-Nowadays, aquaculture projects are beginning to be carried out in many parts of Mexico, where the main obstacle to its success is the lack of knowledge of the basic principles and technical necessary skills to achieve maximum utilization. In Mexico, the projection of a tilapia hatchery has required models established in other countries, making the necessary modifications according to the natural conditions of the country. This work intends to apply multivariate analysis techniques and especially based on main components, due to it is a method widely used to reduce the size of a group of specific data, locating it in the specific variables according to the necessity that is required, maintaining a High-level order of the main components which are determined and ignoring the low level for the decision making. They optimize according to the natural conditions of Mexico and especially the state of Querétaro in the breeding and reproduction of tilapia, constructing predictive models, at the time of developing applicable projects. For this, the first section of this chapter is a review of different concepts related to methods that may be applicable in decision making for the particularity of said project explaining methods of similar solutions giving different points of view for the solution of the same, in later sections will apply numerical methods of analysis that work with the data generated from the analysis based on main components giving a solution of the problem showing concrete results as desired. Finally, it is presented conclusions and references consulted for the accomplishment of this investigation.
vantage of this system is that tilapia Are fed with
hormones from the time they hatch and can eat what results
in a good% of male tilapia, this process can take place
approximately one month after spawning. So, in this case
will work with samples of masculinized issues fry for
the maximum profit and profit of the business. It is worth
emphasizing that when the stocking density of the tilapia
is low, it is possible to dispense with oxygen aerators and
only to carry out water refills, in large areas, the low
intensity crop can shed interesting production volumes
that are the importance to perform a good study to
achieve maximum efficiency in breeding and
reproduction of tilapia. Apply several techniques to apply when
getting the product present quality when it reaches its
final destination that is marketing for it tilapias must have
complete fins without fraying, no white spots or ulcers or
scratching injuries behavior against the wall or the
background [
Nowadays, there are not mathematical models to optimize resources of scientific projects, so the present research is a milestone in the subject seeking a mathematical solution. Additionally, although it will be tested on tilapia breeding projects already implemented, the research will serve as a basis for any similar problem that requires cost optimization. Finally, it will serve to focus the use of economic resources in a more real way. The main social contribution of the research will be to expand the knowledge of the subject in this branch that serves to the future investors. It will be achieved as the budget of each project is determined in real form, to avoid overcharging and underestimating them.
The main objective of this work is to determine the best implementation of a breeding and reproduction project of tilapia depending on the budget to be invested applying mathematical algorithms that help the decision making in carrying out the project.
For the implementation of this project it is worth saying
that the investor must take into account that the budget
that wishes to invest will have to consist of a certain limit
since it is necessary to invest in minimum tools
indispensable for the control and monitoring of the process, in
addition to taking into account the distance in which our
source of supply will be found to determine the cost of
the piping system to be applied. The study is based on
tanks of geomembrane supplied by professional
companies in the sector and it will take into account the different
capacities of the same that are tanks of (3, 6, 9, 12) m wide
and 1.20 m high because they are the most used in This
type of projects applied to the creation of tilapia
(Extensive, Intensive, Semi-Intensive, Super-Intensive) [
Number of fish for the crop Initial budget Actual budget (suggested by the algorithm) Cost per square meter Number of square meters available Actual square meters (suggested by the algorithm) Estimated cost of infrastructure and equipment requested for cultivation Cost of each Alevin (0.5 g) (Constant Value mxn 0.75) Diameter of the pond to be used (3,6,9,12 meters of Diameter) Generalized feeding cost per individual (Constant Value 1.7 kg 12.50 mxn / kg) Existence of blower in the project (1 (SI), 0 (NO)) Number of fish that occupies one cubic meter (Constant Value 50100 individuals) Quantity of general food Number of ponds to be used according to diameter required Metro Square according to pond (Area of pond = 3.14 * r²) Cultivation Methodology to use 2
When the number of pond is equal to or greater than 7, the pond capacity is maximized at the time of planting of the fry as it can plant the pods in full according to the construction follows:
Stage 1 Stage 2
Stage
3 Stage 4
Stage 5
Stage 6
Stage
7
Fig. 1. Pond assignations (Source: own elaboration) In which the capacity of the pond is fully exploited, respecting the planting rate per cubic meter (100 u / m³) SERGIO BARRIOS, ALBERTO OCHOA: APPROACH OF IMPLEMENTATION USING MODELS OF ANALYSIS OF MAIN COMPONENT FOR BREEDING AND REPRODUCTION OF TILAPIA where at the end of the breeding stage the fish reach a MtCult²= ( /7) Amount of times I can applymweight of 150 g / u (Pond 1) Dividing the population of ethodology 2 according to ponds.
( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) MtCult = MtCult¹ y MtCult² Number of times each methodology can be applied.
Mathematical model for methodology 1
= 100 =
∗ Cost of alevin per cubic meter general: The general cost of planting the alevin.
Cp = ∗ 3 ∗ ( ) Amount of feed to be used throughout the breeding cycle up to 500 g per fish.
= .∗ estimated.
The estimated cost for infrastructure and equipment is
= ∑ + ∑
individuals in the following ponds (Pond 2 and Pond 3),
thus respecting the suitability of individual space, and in
turn, at the end of the pre-fattening stage (300 g / Other
ponds (Pond 4, Pond 5, Pond 6, Pond 7) until reaching the
weight per individual of 500 g / u which would be the best
marketing weight [
When the number of ponds is less than 7 does not
comply with the methodology above exposed and as you
cannot divide populations equally as the previous method
does not maximize the capacity of the pond at the time of
planting because you have to think in terms of
Commercialization so that it is necessary to plant the optimal
capacity by square meters that support individuals of 500 g
/ u in the pond so the planting that is carried out in the
pond will not cover the whole of the pond space since the
fry would be sown to Lower amount per cubic meter of
water and should be adequately concentrated. A UML
modeling that explains such a situation is as follows [
Second Stage
Third Stage
≃ Cmd /3, 14*r² ponds per m² available.
=
∗ 3.14 ∗ ² Referes to each meter used with respect to the diameter of the pond used.
Calculation of the number of Estimation of the architectural method to be implemented.
MtCult¹= ology 1 according to ponds.
/7 Amount of times I can apply method( )
Cost of alevin per cubic meter general: =
∗ ( ) ( )
The general cost of planting the alevin.
Cp = ∗ 3 ∗ ( ) Amount of feed to be used throughout the breeding cycle up to 500 g per fish.
= .∗ The estimated cost for infrastructure and equipment is estimated.
= ∑ + ∑ =1 =1 ∗ 0.5 ≤ CAg + Cp ≤ ∗ 0.6 The cost of investment of the fry and feed will comprise between 50% and 60% of the initial budget.
The cost of infrastructure and equipment will comprise between 40% and 50% of the initial budget.
∗ 0.4 ≤ ≤ ∗ 0.5
often occurs between variables: if we take too many
variaThe so-called Common Principal Component Analysis
bles (which usually happens when you do not know too
much about the data or only have an exploratory spirit), it
the more information is considered, which is related to
(ACP) is where you collect information from a sample of
data, most often taking as many variables as possible.
However, if we take too many variables on a set of objects,
for example 20 variables, we will have to consider 180
possible correlation coefficients; If there are 40 variables,
that number increases to 780. Obviously, in this case it is
difficult to visualize relationships between variables.
Another problem that arises is the strong correlation that
often occurs between variables: if we take too many
variables (which usually happens when you do not know too
is normal that they are related or that they measure the
same from different points of view. For example, in
medical studies, blood pressure at the exit of the heart and at
the exit of the lungs is strongly related. It is necessary,
therefore, to reduce the number of variables. It is
important to highlight the fact that the concept of greater
information is related to the one of greater variability or
variance. The greater the variability of the data (variance),
the more information is considered, which is related to the
concept of entropy [
These techniques were initially developed by Pearson at
the end of the 19th century and were later studied by
Hoteling in the 1930. However, the appearance of
computers did not begin to popularize. In order to study the
relationships that exist between correlated variables (that
measure common information), the original set of
variables can be transformed into another set of new variables
that are not correlated with each other (which does not
have a redundancy or redundancy in the information). The
new variables are linear combinations of the previous
ones and are constructed according to the order of
importance in terms of the total variability they collect from
the sample. Ideally, we look for m<p variables that are
linear combinations of the original p and are uncorrelated,
collecting most of the information or variability of the
data. If the original variables are incorrectly matched, then
it is meaningless to perform a principal component
analysis. The
main components analysis is a
mathematical
technique that does not require the multivariate normality
assumption of the data, although if the latter is met it can
be given a deeper interpretation of these components, it
is applied, in this investigation to control the quantity and
capacity of ponds to be implemented with the aim of
maximizing planting space [
The so-called Common Principal Component Analysis
(ACP) is where you collect information from a sample of
data, most often taking as many variables as possible.
However, if we take too many variables on a set of objects,
for example 20 Variables, we will have to consider 180
possible correlation coefficients; If there are 40 variables,
that number increases to 780. Obviously, in this case it is
difficult to visualize relationships between variables.
Another problem that arises is the strong correlation that
much about the data or only have an exploratory spirit), it
is normal that they are related or that they measure the
same from different points of view. For example, in
medical studies, blood pressure at the exit of the heart and at
the exit of the lungs is strongly related. It is necessary,
therefore, to reduce the number of variables. It is
important to highlight the fact that the concept of greater
information is related to the one of greater variability or
variance. The greater the variability of the data (variance),
the concept of entropy [
It is considered a series of variables (x¹, x²... xp) on a group of objects or individuals and try to calculate from them a new set of variables y¹, y²... yp, between each other, whose variances are decreasing progressively. Each yj (where j = 1... p) is a linear combination of the original x¹, x²... xp, that is:
Yj = aj¹x¹ + aj²x² + ... + ajpxp = a0jx Where aºj = (a¹j, a²j... apj) is a vector of constants, and Obviously, if we want to maximize the variance, as we shall see later, a simple way might be to increase the coefficients aij. Therefore, in order to maintain the orthogonally of the transformation it is necessary that the vector module.
A0j = (a¹j, a²j... apj) is
=1
′ = ∑ 2 = 1
( )
The first component is calculated by choosing a¹ so that
y¹ has the largest possible variance. The second main
component is calculated by obtaining a² so that the
obtained variable, y² is wrong with y¹. In the same way, y¹, y²,
and p, are wrongly chosen so that the random variables
obtained will have less and less variance [
Price T 12 14000 4100 10500 20200 30600
From where we can infer in the matrix of own values and in the sedimentation graph that the first 2 components have a high value in terms of% concentration of model information.
Caso T 0,925 0,964 0,010 0,006
Crop Gain = ((Number of Alevin * 80) / 100) * 40 Where the Gain of Farming is going to be equal to the amount of fry 80% of the quantity sown by concept of mustarded by the price of the market that is 40. Each month and a half should be made with the same amount of fry as the applied case, but the investment process will be less because you will not have to invest in infrastructure only in fry to plant and food, giving a higher profit margin at the time of trading after the second cycle, assuming an invariable sale price of mxn 40.00 per kiloAquaculture projects are beginning to take place in many places, where the main obstacle to their success is the lack of knowledge of the basic principles and the necessary technical skills. In Mexico, the projection of an aquaculture farm requires models established in other countries, making the necessary modifications according to the natural conditions in the country. The development of aquaculture in Mexico compared to the world aquaculture development shows a lag in both the diversity and use of resources as in the modernization of the sector. Mexico is in an ideal situation for the cultivation of several species because it has extensive coastlines, abundant wetlands and optimal climates and fish farming has become a necessity in many places where this activity was not previously practiced.
Tilapia farming plays a crucial role in food security and nutrition as a nutrient-rich food source. It also provides significant jobs and income in the rural population, this work tries to demonstrate the feasibility of implementing a tilapia breeding in Mexico where natural conditions do not affect the crop, and serves as a guide to take decisions to The time to develop a project that has specific characteristics and has limited resources and can take full advantage of its development giving the investor the opportunity to know which is the most appropriate model depending on your budget and location.