Economic feasibility of tilapia culture in southern Brazil: A small-scale farm model

Journal Pre-proof Economic feasibility of tilapia culture in southern Brazil: A small-scale farm model L. Castilho-Barros, M.S. Owatari, J.L.P. Mouriño, B.C. Silva, W.Q. Seiffert PII:

S0044-8486(19)30737-9

DOI:

https://doi.org/10.1016/j.aquaculture.2019.734551

Reference:

AQUA 734551

To appear in:

Aquaculture

Received Date: 27 March 2019 Revised Date:

27 September 2019

Accepted Date: 28 September 2019

Please cite this article as: Castilho-Barros, L., Owatari, M.S., Mouriño, J.L.P., Silva, B.C., Seiffert, W.Q., Economic feasibility of tilapia culture in southern Brazil: A small-scale farm model, Aquaculture (2019), doi: https://doi.org/10.1016/j.aquaculture.2019.734551. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

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Running Head: Economic feasibility of O. niloticus in small-scale production

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Castilho-Barros, L.1*; Owatari, M.S.1; Mouriño, J.L.P.1; Silva, B.C.2; Seiffert, W.Q.1

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Title: ECONOMIC FEASIBILITY OF TILAPIA CULTURE IN SOUTHERN BRAZIL: A SMALL-SCALE FARM MODEL

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Aquaculture Department, Center for Agrarian Sciences, Federal University of Santa Catarina Agricultural Research and Rural Extension Company of Santa Catarina (EPAGRI) * Beco dos Coroas Street, 00 – Florianópolis, SC, Brazil; [email protected] 2

Abstract

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Brazil is following the growing trend of world aquaculture production. Among the cultured

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fish species, Tilapia (Oreochromis niloticus) presents itself as a potential commodity in the

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“aquabusiness” sector owing to its versatility. This study was conducted to evaluate the

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economic-financial viability of tilapiculture in southern Brazil. Therefore, small-scale

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production was evaluated over several floodplain configurations in different scenarios (feed

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conversion, stocking density and marketing price) in relation to two segments of activity in

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the region: trade for slaughter (700 g fish-1 of final live average weight – Condition A) and

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“Fish and pay farm” (1100 g fish-1 of final live average weight – Condition B). The

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methodology recommended by the Institute of Applied Economics of the State of São Paulo

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(IAE-SP) was applied to evaluate the production and profitability of tilapiculture in Santa

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Catarina. The activity is profitable in several scenarios with average variations from

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$3,285.09 to $11,288.36 of net revenue per hectare, Annualized Net Present Value up to

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$10,837.85 and Modified Internal Rate of Return of 23.75%. These values support the

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economic feasibility of tilapiculture on a small-scale farm basis and an important economic

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supplement to the family economic base. Among the possible profitable scenarios, we point to

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some of the options best suited to conditions encountered by micro and small producers.

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Keywords: Fish and pay farm; Oreochromis; Economic Analysis; Modified Internal Rate of Return; Annaulized Net Present Value; Operational Costs

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1.

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Tilapia production in Brazil in 2017 was estimated at approximately 283.25 thousand

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tons, which represents a contribution of US $1.58 billion to the Brazilian economy and places

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the country among the four largest producers of this species in the world behind China with

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1.8 million tons, Indonesia at 1.1 million tons, and Egypt with 800 thousand tons in the same

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period (IBGE, 2018; PeixeBR, 2018). According to the Brazilian Fisheries Association

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(PeixeBR, 2018), it is estimated that Brazil will produce 500 thousand tons of tilapia in the

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biennium of 2019/2020.

Introduction

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According to data from the Brazilian Institute of Geography and Statistics (IBGE),

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Brazil has approximately 5.07 million rural properties with enough water for aquaculture,

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including, for example, excavated tanks, lakes and dams. Of this amount, 1.89 million

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properties (37.33%) have up to five hectares of water table (IBGE, 2018). This same report

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indicates that southern Brazil has 22.61% of these rural properties with up to five hectares of

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water.

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In southern Brazil, the states of Paraná and Santa Catarina stand out for their impressive

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aquaculture production. In 2017, Paraná produced 91.72 thousand tons of tilapia, which is

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about 32.28% of the national production, placing it as the largest tilapia producer in Brazil

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(IBGE, 2018). In this ranking, the State of Santa Catarina deserves special attention owing to

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the strong contribution of its production with 24.16 thousand tons (8.53% of the national

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production), occupying fourth place in terms of production, behind only São Paulo with 42.64

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thousand tons (15.05%) and Minas Gerais with 26.42 thousand tons produced (9.33%). The

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tilapiculture from Santa Catarina contributed approximately US $37.43 million to the public

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offers (IBGE, 2018).

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According to data from the IBGE (2018), in 2017, 192,934 agricultural establishments

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had up to five hectares of water surface in Santa Catarina. Silva et al. (2017) reported that fish

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farming in Santa Catarina is characterized by producers who can be classified as Amateur and

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Commercial. Those who cultivate fish species for leisure and eventual commercialization are

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characterized as amateur producers. Commercial producers are distinguished from the former

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by systematic and regular marketing of production.

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Although national aquaculture production, particularly of tilapia, appears to be a

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promising activity for the production of animal protein, challenges still need to be addressed

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so that the chain of production can grow in a harmonic and sustainable way (Schulter &

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Vieira Filho, 2017). Based on the geographic characteristics of the state of Santa Catarina, the

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cultures are predominantly carried out in excavated nurseries.

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The construction of nurseries is costly. In this way, the use of tools that optimize profits

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and reduce costs are necessary (Schulter & Vieira Filho, 2017), so that fish farming is more

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competitive, albeit on a small scale (Jomori et al., 2005).

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Campo & Zuniga-Jara (2018) pointed out that the correct allocation of financial

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resources is beneficial to several sectors, especially for the socioeconomic sustainability of the

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enterprise. The consumer market in Santa Catarina, which absorbs a large quantity of this

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product, is divided into two segments: (i) processing industries and (ii) “fish and pay” farms

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(EPAGRI-CEPA, 20171). In the processing industries, the tilapia market is characterized by

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the commercialization of fresh weight slaughtered fish ranging from 600 g to 800 g (Silva et

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al., 2017). One of the characteristics of the southern and southeastern regions of Brazil is the

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production of fish, among them tilapia, to serve a specific segment of the market, the “fish

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and pay” farms (Kubitza, 2007; Silva et al., 2017). These ventures mainly absorb fish

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weighing more than one kilo, paying up to 15% of the sales value of the slaughtered fish

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(Silva et al., 2017). According to Barroso et al. (2018), this segment has approximately 260

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projects dedicated to recreational and leisure fishing and is capable of absorbing about 50% of

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the tilapia production in Santa Catarina. However, to date, there are no documents

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demonstrating the benefit-cost relationships of the implementation about diferents dimensions

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of fish farms in this region.

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This study aimed to evaluate the economic viability of tilapia production in Santa

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Catarina based on different area dimensions (one, two, three, four and five ha flooded area),

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analyzing two distinct categories of fish commercialization: slaughtered fish (700 g fish-1) and

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"fish and pay" (1100 g fish-1, as final average weight).

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2.

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The production of tilapia (Oreochromis niloticus) cultivated in a single-phase system in

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the subtropical region of Brazil, was evaluated. A single-phase system is a production mode

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whereby 0.5 grams (0.0005 kg) are purchased and grown in tanks until the time of harvest,

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which is the predominant practice in the state.

Materials and Methods

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Infrastructure, flooded area, management and densification data were obtained through

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online questionnaires2 sent to virtual groups of tilapia producers who work in the state and

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extension agents of the Agricultural and Rural Extension Company of Santa Catarina

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(EPAGRI, in Portuguese) from January 2017 to May 2018. It should be noted that the

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Center for Socioeconomics and Agricultural Planning, Research Company in Agriculture and Livestock of Santa Catarina State – http://docweb.epagri.sc.gov.br/website_cepa/publicacoes/Sintese_2016_17_site.pdf. 2 Quiz virtual address – https://forms.gle/wfV1tLfhE2yjMnmB9.

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adhesion to answers to this questionnaire was given voluntarily by the agents (Producers

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and/or extension agents). According to the information obtained, it was decided to carry out a

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feasibility and profitability analysis for five production dimensions with of one, two, three,

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four and five flooded areas (Table 1).

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Table 1 –

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For the economic-financial evaluation of the enterprise, the costs, revenues and profits

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obtained from the production of tilapia (O. niloticus) were subjected to budget analysis to

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compare costs and revenue variations in each scenario (Shang, 1990). Thus, to follow the

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local aquaculture production dynamics, two different scenarios were analyzed economically

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and financially in terms of the forms of commercialization, as follows:

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• Condition A – product to meet the demand of the cutting market with fish slaughtered and marketed at a final average weight of 700 grams (0.7 kg) and

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• Condition B – production of fish to meet the market demand for “fish and pay”

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farms where the animals are marketed live at a final average weight of 1100 grams

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(1.1 kg).

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To evaluate business operation costs, we used the methodology described by Matsunaga

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et al. (1976), as developed by the Institute of Agricultural Economics of the State of São

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Paulo for calculating Effective Operational Cost (EOC), Total Operational Cost (TOC) and

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Total Production Cost (TPC). TPC considers the sum of TOC plus the costs related to the

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depreciation of the constructed facilities (shed, office, excavated tanks, pavements, and the

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like) and the remuneration of the entrepreneur. From the TPC, cash flow was determined.

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It should be noted that the costs related to depreciation, equipment or structural assets

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are not values that effectively leave the coffers of the enterprise, but should be calculated in

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order to avoid underestimating the final amount credited to the profit of the enterprise and to

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provide for the necessary substitutions. Bank interest rates used in the investment and costing

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calculations are based on the National Program to Support the Medium Rural Producer

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(Programa Nacional de Apoio ao Médio Produtor Rural – PRONAMP, in Portuguese) of the

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Plan Safra 2018/2019 released by the Ministry of Agriculture and Livestock with 7.50% and

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6% per annum per item of investment and cost values, respectively.

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Once the cash flow was structured, we followed the method of Martin et al. (1998) to identify the following financial indicators:

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• Gross Revenue (GR) = (Production x Prices);

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• Liquid Revenue (LR) = (GR – TPC annual);

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• Gross Margin (GM) = ((GR – TPC annual) / TPC annual) X 100; and

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• Profitability Index (PI) = (LR / GR) X 100.

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The investment analysis of the enterprise was evaluated based on the Cash Flow (CF)

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for a time horizon stipulated as 10 years where the following are considered:

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• Internal Rate of Return (IRR) is the rate of interest that equals the total costs of the

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returns or total benefits obtained during the period of operation of the enterprise (Sanches et

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al., 2013; Castilho-Barros et al. 2014; 2018). The financial viability of the project is achieved

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when IRR is higher than the Minimum Attractiveness Rate (MAR), defined here at 6.50%,

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higher than the interest that could be received through other financial investments (SELIC

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Rate - Central Bank of Brazil, May 20183).

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• Modified Internal Rate of Return (MIRR) considers that the cash generated by the

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project under analysis is reinvested by the Minimum Attractiveness Rate (MAR) and that the

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financial disbursements are reinvested by the interest rate charged in the market financing

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(Brom and Balian, 2007).

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• Net Present Value (NPV) is the value of benefits at the given moment less the current

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value of costs (Sanches et al., 2014). NPV values above zero indicate the minimum recovery

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of invested capital. In this study, the discount rate of 10% was stipulated.

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• Annualized Net Present Value (ANPV) refers to the periodization of the average

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values of the cash flows of the proposed project, whether the evaluated time horizon is years

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or months (Brom and Balian, 2007), as appropriate for the comparison of investments with

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different time horizons. For this calculation, the attractiveness rate of 10% was stipulated,

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being above the MAR.

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• Return on Invested Capital (RIC) is a method of calculation that does not take into

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account the variation that capital suffers over time; rather, it is widely used for quick market

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decisions with the presumption that the faster the return of capital, the more attractive the

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investment. • Breakeven Point (BP) determines the minimum production required to cover the costs

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at a given sale price per kilogram produced, where BPCondition A =

TPC $ kg-1

Or

BPCondition B =

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Souce: http://www.bcb.gov.br/htms/selic/selicdiarios.asp, acessed in may, 20, 2018.

TPC $ Unit-1

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BP strongly indicates the feasibility of the project since the productivity of the project must be greater than the minimum necessary to cover the investment.

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Economic feasibility studies use these analytical tools to demonstrate the situation of the

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proposed project. Such tools are, therefore, considered important to decision-making

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methodology. The joint analysis of these methodologies provides the evaluator with robust

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elements for decision-making in choosing a particular project when compared to others that

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have the same bases of analysis.

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Finally, in order to obtain the best financial results of the project, four analyses were

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carried out to measure the resilience: (I) different Food Conversion Ratio (FCR) rates ranging

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from 1.2:1.0 to 1.8:1.0 and then compared to that used in this study (1.5:1.0); (II) different

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stocking densities with two, three and four fish meter-2; (III) variation of $ 0.10 on the sale

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value (for more and for less); and (IV) variation of 10% on the value of the feed (for more and

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for less). The first three analyses were performed for each proposed wetland with one, two,

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three, four and five ha2, showing the respective values for TPC, LR, IRR and MIRR, NPV and

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ANPV and RIC. The last sensitivity analysis shows the behavior of IRR against the variation

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of the price of the rations.

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3.

Results

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The economic analysis of the present study considered an exploration time horizon of

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ten years with a total investment value of $20,302.14 (±$4,020.2) fully expended in year zero.

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The details of the investment values of the single-phase production system are presented in

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Table 2. The quantity of items and their corresponding values are referenced according to the

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areas proposed in this study.

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Table 2 –

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According to National Council for the Environment (Conselho Nacional do Meio

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Ambiente – CONAMA, in Portuguese) Resolution 413 of 2009 (BRAZIL, 2009), the flooded

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areas evaluated in this study are classified as having "Small" and "Medium" potential of

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environmental impact severity of the species. Thus, a total amount of $1,857.14 was invested

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to meet licensing and other bureaucratic costs. A Payment for designers of 3% of the total

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investment value for payment of the designers was calculated based on the total values of

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"Civil Construction" and "Equipment" added to the licensing value, resulting in a total of

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$591.33 (±$117.2).

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The "Civil Construction" items represent 53.91% of the total investment costs, followed

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by "Equipment", with 33.73%, and "Documentation and Elaboration" with 12.36%.

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The average Total Production Costs (TPC) were $63,097.47 (±$30,745.5) for fish

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cultures up to 700 g (Table 3 – Condition A) and $92,349.18 (±$45,391.9) for fish cultures

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with 1100 g (Table 3 – Condition B). These amounts correspond to 317.2% and 461.2% of the

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total investment cost in the activity for Conditions "A" and "B", respectively, thus

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demonstrating the high cost of maintaining the activity.

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Manpower was budgeted according to the wetland based on one employee per hectare.

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For Condition A, the item "Permanent employees" ranged from $1,866.67 per productive

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cycle to one hectare of water surface to $9,333.33 for five employees in five hectares of water

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with an average value of $5,600.00 (±$2,951.5). Also, about $42.90 per day was also paid for

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two temporary employees hired only for the expenses. For Condition B, assigning the same

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proportion of employees to the flooded area, $2,333.33 was obtained in the lowest proportion

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and $11,666.67 for the five hectares of water table (average of $7,000.00 ±$3,689.3). Adding

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permanent and temporary labor accounted for 12.88% (±0.9) of the operating costs for

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Condition A and 11.10% (±0.61) for Condition B (Table 3).

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Table 3 –

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The purchase price per thousand tilapia fingerlings was calculated based on the values

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practiced in the southern region of the country, which are traded at $34.29. Considering the

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proposed stocking density (three fish m-2) and the flooded area, the initial amount per cycle

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ranged from 30,000 to 150,000 tilapia fingerlings (Table 1). The costs followed these

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variations, resulting in an average value of $3,085.71 (±$1,626.3) for both conditions. This

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cost represents 5% (±0.3) and 3.46% (±0.2) of the TPC for Conditions A and B, respectively.

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According to the EPAGRI-CEPA (2017), the price of tilapia rations with 32% of Crude

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Protein (CP) was $0.51 kg-1. Thus, the total cost of feeding per cycle based on FCR of 1.5:1.0

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was estimated at $37,944.00 (±$21,376.24) – Condition A – and $59,626.29 (±$33,591.2) –

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Condition B, representing 60.68% (±4.6) and 65.91% (±4.0) of total production costs,

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respectively.

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Other productions inputs, such as chemical fertilization and agricultural limestone,

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when added to telephone and electric energy costs, represented 8.81% (±0.5) and 9.13%

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(±0.5) of TPC for slaughtered commercialized tilapia (Condition A) and live tilapia

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(Condition B), respectively, per cycle.

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The high production costs attributed to the one and two ha water surface scenarios for

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both Conditions (A and B) are $1.44 kg-1, $1.29 kg-1, $1.42 Units-1 and $1.33 Units-1,

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respectively. These results make the activity unfeasible in these situations.

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The depreciation of infrastructure and equipment was estimated at $2,176.46 (±$455.4)

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and $2,670.170 (±$715.6) for Conditions A and B, respectively. Such values do not represent

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a cash outlay for the entrepreneur, but they must be taken into account in order for the activity

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to be maintained throughout the life of the company. Together, these values represent 3.98%

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(±1.6) and 3.29% (±1.1) of the total TPC values (Conditions A and B, respectively). The

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annual average cost of production (TPCAnnual average) per kilo produced from the production of

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tilapia with 700 g was $1.30 (±0.1). For the production of fish with an average weight of 1100

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g, the average annual cost of production was $1.32 (±0.1) (Table 4).

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Table 4 –

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The economic and profitability indicators were calculated in consideration of the

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average selling prices practiced in the region by kilo ($1.34 kg-1), or the living unit of tilapia

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($1.46 units-1). Condition A presented favorable results for projects with at least three hectare

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of water surface with positive IRR and MIRR, and above the MAR (6.50%), positive ANPV

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and ANPVs (Table 5). Condition B presented favorable indicators already from two hectare of

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water surface depth.

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Table 5 –

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The sensitivity analysis for variations of Feed Conversion Ratio (FCR) showed

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significant value oscillations. Maintaining the stocking density of two fish m-2, it is possible to

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observe the behavior of the Total Production Costs (TPC) which tend to decrease according to

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the largest flooded area, i.e., the larger the area used, the smaller the TPC. Also, the decrease

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of TPC is observed as a function of lower FCR. Thus, TPC values ranged from $1.6 (1.8 FCR

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and one hectare water surface) to $1.06 (1.2 FCR and five hectares of water surface) for

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Condition A and $1.59 (1.8 FCR and one hectare of water) and $1.06 (1.2 FCR and five

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hectares of water) for Condition B. Following this behavior are the Liquid Revenue (LR),

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Internal Rate of Return (IRR), Modified Internal Rate of Return (MIRR), Net Present Value

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(NPV), Annualized Net Present Value (ANPV) and Return on Capital Invested (RCI). It

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should be noted that the RCI is highly favorable with less than one year to return the amount

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of investment and operational cost of the one year when when applied to FCR of 1.2 from

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three hectares of water surface in Condition B (Table 6).

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Table 6 –

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In order to assess the sensitivity of tilapia production in the State of Santa Catarina by

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the variation of fish stocking density per m², the most favorable TPC scenario for both

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conditions is obtained with four fishes per m2 grown on five ha of leaf ($1.15 per kg and

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$1.19 per unit). It is also noted that the evaluation of fish farming by ICR is attractive in less

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than five years in Condition A from four ha of water surface to at least three fishes per m2. For

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Condition B, it is feasible to produce tilapia in scenarios with a minimum of two ha and at

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least three fishes per m2, or from one ha with four fishes per m2. In this last condition, the RCI

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is less than one year when produced in five ha with two, three or four fishes per m2. When

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applying two fish per m², except for the scenario with five ha of water, it should be noted that

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fish farming faces high risks of maintaining the activity (Table 7).

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Table 7 –

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When evaluating the behavior of the values regarding variation in the application of

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10%, to plus or minus, on the sales value of the final product in both conditions (Table 8), the

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worst scenario occurs when applying the value of $1.21 per kilogram of tilapia produced in

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Condition A for all flooded areas evaluated. In contrast, the best scenario evaluated is in

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Condition B for five hectares of water surface.

272

Table 8 –

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When analyzing the evolution of the MIRR with respect to the 10% variation on the

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feed price (to plus or minus), we notice a significant difference between the evaluated

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conditions. In Condition A, with just 5 ha of production (10.5%), the value becomes

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financially attractive when compared to MAR (6.5%) with a 10% increase in feed costs. If the

277

values are reduced by 10%, Condition A is more attractive with 2 ha of production (13.81%).

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Condition B, in turn, is profitable from 1 ha of production (10.91%), if one considers the 10%

279

reduction in feed costs, or from 2 ha of production (9.36%), with a 10% increase in feed costs

280

(Figure 1).

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Figure 1 -

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4.

Discussion

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The values used as reference were obtained through online questionnaires. This

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questionnaire had voluntary adhesion of 43 fish farmers and 6 extension agents. When

286

evaluating the obtained results, we verified repetitious responses to certain questions, such as

287

applied densities, initial sizes, sales value, and price of feed. When promoting consumer

288

research for a particular target product, in this case Tilapia, Dayan (2004) used a "target

289

consumer survey ". Thus, when answers do not present variations, or significant additions, it

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is understood that the research can be concluded, even with a low sample number. This

291

supports the formulation of the dimensions evaluated in this study.

292

The present study used the most common financial and profitability ratios to evaluate

293

aquaculture investments (Campo & Zuniga-Jara, 2018). Campo & Zuniga-Jara (2018) affirm

294

a significant increase in the number of economic feasibility studies for aquaculture in the

295

years from 1991 to 2015, highlighting the last five years of the study during which a growth

296

of nine times that of the previous 20 years was experienced. In order to determine the best

297

economic opportunities of aquaculture activity in the State of Santa Catarina, we correlated

298

the different aspects of production, as well as the characteristics applied to the activity.

299

Aquaculture production in the state of Santa Catarina is based on the characteristics of

300

national aquaculture, which is largely made up of small and medium producers (Scorvo-Filho

301

et al., 2010). According to the report of the EPAGRI-CEPA (Agricultural Research and Rural

302

Extension Company of Santa Catarina through the Center of Agricultural Socioeconomy and

303

Planning), tilapia production from Santa Catarina is formed by small properties, with a mean

304

area of 2 ha (Barroso et al., 2018; EPAGRI-CEPA, 2017).

305

The Santa Catarina fish farm is characterized by cultivation in excavated tanks,

306

presenting three modes of production: (i) monoculture with balanced feed; (ii) integrated

307

polyculture with natural feeding in the first three to four months of life and balanced feeding

308

in the fattening phase; and (iii) bicultivation with carp or traíras (Barroso et al, 2018;

309

EPAGRI-CEPA, 2017). This fact corroborates the results from this field research that guided

310

this study, and the average stocking density of three m-2 fishes was also attributed. In this

311

context, the most favorable economic scenarios for tilapia production are (I) fish destined to

312

the processing industry (700 g fish-1 – Condition A) in production scenarios from three ha of

313

water surface and (II) fish marketed live (1100 g fish-1 – Condition B) from two ha of water

314

surface.

315

According to EPAGRI-CEPA (2017), one of the obstacles to the growth of activity is

316

the bureaucracy for the licensing of aquaculture activity, although the licensing costs will not

317

make it unfeasible.CONAMA Resolution No. 413/2009 of the Environment of the Ministry of

318

the Environment (Brazil, 2009) states that the environmental licensing of activities of this

319

nature may vary according to the size of the activity (Small, Medium or Large) and Potential

320

Degrader or Potential Environmental Polluter (medium and low), again classified according to

321

the trophic level of the cultivated species and its place of origin (Native or Exotic). For this

322

purpose, the classification of properties of "Small size of the activity " and "Medium Potential

323

Degrader" was attributed, resulting in a fixed tax of $1,857.14 applied to investment values.

324

In general, Campo & Zuniga-Jara (2018) consider that investments in fish farms present

325

lower risks when compared to other aquaculture activities. These authors affirm that fish

326

farming is among the activities of aquaculture with more variables that can be manipulated by

327

the producer, e.g., weather that can, to some extent, be controlled or mitigated, making the

328

activity less risky. Control of predation, pathogen inputs, some productive predictability and

329

reproductive management of highly prolific fish species are among the characteristics that

330

contribute to the mitigation of productive risks and resultant attractiveness of the activity.

331

The values to invest in the activity ranged from $15,216.93 to $25,387.44 for one to five

332

ha of water, respectively. One of the items that added most to the cost of investment was the

333

construction of the excavated tanks which, on average, accounted for 14.36% (±5.3%) of

334

start-up costs. As a basis for calculating interest rates, we opted for the application of the

335

7.50% interest rate on investments and 6% for funding, as recommended by the National

336

Program to Support the Small and Medium Rural Producer (Programa Nacional de Apoio ao

337

Pequeno e Médio Produtor Rural – PRONAMP, in Portuguese), a Brazilian government credit

338

plan for the promotion of agriculture in the country. Although the burden of production costs

339

is relatively low to the coffers of the producer, Barroso et al. (2018) attributed the low

340

adherence of producers in Santa Catarina to the credit lines as mainly stemming from the

341

difficulty of licensing the activity in the State.

342

Barroso et al. (2018) reached Effective Operating Costs (EOC) for tilapia production

343

from Santa Catarina on the order of $1.08 kg-1 (value of 2015). This result takes into account

344

only the items directly linked to the production cycle without calculating the social, financial,

345

civil and equipment depreciation charges or the remuneration of the entrepreneur. When

346

comparing EOC, the present study reached values variation from $1.44 to $1.29 per kilogram

347

of tilapia (Condition A) and $1.42 to $1.33 per unit of tilapia (Condition B).

348

Among the items that most negatively affect a producer's profit margin, Barone et al.

349

(2017) note the fish selling price, the price of the rations and the Feed Conversion Ratio. Food

350

Conversion (CA) may vary, depending on the mode or phase of production (Barone, 2017).

351

Furlaneto et al. (2010) identified FCR variation from 1.6:1.0 to 1.8:1.0 for tilapia production

352

in net tanks. Barroso et al. (2018) state that the average FCR of the tilapia production in Santa

353

Catarina hovers around 1.3:1.0 when cultivated in excavated tanks. Thus, to evaluate the

354

economic-financial viability of tilapia production from Santa Catarina, a fixed average FCR

355

of 1.5:1.0 was arbitrarily stipulated. Oscillations of FCR can be a reflection of climatic

356

conditions of the region, quality of the rations used, lineage of the cultivated species, type of

357

management, and effective or correct control over the cultivated stock. Minimum variation

358

from 1.8 to 1.7 in Food Conversion can decrease final costs by up to 30% (Barone, 2017).

359

In this study, Condition A present variation from 53.60% (one ha flooded area) to

360

65.78% (five ha flooded area) in the costs attributed to feed utilization. Condition B presented

361

variations from 59.67% to 70.44% from one ha and five ha, respectively. For monoculture

362

aquaculture crops, Scorvo-Filho et al. (2010) affirm that contribution of rations can increase

363

Total Production Costs by up to 60%. Sabbag et al. (2007), when financially assessing the

364

production of tilapia cultivated in net tanks, affirmed that feed expenses comprise 83% of

365

effective cost. Osamaki et al. (2017) report that the costs attributed to rations reached 84% to

366

produce tilapia in tanks excavated in Kenya. However, it should be noted that the high

367

percentage described by Osamaki et al. (2017) mainly results from the non-inclusion of labor

368

costs, which were described as family labor. In 2016, Barone (2017) states that the load on the

369

costs attributed to feed reached values close to 83% in tilapia production in tanks excavated in

370

the state of Paraná.

371

For the most part, Santa Catarina fish farming is also characterized by the use of family

372

labor (Barroso et al, 2018; EPAGRI-CEPA, 2017). Castilho-Barros et al. (2014) and Sanches

373

et al. (2014) affirm that it is essential to apply the costs attributed to family labor, as well as

374

the taxation of social charges, to avoid underestimating the final costs of production.

375

When considering the hiring of a fixed employee per hectare, in addition to the

376

temporary contracting of two employees for fish harvest, costs ranged from 11.44% to

377

12.77% for one and five ha, respectively, in Condition A and 10.13% to 10.88% for one and

378

five ha, respectively, in Condition B. The low percentage of labor costs in relation to the total

379

cost shown here allows the producer/entrepreneur to regularize the activity of the fixed

380

employee, whether a family member or not. However, as suggested by Scorvo-Filho et al.

381

(2006), considering the size of the crop, the activity evaluated here can be allocated on the

382

scale of family production since the producer could feed the fish, maintain the tanks and

383

perform the biometrics, all within the system commonly practiced in family agriculture,

384

implying, in turn, a reduction in the labor costs.

385

Although input costs (feed, fingerlings and energy) increased by 15.30% and 10.90%

386

for Conditions A and B, respectively, the operational costs attributable to "Civil

387

Depreciation", "Equipment", "Investor's Remuneration" and "Interest on invested capital",

388

decreased by up to 66.54% in Condition A and 64.63% in Condition B. This would explain

389

the reduction of TPC per kilogram, or units produced, as planting size increases. The

390

association between TPC and the increase of the area (1 to 5 ha flooded area) present in

391

Condition A present a reduction of 84% in the costs of production. In the same analysis the

392

Condition B presents the reduction about 87% of the TPC. This reduction has direct relation

393

with diluition of the costs in the increase of the production evaluated area.

394

In the Modified Internal Rate of Return (MIRR) we fixed rate of 10%, both for

395

reinvestment and bank rates (financial rates). With results we have two scenarios: 1)

396

Condition A the areas of one and two ha flooded area and Condition B with one ha flooded

397

area present unsatisfact results; and 2) positive yields in Condition A to areas up three ha

398

flooded area and two ha flooded area in Condition B. Tokunaga et al. (2015) present MIRR of

399

7.36% for small-scale aquaponic production in Hawaii, USA. In assessing the viability of

400

Oyster production and trade in Hawaii, USA, Chen et al. (2017) reached unsatisfactory MIRR

401

values (-7.7%) for a time horizon calculated for 10 years. In these last two studies, Tokunaga

402

et al. (2015) and Chen et al. (2017) applied financial and reinvestment rates of 6%.

403

We chose to cover the simple and Annualized Net Present Value (NPV and ANPV,

404

respectively) since the latter presents values that allow its comparison with other activities,

405

provided they have similar characteristics regarding the time horizon, productivity or

406

production areas. When calculating ANPV with the same discount rate (10%), with a time

407

horizon of 20 years in the production of Pacu (Piaractus mesopotamicus) and Piauçu

408

(Leporinus macrocephalus) in excavated tanks, Furlaneto and Esperacini (2009)4 obtained

409

ANPV of $1,673.65 and $1,239.28 cycle-1 ha-1, respectively. In relation to the values obtained

410

in this study, ANPV positive and above those found by Furlaneto and Esperacini (2009) are

411

from 3 ha of water surface in Condition A ($7,855.48), and from two ha in Condition B

412

($23,951.20).

413

4.1. Sensitivity analysis

414

The amplitude of the Feed Conversion Ratio (FCR) of 1.2:1.0 to 1.8:1.0 analyzed here

415

was used to evaluate the economic sensitivity of tilapia production according in the water

416

surface area. This situation is based on an understanding of the main situations encountered

417

by producers in Santa Catarina. By this variation and applying the average stocking density of

418

three fish m-2, the worst scenarios are found in one ha of water surface, except for Condition

419

B, where the FCR evaluated was 1.2:1.0. Efficiency in FCR is an important indicator for

420

assessing the financial health of aquaculture enterprises because low FCR, in turn, lowers feed

421

costs (Boyd et al., 2008).

422

The densities analyzed are supported by studies carried out by the Research Company

423

on Agriculture and Livestock of the State of Santa Catarina (2017), as reported by Silva et al.

424

(2017) and by Barroso et al. (2018). According to Silva et al. (2017), the average productivity

425

per hectare of water in the year 2015 was approximately 7.4 tons. In the conditions evaluated

426

(A and B), the productivity varied from 11.2 tons ha-1 (two fishes m-2) to 22.4 tons ha-1 (four 4

$1.00 = R$ 1.81 in 2009.

427

fishes m-2) for Condition A. In Condition B, we saw a variation from 17.6 tons ha-1 (two fishes

428

m-2) to 35.2 tons ha-1 (four fishes m-2).

429

The sensitivity analysis evaluated with the variation of $0.10 on the price of

430

commercialization of the product (to plus or minus) in both conditions, sought to portray the

431

daily situations of the producer. Depending on the flooded area, this oscillation adversely

432

affects the activity.

433 434

5.

435 436

Conclusions The present study evaluated the different scenarios of fish production in the State of

Santa Catarina.

437

Based on the results obtained, we can conclude that the best production scenarios

438

depend on the product to be exploited. To meet the demand of the cutting industry, for which

439

the average weight of the product delivered is at least 700 g (Condition A), our

440

recommendation is to culture Nile tilapia in areas greater than three hectares of surface water.

441

The fish should be cultured at a stocking density of at least three fishes m-2 and sold at a

442

marketing price close to $1.43, with the aim of optimizing the Feed Conversion Ratio (FCR)

443

below 1.5.

444

In the case of fish production to meet the market demand for “fish and pay” farms in the

445

southern region of Brazil, for which the average weight of the live fish product is 1100 g, the

446

results point to projects with at least two hectares flooded area, minimum of three fish m-2,

447

marketing price above $1.46, and FCR below 1.5.

448

It should be noted, however, that it is important to strengthen entities that represent the

449

aquaculture sector in Santa Catarina. Such entities include producer cooperatives for research

450

and regulation. Such responsible and appropriate strengthening can contribute positively to

451

reduced production costs, whether realized in bargaining power to acquire large volumes of

452

inputs or marketing negotiations.

453 454 455 456 457 458 459 460 461

6.

Bibliographic references

Barone, R. S. C., 2017. Feed is the main input of aquaculture production. Aquaculture Assets Newsletter. Edition 13. Available: https://www.cnabrasil.org.br/assets/arquivos/boletins/ativosaquicultura_0.90337400%201514 917005.pdf. Access: Feb.15.2019. Barroso, R., Muñoz, A., Tahim, E., Webber, D., Albuquerque Filho, A.D.C., Pedroza Filho, M.X., Tenório, R.A.; Carmo, F.J.; Barreto, L.E.G.S.; Muehlmann, L.D.; Silva, F.M. &

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Hein, G., 2018. Diagnosis of the tilapicultura value chain in Brazil. Embrapa Pesca e Aquicultura - Livro técnico (INFOTECA-E), p.186. Boyd, C. E., Lim, C., Queiroz, J., Salie, K., De Wet, L. & McNevin, A., 2008. Best management practices for responsible aquaculture. In USAID/Aquaculture Collaborative Research Support Program, 47Corvallis, Oregon: Oregon State University. Brazil, 2009. Laws, Decrees. Conama Resolution No. 413, of June 26, 2009. Provides for the environmental licensing of aquaculture, and other measures. Diário Oficial da República Federativa do Brasil, Brasília, Jun/30/2009. Section 1, p. 126-129. Brom, L.G. & Balian, J.E.A., 2007. Analysis of investment and working capital: Concepts and applications. São Paulo: Saraiva pp 132. Campo, S.R. & Zuniga-Jara, S., 2018 Reviewing capital cost estimations in aquaculture. Aquaculture Economics & Management, 22:1, 72-93. DOI:500 10.1080/13657305.2017.1300839 Castilho-Barros, L., Almeida, F.H., Henriques, M.B. & Seiffert, W.Q., 2018. Economic evaluation of the commercial production between brazilian samphire and whiteleg shrimp in aquaponic system. Aquaculture International, 26:1187–1206. DOI:10.1007/s10499-018-02778 Castilho-Barros, L., Barreto, O.J.S. & Henriques, M.B., 2014. The economic viability for the production of live baits of White Shrimp (Litopenaeus schmitti) in recirculation culture system. Aquaculture International, 22:0. DOI: 10.1007/s10499-014-9792-4 Chen, J. Q., Haws, M. C., Fong, Q. S. W. & Leung, P., 2017. Economic feasibility of producing oysters using a small-scale Hawaiian fishpond model. Aquaculture Reports 5:4151. Dayan, A, 2004. Les études de marché. Paris: Presses Universitaires de France. 127 p. EPAGRI-CEPA - Síntese anual da agricultura de Santa Catarina. Available: Access: 10/04/2018. FAO, 2018. The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable development goals. Rome. Furlaneto, F.P.B.; Ayroza, D.M.M.R. & Ayroza, L.M.S., 2010. Análise econômica da produção de tilapia em tanques-rede, ciclo de verão, região do Médio Paranapanema, Estado de São Paulo, 2009. Informações Econômicas, 40(4): 5-11. Furlaneto, F.P.B. & Esperancini, M.S.T., 2009 Estudo da viabilidade econômica de projetos de implantação de piscicultura em viveiros escavados. Informações Econômicas 39(2): 5-11 IBGE, 2018. Pesquisa Nacional por Amostra de Domicílios: Síntese de indicadores 2017. Rio de Janeiro: IBGE – Instituto Brasileiro de Geografia e Estatítica. Kubitza, F., 2007 Tilápias na bola de cristal. Panorama da Aquicultura, 17(99): 14-21. Jomori, R.K., Carneiro, D.J., Martins, M.I.E.G. & Portella, M.C. 2005. Economic evaluation of Piaractus mesopotamicus juvenile production in different rearing systems. Aquaculture, 243(1-4): 175-183. Martin, N.B.; Serra, R.; Oliveira, M.D.M., Angelo, J.A. & Okawa, H. 1998. Sistema integrado de custos agropecuários - CUSTAGRI. Informações Econômicas, 28(1): 7-28.

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Matsunaga, M.; Bemelmans, P.F., Toledo, P.E.N. de, Dulley, R.D., Okawa, H. & Peroso, I.A., 1976. Metodologia de custo de produção utilizado pelo IEA. Agricultura em São Paulo, 23(1): 123-139. Osamaki, S.K., Janssen, K., Besson, M. & Komen, H., 2017. Economic values of growth rate, feed intake, feed conversion ratio, mortality and uniformity for Nile tilápia. Aquaculture, 481: 124-132. https://doi.org/10.1016/j.aquaculture.2017.04.013 PEIXE BR (Associação Brasileira da Piscicultura), 2018. Anuário Peixe BR da Piscicultura 2018. São Paulo. 71 p. Sabbag, O.J., Rozales, R.R., Tarsitana, M.A.A. & Silveira, A.N., 2007. Análise econômica da produção de tilápias (Oreochromis niloticus) em um modelo de propriedade associativa em Ilha Solteira/SP. Custo e Agronegócio, 3(2): 86-100. Sanches, E.G., Silva, F.C. & Ramos, A.P.F.D., 2014. Viabilidade econômica do cultivo de robalo-flecha em empreendimentos de carcinicultura no nordeste do Brasil. Boletim do Instituto de Pesca, 40(4): 577-588. Sanches, E.G., Tosta, G.A.M. & Souza-Filho, J.J., 2013. Viabilidade econômica da produção de formas jovens de bijupirá (Rachycentron canadum). Boletim do Instituto de Pesca, 39(1): 15-26. Schulter, E.P. & Vieira Filho, J.E.R., 2017. Evolução da piscicultura no Brasil: Diagnóstico e desenvolvimento da cadeia produtiva de tilápia. Texto para Discussão. p. 33. Scorvo-Filho, J.D., Pinto, C.S.R.M., Verani, J.R. & Silva, A.L., 2006. Custo operacional de produção da criação de tilápias vermelhas da Flórida e tailandesa em tanquesrede de pequeno volume. Informações Econômicas, 36(10): 71-79. Scorvo-Filho, J.D., Frascá-Scorvo, C.M.D., Alves, J.M.C. & Souza, F.R.A., 2010. A tilapicultura e seus insumos, relações econômicas. Revista Brasileira de Zootecnia, 39: 112118 Shang, Y.C., 1990. Aquaculture economics analysis: An introduction. In: SANDIFER, P. A. (Ed.). Advances in World Aquaculture. Baton Rouge: The World Aquaculture Society. p. 211. Silva, B.C, Giustina, E.G.D., Marchiori, N.C., MASSAGO, H. & Silva, F.M., 2017. Desempenho produtivo da piscicultura catarinense. Agropecuária Catarinense, 30(1): 15-18. DOI 10.22491/RAC Tokunaga, K., Tamaru, C., Ako, H. & Leung, P., 2015 Economics of small-scale commercial aquaponics in Hawai’i. Journal of the World Aquaculture Society, 46(1): 564 2032. DOI: 10.1111/jwas.12173

542 543 544

545 546 547 548

Table 1 – Zootechnical indexes of fattening of tilapia (Oreochromis niloticus) in the State of Santa Catarina, April, 2018.

1

Item

Unit

Average Initial Weight Stocking Density Initial amount of fish Initial Biomass Survival Rate Final amount of fish Final Biomass – Cond. A Final Biomass – Cond. B Feed Conversion Ratio Duration of cultivation – Cond. A Duration of cultivation – Cond. B Total ration consumed – Cond. A Total ration consumed – Cond. B Final Productivity - Cond. A Final Productivity - Cond. B

kg Fish-1 Fishes m-2 Unit. kg % Unit. kg kg kgFeed:kgFish Days cycle-1 Days cycle-1 kg cycle-1 kg cycle-1 tons ha-1 tons ha-1

1 ha = 10,000 m²;

1 ha1

2 ha1

30,000 15.00

60,000 30.00

16,000 16,800.00 26,400.00

48,000 33,600.00 52,800.00

25,200.00 39,600.00

50,400.00 79,200.00

3 ha1 0.0005 3 90,000 45.00 80 72,000 50,400.00 79,200.00 1.50:1.00 190 210 75,600.00 118,800.00 16,800.00 26,400.00

4 ha1

5 ha1

120,000 60.00

150,000 75.00

96,000 67,200.00 105,600.00

120.000 84,000.00 132,000.00

100,800.00 158,400.00

126,000.00 198,000.00

549 550

Table 2 – Average investment required to implement a fish farm in the State of Santa Catarina for the single-phase fattening system of tilapia (Oreochromis niloticus), April 20181,2.

551 552

1. Civil construction 1.1. Tractor to build tank 1.2. Construction of office 1.3. Shed 1.4. Eletric systems 2. Equipments 2.1. Net to fishery (25 mm/30 m) 2.2. Aerators (2 hp) 2.3. PVC pipe 100 mm. 2.4. PVC pipe 150 mm 2.5. PVC knee 100 mm 2.6. Fishery balance (50 kg) 2.7. Precision balance (10 kg) 2.8. Fishery balance (150 kg) 2.9. Net to biometry 2.10. pHmetro 3. Documentation and Elaboration 3.1. Licensing 5 3.2. Payment for designers 6

553 554 555 556 557 558 559 560

TOTAL (US$)

Total Value

Life Cycle

Depreciation (a)3

Unit

Qtd.

Hour machine-1 m² m² Conj.

72 (±38.0) 6 (±0.0) 30 (±0.0) 1 (±0.0)

3,085.71 (±1,626.3) 2,057.14 (±0.0) 4,285.71 (±0.0) 1,428.57 (±0.0)

10 10 10

205.71 (±0.0) 428.57 (±0.0) 142.86 (±0.0)

Unit Unit Unit Unit Unit Unit Unit Unit Unit Unit

1 (±0.0) 8 (±4.0) 25 (±0.0) 5 (±0.0) 10 (±0.0) 1 (±0.0) 1 (±0.0) 1 (±0.0) 1 (±0.0) 1 (±0.0)

485.71 (±0.0) 4,320.00 (±2,276.8) 1,285.71 (±0.0) 285.71 (±0.0) 100.00 (±0.0) 17.14 (±0.0) 39.43 (±0.0) 131.43 (±0.0) 17.14 (±0.0) 314.29 (±0.0)

5 5 5 5 5 5 5 5 5 5

97.14 (±0.0) 864.00 (±455.4) 257.14 (±0.0) 57.14 (±0.0) 20.00 (±0.0) 3.43 (±0.0) 7.89 (±0.0) 26.29 (±0.0) 3.43 (±0.0) 62.86 (±0.0)

3%

1,857.14 (±0.0) 591.33 (±117.1) 20,302.18 (±4,020.2)

-

Interest (b)4

115.71 (±61.0) 77.14 (±0.0) 160.71 (±0.0) 53.57 (±0.0)

1

139.29 (±0.0) 44.35 (±8.8)

139.29 (±0.0) 44.35 (±8.8)

2,176.46 (±455.4) 853.15 (±301.5) 5,909.61 (±2,357.3)

Mean values and standard deviation on the different hectares studied; US$ 1.00 = R$ 3.50 in april 2018. 3 Calculation by linear method; 4 Rate of the 7.5% per year on invested capital; 5 Classification “SP” (Small Potential) according with CONSEMA Resolution 98 of 2017 and taxing according with CONAMA Resolution 413 of 2009; 6 Calculation for Payment for designers for Designers and Documentation [3.2. = (1. + 2. + 3.1.) X 3%]. 2

115.71 (±61.0) 282.86 (±0.0) 589.29 (±0.0) 196.43 (±0.0)

18.21 (±0.0) 115.36 (±0.0) 162.00 (±85.4) 3,906.00 (±2,340.2) 48.21 (±0.0) 305.36 (±0.0) 10.71 (±0.0) 67.86 (±0.0) 3.75 (±0.0) 23.75 (±0.0) 0.64 (±0.0) 4.07 (±0.0) 1.48 (±0.0) 9.36 (±0.0) 4.93 (±0.0) 31.21 (±0.0) 0.64 (±0.0) 4.07 (±0.0) 11.79 (±0.0) 74.64 (±0.0)

561 562 563 564 565 566 567 568 569

Total (a+b)

570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610

Table 3 – Operating costs (EOC, TOC and TPC) of the production of Oreochromis niloticus in single-phase system for Condition A (700 g) and Condition B (1100 g) in the State of Santa Catarina, Brazil, April 20181, 2. EOC Permanent Employee Temporary Employee Acquisition of fingerlings Fish feed (FCR 1.5) Chemical fertilization Condiion Agricultural limestone A Telephone costs Electricity costs Building depreciation5 Equipment depreciation5 Investor remuneration Interest on invested capital Total (US$ Cycle-1)

5,600.00 (±2,951.5) 85.71 (±0.0) 3,085.71 (±1,626.3) 36,720.00 (±19,353.1) 857.14 (±451.8) 171.43 (±90.4) 514.29 (±271.1) 4,752.00 (±2,504.5)

1

TOC

2,240.00 (±1,180.6)

470.40 (±247.9) 5.14 (±0.0) 185.14 (±97.6) 2,203.20 (±1,161.2) 51.43 (±27.1) 10.29 (±5.4) 30.86 (±16.3) 285.12 (±150.3)

8,310.40 (±4,380.0) 90.86 (±0.0) 3,270.86 (±1,723.9) 38,923.20 (±20,514.3) 908.57 (±478.9) 181.71 (±95.8) 545.14 (±287.3) 5,037.12 (±,.654.8) 1,399.31 (±455.4)

51,786.29 (±27,248.6)

7,000.00 (±3,689.3) 85.71 (±0.0) 3,085.71 (±1,626.3) 57,702.86 (±30,412.1) 857.14 (±451.8) 171.43 (±90.4) 514.29 (±271.1) 7,467.43 (±3,935.7)

Fixed Costs

58,667.18 (±30,590.3) 3

4

Social burden

Financial burden

TOC

2,800.00 (±1,475.7)

588.00 (±309.9) 5.14 (±0.0) 185.14 (±97.6) 3,462.17 (±1,824.7) 51.43 (±27.1) 10.29 (±5.4) 30.86 (±16.3) 448.05 (±236.1)

10,388.00 (±5,475.0) 90.86 (±0.0) 3,270.86 (±1,723.9) 61,165.03 (±32,236.8) 908.57 (±478.9) 181.71 (±95.8) 545.14 (±287.3) 7,915.47 (±4,171.8) 1,893.03 (±715.6)

Fixed Costs

777.14 (±0.0) 3,500.00 (±0.0) 951.28 (±206.9) 76,884.57 (±40,476.6)

Mean values and standard deviation on the different hectares studied; US$ 1.00 = R$ 3.50 in april 2018; 3 Labor benefits = 40% of the Labor EOC; 4 Finance changes = 7.5% per year of the EOC plus labor benefits; 5 Estimated depreciation, according to useful life. 2

Financial burden4

777.14 (±0.0) 2,800.00 (±0.0) 853.15 (±155.2)

EOC Permanent Employee Temporary Employee Acquisition of fingerlings Fish feed (FCR 1.5) Chemical fertilization Condiion Agricultural limestone B Telephone costs Electricity costs Building depreciation5 Equipment depreciation5 Investor remuneration Interest on invested capital Total (US$ Cycle-1)

Social burden3

86,358.67 (±45,185.0)

TPC 8,310.40 (±4,380.0) 90.86 (±0.0) 3,270.86 (±1,723.9) 38,923.20 (±20,514.3) 908.57 (±478.9) 181.71 (±95.8) 545.14 (±287.3) 5,037.12 (±,.654.8) 1,399.31 (±455.4) 777.14 (±0.0) 2,800.00 (±0.0) 853.15 (±155.2) 63,097.47 (±30,745.5) TPC 10,388.00 (±5,475.0) 90.86 (±0.0) 3,270.86 (±1,723.9) 61,165.03 (±32,236.8) 908.57 (±478.9) 181.71 (±95.8) 545.14 (±287.3) 7,915.47 (±4,171.8) 1,893.03 (±715.6) 777.14 (±0.0) 3.500.00 (±0.0) 951.28 (±206.9) 92,349.18 (±45,391.9)

611 612 613

Table 4 – Total Production Costs (TPC) of the cultivation of Oreochromis niloticus in single-phase system in the State of Santa Catarina, Brazil, April, 2018¹, ². Conditions

614 615 616 617 618 619 620

1 2

TPC

Biomass

TPC

(US$ cycle-1)a

(kg cycle-1)b

US$ kg-1 (a/b)

Flooded area

Conditions A - 700 g (0.7 kg)

1 ha 2 ha 3 ha 4 ha 5 ha

24,207.21 43,652.34 63,097.47 82,542.60 101,987.72

16,800.00 33,600.00 50,400.00 67,200.00 84,000.00

1.44 1.30 1.25 1.23 1.21

Condition B - 1100 g (1.1 kg)

1 ha 2 ha 3 ha 4 ha 5 ha

34,170.42 62,878.76 91,587.09 120,295.43 149,003.76

26,400.00 52,800.00 79,200.00 105,600.00 132,000.00

1.29 1.19 1.16 1.14 1.13

US$ 1.00 = R$ 3.50 in april 2018; 1 ha = 10,000 m²;

Table 5 – Economic indicators of the cultivation of Oreochromis niloticus in singlephase system in the State of Santa Catarina, Brazil, April, 20181. Flooded área 1 ha² Index

Condition A 700 g

Condition B 1100 g

621 622 623 624 625 626 627 628 629 630 631

GR LR GM PI IRR MIRR NPV (10 years)³ ANPV (10 years)³ ANPV (20 years)³ RIC BP

Unit US$ US$ % % % % US$ US$ US$ Years Kg

22,560.00 - 1,647.21 -6.80 -7.30 N/C4 <0.00 - 25,338.34 - 4,123.70 -3,434.59 >10.00 18,026.65

GR LR GM PI IRR MIRR NPV (10 years)³ ANPV (10 years)³ ANPV (20 years)³ RIC BP

US$ US$ % % % % US$ US$ US$ Years Unit

34,971.43 801.01 2.34 2.29 N/C4 <0.00 - 11,142.63 - 1,813.41 -1,085.92 >10.00 23,803.88

2 ha²

3 ha²

4 ha²

Price = $1.34 per kg of fish 45,120.00 67,680.00 90,240.00 1,467.66 4,582.53 7,697.40 3.36 7.26 9.33 3.25 6.77 8.53 N/C4 18.40 31.52 <0.00 13.66 18.30 - 8,741.43 7,855.48 24,452.40 - 1,422.63 1,278.44 3,979.52 -618.37 6,517.84 5,014.06 >10.00 4.43 2.97 32507.06 46,987.48 61,467.89 Price = $1.46 per Unit of fish 69,942.86 104,914.29 139,885.71 7,064.10 13327.19 19,590.29 11.23 14.55 16.29 10.10 12.70 14.00 34.43 57.73 74.39 19.19 24.98 28.10 23,951.20 59,045.02 94,138.85 3,897.95 9,609.31 15,320.66 4,778.97 7,115.06 16,508.74 2.75 1.71 1.34 45,849.09 62,853.89 82,555.69

5 ha² 112,800.00 10,812.28 10.60 9.59 41.24 21.11 41,049.31 6,680.59 7,830.28 2.35 75,948.31 174,857.14 25,853.38 17.35 14.79 87.10 30.12 129,232.67 21,032.02 22,373.62 1.15 102,257.48

¹ US$ 1.00 = R$ 3.50 in april 2018; 2 1 ha = 10,000 m²; ³ NPV and ANPV – With 10% of taxation; 4 Not calculated. Legend: GR – Gross Revenue; LR – Liquid Revenue; GM – Gross Margin; PI – Profitability Index; IRR – Internal Rate of Return; MIRR – Modified Internal Rate of Return; NPV – Net Present Value; ANPV – Annualized Net Present Value; RIC – Return on Invested Capital; and BP – Breakeven Point.

632 633 634

Table 6 – Sensitivity analysis of tilapia (Oreochromis niloticus) in a single-phase system in relation to the rate of feed conversion used in the flooded area in the State of Santa Catarina, Brazil, April, 20181. Condition A – 700 g Flooded area

1 ha

2 ha

3 ha

4 ha

5 ha

635 636 637 638 639 640 641 642 643 644

1

Condition B – 1100 g

Index

Units

1.2:1.0

1.5:1.0

1.8:1.0

1.2:1.0

1.5:1.0

TPC LR IRR MIRR NPV3 ANPV3 RIC TPC LR IRR MIRR NPV3 ANPV3 RIC TPC LR IRR MIRR NPV3 ANPV3 RIC TPC LR IRR MIRR NPV3 ANPV3 RIC TPC LR IRR MIRR NPV3 ANPV3 RIC

US$ US$ % % US$ US$ Years US$ US$ % % US$ US$ Years US$ US$ % % US$ US$ Years US$ US$ % % US$ US$ Years US$ US$ % % US$ US$ Years

1.29 947.67 N/C4 <0.00 -9,393.93 -1,528.82 >10.00 1.14 6,657.42 35.72 19.57 23,147.40 3,767.13 3.67 1.10 12,367.17 60.37 25.52 55,688.73 9,063.08 2.64 1.07 18,076.92 78.89 28.85 88,230.05 14,359.04 2.26 1.06 23,786.68 93.57 31.04 120,771.38 19,654.99 2.07

1.44 -1,647.21 N/C4 <0.00 -25,338.34 -4,123.70 >10.00 1.30 1,467.66 N/C4 <0.00 -8,741.43 -1,422.63 >10.00 1.25 4,582.53 18.40 13.66 7,855.48 1,278.44 4.43 1.23 7,697.40 31.52 18.30 24,452.40 3,979.52 2.97 1.21 10,812.28 41.24 21.11 41,049.31 6,680.59 2.35

1.60 -4,242.09 N/C4 <0.00 -41,282.76 -6,718.58 >10.00 1.45 -3,722.10 N/C4 <0.00 -40,630.26 -6,612.39 >10.00 1.41 -3,202.11 N/C4 <0.00 -39,977.76 -6,506.20 >10.00 1.38 -2,682.12 N/C4 <0.00 -39,325.26 -6,400.00 >10.00 1.37 -2,162.12 N/C4 <0.00 -38,672.76 -6,293.81 >10.00

1.25 4,878.68 27.75 17.08 13,912.88 2,264.26 4.29 1.14 15,219.44 77.99 28.70 74,062.21 12,053.28 2.28 1.10 25,560.20 111.82 33.39 134,211.55 21,842.31 <1.00 1.08 35,900.96 136.82 36.10 194,360.88 31,631.34 <1.00 1.07 46,241.72 156.08 37.90 254,510.22 41,420.37 <1.00

1.42 801.01 N/C4 <0.00 -11,142.63 -1,813.41 >10.00 1.31 7,064.10 34.43 19.19 23,951.20 3,897.95 2.75 1.27 13,327.19 57.73 24.98 59,045.02 9,609.31 1.71 1.25 19,590.29 74.39 28.10 94,138.85 15,320.66 1.34 1.24 25,853.38 87.10 30.12 129,232.67 21,032.02 1.15

1.8:1.0 1.59 -3,276.66 N/C4 <0.00 -36,198.14 -5,891.08 >10.00 1.48 -1,091.24 N/C4 <0.00 -26,159.82 -4,257.39 >10.00 1.44 1,094.19 4 N/C <0.00 -16,121.50 -2,623.70 >10.00 1.42 3,279.61 4.28 7.14 -6,083.18 -990.01 9.00 1.41 5,465.04 13.02 11.39 3,955.13 643.68 6.42

US$ 1.00 = R$ 3.50 in april 2018; 1 ha = 10,000 m²; 3 NPV and ANPV – With 10% of taxation to 10 years of horizon; 4 Not calculated. Legend: TPC – Total Production Costs; LR – Liquid Revenue; IRR – Internal Rate of Return; MIRR – Modified Internal Rate of Return; NPV – Net Present Value; ANPV – Annualized Net Present Value; RIC – Return on Invested Capital; and N/C – Not calculated. 2

645 646

Table 7 – Sensitivity analysis of tilapia (Oreochromis niloticus) in a single-phase system in the State of Santa Catarina, Brazil, April, 20181. Condition A – 700 g Flooded area

1 ha

Units

2 fishes m-2 3 fishes m-2 4 fishes m-2 2 fishes m-2 3 fishes m-2

TPC LR

US$ US$ % % US$ US$ Years US$ US$ % % US$ US$ Years US$ US$ % % US$ US$ Years US$ US$ % % US$ US$ Years US$ US$ % % US$ US$ Years

2.24 1.95 1.81 1.62 - 3,804.23 - 1,647.21 509.80 - 2,636.26 N/C4 N/C4 N/C4 N/C4 <0.00 <0.00 <0.00 <0.00 - 38,097.85 - 25,338.34 - 12,578.84 - 31,486.21 - 6,200.25 - 4,123.70 - 2,047.15 5,124.23 >10.00 >10.00 >10.00 >10.00 1.45 1.47 1.30 1.21 - 2,846.37 1,467.66 5,781.68 189.57 N/C4 28.28 N/C4 N/C4 <0.00 <0.00 17.26 <0.00 - 34,260.43 - 8,741.43 16,777.57 - 16,735.96 - 5,575.73 - 1,422.63 2,730.47 - 2,723.70 >10.00 >10.00 3.24 >10.00 1.39 1.40 1.25 1.18 - 1,888.50 4,582.53 11,053.57 3,015.41 N/C4 18.40 49.85 7.70 <0.00 13.66 23.25 8.89 - 30,423.02 7,855.48 46,133.99 - 1,985.71 - 4,951.21 1,278.44 7,508.09 - 323.17 >10.00 4.43 1.97 6.80 1.36 1.23 1.16 1.37 - 930.64 7,697.40 16,325.45 - 461.15 N/C4 31.52 65.34 21.72 <0.00 18.30 26.49 14.94 - 26,585.61 24,452.40 75,490.40 12,764.54 - 4,326.69 3,979.51 12,285.71 2,077.37 >10.00 2.97 1.52 3.96 1.34 1.21 1.15 1.35 27.22 10,812.27 21,597.33 8,667.07 41.24 77.27 31.49 N/C4 <0.00 18.30 21.11 28.58 - 22,748.20 41,049.31 104,846.82 27,514.79 - 3,702.16 6,680.59 17,063.34 4,477.91 >10.00 2.35 1.29 2.97

IRR MIRR NPV3 ANPV3 RIC TPC LR

2 ha

IRR MIRR NPV3 ANPV3 RIC TPC LR

3 ha

IRR MIRR NPV3 ANPV3 RIC TPC LR

4 ha

IRR MIRR NPV3 ANPV3 RIC TPC LR

5 ha

IRR MIRR NPV3 ANPV3 RIC

647 648 649 650 651 652 653 654 655

1

Condition B – 1100 g

Índex

1.42 801.01 N/C4 <0.00 - 11,142.63 - 1,813.41 >10.00 1.31 7,826.82 34.43 19.19 23,951.20 3,897.95 2.75 1.27 13,327.19 57.73 24.98 59,045.02 9,609.31 1.71 1.25 19,590.29 74.39 28.10 94,138.85 15,320.66 1.34 1.24 25,853.38 87.10 30.12 129,232.67 21,032.02 1.15

4 fishes m-2 1.33 4,238.27 17.60 13.33 9,200.95 1,497.41 3.97 1.24 13,938.63 65.93 26.60 64,638.35 10,519.59 1.51 1.21 23,638.98 93.77 31.07 120,075.76 19,541.78 1.07 1.20 43,478.76 38.73 38.73 175,513.16 28,563.96 <1.00 1.19 43,039.69 128.41 35.24 230,950.57 37,586.14 <1.00

US$ 1.00 = R$ 3.50 in april 2018; 1 ha = 10,000 m²; 3 NPV and ANPV – With 10% of taxation; 4 Not calculated. Legend: TPC – Total Production Costs; LR – Liquid Revenue; IRR – Internal Rate of Return; MIRR – Modified Internal Rate of Return; NPV – Net Present Value; ANPV – Annualized Net Present Value; RIC – Return on Invested Capital; and N/C – Not calculated. 2

656 657 658

Table 8 – Sensitivity analysis of tilapia (Oreochromis niloticus) in a single-phase system in relation to the variation in commercialization value used by flooded area in the State of Santa Catarina, Brazil, April, 20181,2. Condition A – 700 g Flooded area

1 ha

2 ha

3 ha

4 ha

5 ha

659 660 661 662 663 664 665 666 667

1

-1

-1

Condition B – 1100 g -1

Índex

Units

$1.21kg

$1.34kg

$1.47kg

LR

US$ % % US$ US$ Years US$ % % US$ US$ Years US$ % % US$ US$ Years US$ % % US$ US$ Years US$ % % US$ US$ Years

-3,903.21 N/C4 <0.00 -39,200.49 -6,379.70 >10.00 -3,044.34 N/C4 <0.00 -36,465.72 -5,934.63 >10.00 -2,185.47 N/C4 <0.00 -33,730.95 -5,489.56 >10.00 -1,326.60 N/C4 <0.00 -30,996.18 -5,044.48 >10.00 -467.72 N/C4 <0.00 -28,261.41 -4,599.41 >10.00

-1,647.21 N/C4 <0.00 -25,338.34 -4,123.70 >10.00 1,467.66 N/C4 <0.00 -8,741.43 -1,422.63 >10.00 4,582.53 18.40 13.66 7,855.48 1,278.44 4.43 7,697.40 31.52 18.30 24,452.40 3,979.52 2.97 10,812.28 41.24 21.11 41,049.31 6,680.59 2.55

608.79 N/C4 <0.00 -11,476.20 -1,867.70 >10.00 5,979.66 13.70 12.05 18,982.86 3,089.37 2.97 11,350.53 55.22 24.45 49,441.91 8,046.44 1.79 16,721.40 72.89 27.85 79,900.97 13,003.52 1.37 22,092.28 86.85 30.08 110,360.03 17,960.59 1.15

IRR MIRR NPV3 ANPV3 RIC LR

IRR MIRR NPV3 ANPV3 RIC LR

IRR MIRR NPV3 ANPV3 RIC LR

IRR MIRR NPV3 ANPV3 RIC LR

IRR MIRR NPV3 ANPV3 RIC

$1.31 Unit-1 $1.46 Unit-1 $1.60 Unit-1 -2,696.14 N/C4 <0.00 -32,631.06 -5,310.55 >10.00 69.81 -37.19 -24.88 -19,025.66 -3,096.34 >10.00 2,835.76 4.14 7.06 -5,420.26 -882.12 8.06 5,601.72 16.85 13.03 8,185.13 7,332.09 4.68 22,424.81 75.42 28.28 108,165.59 17,603.45 1.32

801.01 N/C4 <0.00 -11,142.63 -1,813.41 >10.00 7,064.10 34.43 19.19 23,951.20 3,897.95 2.75 13,327.19 57.73 24.98 59,045.02 9,609.31 1.71 19,590.29 74.39 28.10 94,138.85 15,320.66 1.34 25,853.38 87.10 30.12 129,232.02 21,032.02 1.15

4,298.15 23.52 11.17 10.345.80 1,683.73 3.74 14,058.39 71.94 27.68 66,928.06 10,892.23 1.38 23,818.62 104.18 32.45 123,510.31 20,100.73 <1.00 33,578.86 127.96 35.19 180,092.56 29,309.24 <1.00 29,281.95 98.74 31.75 150,299.76 24,460.59 <1.00

US$ 1.00 = R$ 3.50 in april 2018; 1 ha = 10,000 m²; 3 NPV and ANPV – With 10% of taxation; 4 Not calculated. Legend: LR – Receita Líquida; IRR – Internal Rate of Return; MIRR – Modified Internal Rate of Return; NPV – Net Present Value; ANPV – Annualized Net Present Value; RIC – Return on Invested Capital; and N/C – Not calculated. 2

668 669 670 671

Condition B

35.0

35.0

30.0

30.0

25.0

25.0

20.0

20.0

MIRR (%)

MIRR (%)

Condition A

15.0 10.0

15.0 10.0 5.0

5.0

-

-

-5.0 -5.0

1 ha

2 ha

3 ha

4 ha

5 ha

Flooded area US$ 0.44 (-10%)

672 673 674 675 676 677 678 679 680

US$ 0.49

US$ 0.53 (+10%)

1 ha

2 ha

3 ha

4 ha

5 ha

Flooded area US$ 0.44 (-10%)

US$ 0.49

US$ 0.53 (+10%)

Figure 1. Evolution of the values of the Modified Internal Rate of Return (MIRR) on the variation of the price of the feed for the different scenarios. Condition A = 700 g fish-1 (Slaughtered fish market); Condition B = 1100 g fish-1 (Live fish market).

Title: ECONOMIC FEASIBILITY OF TILAPICULTURE IN SOUTHERN BRAZIL: A SMALL-SCALE FARM MODEL Author: Leonardo CASTILHO-BARROS HIGHLIGHTS This article provides guidance on small-scale aquaculture activities; The article allows comparing economically different investments in the aquaculture sector; The tools and concepts used in this article allow comparison with other agribusiness activities.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

We declare no conflicts of interest.