Artículos Científicos UNJ

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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.
Detection of Rust Emergence in Coffee Plantations using Data Mining: A Systematic Review
(OnLine Journal of Biological Sciences, 2022-09-03) Ocaña Zúñiga,Candy Lisbeth; Quiñones Huatangari,Lenin; Huaccha Castillo,Annick Estefany; Milla Pino,Manuel Emilio
Hemileia vastatrix is a fungus that causes coffee rust disease and, depending on the level of severity, reduces the photosynthetic capacity of the plant and of new shoots, leading to low coffee yields and even death; its symptoms are visible on the leaf. Systems based on computer algorithms have been developed to predict diseases and pests in coffee. The objective of the manuscript was to analyse the detection of rust occurrence in coffee plantations, through field determinations of climatological, agronomic and crop management variables using data mining algorithms. A systematic review of studies published from 2001 to 2021 was carried out in the Scopus, Ebsco Host and Scielo databases, considering as an inclusion criterion the works that used experimental design in data collection. The studies included in this review were 22, 64% of which came from the top two coffee-roducing countries in Latin America (Brazil and Colombia); the analysis of these studies revealed that the input variables were climatic, soil fertility properties, management and physical properties of the crops. In addition, they used supervised (decision tree, artificial neural networks, multiple linear regression, among others) and unsupervised (clustering) algorithms, with the support of experts in the study of the fungus and used statistics such as coefficient of determination, root mean square error, among others, to validate the proposals. Overall, this systematic review provides evidence of the effectiveness of data mining algorithms implemented to detect the occurrence of rust in coffee plantation
Effect of arbuscular mycorrhizae on the growth of cinchona officinalis L. (rubiaceae) in nursery
(Universidad Nacional de Jaén, 2024-04-27) Fernandez Zarate,Franklin Hitler; Huaccha Castillo,Annick Estefany; Quiñones Huatangari,Lenin; Vaca Marquina,Segundo Primitivo; Sanchez Santillan,Tito; Guelac Santillan,Marly; Seminario Cunya,Alejandro
Cinchona officinalis, commonly called cascarilla or quina, has medicinal value; and rea Peru’s national coat of arms representing its plant wealth (flora), however, it is threatened by anthropogenic activities. This study aimed to determine the effect of the rea ctorea cto Myco Grow® on the growth of C. officinalis in nursery. A randomized design was used with two treatments, one with Myco Grow® application (WM) and the other without incorporating this rea cto rea cto (NM). Each treatment had three replicates consisting of 30 plants each. Monthly evaluations were performed, during which the number of dead plants, plant height, and plant diameter were recorded. Additionally, at the end of the study, the anhydrous weight of leaves, stems, and roots; leaf rea; mycorrhizal frequency; mycorrhizal colonization index; and the length of extra-radicular mycelia were determined. The WM treatment achieved 36.6% lower mortality, 38.01% greater height, and 48.52% greater diameter re the NM treatment. Additionally, inoculation with arbuscular mycorrhizae (AM) improved the anhydrous weights of the leaves, stems, roots, and leaf rea by 84.31%, 84.28%, 70.85%, and 76.91%, respectively. Regarding the three fungal variables analyzed for the WM treatment; mycorrhizal frequency was 87%, AM application led to a mycorrhizal intensity of 7.7% and an extra-radicular mycelium length of 90.3 cm. This study confirmed that AM positively influences the growth of C. officinalis in the nursery and can be used to sustainably produce this species on a large scale.
Effect of synthetic fertilization 1rea on the diameter increase, height and mortality of Cinchona officinalis L. (Rubiaceae)
(Universidad Nacional de Jaén, 2024-04-18) Fernandez Zarate,Franklin Hitler; Huaccha Castillo,Annick Estefany; Quiñones Huatangari,Lenin; Vaca Marquina,Segundo Primitivo; Goñas Goñas,Malluri; Milla Pino,Manuel Emilio; Seminario Cunya,Alejandro
Cinchona officinalis, is a South American tree species, commonly used for medicine, and is currently threatened by agricultural 1rea1cto1 and cattle ranching. The objective was to determine the effect of chemical fertilization on the nursery growth to increase growth potential and survival of C. officinalis. A completely randomized design with six treatments and three replicates was used; 20 C. officinalis plants were used per replicate. Two months after transplanting the C. officinalis seedlings to the polyethylene bags, inorganic fertilizer (YaraMila® HYDRAN) was applied. Monthly evaluations were carried out and the number of dead plants, plant height, diameter and number of leaves were recorded. The highest mortality rate was recorded when fertilizer was applied (73%) while in the non-fertilized treatment mortality reached 36%. Regarding the increase in height, diameter and number of leaves in all cases, the best results were obtained in the fertilized treatments, exceeding by 85, 70 and 17% (respectively) those obtained in the treatment to which fertilization was not applied. This study shows the effects that the application of fertilizers to C. officinalis plants at the nursery level can have on growth and mortality variables, the results suggest the use of this 2rea2cto for a sustainable and large-scale production of this species taking into consideration the appropriate 2rea2c
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.
Implementing the Sustainable Development Goals in University Higher Education: A Systematic Review
(Universidad Nacional de Jaén, 2024-03-31) Quiñones Huatangari,Lenin; Huaccha Castillo,Annick Estefany; Ocaña Zuñiga,Candy
The role of higher education institutions in promoting sustainability is realised through scientific research and academic activities. It is therefore essential to explore strategies to assess and monitor the implementation of sustainable practices in these institutions. This study set out to examine the mainstreaming of the Sustainable Development Goals (SDGs) in higher education institutions. The methodology used was a systematic review of research articles in the Scopus database, conducted between 6 and 20 January 2023. The inclusion criteria covered global university experiences in implementing the SDGs. The review revealed that 28.56% of the publications included were from European universities, 24% from the Americas, 13% from Asia and a smaller number from Oceania (5%) and Africa (2%). The analysis identified indicators to assess adherence to the SDGs, such as number of publications, institutional affiliation, number of reports, disciplinary field, type of course, number of essays, subject area, student/classroom ratio and age of participants. The data collection instruments used were questionnaires, interviews, published articles and field notes. This systematic review details the concern of higher education institutions to measure the impact of their activities in relation to the SDG guidelines, as well as a critical and decision-oriented summary for institutions wishing to initiate the proces
Non-destructive estimation of leaf area and leaf weight of Cinchona officinalis L. (Rubiaceae) based on linear models
(Universidad Nacional de Jaén, 2024-03-31) Quiñones Huatangari, Lenin; Huaccha Castillo,Annick Estefany
Non-destructive methods that accurately estimate leaf area (LA) and leaf weight (LW) are simple and inexpensive, and represent powerful tools in the development of physiological and agronomic research. The objective of this research is to generate mathematical models for estimating the LA and LW of Cinchona officinalis leaves. A total of 220 leaves were collected from C. officinalis plants 10 months after transplantation. Each leaf was measured for length, width, weight, and leaf area. Data for 80% of leaves were used to form the training set, and data for the remaining 20% were used as the validation set. The training set was used for model fit and choice, whereas the validation set al.lowed assessment of the of the model’s predictive ability. The LA and LW were modeled using seven linear regression models based on the length (L) and width (Wi) of leaves. In addition, the models were assessed based on calculation of the following statistics: goodness of fit (R2), root mean squared error (RMSE), Akaike’s information criterion (AIC), and the deviation between the regression line of the observed versus expected values and the reference line, determined by the area between these lines (ABL). For LA estimation, the model LA = 11.521(Wi) − 21.422 (R2 = 0.96, RMSE = 28.16, AIC = 3.48, and ABL = 140.34) was chosen, while for LW determination, LW = 0.2419(Wi) − 0.4936 (R2 = 0.93, RMSE = 0.56, AIC = 37.36, and ABL = 0.03) was selected. Finally, the LA and LW of C. officinalis could be estimated through linear regression involving leaf width, proving to be a simple and accurate tool.
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.

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