RISKS AND ECONOMIC ANALYSIS OF ORANGE
CULTURE: CASE STUDY OF A PRODUCER FROM THE INTERIOR OF SÃO PAULO STATE, BRAZIL
Fernando Rodrigues de Amorim
Universidade Estadual de Campinas, Brazil
Email: fernando.amorim@feagri.unicamp.br
José Claudenir Nanetti
Junior
Faculdade de Tecnologia
de Taquaritinga,
Brazil
Email: juninhounespcta@hotmail.com
Pedro Henrique Camargo
de Abreu
Faculdade de Tecnologia
de Taquaritinga,
Brazil
Email: pedro.abreu7@fatec.sp.gov.br
Submission: 09/12/2017
Accept: 06/03/2018
ABSTRACT
The Brazilian citriculture is one of the activities that
generate the most income within the agribusiness, being responsible for
providing opportunities for thousands of direct and indirect workers, besides
being a sector that moves a great amount of financial resources. The orange
crop has been going through great price swings in the recent years, and with
this, many farmers are failing to invest in potential, as a result of the risks
involved in the activity. A large part of these risks is related to the buyer
market, which is controlled by the large juice industries and by the high
capital required for the implementation of the new orchards. The objective of
this work is to identify the risk factors for attractiveness and to analyze the
economic viability of the orange crop in a farm in the municipality of Bauru,
in the state of São Paulo. For this, the Monte Carlo method be used to simulate
the probabilities of success in the scenarios analyzed and the NPV, IRR and
Payback to determine the feasibility of the project. The research is
characterized as a case study. The results obtained showed that the investment
is feasible, only in the real and optimistic scenario and will provide a return
between the 6th and the 7th year of the project, providing a balance of
approximately R$ 4,190,252.94 after 10 years of investment which represents an
attractive compared to the initial investment value of R$ 937,500.00.
Keywords: Risk analysis; profitability; citriculture; Monte Carlo method;
simulation
1. INTRODUCTION
Among the fruit grown in Brazil, orange
occupies the largest area planted (415,232 hectares) and exerts great
importance in the trade balance (FUNDECITRUS, 2017). In Brazil, the fruit was
introduced early in the colonization, finding in the country better conditions
for its development than in its own place of origin and thus expanding
throughout the territory (NEVES et al., 2010).
Oranges can have three basic
destinations: processing industry, domestic market and external market. In the
states of Bahia and Sergipe, 77% of production is absorbed by the fresh fruit
market. In the Brazilian citrus belt (including the state of São Paulo,
Triângulo Mineiro and northwest of Paraná), 86% of the production is destined
for the processing industry (NEVES et al., 2014). According to the latest
estimate by FUNDECITRUS (2017), the citrus belt has 191,694,410 plants. The
2017/18 orange crop of Brazil's main citrus park  which includes 349
municipalities in São Paulo and Minas Gerais  is expected to be 364.47 million
boxes of 40.8 kg. Production is 14% higher than the historical average of the
last ten years.
The 2016/17 crop, considered one of the
smallest in history, closed with a production of 245.31 million boxes of 40.8
kg. With this reduction in production, the price paid for the fruit reached
high levels during the harvest, where the industry paid on average, R$ 21.24
per box (CEPEA, 2017).
According to Kalaki (2014), despite the
Brazilian superiority in the production of orange juice, the country is highly
dependent on the foreign market, and this dependence has become the chain's
major concern in recent years, ranging from producers to industry. Of the total
drinks consumed in the world in 2010, the orange flavor represented only 0.91%,
but among fruit drinks, orange juice is the most taken, with a 37% share of
juices in 2012.
As observed by Silva (2016), the greater
the surplus of orange in the market, the verticalization of the industry, and
the concentrated juice store, the greater the purchasing power of the orange
processing industry. Because it is an oligopsony market, the producer usually
has no control over the price paid for the fruit, so it is up to him to manage
production costs so that the business is viable over time.
For Adami (2010), Brazilian citriculture
is seen as a profitable activity in the long term, but characterized by the
high level of risk. In addition to the common risks as a market for inputs and
products and climatic risks, citriculture is influenced by factors such as
soil, age, pests, diseases and cultural management. All of these factors affect
the profitability of citrus, as they impact on the costs of production,
reducing the income expected by the producer.
The dynamics of these variations make
the planning of property extremely complex, where the producer must monitor his
production costs, manage the activities of the property and at the same time
follow the changes of the buyer market. But often the producer cannot quantify
his production costs as well as the profit or loss obtained during the harvest.
The Brazilian citrus agroindustrial
system is a consolidated and important system for the economic and social
development of the country. There is a clear perception that the sector is
devoid of definitive organization, a plan and a policy elaborated by all links
and for all links (KALAKI; NEVES, 2017). According to Silva and Marques (2015)
the agricultural producer, in this case the citrus grower, is faced with
uncertainty both in terms of quantity produced, productivity fluctuations per
hectare and prices received for the sale of the orange.
The objective of this work is to analyze
the impact of production on costs, as well as to evaluate the viability of the
implantation of new orchards.
2. THEORETICAL FOUNDATION
Even with the great national production
of the fruit, its products and byproducts, there are still points in the chain
that lack information. For Silva (2016), information on production costs and
the economic viability of these investments are not clear and objective. Therefore,
it is necessary to prepare economic studies that guarantee the continuation of
the expansion of the citrus chain in a sustainable way.
The cost of production is defined by
Matsunaga et al. (1976), as the sum of the values of the productive services
of the factors applied in the production, being this value equivalent to the
total monetary sacrifice of the firm that produces. For Silva (2016),
production costs can be divided into two components: fixed costs and variable
costs. The fixed costs do not vary with the increase or decrease of production,
since the variable cost varies according to the volume produced.
The activity analysis encompasses the
entire production process, from the plant formation phase to the end of the
productive cycle. This analysis includes all costs related to production such
as: machinery, labor, taxes, diesel oil, electricity, administrative expenses,
equipment, improvements, among others (ADAMI, 2010).
The expenses that make up all items of
variable costs and fixed costs, which are directly related to the
implementation of the activity, are part of the operational cost of production
(HORNGREN, 1989). According to Silva (2016), the operational cost is equivalent
to the disbursement made for the costing of the activity in a period, that is,
the costs incurred during a harvest.
For Silva and Marques (2015), product
price fluctuations are causing uncertainty (risk) in agricultural activity. In
addition to climate risks, pests and diseases that increasingly impact the cost
of production, the citrus farmer faces more price instability than the
industry. The orange processing industry is an oligopsonic market structure,
that is, with many suppliers (citrus growers) and few demanders (processing
industries).
For companies it is very important to
measure the risks of an activity, especially the market volatility. According
to Adami (2010), investments made in agricultural activity compromise a
considerable amount of capital that although there is a probability of return,
is subject to partial or total losses. However, there are measures that
evaluate these risks as well as assess the feasibility of a project.
According to Adami (2010), the cash flow
components that present the greatest variability (risk) are those related to
inputs (fertilizers and pesticides), on the cost side; the selling price of the
fruit and the productivity of the orange, on the revenue side. These components
of the activity's cash flow can affect the profitability of the crop over time.
According to Zuin and Queiroz (2006),
the implementation of a strategic planning becomes fundamental in the search
and maintenance of competitiveness in an enterprise, having to count on the
participation and commitment of all those involved in the process and sustained
by an efficient and clear communication between the managers.
3. MATERIALS AND METHODS
The present work was accompanied by a
property located in the municipality of Bauru, in the state of São Paulo (22º
18 '53 "S and 49º 03' 38" W), this municipality has an area of
67,350 hectares, of which 56,062 hectares are belonging to the rural area.
The main farms are: Braquiária with 37,291.6 ha, Eucalyptus with 4,011.8 ha and
Laranja with 2,293.3 ha (LUPA, 2008).
The area occupied with orange is divided
among 38 properties, and even being the third most planted in the municipality
is the one that generates the highest production value of R$ 10,124,000.00
against R$ 7,130,900.00 generated by cattle and R$ 8,824. 000.00 of eucalyptus
(LUPA, 2008). This shows the importance of citriculture to producing
municipalities, generating jobs, raising taxes and moving the local economy.
The property surveyed has an area of
93 hectares and 71.4 hectares are occupied with Orange varieties of Valencia
Americana, Pêra Rio and Valencia. The orchards of his property are with an
average of 9 years, that is to say, the planting is in production phase.
However, the production estimated for the other years was analyzed from other
farms of the same owner, with very similar conditions of soil and management.
According to Gitman (2002), Net Present
Value (NPV) can be understood as an investment analysis, which discounts the
cash flows at a specific rate, referring to the minimum return to be obtained
in the project. According to Silva (2016), the investment is considered
attractive when the NPV has a value equal to or greater than zero. In
situations where this indicator is negative indicate that the return is less
than the minimum rate required for the investment, showing that the project is
not feasible in the long term.
Another measure used to analyze the
feasibility of a project is the Internal Rate of Return (IRR), defined as the
discount rate that equals the present value of the cash inflows to the project
investment (GITMAN, 2002). That is, IRR seeks to synthesize the merits of a
project (ROSS et al., 2007). The acceptance or rejection of a given project is
defined by comparing the internal rate of return obtained with the minimum
profitability required by the company for its investors (SILVA, 2016).
Even if a project proves feasible, the
time required to recover the invested capital is undoubtedly a pertinent
question for those who wish to make an investment. Payback consists of the time
necessary for the capital expenditure to be recovered through the cash benefits
(cash flows) promoted by the investment project. Payback is often interpreted
as the indicator of the risk level of an investment project. The longer the
term, the greater the risk involved in the project (SILVA, 2016).
3.1.
Exploratory
research
The exploratory research aims to provide
greater familiarity with the problem thus making it more explicit. According to
Gil (1999), exploratory research has as main purpose to develop, clarify and
modify concepts and ideas, in order to formulate more precise problems or
searchable hypotheses for later studies.
Exploratory research aims to deepen the
knowledge of the researcher on the subject studied. It can be used to
facilitate the elaboration of a questionnaire or to serve as a basis for future
research, helping to formulate hypotheses, or the more precise formulation of
research problems (MATTAR, 2001). According to Cervo and Silva (2006), this
form of research establishes criteria, methods and techniques for the
elaboration of a research and aims to offer information about its object and
guide the formulation of hypotheses.
3.2.
Case study
According to Yin (2001), the case study
represents an empirical investigation and comprises a comprehensive method,
with the logic of planning, collecting and analyzing data. It can include both
single and multiple case studies as well as quantitative and qualitative
research approaches.
The case study may be understood as
exploring a limited system or a case, involving indepth data collection and
multiple sources of information, in a given context. The case can be an event,
an activity or even individuals; thus, the notion of a limited system is
related to the definition of time and space (CRESWELL, 1998). According to
Fonseca (2002), for a deeper analysis the case study may follow according to an
interpretative perspective, or a pragmatic perspective, that simply aims at
presenting a global, as much as possible complete and coherent perspective of
the object of study from the researcher's point of view.
3.3.
Quantitative analysis
Quantitative research seeks the
validation of hypotheses through the use of structured, statistical data, with
analysis of a large number of representative cases. It quantifies the data and
generalizes the sample results to those interested (MATTAR, 2001). For
Richardson (1999), the quantitative research is characterized by the use of
quantification, both in the information collection modalities and in the
treatment of them by means of statistical techniques.
The quantitative approach is
characterized by the formulation of hypotheses, operational definitions of
variables, quantification in data collection and information modalities, and
use of statistical treatments. The quantitative model establishes hypotheses
that demand a relation between cause and effect and supports its conclusions in
statistical data, tests and tests. The criteria of scientificity are
verification, demonstration, tests and mathematical logic (GRESSLER, 2003).
3.4.
Monte Carlo method
According to Nascimento and Zucchi
(1997), the probabilistic simulation models had their origin in the Monte Carlo
method and are focused on random phenomena, introducing risk analysis,
incorporating the environmental variables and, consequently, the elements of
inherent uncertainty.
Monte Carlo simulation (MCS), although
widely used in project management, gets some exposure in cost management, and
is used to promote the quantification of risk and uncertainty levels related to
project costs (KWAK; INGALL, 2009). According to Williams (2003), MCS has some
advantages over other methods of project analysis that try to incorporate
uncertainty, because although there are many analytical approaches to project
planning, the problem with these approaches lies in the assumptions required,
rendering them unusable in any practical situation.
According to Samanez (2007), the essence
of MCS is to simulate ways for the evolution of a phenomenon until an
approximation is found that explains it satisfactorily, that is, uncertain
situations are simulated to find expected values for the unknown variables.
According to Yoriyaz (2009), the Monte Carlo technique involves some primary
components necessary for any kind of simulation: probability density functions;
random number generator; and sampling techniques.
In cost management, the project manager
can use MCS to better understand the project budget and estimate the final
budget at completion. These estimates are directed toward consolidating a
probability distribution of the final cost of the project, with project
managers often using this distribution to reserve a project budget reserve to
be used when contingency plans are required to respond to events of risk (KWAK;
INGALL, 2009).
Thus, a projective financial model using
MCS would be converted from a deterministic model, which does not incorporate
any probabilistic element, to a stochastic, which incorporates probabilistic
components essential for decision making in uncertain environments (OLIVEIRA;
MEDEIROS NETO, 2012).
According to Yoriyaz (2009), all Monte
Carlo simulation is performed by means of samplings of the probability density
functions and the use of cumulative probability functions. These samplings are
performed using random numbers, so any computer program that uses the Monte
Carlo method requires a random number generator.
It should be emphasized that the
analyzed populations should have certain parameters, such as mean and standard
deviation, and may present several behaviors such as Normal, Exponential and
Uniform, and it is important to note that the samples obtained should be
random. For this, a sequence of random numbers must be obtained (GARCIA et al.,
2007).
Random number generators are based on
mathematical algorithms that generate numbers, whose occurrences follow a
randomness, and which simulate the true randomness found in nature. In this
sense, the numbers generated by these algorithms are formally called
pseudorandom numbers (YORIYAZ, 2009).
In the case of the property studied the
project was done considering a period of useful life of the orchards of 15
years. The culture begins its production relatively early, however this
production begins to be expressive from the 4 years of age, period known as
formation. The implantation of the culture, demand of great volume of
investment, needing a long period for the recovery of the same.
4. RESULTS AND DISCUSSIONS
In a market highly dependent on the
juiceproducing industries, the producer usually cannot impose the price to be
paid for his production, and it is his function to reduce his production cost
to the maximum, as well as to seek the increase of productivity by area. As
observed by Adami (2010) the producer is a price taker, that is, not influence
by the prices that he pays of the inputs, nor, on the prices of his production.
The analyzed property divides its
expenditures by cost center, which is the logical and segmented organization of
different sectors and activities within the property. The division of sectors
and activities by cost center helps to understand in a simplified way where the
biggest expenses are located.
Table
1: Production cost referring of the 2015/2016 harvest
Cost center 
Value 
Labor force 
R$ 132.145,20 
Machining 
R$ 63.715,00 
Inputs 
R$ 135.160,20 
Irrigation 
R$ 42.617,60 
Outsourced services 
R$ 8.863,40 
Administration 
R$ 41.457,80 
Harvest 
R$ 312.724,80 
TOTAL 
R$ 736.684,00 
Cost control in a segmented way shows in a simplified way where the most
significant production costs are, and is an important source of information for
decision making. This type of control allows us to focus on the expenses that
have an impact on production, having a larger share of the operational cost of
production, which is shown in Graph 1.
As can be seen in the graph above,
harvesting is the largest expense in fruit production, accounting for 42% of
expenditures, followed by inputs with 18% and labor with 18%. The sum of these
three accounts corresponds to 78% of the total cost of production, thus
requiring more attention from the producer on the actual expenses with each
cost center.
Graph 1: Cost production analysis by
cost center
Even with less participation, the costs
of mechanization, irrigation, administration and thirdparty services must also
follow a control criterion, in order to ensure that they do not influence
negatively the cost of ownership. Especially irrigation, where inadequate
management of the system can generate unnecessary expenses with electricity.
The productivity of the property in the
2015/2016 harvest closed with an average of 1231 boxes of 40.8 kg per hectare,
far above the average of the citrus belt that closed in 745 boxes of 40.8 kg
per hectare (FUNDECITRUS, 2017). This high productivity is due to the use of
irrigation as well as management techniques that allows more efficient control
of pests and diseases.
With a total cost of R$ 736,684.00, the
farm has a cost of R$ 10,317.00 per hectare. The commercialization of orange
occurs through boxes of 40.8 kg, where the industry considers this weight as a
fruit box. The cost per box is diluted according to the increase in production,
and the higher the productivity per area, the higher the revenue per box.
Kalaki (2014) shows that the cost of
production per hectare in the year of 2012 was around R$ 9,355.00, of which R$
3,241.00 of this amount refers to the harvesting and transportation of the
fruit, and the rest corresponds to the operating cost. In the case of the
property analyzed, the cost with the harvest is the one that has the greatest
impact within the production, being responsible for 34.64% of the expenses with
the crop.
According to Adami (2010), there is
great concern in the citrus production sector, because it is believed that it
can be a decisive factor for the producer's permanence in the activity. With an
average productivity of 1237 boxes per hectare at a cost of R$ 10,315.00, the
property has a cost per box of R$ 8.34. The average selling price of the carton
in the last two years (2015 and 2016) in the industries of the region of
Araraquara was R$ 16.45, obtaining a profit of R$ 8.11 per box.
The Table 2 presents the comparison
between productivity and selling price.
Table
2: Selling price vs. Productivity per hectare (revenue per hectare)
Value
per box (R$) 
Productivity
box/ha 

700 
800 
900 
1000 
1100 
1200 

13 
9100 
10400 
11700 
13000 
14300 
15600 
Damage 

13,5 
9450 
10800 
12150 
13500 
14850 
16200 

14 
9800 
11200 
12600 
14000 
15400 
16800 
Point
of Equilibrium 

14,5 
10150 
11600 
13050 
14500 
15950 
17400 

15 
10500 
12000 
13500 
15000 
16500 
18000 
Gain 

15,5 
10850 
12400 
13950 
15500 
17050 
18600 

16 
11200 
12800 
14400 
16000 
17600 
19200 

16,5 
11550 
13200 
14850 
16500 
18150 
19800 

17 
11900 
13600 
15300 
17000 
18700 
20400 
As can be seen in Table 2, productivity and sales price directly
influence revenue. However, if the productivity of the property is in the citrus
belt average (745 boxes per hectare), the producer will only cover his
production costs, with the orange box price at R$ 15.00 remaining at break
even.
For the implementation of orchards, it
is necessary to acquire machines suitable for the conduction of the plantation
throughout the productive cycle. In this initial investment, drip irrigation
was also recorded, which was installed immediately after planting the
seedlings.
The Table 3 describes the initial
investment with equipment.
Table
3: Investment with machines and irrigation
Item 
Quantity 
Unit Value (R$) 
Total Value(R$) 
Tractor 85 HP 
2 
90.000,00 
180.000,00 
Bilateral atomiser 4.000 liters 
1 
47.000,00 
47.000,00 
Feeding machine 
1 
40.000,00 
40.000,00 
Double Splitter 3,2 m 
1 
29.000,00 
29.000,00 
Unilateral herbicide bar 
1 
5.000,00 
5.000,00 
Sprayer 2000 liters 
1 
14.500,00 
14.500,00 
Agricultural trailer 4.000 kg 
1 
5.800,00 
5.800,00 
Light utility 
1 
45.000,00 
45.000,00 
Irrigation (/ha) 
71,4 
8.000,00 
571.200,00 
The producer invested R$ 937,500.00 at the beginning of the project with
acquisition of machines and irrigation system, this investment increases what
is necessary for the return of the applied capital (Payback). The return on
capital is undoubtedly one of the factors that makes investments in culture
more difficult, in the current scenario where the great increase of pests and
diseases that diminish the useful life of the orchard, besides the instability
in the prices received by the fruit box discourages the entrance of new citrus
producers.
Because it is a perennial crop, the
orange needs time to develop and go into production during this period known in
the citriculture as formation, at this stage the plant for not producing in a
way means the constant investment in the planting is necessary.
Table 4 below presents the IRR, NPV and
Payback indicators in each of the three investment assessment scenarios
(Pessimistic, Real and Optimistic).
Table
4: Feasibility indicators in each of the scenarios
Optimistic 
Real 
Pessimistic 

TIR 
18% 
16% 
14% 
VPL 
R$
310.513,63 
R$
98.531,95 
R$
113.449,59 
PAYBACK 
6 years 
7 years 
7 years 
The period for the recovery (Payback) of the investment in the Real
scenario is 7 years, that is, it takes practically half the useful life of an
orchard, so that it acts the return of the invested capital. With the delay in
return on investment, and an increasing rate of pests and diseases, citrus
farming has become a risky business.
The internal rate of return (IRR)
obtained in the Real scenario was 16%, which is only 1% above the minimum
calculated rate of 15%. In this case, the investment may not be attractive,
leaving the producer waiting for better scenarios to sell his production, or
the move to the fresh fruit market, which may be more profitable in some cases.
In addition, a NPV of R$ 98,531.95 was obtained in the Real scenario, where the
project can be considered acceptable, however, the risks present in the
cultivation activities should be considered, as they may result in the project
not being viable.
In the Optimistic scenario, a revenue
increase of 10% was established, which directly interferes with IRR and NPV,
making the project more attractive. With the increase in revenue, the period
for the recovery of the investment reduces to 6 years, and the internal rate of
return rises from 16% to 18%, as well as the NPV that more than triples in
relation to the Real scenario, making the investment more attractive for future
investors. The 6year repayment obtained in this scenario significantly reduces
the risk in relation to production, in addition to requiring a smaller
investment in the training period, where revenue from the sale of production
helps to cover expenses with the culture.
In the Pessimistic scenario, with a
decrease of 10% of revenue in relation to the Real scenario, the investment
with the orange crop proves impracticable, with a IRR of 14%, which is lower
than the minimum rate imposed on the project, and with a NPV negative in the
amount of R$ 113,449.59. In this case, the project would not be accepted, and the
investment should not be made.
It is possible to compare the work of
Bruni et al. (1998), where the authors carried out the analysis of three
investment projects and obtained NPV of R$ 40,359.49, R$ 49,074.06 and R$
53,107.61 for projects that presented IRR equivalent to 100.8%, 37.7% and
23.34%, respectively. Thus, it is evident that the cases presented by the
authors present a large variation in the IRR, which does not occur in the
present work, in which the IRR presented a small amplitude of variation among
the three scenarios (of only 4%).
The investment treated in the present
work presents an IRR to those provided in the Pagliuca study (2014), where the
author obtained IRR indicators of 4.1% and 6.6%, respectively, for small and
largescale production. In the same study, Pagliuca (2014) obtained an NPV of
R$ 1,066,164.74 and an IRR of 13.36% for mediumscale production, with an
analysis period of 49 months.
Bendlin et al. (2016) analyzed two forms
of investment, reaching a NPV of R$ 3,400.00 and a 40% IRR for the first
planting scenario, together with a NPV of R$ 20,193.00 and a 30% IRR for the
according to the investment model.
In the study by Alcantara (2017), the
author analyzed the orange cultivation carried out by medium producers, where
she obtained 0% IRR in scenarios where NPV was negative and, with NPV positive,
IRR ranged from 10% to 11%. In addition, Alcantara (2017) also analyzed the
case of large producers, where it was verified that the investment proves
feasible, because the crop provides a positive NPV and IRR values between 21%
and 22%.
In order to analyze in a stochastic way,
the feasibility of the investment in question, the Monte Carlo simulation was
applied using the values established in each of the scenarios, where they
were structured in tabular form for the definition of the input variables of
the simulation. According to Rodrigues et al. (2010), the essence of MCS is: to
establish a probability distribution (model) that responds to the random
variables for the risk analyzed; to simulate events from these variables, with
a number of iterations large enough to provide the desired confidence; and to
analyze the results obtained statistically.
The software used to run the simulations
was Oracle Crystal Ball, which acts
as an extension of Microsoft Excel.
According to Damasceno and Couto (2008), a determinant factor for the
application of MCS is its number of iterations, which, the larger, favors the
convergence of the result to a probability distribution closer to the real.
Therefore, it was established that they would be performed 50,000 iterations
for the generation of estimates about the investment. According to Souza
(2004), this number of iterations is already more than sufficient to allow the
results (mean and standard deviation of the output variables) to stabilize and
that graphs are provided with a high density of points to be analyzed.
In order to promote the generation of
estimates for the analysis of cultivation scenarios in the property analyzed,
triangular distributions were defined for each of the project years, starting
from the initial investment value and the previously established cash flow
projections. The assumptions adopted for the deterministic model were
replicated for the stochastic model.
According to Machado and Ferreira
(2012), to consolidate the use of triangular distributions, it is necessary to
define three distinct points of distribution for each input variable, that is,
to estimate the individual or partial costs or deadlines of the project: the
lowest possible value, the most probable value, and the estimated maximum
possible value. These three values represent the opinion of the experts and the
possible scenarios will be randomly generated based on these estimates.
Table 5 presents the cash flow analysis
according to each of the scenarios previously raised. In addition, the table
presents the assumptions and forecast cell for MCS application. It is worth
mentioning that the forecast cell is characterized by the sum of the investment
value and the stipulated cash flows for each of the scenarios, and Crystal Ball will generate the random
numbers through the defined distributions for the simulation.
Table
5: Disposition of cash flow scenarios for simulation application
According to the Table 5, in the first three years since the investment,
the producer continues to invest in the orchard, although the investment
decreases in the second year due to the start of production. However, in each
scenario, this production starts to be profitable from the fourth year where
productivity reaches about 650 boxes per hectare.
Through the application of the
simulation and the execution of the interactions with the values raised, the
cumulative frequency graph of the total investment project balance, which is
presented in Figure 1.
Figure 1: Cumulative frequency graph of
the total investment balance
When carrying out the simulation and
analyzing the estimates provided, it is necessary to point out that the minimum
balance was R$ 3,877,963.15, which is R$ 384,874.78 higher than the sum of the
Pessimistic scenario. The maximum balance obtained in the simulation was R$
4,506,761.17, which is R$ 380,650.20 lower than the sum of the Optimistic
scenario. The average balance provided by the simulation was R$ 4,190,252.94,
which is only R$ 3.15 higher than the sum of the Real scenario, evidencing a
high equivalence in the sample distribution between the input variables and the
output variables provided by the stochastic model. In addition, the simulation
obtained a standard deviation of R$ 78,525.27 and a median of R$ 4,190,588.75.
Through interval analysis, it was found
that the probability of the project being finalized with a total balance above
R$ 4,300,000.00 is only 8.1%. In order to analyze another interval for the
total balance of the project, the amounts R$ 4,150,000.00 and R$ 4,300,000.00
were defined as being the minimum and the maximum to be portrayed in the
cumulative frequency graph represented in Figure 2.
Figure 2: Cumulative frequency graph of
the total investment balance
As shown in Figure 2, the range of R$
4,150,000.00 and R$ 4,300,000.00 is responsible for 61.4% of the simulation
figures, which is an important range to be considered for decision making,
considering the high probability of materializing. In this way, it is possible
to state that the sum of the 15 triangular distributions exerts a tendency for
the total value to be located in a centrally standardized range according to
the limits established in the distributions, and the larger the number of iterations,
the higher the percentage of output variables located in the central sampling
interval.
Table 6 shows the percentiles provided
by the simulation, according to the forecast values for the total investment
balance.
Table
6: Percentiles of the simulation of the total investment balance
Percentiles 
Forecast values 
0% 
R$ 3.877.963,15 
10% 
R$ 4.089.526,70 
20% 
R$ 4.123.661,02 
30% 
R$ 4.149.004,73 
40% 
R$ 4.170.395,91 
50% 
R$ 4.190.588.50 
60% 
R$ 4.210.382,55 
70% 
R$ 4.231.559,58 
80% 
R$ 4.256.263,91 
90% 
R$ 4.290.832,72 
100% 
R$ 4.506.761,17 
By analyzing the percentiles of the total investment balance, it is
possible to investigate the forecast intervals, which complement the estimates
provided by the simulation and help in defining parameters about the success
and viability of the project. It is clear that the balances defined in the
Pessimistic and Optimistic scenarios were not reached in the simulation and
consequently are not included in the percentiles intervals, however, they
should be regarded as important specifications about the success of the
project, since they originate from variables which have individually manageable
distributions.
In this way, it is possible to affirm
that the probability of the total investment balance reaching the most pessimistic
or optimistic indexes within the MCS is 0%. On the other hand, it is worth
mentioning that each simulation in Crystal
Ball will present different output variables and estimates among them, even
if they are performed with the same input values.
5. FINAL CONSIDERATIONS
Orange can be a profitable crop in the
long run, especially when high productivity is achieved, and the producer
manages to keep his costs low. However, the investment in the crop presents a
series of risks to be considered for the decision making in the face of the
instability of the price of the orange box in the last years, since the
commercialization of the orange occurs in an extremely centralized market.
The orange juice market signals a new
phase in which the production chain must pass, as world juice stocks are
currently low, and production expectations for the next harvest (2018/2019) are
relatively low, which should keep the price of the fruit at levels higher than
those found in recent years.
After analyzing the cash flows and the
NPV, IRR and Payback indicators in each of the scenarios, it was possible to
determine that the investment is attractive only in the real and optimistic
scenario, as it would provide a value above the stipulated financial return,
besides providing a final balance of nearly R$ 700,000.00 provided in the
optimistic scenario, when compared to the Real scenario.
Through the Monte Carlo simulation, it
was possible to obtain estimates of the probabilities of viability of
investment success, being that the project presents a 61.4% chance of providing
a total balance between R$ 4,150,000.00 and R$ 4,300,000.00, being this a
decisive indicator for the decision making, considering that it would be the
most probable to materialize.
As a suggestion for future works, it is
possible to indicate the performance of studies aimed at the economic analysis
of the systems of application of fixed rate and variable rate of fertilizers
and agrochemicals in the orange crop, comparing multicasts that relate
different productive scenarios to the crop comparing their respective operating
costs.
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