A Maize Production Model Using
Structural Equation Modeling in Chicontepec, Veracruz, Mexico
Modelo de producción
de maíz con ecuaciones estructurales en Chicontepec Veracruz, México
José Osorio-Antonia1
Fredy Juárez-Pérez2
Published Instituto
Tecnológico Superior Corporativo Edwards Deming. Quito - Ecuador Frequency April–June Vol.
1, No. 29, 2026 pp. 16–52 http://centrosuragraria.com/index.php/revista Dates of receipt Received: January 12, 2026 Approved: March 25, 2026 Corresponding author Creative Commons License Creative Commons License,
Attribution-NonCommercial-ShareAlike 4.0
International.https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es jose.oa@alamo.tecnm.mx Professor
and Researcher: Instituto Tecnológico Superior de Álamo Temapache,
Veracruz, Mexico https://orcid.org/0000-0001-9049-9885 fredy.jp@alamo.tecnm.mx
Research Professor: Instituto
Tecnológico Superior de Álamo Temapache, Veracruz, Mexico
https://orcid.org/0000-0002-9172-8173
Key words: transplanting, flowering, fruiting, stages,
physiology.
Abstract: This article models corn production in Chicontepec, Veracruz, using structural equation modeling,
based on the factors identified in the literature review, which are:
organization, yield, support policies, production costs, climatic conditions,
and marketing. The objective was to validate, using SPSS software, 324
questionnaires completed by corn producers. Additionally, confirmatory factor
analysis was performed using the scientific software LISREL for analysis and
modeling with structural equation modeling (SEM), in accordance with
Keywords: Lisrel, structural equations, confirmatory factor analysis
Resumen: En este artículo se modela la producción de maíz
en Chicontepec Veracruz con ecuaciones estructurales, a través de los factores
que incidieron en el análisis de la literatura las cuales son: Organización,
rendimiento, políticas de apoyo, costos de producción condiciones climáticos, y
comercialización. El objetivo consistió en validar a través el software SPSS,
324 test contestados por los productores de maíz, así mismo se realizó el
análisis factorial confirmatorio con el software científico LISREL para el análisis
y modelación con ecuaciones estructurales o structural ecuation modeling (SEM),
de acuerdo con
Palabras clave: Lisrel, ecuaciones estructurales, factorial
confirmatorio
Introduction
Corn cultivation in
Mexico takes place in diverse geographical contexts—altitude, latitude,
climatic conditions, temperature, humidity, and soil type—ranging from
traditional farming practices to the use of production technologies in some
states.
Based on the above
description, these are classified into 11 regions corresponding to relatively
homogeneous geographic areas in terms of the climatic, agroecological, and
socioeconomic factors that influence maize production in Mexico (Donnet, 2012).
Figure 1. Corn-growing regions in Mexico.
Source: The market
potential of improved maize seeds in Mexico (CIMMYT 2012) cited by Donnet, et al. (2012).
Figure 1.5 shows a
map of the United Mexican States, illustrating the maize-growing regions, which
are divided into eleven regions: Valles Altos comprises the states of Tlaxcala,
Hidalgo, San Luis Potosí, and Puebla;
Bajío comprises the states of Guanajuato and Querétaro; Central Valleys
comprises the states of Northern Oaxaca, Northern Guerrero, Puebla, Southern ,
Tlaxcala, Mexico City, Morelos, and the State of Mexico; Lower Pacific Tropics
comprises Guerrero, Jalisco, and Michoacán;
the Lower Atlantic Tropics include the states of Yucatán, Quintana Roo, central Campeche, and northern Veracruz; the
Humid Tropics include the states of Chiapas, Tabasco, northern Oaxaca, and
southern Puebla; Intermediate Zone includes the states of Zacatecas, Durango,
Aguas Calientes, San Luis Potosí, and Northeast Sonora; North Pacific includes
Northwest Sonora, Sinaloa, Baja California Norte, and Baja California Sur; Chihuahua includes the state of Chihuahua,
northern Coahuila, northern Tamaulipas, and northern Nuevo León; the Northern
Gulf region includes the states of Tamaulipas and Nuevo León.
Corn production in
the state of Veracruz, Mexico, is concentrated in approximately 20
municipalities. Likewise, the 10 municipalities with the highest production
over the last five years are: Papantla, San Andrés Tuxtla, Minatitlán,
Soteapan, Las Choapas, Hueyapan de Ocampo, Álamo Temapache, Texistepec, Perote,
and Tierra Blanca.
In Mexico, corn has
been part of the daily diet since ancient times. Understanding corn production
in different regions could facilitate decision-making at the local, regional,
and national levels to increase or ensure the necessary yield results, as is done
in other countries. This research aims to model the variables that influence
corn production in the municipality of Chicontepec,
Veracruz.
Although the
municipality of Chicontepec is not among the top 10,
it is a major corn producer and has significant potential. It would be of
utmost importance to develop projects at all three levels of government to
revitalize corn production and support farmers’ livelihoods.
According to
Rodríguez (2012), the state of Veracruz, with a population of 7.6 million,
consumes approximately 1.2 million tons of corn.
Veracruz accounts for
5.7% of Mexico’s total corn production. Additionally, it is noted that in some
areas there are two types of corn production: the commercial system and the
subsistence system; the former is market-oriented, as production aims for the
intensive use of resources to benefit producers, while the latter is based
essentially on the intensive use of family labor, as is the case in the
municipality of Chicontepec, Veracruz
Corn varieties in Veracruz: Tuxpeño, Celaya, Cónico, Cónico Norteño,
Chalqueño, Elotes Cónicos, Elotes Occidentales, Olotillo, Bolita, Dzit-Bacal,
Nal-Tel, Pepitilla, Mushito, Cacahuacintle, Palomero, Tepecintle, Arrocillo
Amarillo, Olotón, Coscomatepec
In a survey conducted
in the state of Veracruz in the so-called Priority Attention Zones of the
Center for Studies on Sustainable Rural Development and Food Sovereignty
(CEDRSSA) of the Chamber of Deputies; Mexico City, they found localities where
75.9% of the population lived in poverty and 25.5% in extreme poverty; within
these, there are 94.8 million corn producers who produce 610,200 tons of corn
with a yield of 2.24 tons per hectare and 53.7% of production for subsistence
consumption; human consumption of corn in these 43 ZAP municipalities is
estimated at 214,300 tons
The three main
corn-producing municipalities in the state of Veracruz, which remained among
the top three during the Spring–Summer and Fall–Winter 2019 cycle in the
category of irrigated and rain-fed grain corn, are as follows: San Andrés
Tuxtla, Papantla, and Soteapan; however, there are 17
out of 212 municipalities in the state of Veracruz that are dedicated to and
contribute significantly to corn production; among this group is the
municipality of Chicontepec de Adalberto Tejeda.
Veracruz is ranked as
one of the states with the greatest potential to excel in the agricultural,
livestock, forestry, and fishing sectors, accounting for 96.29% of the
production value in these sectors.
However, it faces
significant challenges due to its structural characteristics, including
inefficient marketing systems, difficulties in accessing government resources,
and potential misuse of those resources. These issues result in products with
no added value, low yields, narrow profit margins, and consequently, low income
levels.
Perhaps the
opportunities for developing the rural sector include, among others, leveraging
the capacity and diversity of existing natural resources, surpluses of primary
production, available human potential, water resources in a sustainable manner,
and the robust education and research system throughout the state.
In the state of
Tlaxcala, Lazos (2014) notes that the annual harvest ranges between 100,000 and
120,000 hectares. It is confirmed that yields were previously higher; the
decline was due to a lack of credit, low corn prices, and the high frequency of
natural disasters, hectares.
In the study
“Analysis of Corn Production Costs in the Bajío Region of Guanajuato” by
Guzmán, De la Garza, González, & Hernández (2014), the authors break down
and analyze corn production costs for a spring-summer production cycle using
three technologies: rainfed, irrigated with livestock, and irrigated without
livestock. the results show that, among marketable inputs, fertilization
represented the highest cost for the producer; it accounted for 75% of total
costs per hectare under the two contexts in which the three technologies were
analyzed, both excluding and including the planted area.
In 2018, Mexico had a
total consumption of 44.1 billion tons; in 2019, it increased to 44.5 billion
tons, an increase of 400,000 tons; currently, Veracruz contributes 5% of the
country’s corn production
Table 1.1 explains the
factors or variables identified in the literature review for this study.
Table 1. Variables in the corn production
process.
|
Variable |
Empirically Verified Findings |
Empirically
verifiable explanations |
|
Organization |
This is the amount of corn
grain per hectare produced by a region based on the variety or type of corn
used. According to |
This is the
classification system used by corn producers within the organizational
culture in sectors such as: ejidos, smallholdings, and communal lands, most
of which plant native corn varieties or hybrid corn varieties, as well as
various crops suited to the climatic conditions of Chicontepec,
Veracruz, related to production chains (INEGI, 2017) |
|
Yield |
This
refers to the corn production process, which may rely on technologies such as
rain-fed farming, irrigation, or intercropping with other crops to maximize
yield. In Mexico, there are six programs supporting agricultural activity;
one of them is the irrigation modernization program |
It is the
process of corn production that can be based on technologies such as:
rain-fed, irrigated, or intercropped with other crops to maximize yield. In
Mexico, there are six programs supporting agricultural activity, one of which
is the modernization of irrigation |
|
Support Policies |
These are the laws that
regulate and require different levels of government to implement programs to
incentivize corn production (ECLAC, 2012) |
These are programs to
promote agricultural activity or comprehensive production: Agricultural
Production Systems (SISPROA), PROAGRO Productivo,
Irrigation Modernization, Agri-Food Innovation, Modernization of Machinery
and Equipment, Incentive Program for Corn and Bean Producers (SAGARPA, 2016). |
|
Production Costs |
Production costs include:
labor, fertilizers, land preparation machinery, harvesting machinery, seeds,
inputs, spraying, and harvesting |
These are the expenses
incurred to ensure the growth, development, harvest, and marketing of an
agricultural crop in different environmental, , and water-technological
conditions (also called operating costs), which are necessary to maintain
corn production, & Hernández (2014), Vaz & Leyva (2015) |
|
Climatic conditions |
These are meteorological
phenomena (temperature, humidity, atmospheric pressure, precipitation, winds,
frosts, dry seasons) that determine whether or not a crop yields a harvest
(Esparza, 2012), (Grammont, 2010), (Ortíz, 2013), (Villa & Bracamonte, 2013),
(López, 2014). |
It encompasses determining factors such as excessive rain, drought,
and flooding (Grammont, 2010), (Ortíz, 2013), (Villa & Bracamonte, 2013),
(López, 2014), (Esparza, 2012). |
|
Marketing |
These are meteorological
phenomena (temperature, humidity, atmospheric pressure, precipitation, winds,
frost, dry seasons) that determine whether or not a crop yields a harvest
(Esparza, 2012), (Grammont, 2010), (Ortíz, 2013), (Villa & Bracamonte, 2013),
(López, 2014). |
Product distribution and
sales channels to generate the necessary profit. Barajas, M., Vázquez, M., Sapien, A., &
Gutiérrez, M. (2015) |
|
|
Facts based on conjecture, but not proven |
|
|
|
|
|
Source: Prepared by
the author based on the literature review.
The variables to be used
in this research are shown in the ex ante diagram in
Figure 1.1; they are grounded in the theoretical and contextual framework and
the state of the art presented previously, which will serve to describe the
evolution of corn production in the municipality of Chicontepec
using agent-based modeling.
Figure 2. Ex-ante diagram
Source: Prepared by
the author based on the theoretical framework presented in this research
Figure 2.3 shows the
ex-ante diagram based on the empirical evidence documented in the state of the
art. From this perspective, this research adopts an approach focused on the
evolution of competitiveness in corn production in Chicontepec,
Veracruz, considering the independent variables of organization, yield, support
policies, production costs, marketing, and climatic conditions.
Methodology
In the first stage, the context of the research topic was reviewed from
the perspectives of international, national, regional, and local corn
production; the local component corresponds to corn production in the
municipality of Chicontepec.
The theoretical framework and state of the art were reviewed, taking
into account the following: competitiveness, agricultural competitiveness,
competitiveness of corn production, and agent-based modeling.
The second stage included the problem statement, general and specific
objectives, the definition of the study method and type of research, as well as
the determination of the sample size of male and female producers engaged in
corn production in the municipality of Chicontepec.
In the third stage, which involved data collection, studies were
conducted in several communities within the municipality of Chicontepec,
both in Spanish and in Nahuatl—the latter being the native language of the
study population. Statistical analysis was then performed using SPSS to assess
the validity of the instrument, the degree of correlation between independent
and dependent variables, and the model will be validated against the theory
through confirmatory factor analysis, structural equation modeling, and the
agent-based model.
Given the characteristics and nature
of the problem addressed in this research, the study will have a quantitative
and qualitative approach, as it will describe the evolution of competitiveness
using a measurement instrument that applies statistical techniques and tests;
subsequently, the characteristics or qualities of the data will be analyzed
based on the results obtained.
The research is mixed-methods and
explanatory in nature, as it employs a structural and statistical approach; so
it may become descriptive and correlational, as it addresses the questions
raised regarding the variables to describe the relationship in terms of the
magnitude of each variable, as well as the use of multivariate statistical
techniques to test and estimate causal relationships based on statistical data
and qualitative assumptions about causality.
Analysis of complex systems, as
agent-based modeling will be used to simulate the production or behavior of
agents in corn production.
The variables in this research are
grounded in the contextual framework of corn production, as well as in the
theoretical framework and state of the art, which is developed to measure the
competitiveness of corn producers in the municipality of Chicontepec
using agent-based modeling.
A model is designed to describe and
simulate the conditions under which this agricultural activity takes place in
the municipality of Chicontepec.
Within the theoretical framework,
information related to the research topic was analyzed in scientific articles
found in highly prestigious journal databases regarding the subject of study,
which are: competitiveness, agricultural competitiveness, the competitiveness
of corn production, and agent-based modeling.
After obtaining the measurement
scale for the independent variables and specifying the coding or values, the
pilot test was conducted.
It was administered to 324 corn
producers, both women and men, from communities in the municipality of Chicontepec, Veracruz, in the language they speak fluently,
which is Nahuatl. Annexes III and IV show the operationalization of the
independent and dependent variables, and Annex V shows the evaluation
instrument for this research.
The following statistical tests were
performed:
After administering the 324 surveys
to the corn producers, the data were entered into the statistical software SPSS
(Statistical Package for the Social Sciences) version 25. The reliability of
the measurement instrument was then assessed using Cronbach’s alpha, yielding a
result of .857 (APPENDIX II), which means that the measurement instrument is
reliable because it has a Cronbach’s alpha greater than 0.7
Next, confirmatory factor analysis
and structural equation modeling were performed.
Results
Based on the validation in the
statistical software of the 324 tests answered by the corn producers of Chicontepec, Veracruz, confirmatory factor analysis was
performed.
The results of the confirmatory
factor analysis are shown; this is a necessary step for structural equation
modeling (SEM), According to
Thus, structural equation modeling
requires two stages: confirmatory factor analysis, which validates the
measurement model of the variables or constructs, and the verification of the
parameters among the latent variables
Structural equation modeling was
performed using LISREL, a scientific software package that allows researchers
in the social sciences, management sciences, behavioral sciences, and other
fields to evaluate their theories. It is classified as statistical software It
can run on 64-bit computers for structural equation modeling and is available
for data obtained from surveys on categorical variables, as well as for data
from random samples.
Based on the validation of the
obtained data, we proceed to design the agent-based modeling of the performance
variable and the validated items for the SEM model.
Confirmatory factor analysis.
In the factor analysis, the validity
of the results obtained in the field study was verified, classified or
categorized with their respective independent and dependent variables based on
the dimensions, indicators, and items presented in this research, as shown in
Appendices II, III, and V.
In the first stage, several
constructs were analyzed simultaneously along with their respective indicators
(items), which are O54, O55, O35, O36, O37, O38, O40, O34, and O39
corresponding to the Organization variable; R7, R8, R11, R45, R12, R9, R10,
R43, R44, R20, R25, R27, R28, R29, R30, R31, R32, R33, R41, R42, corresponding
to the Performance variable; PA24, PA51, PA26, PA50, PA52, PA53, corresponding
to the Support Policies variable; CP5, CP1, CP2, CP4, CP6, CP3, corresponding
to the Production Costs variable; CO56, CO57, CO58, CO59, for the Marketing
variable; CC13, CC14, CC19, CC17, CC18, CC15, CC16, CC23, CC21, CC22,
indicators of the Climatic Conditions variable; and PR46, PR47, PR48, PR49, for
the dependent variable, corn production.
Based on the analysis run in the
LISREL software, Figure 1.2 is shown
Figure 3. Confirmatory factor analysis
Source: Author’s own work based on the
confirmatory factor analysis with latent variables
The yield variable “R” shows a
strong positive relationship with item R8 “use of organic fertilizer” and item
R10 “use of insecticides to combat pests,” as the correlation coefficients in
these cases are 0.99 and 0.98, respectively.
As for the production variable “PR,”
it shows a strong positive relationship with item PR47 “harvest more than one
ton per hectare,” since the correlation coefficient between these two variables
is 0.56
We can also observe that the
production variable “PR” shows a weak positive relationship with items R20
“lack of rain does not affect my crop because I have irrigation” and R48 “I
harvest more than 2 tons per hectare,” since the correlation coefficients are
0.31 and 0.41, respectively.
In an effort to validate these
latent variables and the relationship between them, as well as the dependent
variable, Figure 1.2. shows the results, where the Yield (R) variable validates
items 8 and 10 with reliable values of 0.99 and 0.98; likewise, the dependent
variable Production (PR) validates the relationship with items 20, 4, and 48,
thus establishing two latent variables with their respective indicators.
Likewise, in the figure we can
identify four principal indices indicating that the Confirmatory Factor
Analysis is valid because the Chi-square value is 7.28, and the RMSEA (Root
Mean Square Error of Approximation) value is 0.052, for which the measurement
criterion is that a result of 0.8 or less is satisfactory;
The following code was used for the
confirmatory factor analysis.
Correlation Matrix From File
JOSE.COR
Sample Size 324
Latent Variables: O R PA CP CO CC PR
Relationships:
PR48 PR47 R20 = PR
R8 R10 = R
Number of Decimals = 3
Wide Print
Print Residuals
Path Diagram
End of Problem
Sample Size = 324
Table 3: Correlation matrix resulting from
confirmatory factor analysis (CFA).
|
|
R8 |
R10 |
R20 |
PR47 |
PR48 |
|
R8 |
1,000 |
|
|
|
|
|
R10 |
0.011 |
1,000 |
|
|
|
|
R20 |
0.178 |
0.270 |
1,000 |
|
|
|
PR47 |
0.167 |
.178 |
0.537 |
1,000 |
|
|
PR48 |
0.069 |
0.215 |
0.638 |
0.525 |
1.000 |
Source: Results of confirmatory
factor analysis
In the correlation matrix in Table 1.2,
it can be seen that the strongest correlations occur between item R20 “the lack
of rain does not affect my crop because I have irrigation” and PR47 “I harvest
more than one ton per hectare” and PR48 “I harvest more than two tons per
hectare”; At the same time, it is observed that the latter items mentioned have
a strong correlation with each other.
On the other hand, the weakest
correlations are between item R8 “I use organic fertilizer” and items R10 “I
use insecticides to control pests” and PR48 “I harvest more than two tons per
hectare”
Structural
Equation Modeling (SEM).
Structural equation modeling (SEM)
has the capacity to model constructs, which are referred to as latent or hidden
variables in the individual modeling of the structural model; these are
estimated in the model based on the variables measured in the indicators,
allowing for the estimation of the model’s average reliability.
In the literature review, latent
variables or constructs were identified by specifying the dimensions and
indicators that explain their relationship, as proposed in the ex ante model
From this perspective, all those
presented in this study were analyzed.
Figure 4. Final model obtained from the LISREL
software.
Source: Author’s own work. Result:
Structural Equation Modeling (SEM).
According to the structural equation
modeling in Figure 1.3, the only independent variable that explains production
(dependent variable) is yield.
The complete model, which considers
the variables Support Policies (SP), Production Costs (PC), Climatic Conditions
(CC), Marketing (MO), Organization (O), and Yield (Y) (independent variables),
does not explain PR due to two possible situations:
1. That the dependent variable (PR)
is explained solely by the independent variable (R), since it is a variable
that encompasses many dimensions, indicators, and items, making it a highly
relevant variable for SEM modeling.
2. PA, CP, CC, CO, and O need to be
redesigned.
3.- CP, CC, O, and PA are
independent variables of Performance (R), and therefore it is necessary to
redefine the model in further research.
Correlation Matrix From File
JOSE.COR
Sample Size 300
Latent Variables: O R PA CP CO CC PR
Relationships:
PR48 = 1*PR
R20 =
PR
R8 = 1*R
R10 = R
PR = R
Number of Decimals = 3
Wide Print
Print Residuals
Path Diagram
End of Problem
Sample Size =
324
The most important indicators
validating the model are described below:
1.-It can be observed that the
CHI-SQUARE value is 1.845; therefore, it can be concluded that this is an
acceptable model.
Normal Theory Weighted Least Squares
Chi-Square = 1.840 (P = 0.175)
2.-Root Mean Square Error of
Approximation (RMSEA) = 0.0530
We can see that RMSEA = 0.0530,
which indicates that it is satisfactory; this means there is no need to adjust
the model
Root Mean Square Error of
Approximation (RMSEA) = 0.0530
3.-In this case, we observe that the
Normed Fit Index (NFI) = 0.989, which means that the theoretical model improves
the fit relative to the null model to an acceptable degree.
Normed Fit Index (NFI) = 0.989
4.-As we can see, the CFI = 0.995,
which is a value very close to 1; this means that the comparison between the
theoretical model and the null model has a good fit.
Goodness of Fit Index (GFI) = 0.997
Table 5. Correlation matrix; structural
equation modeling (SEM)
|
|
R20 |
PR48 |
R8 |
R10 |
|
R20 |
1,000 |
|
|
|
|
PR48 |
0.638 |
1,000 |
|
|
|
R8 |
0.174 |
0.118 |
1,000 |
|
|
R10 |
0.273 |
0.184 |
0.011 |
1,000 |
|
|
|
|
|
|
Source: Author’s
own work, based on SEM analysis
In correlation matrix 1.3, it can be
observed that, among all possible item combinations, the highest correlation is
between R20 “lack of rain does not affect my crop because I have irrigation”
and PE48 “I harvest more than two tons per hectare,” which means that having
irrigation increases production by more than two tons per hectare; Conversely,
the lowest correlation is between item R8 “use of organic fertilizer ” and R10
“I use insecticides to combat pests,” which means that using little organic
fertilizer also leads to using little insecticide.
Conclusions
Based on the general objective of
this research to “design a competitiveness model for corn production in Chicontepec, regarding types of organization, support
policies, climatic conditions, production costs, yield, and marketing, using
agent-based modeling,” the agent-based model was designed based on the two
variables validated in the confirmatory factor analysis and structural equation
modeling. Additionally, six specific objectives were formulated with six
research questions that allowed for the formulation of hypotheses.
Data collection was conducted using
a measurement instrument administered to 324 small-scale corn farmers in the
municipality of Chicontepec. The data obtained were
subjected to statistical tests, and the variables were validated using the
scientific software LISREL to design the agent-based model.
Regarding the fulfillment of the
general and specific objectives, it is concluded that:
The general objective was achieved
by designing the agent-based model based on the SEM validation of the yield and
production variables with their respective indicators: “lack of rain affects my
crop” with a correlation of 1.000, “I harvest more than one ton per hectare”
with a correlation of 0.638, and “I harvest more than 2 tons per hectare” with
a correlation of 0.525. On the other hand, there is a weak correlation with
“use of fertilizers”; however, it was taken into account for the simulation
because it is a relevant input for yield according to Table 4.2; From the two
variables mentioned, the following parameters were used for the simulation:
available land area, rainfall, fertilizers, planted area, and planting cycle;
“support programs” were also taken into account as a complementary factor.
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