Artículos Científicos UNJ

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Evaluation of the Optimal Dose of Gibberellic Acid (Full Gib) in the Induction of Sprouting of Yungay Potato Variety Tuber (Solanum tuberosum L.)
(Journal of Biological Sciences, 2024-07-24) Garrido Campaña,Zadith Nancy
This research was carried out in the district of Luya, province of Chachapoyas, Amazonas, with the objective of evaluating the optimal dose of gibberellic acid (Full Gib) in the induction of sprouting of potato tubers (Solanum tuberosum L.) Yungay variety. A Completely Randomized Design (CRD) with two factors three replications and seven treatments was used. Minitat 17 statistical software, ANOVA, and the Tukey mean comparison test at 5% were used for data analysis. The treatments were: Nothing was applied (T1), 7.5 mL of Full Gib in 20 L of water, for 15 min (T2), 7.5 mL of Full Gib in 20 L of water, for 20 min (T3), 10 mL of full Gib in 20 L of water, for 15 min (T4), 10 mL of full Gib in 20 L of water, for 20 min (T5), 12. 5 mL of full Gib in 20 L of water, for 15 min (T6), 12.5 mL of full Gib in 20 L of water, for 20 min (T7). The variables evaluated were: Sprouting percentage, number of sprouts, sprout length, and sprout diameter per tuber. According to the results obtained, it can be observed that treatments 5 and 3 obtained the highest germination percentages with 86.17 and 85.67%, respectively, surpassing the other treatments; there were no significant differences between treatments in the number of sprouts per tuber. Treatments 5, 3, and 4 were better, with results of 8.93, 8.83, and 8.68, respectively; on the other hand, the variable shoot length obtained the best results in treatments 5 and 3 with 7.69 and 7.50 cm, respectively. Similarly, the shoot diameter variable had positive results in treatments 3 and 5 with 6.0 and 5.9 mm, respectively
Políticas públicas para afrontar sequías en el Perú
(Revista de Climatología, 2022-06-29) Garrido Campaña,Zadith Nancy
El presente estudio se refirió a las Políticas Públicas para afrontar sequías en el Perú, aludiendo a la Estrategia Nacional de Lucha Contra la Desertificación y la Sequía en el Perú (ENLCDS) por ser un importante instrumento de gestión que promueve la participación y movilización de actores públicos y privados, para ejecutar acciones orientadas a promover el manejo sostenible de la tierra (MST), siendo este artículo de valía porque trae acotación el llamado a las instituciones peruanas responsables de esta problemática a que puedan priorizarlo por ser el Perú uno de los países con mayor extensión de tierras secas en América del Sur, en donde la sequía puede causar mayores consecuencias; describiéndose las sequías meteorológicas en la provincia de Candarave, Departamento de Tacna; Perú.
Comparison of Collinearity Indices for Linear Models in Agricultural Trials
(Journal of Biological Sciences, 2023-12-28) Garrido Campaña,Zadith Nancy
The deleterious consequences of collinearity in linear regression on the precision of estimators of regression coefficients and the interpretability of the fitted model are widely recognized. In this study, we compare several methodologies for assessing collinearity in linear models and explore the effect of outliers on collinearity. The robustness of collinearity measures (individual and overall) is validated through two detailed Monte Carlo simulation study which also considers the effect of outliers on collinearity indices. The methods are illustrated with two real-world agricultural and fish morphology l data sets to show potential applications. The results do not provide any evidence for an effect from outliers on collinearity identification using the collinearity indices (individual and overall). The FG and Fj collinearity indices more robust as both sample size and collinearity degree increase. The VIF (individual measure) had a better performance on the fitted model with a greater number of parameters.
Impact of forest fire severity on soil physical and chemical properties in pine and scrub forests in high Andean zones of Peru
(Agriculture, 2024-12-27) Ocaña Zúñiga,Candy Lisbeth
Agroforestry systems can influence the occurrence and abundance of pests and diseases because integrating crops with trees or other vegetation can create diverse microclimates that may either enhance or inhibit their development. This study analyzes the severity of coffee rust in two agroforestry systems in the provinces of Jaén and San Ignacio in the department of Cajamarca (Peru). This research used a quantitative descriptive approach, and 319 photographs were collected with a professional camera during field trips. The photographs were segmented, classified and analyzed using the deep learning MobileNet and VGG16 transfer learning models with two methods for measuring rust severity from SENASA Peru and SENASICA Mexico. The results reported that grade 1 is the most prevalent rust severity according to the SENASA methodology (1 to 5% of the leaf affected) and SENASICA Mexico (0 to 2% of the leaf affected). Moreover, the proposed MobileNet model presented the best classification accuracy rate of 94% over 50 epochs. This research demonstrates the capacity of machine learning algorithms in disease diagnosis, which could be an alternative to help experts quantify the severity of coffee rust in coffee trees and broadens the field of research for future low-cost computational tools for disease recognition and classification
Current and Future Spatial Distribution of the Aedes aegypti in Peru Based on Topoclimatic Analysis and Climate Change Scenarios
(Insects, 2025-05-02) Ocaña Zúñiga,Candy Lisbeth
Dengue, a febrile disease that has caused epidemics and deaths in South America, especially Peru, is vectored by the Aedes aegypti mosquito. Despite the seriousness of dengue fever, and the expanding range of Ae. aegypti, future distributions of the vector and disease in the context of climate change have not yet been clearly determined. Expanding on previous findings, our study employed bioclimatic and topographic variables to model both the present and future distribution of the Ae. aegypti mosquito using the Maximum Entropy algorithm (MaxEnt). The results indicate that 10.23% (132,053.96 km2) and 23.65% (305,253.82 km2) of Peru’s surface area possess regions with high and moderate distribution probabilities, respectively, predominantly located in the departments of San Martín, Piura, Loreto, Lambayeque, Cajamarca, Amazonas, and Cusco. Moreover, based on projected future climate scenarios, it is anticipated that areas with a high probability of Ae. aegypti distribution will undergo expansion; specifically, the extent of these areas is estimated to increase by 4.47% and 2.99% by the years 2070 and 2100, respectively, under SSP2-4.5 in the HadGEM-GC31-LL model. Given the increasing dengue epidemic in Peru in recent years, our study seeks to identify tools for effectively addressing this pressing public health concern. Consequently, this research serves as a foundational framework for assessing areas with the highest likelihood of Ae. aegypti distribution in response to projected climate change in the second half of the 21st century.
Geospatial Landslide Risk Mapping Using AHP and GIS: A Case Study of the Utcubamba River Basin, Peru
(Applied Sciences, 2025-08-28) Ocaña Zúñiga,Candy Lisbeth
This study examines the use of a spatial multi-criteria approach based on GIS and AHP techniques to model landslide risk in the Utcubamba river basin, Peru. The methodology consisted of selecting twelve triggering variables: slope angle, geology, precipitation, distance to faults, drainage density, TWI, relative relief, profile curve, land use, elevation, distance to roads, and distance to population centers. These variables were then analyzed using the AHP method and then integrated into a GIS environment, where the weighted linear combination (WLC) method was used to map landslide risk. The risk was categorized into five classes, ranging from very low (1) to very high (5). The main results indicate that 32.81% of the area analyzed in the Utcubamba river basin presents a high and very high risk of landslides. The high-risk areas are mainly located in the southern part of the basin and coincide with areas with steep slopes, high rainfall, and proximity to population centers or communication routes. The model generated was highly accurate (AUC of 0.82), confirming that the integration of the AHP method with GIS allows for the precise identification of critical areas, which is useful for territorial planning, the prioritization of interventions, and emergency management, making it a reliable and replicable methodology in other parts of Peru.
Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical Ecosystems
(Agriculture, 2024-12-27) Ocaña Zúñiga,Candy Lisbeth
Agroforestry systems can influence the occurrence and abundance of pests and diseases because integrating crops with trees or other vegetation can create diverse microclimates that may either enhance or inhibit their development. This study analyzes the severity of coffee rust in two agroforestry systems in the provinces of Jaén and San Ignacio in the department of Cajamarca (Peru). This research used a quantitative descriptive approach, and 319 photographs were collected with a professional camera during field trips. The photographs were segmented, classified and analyzed using the deep learning MobileNet and VGG16 transfer learning models with two methods for measuring rust severity from SENASA Peru and SENASICA Mexico. The results reported that grade 1 is the most prevalent rust severity according to the SENASA methodology (1 to 5% of the leaf affected) and SENASICA Mexico (0 to 2% of the leaf affected). Moreover, the proposed MobileNet model presented the best classification accuracy rate of 94% over 50 epochs. This research demonstrates the capacity of machine learning algorithms in disease diagnosis, which could be an alternative to help experts quantify the severity of coffee rust in coffee trees and broadens the field of research for future low-cost computational tools for disease recognition and classification
Non-Destructive Estimation Of Leaf Area In Cinchona Micrantha And Cinchona Pubescens Using Linear Regression Models
(International Information and Engineering Technology Association, 2025-07-25) Huaccha Castillo,Annick Estefany
Accurate quantification of leaf area is essential for ecophysiological, agronomic, and conservation studies, especially in threatened species such as Cinchona micrantha and Cinchona pubescens. This study evaluated simple, quadratic, and composite linear regression models to estimate leaf area non-destructively using morphometric measurements (length and width). A sample of n=800 leaves from 32 individuals was systematically collected and analyzed using a standardized photographic protocol with digital processing in ImageJ. The most robust models were those incorporating composite variables such as the product of length and width (L × W) and the sum squared of both dimensions ((L + W)²), reaching coefficients of determination higher than 0.97. These models consistently outperformed models based on single variables, providing higher accuracy and lower prediction error. High correlations were observed between leaf dimensions and area, and C. pubescens showed greater morphological variability. These findings establish that simple linear models based on L × W are efficient, replicable, and low-cost tools for non-destructive estimation of leaf area, which improves ecological monitoring and supports sustainable forest management, essential for the conservation of these Cinchona species, important from an ecological and medicinal point of view, in tropical ecosystems.
Sustainable Rice–Fish Farming Systems: A Systematic Review and Meta-Analysis
(Aquaculture Research, 2025-06-12) Huaccha Castillo,Annick Estefany
The rice–fish farming system is an efficient ecological model with economic, ecological, and social benefits, reduces environmental impacts and optimizes the use of resources. The objective of the research was to explore and analyze scientific publications through a systematic review and meta-analysis related to rice–fish intercropping. A review of publications hosted in the Scopus and PubMed database from January 2000 to April 2025 was conducted. Research articles were selected, excluding review articles, com-mentaries, book chapters, and letters, and only documents published in English were analyzed. The analysis shows that the countries with the highest number of publications were China and Bangladesh, with a proportion of 48% and 24% respectively, followed by Thailand with 10% and Pakistan, Indonesia, Malaysia, and India with 5% each. The fish species used in rice–fish systems were reported to be Cyprinus carpio (37%), Oreochromis niloticus (29%), Barbonymus gonionotus, Micropterus salmoides and Pelteobagrus fulvidraco (8%), Amblypharyngodon mola (5%), and Labeo rohita and Monopterus albus (3%). On average, fish settle in the rice–fish system 27 days after rice planting, with a density of 13,390 fish/ha. Between rice planting and harvesting 132 days pass, obtaining an average yield of 4397 kg of rice/ha and 1383 kg of fish/ha. It is recommended to prioritize integrated research on unstudied fish species, optimal densities, fertilization, culture models, and emerging technologies in rice–fish systems, considering regional variations to improve sustainability, productivity, and food security at a global level.
Comparison of non-destructive methods for estimating the leaf area of Cinchona officinalis L. using digital image processing
(Revista Cubana de Ciencias Forestales, 2025-09-11) Huaccha Castillo,Annick Estefany
Cinchona officinalis it is an important plant species and was the only treatment for malaria for over three centuries. The aim of this study was to compare the accuracy of four non-destructive digital image processing methods (LeafArea and three ImageJ algorithms)for estimating the leaf area of young C. officinalis plantations under two establishment conditions: forest stands and enrichment strips. Leaves were photographed at a distance of 8 cm using a 24 MP smartphone and processed with the evaluated methods. Statistical analysis included box and whisker plots, Pearson correlation, and Friedman test. The results showed that ImageJ methods M3 and M4 had the highest accuracy (r = 0.99), with no significant differences between them, and overestimations detected in M1 and M2. It is concluded that M3 and M4 are fast, low-cost, and highly accurate options for foliar monitoring of C. officinalis in the field.
Estimation of diurnal greenhouse gas (GHG) emissions from unfertilized coffee soils using recurrent neural networks (RNN). A case study for Chirinos, San Ignacio Province, Cajamarca, Peru
(Clean Energy Science And Technology, 2025-12-10) Huaccha Castillo,Annick Estefany
Global warming, driven by rising greenhouse gas (GHG) concentrations, has agriculture as a major source of emissions. In coffee plantations, low sampling frequency and the absence of diurnal baselines introduce bias in emission estimates. The objective of this research was to estimate diurnal CO₂, N₂O, and CH₄ emissions from unfertilized coffee soils using recurrent neural networks (RNN). Gas fluxes were measured with a closed dynamic chamber (CDC) at 20-minute intervals between 8:00 and 18:00 over 22 days. For the estimation of GHG emissions, climatic data measured through a meteorological station were used, in addition to environmental parameters incorporated in the CDC. Five RNN models composed of two hidden layers of 20, 25, and 50 neurons were developed, trained, and validated for each GHG. Results indicate that N₂O contributed most to total emissions (734,689 ppm CO₂-eq), with CO₂ (237,579 ppm CO₂-eq) and CH₄ (215,426 ppm CO₂-eq) contributing less. Model performance was strong, with R² values of 0.98 (CO₂), 0.96 (N₂O), and 0.94 (CH₄). It is concluded that the RNNs proved to be reliable models for predicting GHG emissions in unfertilized coffee soils, with this study presenting a replicable framework with the potential to improve temporal estimation and reduce uncertainty in GHG inventories.
Determination of hydration kinetic of pinto beans: A hyperspectral images application
(Measurement: Food, 2024-03-30) Arteaga Miñano,Hubert Luzdemio
Hydration is a typical operation applied to legumes before cooking, reducing time and the associated energy cost. To monitor the process, mass balance method is the most used methodology, despite this method is destructive, repetitive, and time-consuming. For that reason. hyperspectral techniques are presented as an alternative for assessing the hydration process since it is a noninvasive method. Therefore, the objective of this work was to evaluate the technique of hyperspectral imaging for studying the hydration kinetics of pinto beans. For this purpose, a sample of pinto beans was hydrated in distilled water, determining moisture content during the process and taking hyperspectral images by reflectance mode, in the range 400 to 800 nm until constant mass. The moisture content was modelled using Peleg and a sigmoidal model. Next, the images were pre-treated and the median spectral profile for each bean was obtained. Then, a regression model was fitted, using the wavelength that maximized the coefficient of determination (R2) and minimized the root mean square error (RMSE). The results show that Peleg model fit experimental data with R2 in the range of 0.974 to 0.989 while sigmoidal model of 0.997 to 0.999. On other hand, mean spectral profiles at 632 nm and sigmoidal model give the higher metrics 0.997 and 38.3 for R2 and RMSE respectively. The results showed that hyperspectral imaging in reflectance mode is a tool
Impact of Magnetic Biostimulation and Environmental Conditions on the Agronomic Quality and Bioactive Composition of INIA 601 Purple Maize
(Foods, 2025-06-10) Arteaga Miñano,Hubert Luzdemio
The utilization of magnetic fields in agricultural contexts has been demonstrated to exert a beneficial effect on various aspects of crop development, including germination, growth, and yield. The present study investigates the impact of magnetic biostimulation on seeds of purple maize (Zea mays L.), variety INIA 601, cultivated in Cajamarca, Peru, with a particular focus on their physical characteristics, yield, bioactive compounds, and antioxidant activity. The results demonstrated that seeds treated with pulsed (8 mT at 30 Hz for 30 min) and static (50 mT for 30 min) magnetic fields exhibited significantly longer cobs (16.89 and 16.53 cm, respectively) compared with the untreated control (15.79 cm). Furthermore, the application of these magnetic fields resulted in enhanced antioxidant activity in the bract, although the untreated samples exhibited higher values (110.56 µg/mL) compared with the pulsed (91.82 µg/mL) and static (89.61 µg/mL) treatments. The geographical origin of the samples had a significant effect on the physical development and the amount of total phenols, especially the antioxidant activity in the coronet and bract. Furthermore, a total of fourteen phenols were identified in various parts of the purple maize, with procyanidin B2 found in high concentrations in the bract and crown. Conversely, epicatechin, kaempferol, vanillin, and resveratrol were found in lower concentrations. These findings underscore the phenolic diversity of INIA 601 purple maize and its potential application in the food and pharmaceutical industries, suggesting that magnetic biostimulation could be an effective tool to improve the nutritional and antioxidant properties of crops
Dielectric spectroscopy for the prediction of pork quality during the post-mortem time
(Journal of Food Composition and Analysis, 2025-08-05) Arteaga Miñano,Hubert Luzdemio
Dielectric spectroscopy was used in this study to predict and classify pork quality during the post-mortem time. Eighty ∼1 kg- longissimus dorsi muscles were collected and stored at 4 ± 1 °C and pH, instrumental color, and dielectric properties (ɛ' and ɛ'') were subsequently determined in the microwave range (0.5–9 GHz) at 3, 4, 5, 6, 7, 8, 9, 10 and 24 h post-mortem (hpm), as well as moisture at 8 hpm and drip weight loss at 24 hpm. Of the 80 pork samples, two types of meat were found. RFN (33) and DFD (47) between males and females. Quality parameters: RFN (pH=5.708–5.714; L*=43.341–43.692; moisture (%) = 68.857–69.604; drip loss = 1.655–1.833) and DFD (pH=6.154–6.177; L*=40.152–41.91; moisture (%) = 69.032–69.9; drip loss = 1.129–1.693). Quality parameter predictions during muscle-to-meat transformation showed R² of 0.743 (pH), 0.811 (L*) and 0.603 (C*) for DFD meats with PLSR (full) and R2 of 0.359 (pH), 0.558 (L*) and 0.284 (C*) for RNF meats with PLSR (optimized) from male pigs. of 0.412–0.637 for pH, L* and c* for RFN and DFD meats from female pigs with PLSR (optimized). Dielectric spectroscopy predicts pork quality moderately well, but models that are more robust are needed to improve predictions of internal pork quality
Climatic Aggressiveness and Precipitation Concentration in a Peruvian Amazon Basin: Alto Huallaga Interbasin
(Revista Politecnica, 2025-05-31) Piedra Tineo,José Luis; Cayatopa Calderon,Billy Alexis
Precipitation in the central Peruvian Amazon is characterized by being seasonal and with strong intensities during the first months of the year, leading to flooding and the subsequent collapse of the local infrastructure in provinces of San Martin, Huanuco, Pasco and La Libertad Regions of Peru, which are located within the delimitation of the Alto Huallaga Interbasin. Therefore, the objective of this research was to evaluate the climatic aggressiveness and concentration precipitation in the Amazon basin, applying three indices of aggressiveness and a precipitation concentration index (ICP), estimated from the precipitation record of the climatic stations in the study area. The results show a very high correlation between mean precipitation and altitude (R2 = 0.72) and with respect to the aggressiveness, the modified Fournier-Maule index (IFMM) was the one with the best correspondence with respect to altitude (R2 = 0.72) and mean precipitation (R2 = 0.98), however, the ICP shows moderate correlations with altitude (R2 = 0.21) and mean precipitation (R2 = 0.16). Likewise, the extreme values of the different indices were estimated for different return periods and a multiple linear regression model was developed to relate climatic aggressiveness and the ICP to estimate the mean precipitation (R2 = 0.99). Finally, it is concluded that, the Alto Huallaga Interbasin presents a very low climatic aggressiveness and the concentration of rain is moderately seasonal.
Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works
(Applied Sciences, 2025-08-07) Piedra Tineo,José Luis; Cayatopa Calderon,Billy Alexis
The objective of the research was to use sigmoidal mathematical models for the planning and control of rigid pavement works. A dataset was constructed using 140 technical files, which were then analyzed to extract the valued work schedules. These schedules contained the variables time and cost per month. Subsequently, two groups were created from the dataset: a training group comprising 80% of the data and a test group comprising the remaining 20%. Subsequently, the variables were normalized and adjusted with the proposed logistic, Von Bertalanffy, and Gompertz models using Python 3.11.13. Following the implementation of training and validation procedures, the logistic model was identified as the optimal fit, as indicated by the following metrics: R2 = 0.9848, MSE = 0.0026, RMSE = 0.0506, and MAE = 0.0278. The implementation of the aforementioned model facilitates the establishment of an early warning system with a high degree of effectiveness. This system enables the evaluation of the discrepancy between the actual progress and the planned progress with an R2 greater than 98%, thereby serving as a robust instrument for the adjustment and revalidation of activities before and following their execution
Estimation of the Physical Progress of Work Using UAV and BIM in Construction Projects
(Civil Engineering Journal, 2024-02-01) Piedra Tineo,José Luis; Cayatopa Calderon,Billy Alexis
The delay in the physical progress of construction creates additional costs, missed deadlines, and quality issues. The research aimed to estimate the physical progress of the project by using unmanned aerial vehicles (UAVs) and building information modeling (BIM). The methodology comprised capturing 848 high-resolution images of the Civil Engineering Laboratory construction site at the National University of Jaen, Cajamarca, Peru, using the Phantom 4 RTK drone. The photographs were processed using Agisoft 2.0.1 software, resulting in a point cloud. This was then imported into ReCap Pro 2023 software, which was used to assess the quality of the points. The Revit 2023 software was subsequently utilized to establish the phase parameters, linking the BIM model with the point cloud, filtering the model, and eventually exporting it to the Power BI 2023 software. The work's estimated progress utilizing the proposed methodology was 42.82%, which was not statistically significant compared to the Public Works Information System (INFOBRAS) of 43.14%. This allows for the automation of customary processes, the identification of crucial issues, and prompt decision-making. The study's originality lies in the suggestion of integrating aerial imagery with drones and BIM modeling for the real-time and precise estimation of work progression. This method provides a precise and effective substitute for traditional techniques for gauging the tangible advancement of projects.
Estabilidad del nivel del agua en un tanque con variaciones generadas por la demanda del servicio mediante modelos matemáticos
(Revista Científica Pakamuros, 2023-09-13) Arce Fernández,Nilthon
El objetivo de la investigación fue mantener estable el nivel del agua en un tanque con variaciones generadas por la demanda del servicio. Se emplearon dos modelos matemáticos que permitieron estudiar la estabilidad, el modelo no lineal representado por una ecuación diferencial y el modelo lineal por una función de transferencia. Se utilizó el software Matlab/Simulink mediante un método numérico de Runge-Kutta para modelar y simular el proceso. Con las condiciones iniciales del 90 % en la apertura de la válvula de entrada y del 50 % en la apertura de la válvula de salida, la solución de ambos modelos alcanzó un nivel estacionario de h=1,8m. Debido a la demanda del servicio, la válvula de entrada se reajustó en +5 %, alcanzando la solución del sistema un nivel estacionario de h=2,005 m para el modelo no lineal y h=1,999 m para el modelo lineal. Finalmente, la válvula de salida se reajustó en +3 %, alcanzando la solución del sistema un nivel estacionario de h=1.785 m para el modelo no lineal y h=1.784m para el modelo lineal. Los modelos matemáticos empleados, permitieron estimar en menor tiempo con resultados esperados el nivel del agua, logrando así identificar la estabilidad del sistema.
Minimización de la penalidad generada por los retrasos en la entrega de proyectos mediante un modelo de programación lineal entera
(Revista Científica Pakamuros, 2023-09-17) Arce Fernández,Nilthon
La presente investigación tuvo por objetivo minimizar la penalidad total generada por el retraso en la entrega de proyectos operados por una máquina (retroexcavadora). Se empleó el método doble simplex de paso largo con el software GNU Octave (versión 6.2.0). Para estimar la fecha y el orden de entrega de los proyectos antes de la ejecución, se implementó un plan de trabajo para la máquina. La empresa Consultores & Ejecutores Jhothiza S.R.L. verificó los plazos de entrega ejecutando el modelo y concluyendo que la penalidad óptima fue de S/. 8100.
Posicionamiento de una universidad privada en la provincia de Jaén-Perú, caso del año 2018
(Revista Científica Pakamuros, 2024-09-30) Arce Fernández,Nilthon
El término "posicionamiento" se ha convertido en una de las principales preocupaciones de las universidades; un buen posicionamiento influye en la preferencia, el valor y la lealtad de los solicitantes hacia la institución. La presente investigación estudió el posicionamiento de una universidad privada en la provincia de Jaén-Perú, identificando los elementos necesarios para visualizar el panorama adecuado hacia el cual debe dirigirse la institución. Se obtuvo una muestra representativa de 341 alumnos de quinto grado de secundaria y 340 padres de familia. La mayoría de los encuestados percibieron la calidad de la enseñanza como regular, por lo que se debió valorar la importancia de mejorar la calidad de la educación en todos los programas de estudio ofertados, considerando siempre el costo de la educación de acuerdo con el mercado. El estudio demuestra carencias en el posicionamiento de la UDCH - Sede Jaén durante su etapa operativa, que requerían atención inmediata para proyectar la marca y mejorar su posicionamiento en el mercado. Esta investigación es de importancia social ya que servirá de base para estudiar el posicionamiento de las universidades privadas y públicas, mejorando así la calidad de la educación superior en América Latina.

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