© The Authors, 2023, Published by the Universidad del Zulia*Corresponding author: rameleanro@ucvvirtual.edu.pe
Keywords:
Planning
Control
Management
Production
Technology
Finance
Banana sector
Production management in banana production units, El Oro province - Ecuador
Gestión de la producción en unidades de producción bananera, provincia de El Oro–Ecuador
Gestão da produção em unidades de produção de banana, província de El Oro-Equador
María Eugenia Palomeque Solano
1
Armando José Urdaneta Montiel
2
Rosana A. Meleán Romero
3*
Rev. Fac. Agron. (LUZ). 2023, 40(2): e234019
ISSN 2477-9407
DOI: https://doi.org/10.47280/RevFacAgron(LUZ).v40.n2.09
Socioeconomics
Associate editor: Dra. Maritzabel Materán Jaimes
University of Zulia, Faculty of Agronomy
Bolivarian Republic of Venezuela
1
Auditoria y Contabilidad, Ingeniera Comercial, Ingeniera
en Contabilidad y Auditoría, Universidad Metropolitana del
Ecuador.
2
Postdoctorado en Integración y Desarrollo de América
Latina, Doctor en Ciencias Económicas, Doctor en Ciencias
Gerenciales, Magíster en Telemática, Magíster en Gerencia
Empresarial, Ingeniero en Computación, Profesor Titular
Agregado I, de la Universidad Metropolitana del Ecuador,
Docente Titular invitado en Universidad del Zulia, Venezuela.
3
Posdoctorado en Agronegocios, Doctora en Ciencias
Sociales, Magíster en Gerencia de Empresas, Licenciada en
Administración. Docente de la Universidad Cesar Vallejo,
Received: 05-12-2022
Accepted: 13-04-2023
Published:
01-06-2023
Abstract
This research analyzed production management of 74 agricultural
production units, belonging to the Coast Banana Farmers Association
(ASOAGRIBAL) from El Oro province - Ecuador. Latent variables were
research was positivist, quantitative, analytical, non-experimental, with a
was the questionnaire, applied through the survey technique. Descriptive
results indicate that the variables situational analysis (3.56), planning and
control processes (3.40) and production technology platform (3.51) obtained
of excellence. The estimated correlations showed that none of the dimensions
under study reached levels of excellence. Results of structural equations
model analysis showed a high degree of linear association and positive
dependence between the four latent variables; that is, they vary jointly
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2023, 40(2): e234019. Abril-Junio. ISSN 2477-9407.
2-8 |
Resumen
La investigación analizó la gestión de la producción de 74
unidades de producción agrícola, pertenecientes a la Asociación de
Agricultores Bananeros del Litoral (ASOAGRIBAL) de la provincia
de El Oro – Ecuador. Fueron precisados variables latentes como
indicador clave en este tipo de organizaciones. La investigación
fue positivista, cuantitativa, analítica, no experimental, con diseño
transeccional, de campo. El instrumento de recolección de datos
diseñado fue el cuestionario, aplicado mediante la técnica de la
encuesta. Los resultados descriptivos indican que las variables
(3,40), y plataforma tecnológica de la producción (3,51), obtuvieron
que los valores obtenidos para la rentabilidad (3,36) la catalogan
correlaciones estimadas, ninguna de las dimensiones sujetas a estudio
alcanza niveles de excelencia. Al aplicar el modelo de ecuaciones
estructurales, los resultados evidencian un alto grado de asociación
lineal y dependencia positiva entre las cuatro variables latentes; es
decir, las mismas varían de manera conjunta respecto a su media. Se
debe trabajar en la integración de las labores de producción como
mecanismo para concretar mayores niveles de rentabilidad, que
agrícolas.
Palabras clave:
Resumo
A pesquisa analisou a gestão da produção de 74 unidades
de produção agrícola, pertencentes à Associação de Produtores
de Banana do Litoral (ASOAGRIBAL) da província de El Oro
situacional, planejamento e controle, tecnologia de apoio nos
como um indicador chave neste tipo de organização. A pesquisa é
positivista, quantitativa, analítica, não experimental, transeccional, de
campo. O instrumento de coleta de dados elaborado foi o questionário,
aplicado por meio de inquéritos. Os resultados descritivos indicam
que ao nível da análise situacional (3,56), processos de planeamento
e controlo (3,40), plataforma tecnológica de produção (3,51) foram
obtidas médias estatísticas que permitem interpretar a avaliação
como boa, enquanto os valores obtidos pois a Rentabilidade (3,36) é
aplicar o modelo de equações estruturais, os resultados mostram um
variáveis latentes; isto é, eles variam conjuntamente com relação
à sua média. Em conclusão, é preciso trabalhar a integração das
tarefas produtivas como mecanismo para alcançar maiores níveis de
empresas agropecuárias.
Palavras-chave: planejamento, controle, gestão, produção,
Introduction
Worldwide, the agri-food sector fosters the development of highly
multiple economic, political, legal, social and environmental factors
aimed at carrying out diagnoses, analyzes and comparisons, on
levels of growth and evolution of this sector, as well as the study
of factors that enable or restrict its growth and development found:
a) highly unbalanced productive structures socially and territorially,
b) problems with agricultural borders, slowdown in growth, increase
in hectares of land dedicated to pasture, c) prices and losses, and
d) agricultural exports and decrease in prices of agrifood products,
among others (Pino et al., 2018; García, 2006).
Particularly Ecuador, a predominantly primary and exporting
agricultural sector, permanently consider a dialectical relationship with
actions for an adequate allocation of resources, goals and consensual
organizational objectives (Chandler, 1962; Kenneth, 1980).
derived from active crops that direct their production, showing the
Ecuadorian agricultural wealth, where banana crop represents an
important economic activity, with an increase in extensions that
concentrate more than 45 % of the banana farms (Zhiminaicela et al.,
2020) and where El Oro province is the largest producer of bananas
(24.8 %) (National Institute of Statistics and Censuses, 2019); It is
the Caribbean (United Nations Food and Agriculture Organization,
2020).
Bananas, with an important nutritional contributions and good
levels of international demand (Zhiminaicela et al., 2020) are
positioned and supported by international organizations (Trade
Center, ITC, 2018; Joint Agency of the World Trade Organization and
the United Nations. Trade for Sustainable Development - Tradefor
Sustainable Development - T4SD) that establish guidelines to
achieve greater transparency and sustainability of international and
sustainable trade; All of this is carried out by Ecuador, a country with
(Zhiminaicela et al., 2020).
Given this scenario, the objective of this research, is to analyze
the banana production management of 74 agricultural units from the
Coast Banana Farmers Association (ASOAGRIBAL) of the El Oro
province - Ecuador, specifying elements such as situational analysis,
planning and control, support technology in processes and adding
This research was supported by classical theories of administration
(1989), which allows recognizing the internal and external context
of the organization by giving its importance from its analysis and
=
²
2
(
− 1
)
+
²
1
=
92(1,96)²(0,5)
(0,50
)
(0,05)
2
(
92 − 1
)
+ (1,
96)²
(0,5)(0,5)
1
=
88,36
1,19
= 74,25
≈ 74
1
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
3-8 |
decisions made by companies are required. From the technological
point of view, is central name Chandler, due to the importance of
technologies in production processes.
Materiales and methods
The research is analytical-descriptive with quantitative data,
and a non-experimental design (Hernández et al., 2014), also with
from the agricultural sector (Hurtado, 2010); it was developed in El
Oro province, Ecuador, which is assumed as the study area, formed by
around the world. The extension of the study area is more than 5,100
cultivated hectares. The associated producers’ plantations are located
at El Oro province, mainly in Guayas and Los Ríos, whose tropical
climate is ideal for banana production.
The population was made up of agricultural production units
belonging to the Coast Banana Farmers Association (ASOAGRIBAL)
of El Oro province (agricultural registration units, 2021), which are
attended by 8 agronomists who attend the production administrative
representative portion of the population, through the calculation
of the sample size for proportions, using the maximum variance
The following formula
was applied to calculate the sample:
Where:
N: population size = 92
e: permissible maximum error = 0.05
P: probability of success = 0.50
Q: probability of failure = 0.50
Substituting the values in the formula results in a sample size of
n = 74
As a data collection instrument, a questionnaire consisting of 95
items validated by experts was designed, the reliability turned out to
before instrument was applied to the sample attended by the engineers,
through surveys and semi-structured interviews in order to provide
information about the production units grouped in ASOAGRIBAL.
The core variable studied was production management, a
functional area responsible for achieving the transformation of raw
materials and goods into intermediate products capable of feeding
agribusiness. In the production area, the very essence of the processes
is developed, and is supported by essential production factors: Land,
et al
Palomeque et al. Rev. Fac. Agron. (LUZ). 2023 40(2): e234019
units, El Oro province - Ecuador.
management
It carries out complete
surveys of the organizational
environment, considering,
on the one hand, external
elements and on the other,
internal aspects.
F2: Production planning
and control processes
Two essential functions in
management, represents
closely related processes.
The plans are the frame of
reference within which the
the control phases.
platform
implies the transmission of
promoting the broadening
of the perspectives of a
communication (González-
Díaz et al., 2020; Zurita and
Monge, 2018). Technological
platforms represent a
transcendental tool to
increase the competitiveness
of industries (Ojeda-Beltrán,
2022).
They show return on
investment by carrying out
various activities in a certain
period of time, it is one of
indicators to measure the
success of a business (De La
Hoz Suárez et al., 2008).
Source: own elaboration based on the referenced authors
variables (initial indicators) (table 2), which were measured with
almost always, sometimes, never, almost never and never). The
statistical analysis began with a descriptive analysis of means,
standard deviations, maximum and minimum values to characterize
the latent variables, whose results were evaluated qualitatively by
a scale constructed by estimating a class interval (table 3). Then, a
non-parametric Rho Spearman correlation analysis was applied to
determine the degree of relationship between the variables studied.
Next, to determine the relationships between observable and latent
variables, structural equation models (SEM) were used, which are
originated in biology; their use is basically aimed at the construction
of abstract and unobservable situations (Westland, 2015).
The SPSS version 25 (2020) was used as a tool to perform the
statistical analysis and the AMOS IBM SPSS software for the modeling
of structural equations, in order to support the theory proposed by
Byrne (2001) and Kline (2015). According to the results issued by
the software, the situational analysis, the planning processes and
administrative control of production and the technological platform
=
²
2
(
− 1
)
+
²
1
=
92(1,96)²(0,5)
(0,50
)
(0,05)
2
(
92 − 1
)
+ (1,
96)²
(0,5)(0,5)
1
=
88,36
1,19
= 74,25
≈ 74
1
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2023, 40(22): e234019. Abril-Junio. ISSN 2477-9407.
4-8 |
model was carried out, using the common indices of absolute and
Results and discussion
Table 4 shows characterization results of latent variables, it
is observed that situational analysis, management of production
planning and control processes, and the implementation of production
regular. However, the objective is to reach levels of excellence, which
on the part of banana producers.
This made it possible to reduce the number of observable variables
observable variables
and the structural model
IC1 Total Cost Leadership Strategies
IC4. potential competitors
IC5. Bargaining power with customers
IC6. Substitute products
IC7. Bargaining power with suppliers
IC8. competitiveness
IC1 Total Cost Leadership Strategies
IC5 Bargaining power with customers
F2: Production planning and control
processes
IC9 Simulation of operations analysis
IC10 Production process
IC11 Quality control system
IC12 Plants Location and distribution
IC13 Performance measurement
IC14 Strategies to increase production capacity
IC15 Waiting lines
IC16 Programming of operations
IC9 Simulation analysis of operations
IC11 Quality control system
IC12 Location and distribution of plants
IC13 Performance Measurement
IC14 Strategies to increase production capacity
IC17 Production management
IC18 Planting and harvest planning
IC19 Irrigation system
IC20 Soil Study
IC21 Soil preparation
IC22 Agricultural machinery
IC23 Seed supply
IC24 Seed fertilization
IC25 Pest and disease control
IC26 Fertilization inputs
IC27 Elimination of undesirable plants.
IC28 Transport of crops.
IC17 Production management
IC19 Irrigation system
IC20 Soil study
IC21 Soil preparation
IC22 Agricultural machinery
IC23 Seed supply
IC24 Seed fertilization
IC25 Pest and disease control
IC26 Fertilization inputs
IC27 Elimination of undesirable plants.
IC28 Transport of crops
IC30 Net present value estimates
IC31 Internal rate of return estimates
IC32 Capital recovery factor
IC34 Equivalent annual cost
IC36 Fixed costs
IC37 Variable costs
IC38 Total income
IC40 Marginal cost
IC41 Marginal income
IC36 Fixed costs
IC37 Variable costs
Source: own elaboration.
variable, El Oro province - Ecuador.
Range
[1.0 ; 1.8)
[1.8 ; 2.6)
[2.6 ; 3.4) Regular
[3.4 ; 4.2) Good
[4.2 ; 5.0) Excelent
Source: own elaboration.
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5-8 |
N Average Extreme values
74 3.5676 0,756070 2,14-4,79 Good
Production planning and control processes 74 3.4054 0,63793 2,36-4,91 Good
74 3.5135 0,69310 2,22-4,79 Good
74 3.3649 0,63190 1,91-4,84 Regular
74
Source: Own elaboration based on data analyzed by SPSS Ver 25.0 (2022)
covariances and their dependency relationships in the
- Ecuador.
on the covariances of the latent variables F1 (Situational
analysis and strategic objectives); F2 (Planning processes
and production control); F3 (Production Technological
Platform); ICn: Indicators (observable variables); in
(estimation errors).
Source: Own elaboration based on data
analyzed by AMOS IBM SPSS software
Table 6. Meassurement model adjustment validation with
respect to the latent variables and their dependency
El Oro province - Ecuador.
Measure Threshold
CMIN 262.203 -- --
DF 221.000 -- --
CMIN/DF 1.186 Between 1 and 3 Excellent
CFI 0.940 >0.95 Aceptable
RMSEA 0.051 <0.06 Excellent
PClose 0.474 >0.05 Excellent
Source: own elaboration
Regarding the correlations between the variables (table 5), it can
be observed that there is a high degree of linear association between
values oscillate between 76.9 % and 87.4 % explaining through
themselves between 59.14 % and 76.9 % of their variance, indicating
cohesion between them.
between the latent variables evaluated in banana
and
and
control
technology
1.000 0.780
**
0.769
**
0.785
**
control
0.780
**
1.000 0.811
**
0.874
**
technology
0.769
**
0.811
**
1.000 0.788
**
0.785
**
0.874
**
0.788
**
1.000
Source: Own elaboration based on data analyzed by SPSS Ver 25.0 (2022)
None of the dimensions under study reached levels of excellence.
However, four latent variables were found out of the twenty-three
observable variables, thus, it was developing a structural equation
model with the purpose of explaining which of the latent variables
the long-term investment projects, which obtained a lower score in
the descriptive analysis performed.
Figure 1 shows the measurement model, which is recursive, since
it is made up of the four latent variables (explanatory variables) with
each of its observable variables (explained variables) indicated in
These represent the relationships between the indicator variables
of the model with their latent constructs and also by the covariance
relationships between the latent variables (Vogt, 2015). Each latent
with its indicators form a part of the measurement model.
Table 6 shows the validation of the measurement model built by
indicator (CFI) is considered acceptable (Hu & Bentler, 1999), based
on this, it is concluded that the Fit measurement model is excellent.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2023, 40(22): e234019. Abril-Junio. ISSN 2477-9407.
6-8 |
to the observed data, insofar as the covariance matrix implicit in the
model is equivalent to the empirical covariance matrix. Once a model
other discrepancy functions to minimize (Schermelleh-Engel et al.,
2003). When the estimation procedure has converged to a reasonable
degree of adjustment between the structural equation model and the
sample data.
absolute adjustment measures RMSEA (Root Mean Squared Error
excellent. According to Mahatme (2018), these indicate to what
extent the structural equation model corresponds to the empirical
data. Regarding the descriptive measures based on comparisons of
which are considered at acceptable levels according to Hu & Bentler
(1999), but not the NFI (Index of normed adjustment) that is in critical
levels. According to Schermelleh-Engel et al
a baseline model
serves as a comparison value. The issue is whether the target model
is an improvement over the reference model. Table 7 shows three
Regarding the descriptive measures of the parsimony model,
a progressive decrease in the values of the parsimony goodness of
information criterion (AIC) and Chi-Square Normalized was
data better; that is why parsimony is considered important to evaluate
et al. et al., (2003). The CMIN/DF
(Chi-Square Normalized) is the most important indicator; it must be
After validating both the measurement model and the structural
model, the correlations and covariances between the latent variables
were analyzed (Table 8), showing a high degree of linear association
and positive dependence between the four latent variables; that is, they
case under study, the high degree of integration of the aforementioned
latent variables, which in turn represent the scope of action of the
production management variable for the ASOAGRIBAL case.
Table 9 shows how F2: Production planning and administrative
control processes, explains the perception that the production
engineers of the 74 agricultural production units have about the
in long-term productive projects; while F1: Situational analysis and
strategic objectives and F3: Technological platform of production are
model are shown.
F4 (Economic-
variables F1 (Situational analysis and strategic objectives);
F2 (Planning processes and production control); F3
(Production Technological Platform); ICn: Indicators
(observable variables); in (estimation errors). Source: own
elaboration based on data analyzed by AMOS IBM SPSS software
measurements
Incremental adjustment measures
PCLOSE RMSEA CFI TLI NFI PGFI PNFI AIC
0.179
0.179 0.062 0.907 0.895 0.693 0.803 0.614 437.463 1.283
0.358 0.358 0.055 0.928 0.919 0.710 0.625 0.818 424.28 1.220
0.415 0.415 0.053 0.934 0.925 0.715 0.627 0.820 421.063 1.203
0.474 0.474 0.051 0.940 0.931 0.720 0.52 0.05 418.203 1.186
Source: Own elaboration
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|
Table 8. Correlations and covariances between latent variables
province - Ecuador.
S.E. C.R. P Label
F2 <--> F1 ,321 ,098 3,276 ,001 par_19
F2 <--> F3 ,169 ,071 2,378 ,017 par_20
F1 <--> F3 ,216 ,084 2,559 ,010 par_21
F4 <--> F1 ,402 ,107 3,755 *** par_23
F4 <--> F3 ,215 ,084 2,551 ,011 par_24
F4 <--> F2 ,301 ,094 3,211 ,001 par_25
e4 <--> e6 ,332 ,097 3,431 *** par_26
e18 <--> e22 ,169 0,82 2,073 ,038 par_27
e20 <--> e21 ,172 ,082 2,104 ,035 par_28
F2 <--> F1 1,171
F2 <--> F3 ,998
F1 <--> F3 1,068
F4 <--> F1 1,100
F4 <--> F3 ,955
F4 <--> F2 ,985
e4 <--> e6 ,449
e18 <--> e22 ,267
e20 <--> e21 ,271
F1: Situational analysis and strategic objectives, F2: Planning processes and
production control; F3: Production Technological platform, and F4: Economic-
As a result of the analysis, the following structural model was
obtained:
4
= 0,278
1
+ 1,590
2
− 0,714
3
1
a b
S.E. C.R. P Label
F4 <---F1 0,0278 0,899 0,31 0,757 par_23 F4 <-- F1 0,249
F4 <---F2 1,59 0,377 4,214 *** par_24 F4 <-- F2 1,086
F4 <---F3 -0,714 4,438 -0.161 0,872 par_25 F4 <-- F3 -0,394
Source: own elaboration based on data analyzed by AMOS and SPSS Ver 25.0 (2022)
Conclusions
variables: situational analysis, production planning and control, and
Palomeque et al. Rev. Fac. Agron. (LUZ). 2023 40(2): e234019
The correlational analysis between the variables, showed that
there is a high degree of linear association between the dimensions
between the latent and observable variables.
The applied model also allowed us to show a high degree of
linear association and positive dependence between the four latent
variables; that is, they vary jointly with respect to their mean.
integration of the aforementioned latent variables, which in turn
represent the production management variable scope of action, for
the ASOAGRIBAL case.
the processes of planning and administrative control of production,
which should consider the situational analysis and strategic objectives
of agricultural enterprises, as well as its technological platform for
production in order to have a greater impact of strategic planning in
the administrative management of agricultural production, in El Oro
Province.
Finally, the previous analysis is based on the fact that results of
this study show that only factor 2 (F2) planning and administrative
explained in the study, and that the other factors F1 situational analysis
and strategic objectives and F3 technological platform of production
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