7 5,331.5de� 705.4 816.2e� 132.125–31 24ac� 17 1,521.0bc� 1,060.0 3,940.4bc� 644.9 574.6bcd� 94.1

Medium 15–19 16–19 23c� 8 1,436.6bc� 635.9 4,571.0cd� 861.8 740.1de� 141.820–24 30ac� 12 2,206.1bc� 1,010.8 4,649.9cd� 553.8 733.2ce� 96.6

Mediumhigh

20–24 20–24 28ac� 15 2,674.8ab� 1,885.4 5,922.6e� 710.4 1,052.5f� 155.325–31 �12bc� 79 1,282.9bc� 2,124.7 5,454.0de� 1,358.6 801.1e� 188.3

High 25–30 12–15 33ac� 9 4,088.3a� 1,454.4 8,182.6f� 682.4 1,690.7h� 274.216–19 12c� 12 962.6bc� 774.7 6,053.6e� 846.3 1,054.7f� 186.625–31 2c� 29 707.8c� 1,731.4 7,980.1f� 930.5 1,302.3g� 180.6

aOperating profit margin ratio might not coincide if it is calculated directly from table values, becauseresults were calculated with raw data.

Means (�SD) in the same column with different letters are significantly different (p< 0.05).

TABLE 4 Production Results for Stocking Density-Culture Time Scenarios

Density PL=m2

CultureTime

(Weeks)IndividualWeight (g)

Survivial%

FeedConversion

ratio Yield (Kg=ha)

Low 6–9 16–19 25.1ad� 3.0 39b� 12 1.5a� 0.2 607.7g� 164.520–24 27.1a� 1.6 31bcd� 8 1.9ad� 0.3 615.8g� 194.1

Medium low 10–14 12–15 21.4ef� 1.5 34bed� 13 1.9ad� 0.3 750.0fg� 316.916–19 22.2ef� 1.9 35bc� 8 1.5a� 0.2 986.8df� 179.420–24 25.5ab� 1.8 53a� 12 2.0ad� 0.2 1,391.9bcd� 270.625–31 26.6abc� 1.3 21cde� 7 1.8ad� 0.2 796.5efg� 246.2

Medium 15–19 16–19 22.8cde� 2.4 26cde� 8 2.3cd� 0.5 973.4ef� 216.620–24 24.0bde� 1.3 29bde� 7 2.2bcd� 0.4 l,079.8cdf� 242.7

Medium high 20–24 20–24 19.0f� 4.5 38b� 10 1.9acd� 0.3 l,497.3b� 315.525–31 28.0a� 1.7 17e� 9 3.0e� 1.4 984.2ef� 500.9

High 25–30 12–15 13.9g� 1.5 65a� 8 1.4ab� 0.1 2,509.8a� 230.416–19 19.2f� 2.8 22de� 7 2.4de� 0.1 l,222.0bde� 286.025–31 21.9ef� 3.0 22de� 5 3.2e� 0.9 l,405.2bc� 329.3

Means (�SD) in the same column with different letters are significantly different (p< 0.05).


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for 16–19 weeks and 10–14 PL=m2 16–19 weeks. Significantly higher yieldsresulted from stocking 25–30 PL=m2 for 12–15 weeks, contrary to the lowstocking density (6–9 PL=m2) scenario where yield was the lowest. Nostatistical difference was found in yield between 10–14 PL=m2 16–19 weeksand 20–24 PL=m2 25–31 weeks; neither in the comparison between10–14 PL=m2 20–24 weeks and 20–24 PL=m2 20–24 weeks.

Target (35%) and limit (27%) reference points (TRP and LRP) wereestablished according to the median and the 25th percentile of the operat-ing profit margin ratio observed for ponds with positive returns in thisstudy (n¼ 168). The owner of the farm was interviewed to set the TRP valuein accordance with his preferences, needs and experience.

Scenarios with low (6–9 PL=m2 16–19 weeks) and medium-low(10–14 PL=m2 16–19 and 20–24 weeks) stocking densities achieved theproposed target reference point for the operating profit margin ratio.Other scenarios (6–9 PL=m2 20–24 weeks; 15–19 PL=m2 20–24 weeks;20–24 PL=m2 20–24 weeks; and 25–30 PL=m2 12–15 weeks) had an operat-ing profit margin ratio above the established limit reference point.However, no statistical differences (p< 0.05) were found in the operatingprofit margin ratio groups of scenarios.

Profit levels were statistically similar across the majority of thescenarios (Table 5). The highest profit was observed for the 25–30 PL=m2

12–15 week scenario and the 10–14 PL=m2 20–24 week scenarios, butthese were statistically similar to the 20–24 PL=m2 20–24 week scenario.Total costs increased with stocking density. The lowest total cost wasobserved in scenarios with low stocking densities (6–9 PL=m2 16–19 and20–24 weeks) while the highest cost was found with the high stockingdensity (25–30 PL=m2).

Breakeven yield (BEY) exhibited a similar trend to that of total cost,increasing with the stocking density. However, since production results forall scenarios exceeded the breakeven yield, the best results were for thosescenarios with the highest yield value related to their respective breakevenyield. These were the scenarios with the low density of 6–9 PL=m2 for16–19 weeks, 6–9 PL=m2 for 20–24 weeks and some with medium-low10–14 PL=m2 for 16–19 weeks, and 10–14 PL=m2 for 20–24 weeks.

The relationship between profit, total cost and operating profit marginratio is shown in Figure 1. The scenarios of 10–14 PL=m2 for 20–24 weeksand 6–9 PL=m2 for 16–19 weeks had the best operating profit margin ratios,41% and 40%, respectively. These similar values occurred even though totalcost ($5,332=ha and $2,285=ha, respectively) and profit ($3,766=ha and$1,650=ha, respectively) values differed. This is because similar proportionsof inputs and profits were found between these two scenarios. This was notthe case for the 25–30 PL=m2 12–15 weeks scenario where the totalcost-profit difference brought about a lower operating profit margin ratio


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(33%). The same situation occurred for all other scenarios of high (25–30 PL=m2), medium-high (20–24 PL=m2) and medium (15–19 PL=m2)stocking densities as compared to low (6–9 PL=m2 20–24 weeks) andmedium-low (10–14 PL=m2 16–19 weeks) density scenarios (Table 5).

The total operating cost structures (per hectare) for stockingdensity-culture time scenarios are shown in Figure 2 (6–9 PL=m2 20–24weeks scenario was not included since its results did not differ statistically(P< 0.05) from that of 6–9 PL=m2 16–19 weeks). Figure 2a represents thevalue of each cost item. Total operating costs for the 6–9 PL=m2 16–19weeks scenario were $1,383=ha without marketing costs. Inclusion ofmarketing costs increased total operating costs by $903=ha. Marketing costswere greatest for the 25–30 PL=m2 12–15 weeks scenario. Results from thisstudy for all possible scenarios indicate that direct marketing by the farmerwould increase total operating costs by 33%. Differences in total operatingcosts among all scenarios are not that large if only production costs arecompared, but the disparity increases when marketing costs are included.For example, the difference between 20–24 PL=m2 20–24 weeks and25–30 PL=m2 12–15 weeks scenarios is only $920=ha for production costsand $2,260=ha with marketing costs included.

FIGURE 1 Operating profit margin ratio (OPMR) generated from different stocking density-culturetime production scenarios.


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Feed was the most important component of the total operating costs ofproduction, comprising 41% of total costs in the 10–14 PL=m2 density for16–19 weeks up to 51% in 20–24 PL=m2 for 20–24 weeks (Fig. 2b).Post-larvae had the second largest cost share (15%) in 10–14 PL=m2

20–24 weeks; this cost share increased to 30% in the 25–30 PL=m2 12–15weeks. Marketing costs (Fig. 2c) represented from 33% (in 15–19 PL=m2

20–24 weeks) up to 42% (in 25–30 PL=m2 12–15 weeks) of the total costwhen harvesting, processing (frozen, head-on shrimp) and merchandisingthe product was taken into account.

A sensitivity analysis was conducted for the best scenarios by varyingfeed cost and sales price by �10%, 20%, and 30%. Sales price had thegreatest effect on the operating profit margin ratio (Fig. 3a). A decreaseof 10% caused scenarios 25–30 PL=m2 12–15 weeks, 15–19 PL=m2 20–24

FIGURE 2 Total cost components per hectare for different stocking density-culture time productionscenarios (2a) and its proportions without (2b) and with marketing costs (2c).


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weeks and 20–24 PL=m2 20–24 weeks to fall bellow the limit referencepoint (27%). Reductions in sales price larger than 20% were necessaryto drop the operating profit margin ratio below the limit reference pointin the scenarios of 10–14 PL=m2 20–24 weeks and 6–9 PL=m2 16–19 weeks.Feed cost increases up to 30% did not cause production scenarios6–9 PL=m2 16–19 weeks, 10–14 PL=m2 20–24 weeks, 10–14 PL=m2 16–19weeks, 25=30 PL=m2 12–15 weeks to fall below the limit reference point(Fig. 3b).

Risk analysis results (Table 6) clearly favored scenarios with low(6–9 PL=m2) and medium-low (10–14 PL=m2) stocking densities, inwhich the probabilities of achieving the target reference point of 35%operating profit margin ratio were higher. The probabilities of achievingthe target reference point for scenarios with medium (15–19 PL=m2),medium-high (20–24 PL=m2) and high (25–30 PL=m2) stocking densitieswere very low, and would not be considered by the farmer as the bestchoice.

FIGURE 3 Sensitivity analysis on operating profit margin ratio (OPMR, y axis) given selling price (3a)and feed cost (3b) variations (x axis), for different stocking density-culture time production scenarios.


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DISCUSSION

What are the risks associated with production management decisionsthat jointly adjust shrimp stocking densities and growout cycle length?Large-size shrimp (>22 g) with better market prices were obtained inproduction scenarios where the target reference point (operating profitmargin ratio �35%) was achieved (Tables 4 and 5). Lower prices offeredfor small-sized shrimp (mean 13.9 g) affected the 25–30 PL=m2 12–15 weeksscenario that had the best production performance indicators. Higherprices for larger shrimp (25.5 g) favored the medium-low (10–14 PL=m2

20–24 weeks) stocking density scenario in spite of the lower yield ascompared to some other scenarios due to lower production costs. Thiscorroborates findings of Valderrama and Engle (2002) who found thatproduction strategies of 15 PL=m2-21 weeks and 12 PL=m2-19 weeks, maxi-mize profits by targeting the production of large tail counts, as comparedto strategies with higher stocking densities (20 and 25 PL=m2) and differentculture times.

In this study the best operating profit margin ratio was found for sce-narios with the lowest feed conversion ratio (Table 4), with the exceptionof the 10–14 PL=m2 20–24 weeks scenario. High correlations (Spearman,r¼�0.75) exist between feed conversion ratios and operating profitmargin ratios. On the other hand, survival and feed conversion ratios werenot related to stocking density. These results coincide with Valderramaand Engle (2002).

TABLE 6 Certainty Levels for Stocking Density-Culture Time Production Scenarios for Achieving theEstablished Target Reference Point of 35% in the Operating Profit Margin Ratio (OPMR)

Density (PL=m2) Culture Time (Weeks) Ponds=Year Probability OPMR �35%

Low 6–9 16–19 6=2004 76%3=2005 100%9=2005 70%

20–24 7=2005 48%3=2005 100%7=2005 17%

Medium low 10–14 16–19 3=2001 5%20=2002 55%11=2004 34%

20–24 13=2001 72%3=2003 99%

Medium 15–19 20–24 3=2003 38%9=2003 24%

Medium high 20–24 20–24 4=2004 100%4=2005 0%3=2005 12%

High 25–30 12–15 7=2001 43%


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At harvest, the shrimp can be sold either fresh (on farm) or processed(frozen, head-on=off, etc). If the farmer decides to process his product, anadditional expense will be required, and this extra cost needs to beassessed. For example, the extra income gained for processed product doesnot compensate for the extra cost of processing shrimp that are small.Variations in sales price had a greater effect on the operating profit marginratio than did variations in feed cost. This supports findings from Hatchet al. (1987) and Hernandez-Llamas et al. (2004) who reported that salesprice had the largest effect on income variations in shrimp farming inPanama and Mexico, respectively.

Scenarios with low (6–9 PL=m2) and medium-low (10–14 PL=m2)stocking densities for different culture times (16–19 weeks, 20–24 weeks)were favored according to the risk analysis results. These results indicatethat there is a higher risk level associated with higher stocking densities.Moreover, market conditions favored the production of large shrimp sizeswhen cultured in scenarios at lower stocking densities. For example,comparing 10–14 PL=m2 for 20–24 weeks and 25–30 PL=m2 12–15 weeksscenarios, the probabilities of achieving the target reference point in eachcategory that integrates these scenarios were higher for the former (72%,99% vs. 43%). Ponds integrating the 10–14 PL=m2 20–24 weeks scenario,showed good survival rates (mean¼ 53%). However, lower survival ratesare likely with a shorter production cycle (Table 4). The riskanalysis showed that the economic performance of the medium-low stock-ing density (10–14 PL=m2) scenarios was lower than that of the lowstocking density (6–9 PL=m2) scenarios. As a result, the farmer faces asituation in which two options can be possible: (1) Medium-low stockingdensity (10–14 PL=m2) increases profit levels, and (2) low stocking density(6–9 PL=m2) scenarios ensures elevated operating profit margin ratio dueto lower costs. In both cases, the risks of total production lost due to viraldiseases outbreaks are present.

Stocking densities to optimize farm profit under risk conditions (TSV andWSSV presence) were evaluated by Valderrama and Engle (2002). They didnot find significant differences in risk levels by stocking 5 PL=m2 or 12–15 PL=m2 in production cycles of at least 19 weeks, to ensure harvesting largesizes of shrimp. A very low potential for financial loss was shown by 5 PL=m2

results. However, 12–15 PL=m2 was not a risky option either; consequently, itwas chosen as the first option because profit levels would be higher.

Shrimp farms in Sinaloa for example, have reduced stocking densities(2003–10 PL=m2, 2004–10 PL=m2, 2005–8 PL=m2 CESASIN, 2006), com-pared to those considered in Martinez and Seijo (2001a, 2001b), of 20 PL=m2

and 20–35 PL=m2 in semi-intensive production systems in Sonora. Reducingstocking densities as a measure to counteract the effects of diseases is consid-ered a good management practice (GMP). Hatch et al. (1987) pointed out


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that if farm survival is in doubt, more conservative farm plans will generallybe adopted.

There were no partial harvests during the five years of study. Thispractice reduces stress to shrimp from a lack of oxygen, overcrowding,and disease. Partial harvests in this study may have improved survival andgrowth rates (Martinez and Seijo, 2001a).

The best operating strategy for a semi-intensive shrimp farm in Sinaloawould be the use of a combination of low (6–9 PL=m2) or medium low(10–14 PL=m2) stocking densities, with a rearing cycle of 16 to 24 weeks.These options balance risk levels with income necessities. The results of thisanalysis showed that production strategies with conservative stocking densi-ties performed better in terms of profit and operating profit margin ratiounder risk analysis. These results parallel industry trends in 2006.

ACKNOWLEDGMENTS

The authors thank CONACYT for a scholarship for postgraduate studies.Also the authors acknowledge the company administrative and operationpersonnel for their collaboration. Special thanks for the insightfulcomments from two anonymous reviewers.

NOTE

1. Information about fixed and infrastructure costs was not available and therefore not included in theanalysis (not provided by the company, ranked as highly confidential).

REFERENCES

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Comite Estatal de Sanidad Acuicola de Sinaloa (CESASIN), A.C., Online http://www.cesasin.org/,Sept=26=2006. (In Spanish)

Food and Agriculture Organization, FAO: Fisheries Global Information System, FIGIS, Online http://www.fao.org/, Oct=10=2006.

Galaviz-Silva, L., Molina-Garza, Z.J., Alcocer-Gonzalez, J.M., Rosales-Encinas, J.L., & Ibarra-Gamez, C.(2004) White Spot syndrome virus genetic variants detected in Mexico by a new multiplex PCRmethod. Aquaculture, 242, 53–68.

Hatch, U., Sindelar, S., Rouse, D., & Perez, H. (1987) Demonstrating the use of Risk programmingfor aquacultural farm management: The case of penaeid shrimp in Panama. Journal of the WorldAquaculture Society, 14, 260–269.

Hernandez-Llamas, A., Gonzalez-Becerril, A., Hernandez-Vazquez, S., & Escutia-Zu~nniga, S. (2004) Bio-economic analysis of intensive production of the blue shrimp Litopenaeus stylirostris (Stimpson).Aquaculture Research, 35, 103–111.

Kazmierczak, R.F.JR., & Soto, P. (2001) Stochastic economic variables and their effect on net returns tochannel catfish production. Aquaculture Economics and Management, 5(1=2), 15–36.


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Lem, A. (2006) An overview of global shrimp markets and trade. In: Shrimp Culture Economics,Market, & Trade (eds P.S. Leung & C.R. Engle), pp. 3–10. World Aquaculture Society & BlackwellPublishing, Baton Rouge, Louisiana, USA.

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Documents

Economic Risk Assessment of a Semi