Artículos científicos

URI permanente para esta colecciónhttps://repositorio.inia.gob.pe/handle/20.500.12955/8

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Phenotypic diversity of morphological traits of pitahaya (Hylocereus spp.) and its agronomic potential in the Amazonas region, Peru
(MDPI, 2024-11-02) Santos Pelaez, Julio Cesar; Saravia Navarro, David; Cruz Delgado, Julio H. I.; Del Carpio Salas, Miguel Angel; Barboza, Elgar; Casanova Núñez-Melgar, David Pavel
Pitahaya (Hylocereus spp.) is an economically significant cactus fruit in Peru, renowned for its rich nutritional profile and antioxidant properties while exhibiting wide biological diversity. This study aimed to morphologically characterize seven pitahaya accessions using qualitative and quantitative descriptors related to the cladodes, flowers, and fruits. Univariate and multivariate (FAMD, PCA, MCA, and clustering) analyses were employed to identify and classify the accessions based on their morphological traits. The analyses revealed three distinct groups: one consisting solely of AC.07; another with AC.02, AC.04, and AC.06; and a third including AC.01, AC.03, and AC.05. The first group exhibited superior characteristics, particularly in fruit traits such as the stigma lobe count (23.3), number of bracts (26.5 mm), and length of apical bracts (15.75 mm). The second group recorded the highest spine count (3.21), bract length (16.95 mm), and awn thickness (5.12 mm). The third group had the highest bract count (37) and an average locule number (23.65). These findings highlight the significant morphological diversity among the accessions, indicating the potential for classification and selection in pitahaya cultivation. The potential of AC.07 stands out in terms of its agronomic qualities, such as its fruit weight (451.93 g) and pulp weight (292.5 g), surpassing the other accessions.
Draft genome sequence data of Fusarium verticillioides strain REC01, a phytopathogen isolated from a Peruvian maize
(Elsevier, 2024-09-23) Estrada Cañari, Richard; Aragón, Liliana; Pérez Porras, Wendy E.; Romero Avila, Yolanda; Martínez Vidal, Gabriel; García, Karina; Cruz Luis, Juancarlos; Arbizu Berrocal, Carlos I.
Fusarium verticillioides represents a major phytopathogenic threat to maize crops worldwide. In this study, we present genomic sequence data of a phytopathogen isolated from a maize stem that shows obvious signs of vascular rot. Using rigorous microbiological identification techniques, we correlated the disease symptoms observed in an affected maize region with the presence of the pathogen. Subsequently, the pathogen was cultured in a suitable fungal growth medium and extensive morphological characterization was performed. In addition, a pathogenicity test was carried out in a DCA model with three treatments and seven repetitions. De novo assembly from Illumina Novaseq 60 0 0 sequencing yielded 456 contigs, which together constitute a 42.8 Mb genome assembly with a GC % content of 48.26. Subsequent comparative analyses were performed with other Fusarium genomes available in the NCBI database.
Inoculation with Azospirillum brasilense as a strategy to reduce nitrogen fertilization in cultivating purple maize (Zea mays l.) in the Inter-andean valleys of Peru
(MDPI, 2024-10-21) Condori Ataupillco, Tatiana; Alarcón Romani, Susan; Huasasquiche Sarmiento, Lucero; García Blásquez, Cayo; Padilla Castro, César; Velásquez Mantari, José; Solórzano Acosta, Richard
Purple maize has gained global significance due to its numerous nutraceutical benefits. However, sustaining its production typically requires high doses of nitrogen fertilizers, which, when applied in excess, can contaminate vital resources such as soil and water. Inoculation with nitrogenfixing microorganisms, such as those from the Azospirillum genus, has emerged as an alternative to partially or fully replace nitrogen fertilizers. This study aimed to evaluate the inoculation effect with A. brasilense and varying nitrogen fertilization levels on the yield and quality of purple maize. The experiment was carried out using a randomized complete block design (RCBD) with a 2 × 5 factorial arrangement and five replications. Treatments comprised two inoculation levels (control without inoculation and inoculation with A. brasilense) under five nitrogen doses (0, 30, 60, 90, and 120 kg・ha−1, applied as urea). Inoculation with A. brasilense resulted in a 10.5% increase in plant height, a 16.7% increase in root length, a 21.3% increase in aboveground fresh biomass, a 30.1% increase in root fresh biomass, and a 27.7% increase in leaf nitrogen concentration compared to the no inoculated control. Regarding yield, the inoculated plants surpassed the control in both purple maize yield (kg・ha−1) and cob weight by 21.8% and 11.6%, respectively. Across all fertilization levels and parameters assessed, the inoculated treatments outperformed the control. Furthermore, for parameters, namely plant height, leaf nitrogen content, and cob dimensions (length, diameter, and weight), the A. brasilense inoculation treatment with 90 kg N・ha−1 was statistically equivalent or superior to the non-inoculated control with 120 kg N・ha−1. These results indicate that inoculation with A. brasilense positively impacted purple maize at all nitrogen levels tested and improved nitrogen use efficiency, enabling a reduction of 30 kg N・ha-1 without compromising performance in key parameters.
Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging
(MDPI, 2024-10-06) Urquizo Barrera, Julio Cesar; Ccopi Trucios, Dennis; Ortega Quispe, Kevin; Castañeda Tinco, Italo; Patricio Rosales, Solanch; Passuni Huayta, Jorge; Figueroa Venegas, Deyanira; Enriquez Pinedo, Lucia; Ore Aquino, Zoila; Pizarro Carcausto, Samuel
Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines with an R2 of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose.
Forage yield and nutritive value of plantain and chicory for livestock feed at high altitudes in Peru
(John Wiley & Sons Inc., 2024-10-10) Vallejos Fernández, Luis A.; Guillén, Ricardo; Pinares Patiño, César; García Ticllacuri, Rubén; Muñoz Vilchez, Yudith Yohany; Quilcate, Carlos Enrique; Álvarez García, Wuesley Yusmein
Background: Evaluation of forage resources is vital for the sustainability of livestock farming in the South American Andes, especially under conditions of low water availability for irrigation and acid soils. Methods: We evaluated the productivity and nutritive value of two cultivars of chicory (Cichorium intybus L.) and one of plantain (Plantago lanceolata L.) in three high‐altitude sites (AL) of the northern highlands of Peru: AL‐I: 2300–2800 m.a.s.l, AL‐II: 2801–3300 m.a.s.l. and AL‐III: 3301–3800 m.a.s.l., for 1 year. The parameters evaluated were dry matter yield (DMY), plant height (PH), growth rate (GR) and nutritional value. Results: Plantain achieved the greatest annual DMY (ADMY), PH and GR compared to the two chicory cultivars (9.34, 9.56 and 13.39 Mg ha−1 for Puna II and Sese 100 chicory and Tonic plantain, respectively; p = 0.0019). The greatest ADMY and GR occurred at AL‐I. Regarding nutritional value, differences were observed only for in vitro digestibility of dry matter and metabolisable energy with chicory cultivars higher than plantain. Conclusions: The results indicate that the three cultivars evaluated may be used as a nutritional supplement in cattle feed, associated with grasses because they have high nutritive value suitable for milk production in the mountain regions of Peru.
Molecular identification of the most frequent pathotypes of Escherichia coli in calves with diarrhoea in the Cajamarca region of Peru
(Faculty of Veterinary Medicine, University of Tripoli, 2024-09-30) Cabrera González, Marco; Quilcate Pairazamán, Carlos; Alvarez García, Wuesley; Cabrera Hoyos, Héctor; Tayca Saldaña, Antony; Aliaga Tambo, Fernando; Rojas Valdez, Deisy; Cueva Rodríguez, Medali
Background: Colibacillosis caused by Escherichia coli causes significant economic losses in the livestock sector worldwide and is one of the calves’ leading causes of diarrea Aim: This study aimed to identify the most frequent E. coli molecularly pathotypes in calves with diarrhea in six provinces of the Cajamarca region in the northern highlands of Peru. Methods: Twenty-eight herds of dairy cattle under a semi-intensive rearing system were evaluated; 95 samples were isolated from calves with diarrhea up to the first month of life, 62 males and 33 females, during the rainy season. Results: The presence of virulence genes of E. coli strains was more prevalent in males; the astA (89.47%), st (83.15%), and f5 (57.89%) genes were more expressed, and the lt (17.89%) and stx2 (1.05%) genes were less expressed. The eae gene (21.05%) was more present in females. Conclusion: When E. coli strains express virulence genes astA, st, and f5 and their atypical double, triple, and quadruple combination between different observed pathotypes, they give rise to the formation of several pathotypes by the horizontal transfer of virulence genes, which can cause colibacillosis processes in more virulent calves, which is one of the most important causes of diarrhea in calves in the region of Cajamarca, compromising the sanitary viability in the herds.
Responses of Megacyllene andesiana and Oreodera bituberculata (Coleoptera: Cerambycidae) to anti-2,3-hexanediol, fuscumol, and fuscumol acetate in Peru
(Cambridge University Press, 2024-09-25) Aguirre Gil, Oniel Jeremias; Paredes Espinosa, Richard; Egoávil Jump, Giannfranco; Allison, Jeremy Dean
Management of phytosanitary and biosecurity risks associated with the Cerambycidae focuses on prevention and early detection. Semiochemical-baited traps are an important component of these management efforts. Cerambycid pheromones are often screened in field trials to develop inventories of which species can be surveyed with which semiochemicals. We report field trials of two types of intercept traps (four- and three-sided panel traps) baited with known Cerambycidae pheromones aimed to capture Peruvian fauna. Intercept traps were baited with anti-2,3-hexanediol, fuscumol, and fuscumol acetate alone and in binary and ternary blends. The most frequently captured species was Megacyllene andesiana (Casey) (Coleoptera: Cerambycidae) (n = 268), followed by Oreodera bituberculata Bates (Coleoptera: Cerambycidae) (n = 59), Discopus eques Bates (Coleoptera: Cerambycidae) (n = 37), and Aegomorphus longitarsis (Bates) (Coleoptera: Cerambycidae) (n = 31). Trap type did not affect capture rates. Male and female M. andesiana were attracted by anti-2,3-hexanediol. The addition of fuscumol, fuscumol acetate, or the combination of fuscumol and fuscumol acetate reduced male M. andesiana captures, whereas the addition of fuscumol and the combination of fuscumol and fuscumol acetate reduced the response of female M. andesiana. Male O. bituberculata were attracted to traps baited with fuscumol, and this response was reduced by the addition of fuscumol acetate, whether or not anti-2,3-hexanediol was present.
Implementing artificial intelligence to measure meat quality parameters in local market traceability processes
(John Wiley & Sons Inc., 2024-09-20) Alvarez Garcia, Wuesley Yusmein; Mendoza, Laura; Muñoz Vílchez, Yudith Yohany; Casanova Núñez-Melgar, David; Quilcate Pairazaman, Carlos
The application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri-food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products’ production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as the automation of inspection processes, accurate product classification, traceability, and food safety. While the potential of artificial intelligence associated with sensor development in the meat industry is promising, it is crucial to recognize that this is an evolving field. This technology offers innovative solutions that enable efficient, cost effective, and consumer-oriented production. However, it also underlines the urgent need for further research and development of new techniques and tools such as artificial intelligence algorithms, the development of more sensitive and accurate multispectral sensors, advances in computer vision for 3D image analysis and automated detection, and the integration of advanced ultrasonography with other technologies. Also crucial is the development of autonomous robotic systems for the automation of inspection processes, the implementation of real-time monitoring systems for traceability and food safety, and the creation of intuitive interfaces for human-machine interaction. In addition, the automation of sensory analysis and the optimisation of sustainability and energy efficiency are key areas that require immediate attention to address the current challenges in this agri-food and agri-industrial sector, highlighting and emphasising the importance of ongoing innovation in the field.
Litter decomposition rates of four species of agroecological importance in the Peruvian coast and Andean highland
(MDPI, 2024-09-13) Samaniego Vivanco, Tomás Daniel; Ramirez Aparicio, Jorge Adrian; Solórzano Acosta, Richard Andi
Crop residue decomposition is fundamental for ecosystems, influencing carbon cycling, organic matter accumulation, and promoting plant development through nutrient release. Therefore, this study aimed to ascertain the rate of decomposition of four commonly cultivated crops (alfalfa, maize, avocado, and eucalyptus) along the northern coast of Lima (Huaral) and in the Ancash Mountain range (Jangas) areas. Decomposition rates were assessed using mass loss from decomposition bags measuring 15 × 10 cm, filled with 10–15 g of material tailored to each species, and buried at a depth of approximately 5 cm. Sampling occurred every three months over a year, totaling four sampling events with three replicates each, resulting in ninety-six experimental units. The findings demonstrate that the decomposition rates and the release of nutrients were markedly greater in Huaral for maize and avocado. In contrast, these rates were notably elevated in Jangas for alfalfa and eucalyptus. The leaf litter of avocado and eucalyptus (tree) had periods of accumulation and release of heavy metals such as Cd. The initial C/N ratio was one of the main factors related to the nutrient decomposition rate; in contrast, there were no significant relationships with soil properties at the study sites.
Mango varietal discrimination using hyperspectral imaging and machine learning
(Springer Nature, 2024-07-29) Castro, Wilson; Tene, Baldemar; Castro, Jorge; Guivin, Alex; Ruesta Campoverde, Nelson Asdrubal; Avila George, Himer
Mango is a highly diverse tropical fruit with numerous varieties that differ in flavor, texture, and chemical composition. Consequently, identifying fraudulent substitutions of mango varieties poses a significant challenge using traditional techniques. Therefore, there is an increasing need for new methods to discriminate between mango varieties. Hyperspectral imaging coupled with machine learning techniques presents a promising approach for varietal discrimination. In this study, mango samples of eleven varieties were collected from a germplasm bank, with four slices obtained from each sample. Hyperspectral images were acquired in the Vis–NIR and NIR ranges for each slice, and spectral profiles were extracted and pretreated. Three discrimination models, linear discriminant analysis, K-nearest neighbor, and artificial neural networks, were implemented and validated using relevant wavelengths selected through a covering array feature selection algorithm. The performance of these models was evaluated using precision, accuracy, and F-score metrics. The average spectral profiles of the studied varieties exhibited a similar behavior with slight differences, which could be used for classification within the evaluated ranges. The optimal number of variables selected to refine the models was 17 for the UV–Vis–NIR range and 21 for the NIR range, with an accuracy ranging between 0.752 and 0.972. This study concludes that hyperspectral imaging combined with machine learning techniques can effectively discriminate between different varieties of mango.
Methodology for the phenotypic evaluation in Guazuma crinita trees in Ucayali, Peru
(Universidade Federal de Santa Maria, 2024-02-20) Revilla Chávez, Jorge Manuel; López Galán, Edinson Eduardo; Gonzales Alvarado, Antony Cristhian; Sáenz Ramírez, Lyanna Hellen; Mori Vásquez, Jorge Arturo; Rojas Mego, Krystel Clarissa; Abanto Rodríguez, Carlos; Sebbenn, Alexandre Magno
The objective of this study was to present a methodological tool for the phenotypic evaluation in progeny tests of Guazuma crinita in producer plots of the Aguaytía river basin, Ucayali, Peru, which allows field technicians to standardize the morphological evaluation criteria of trees in forest plantations. Therefore, the phenotypic traits were evaluated for plant height (m), diameter at the height of the base (cm), number of branches, number of rings, stem form, branch orientation, presence and quantity of leaves. The heritability and genetic and phenotypic correlations between traits were also estimated. Therefore, 32 morphological categories were plotted based on the significant correlations (p≤ 0.05) shown between the place of planting, the stem form, the orientation of the branches and the presence of leaves. For the same reason, the progeny showed low morphological patterns, being a low factor of phenotypic variability. It is concluded that the correlations between the biometric and morphological traits evaluated, allowed to validate the phenotypic evaluation procedures of Guazuma crinita progeny tests at 36 months of age.
Efficiency of a compound parabolic collector for domestic hot water production using the F- chart method
(International Hellenic University School of Science and Technology, 2024-06-01) Ortega Quispe, Kevin Abner; Huari Vila, Oscar Paul; Ccopi Trucios, Dennis; Lozano Povis, Arlitt Amy; Enriquez Pinedo, Lucia Carolina; Cordova Torres, Betty
Among solar energy technologies, differences exist in terms of costs, performance, and environmental sustainability. Flatplate solar collectors, solar towers, and parabolic dish systems offer high thermal efficiency and versatility, but they may be more costly and bulky compared to other collector models. This study focused on evaluating the efficiency of a cylindrical parabolic collector (CPC) for the production of domestic hot water in a high Andean region of Peru, using the F-Chart method. Its performance was estimated considering the energy demand for hot water in a single-family home with four occupants, in accordance with national regulations and international recommendations. Additionally, the collector area, water temperature, and incident solar radiation were determined based on meteorological data obtained using the PVsyst software. On the other hand, the F-Chart methodology was employed to find the dimensionless factors X and Y of the CPC collector, which allowed estimating the solar fraction factor and the monthly useful energy that can be provided by the designed CPC system. The results showed that, during months of maximum solar radiation, the CPC is capable of satisfying between 129% and 144% of the energy demand for hot water. This indicates that there is a surplus of usable solar energy in the collector during the summer, while in autumn and winter, the solar contribution balances and slightly exceeds the demand. CPC can significantly contribute to the development of high Andean areas by improving quality of life, reducing costs, and promoting environmental sustainability compared to other available technologies.
Matrix-assisted laser desorption ionization time-of-flight mass spectrometry combined with chemometrics for protein profiling and classification of boiled and extruded quinoa from conventional and organic crops
(MDPI, 2024-06-17) Galindo Luján, Rocío; Pont, Laura; Quispe Jacobo, Fredy Enrique; Sanz Nebot, Victoria; Benavente, Fernando
Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.
El biofertilizante líquido fermentado de residuos de camal mejora el rendimiento del cultivo de maíz
(Universidad Autónoma de Yucatán, 2024-08-06) Samaniego Vivanco, Tomás Daniel; Pérez Porras, Wendy Elizabeth; Lastra Paúcar, Sphyros; Verme Mustiga, Ezio; Solórzano Acosta, Richard Andi
La aplicación exclusiva de fertilizantes sintéticos u orgánicos sigue generando polémica. La evidencia muestra que su aplicación conjunta puede mejorar la nutrición de los cultivos, evitar el uso excesivo de fertilizantes sintéticos y amortiguar su efecto contaminante en el suelo. Objetivo: Evaluar el uso de dosis de fertilización orgánica y mineral sobre el crecimiento y rendimiento del maíz amarillo duro Megahíbrido 619 INIA empleando un biofertilizante líquido derivado de la fermentación de residuos de camal. Metodología: Mediante un diseño experimental de bloques completos al azar con arreglo factorial 4x2, se ensayaron cuatro dosis de fertilización química NPK y la aplicación del biofertilizante. La fertilización mineral se fraccionó en dos partes, mientras que las aplicaciones del biofertilizante fermentado de residuos de camal se realizaron vía drench durante el crecimiento vegetativo y entre las etapas de panojamiento y llenado de grano a una dosis de 50 L. ha-1 de producto. Resultados: El uso del biofertilizante líquido (K1) tuvo un impacto positivo en el crecimiento, con un efecto equiparable en la altura y área foliar de la planta al aplicar una dosis media de fertilización química (F2_K1). La dosis más baja de fertilización química en combinación con el biofertilizante (F1_K1) obtuvo un índice de cosecha estadísticamente superior (+14%) en comparación con el la fertilización completa (F3_K1). Implicaciones: Si bien las fertilizaciones más altas no produjeron rendimientos superiores, es posible que en otras condiciones y con otros híbridos de maíz sí se observen diferencias significativas. Conclusión: La aplicación del biofertilizante líquido junto con una dosis reducida de fertilización mineral permite obtener un mayor índice de cosecha y rendimientos comparables con el uso de una fertilización mineral completa en el maíz amarillo.
Evaluación del prendimiento y desarrollo de clones de caucho (Hevea brasiliensis) propagados por injerto en la selva peruana
(Universidad Autónoma de Yucatán, 2024-07-15) Navarro, Jorge M.; Alva Arévalo, Alberto; Lozano, Andi; Alva Arévalo, Celso Misael; García Gonzales, Patricia; Gaona Jimenez, Nery; Baselly Villanueva, Juan Rodrigo; Saavedra Ramírez, Jorge; Vallejos Torres, Geomar
The latex yield in Hevea brasiliensis trees is highly variable; Therefore, it is important to propagate commercial clones of this species by grafting in order to standardize or improve latex yields and therefore improve the economic income of producers in this part of the Amazon region. Objective. To evaluate the attachment and development of rubber clones propagated by grafting in the Peruvian jungle. Methodology. Nine-month-old patterns installed in the field were used. These were grafted with budding rods from the commercial clones TR-1, FX-3864, RRIM-600, IAN-873 and MDF-180. A completely randomized block design was used with five treatments and three blocks with 15 experimental units; each containing 30 repetitions. The data obtained were subjected to analysis of variance and Tukey's multiple range test (p<0.05) and the Kruskal Wallis test. Results. The best results in attachment and number of shoots were obtained in grafts with clone MDF-180 with values of 73.33 % and 7.83 shoots respectively; Regarding days to the appearance of the shoot and length, clone TR-1 presented it with 36.63 days and 27.47 cm; The largest leaf area was presented by the graft with clone IAN-873 with 55.46 cm2, while the highest chlorophyll content was obtained in grafts with clone RRIM-600 with 47.18 SPAD. Implications. More studies are required to establish the quality and adaptability of rubber clones propagated by grafting in different site conditions and establish patterns of tree productivity in a short time; Likewise, it is desired to evaluate the quality and quantity of latex produced by these clones. Conclusions. These results indicate the feasibility of propagating Hevea brasiliensis through shoot grafting in the field, which opens great possibilities for propagating rubber trees with better commercial characteristics, particularly with greater latex production.
Bioestimulante y tiempos de imbibición sobre la germinación de semillas de maracuyá (Passiflora edulis S., Passifloraceae)
(Universidad Autónoma de Yucatán, 2024-05-28) Ruesta López, Madai; Zurita Chinguel, Lesly; Lizano Pintado, Maribel; Delgado Vite, María; Zapata Durand, Diana; Jiménez Castillo, Jaira; Peña Castillo, Ricardo; Galecio Julca, Miguel; Chanduví García, Roger; Morales Pizarro, Davies Arturo
El maracuyá (Passiflora edulis S.) es un fruto tropical de interés socio-económico en Perú, usado como ingrediente en la industria alimentaria, estética y medicina. No obstante, este cultivo presenta un bajo poder germinativo, por lo cual es necesario realizar tratamientos pre germinativos, para incrementar el porcentaje de germinación y el desarrollo inicial de las plántulas. Objetivo: evaluar el efecto de diferentes bioestimulantes a 12 h y 24 h de hidratación sobre la germinación de semillas de maracuyá. Metodología: se recolectaron semillas de maracuyá de frutos grandes y redondos, libre de patógenos. Las semillas sin arilo fueron tratadas: T0 (agua destilada), T1 (azufre 25 g/10 L), T2 (azufre 50 g/10 L), T3 (biol 3%), T4 (biol 5%), T5 (canela 25%), T6 (canela 50%), T7 (suero de leche 25%) y T8 (suero de leche 50%) durante 12 h y 24 h de imbibición. Las variables evaluadas fueron: agua absorbida, tasa de imbibición (TI), porcentaje de germinación (PG), velocidad de germinación (VG), índice de germinación (IG) y primer día de germinación (PDG). Resultados: la mayor absorción de agua se da en las 9 primeras horas. Los Tratamientos T4, T5 y T6 mejoraron significativamente el PG, VG, IG, PDG a 12 h de imbibición. Sin embargo, a 24 h de imbibición estos valores disminuyen. El PG presentó una alta correlación positiva con VG e IG. Asimismo, la VG presentó una correlación altamente negativa con PDG. Implicaciones: El uso de bioestimulantes incrementa la germinación. Conclusiones: El extracto de canela a 25% y 50%, y biol 5% a 12 h de imbibición mejoran significativamente las variables evaluadas comparadas con la imbibición a 24 h. La correlación positiva entre las variables indica una relación directa entre estas.
Niveles de glomalina y carbono en función de los agregados del suelo en la Amazonía peruana
(Universidad Centroccidental Lisandro Alvarado (UCLA), 2024-09-01) Vallejos Torres, Geomar; Mendoza López, Karla; Ordoñez Sánchez, Luis; Lozano Sulca, Yimi; Gaona Jimenez, Nery; Baselly Villanueva, Juan Rodrigo
La selva amazónica de Perú ha experimentado grandes cambios en el uso de suelos, mediante la deforestación del ecosistema forestal para la instalación de silvopasturas y cultivos agrícolas como café y cacao, lo que ha contribuido al aumento de CO2 en la atmósfera. Este estudio evaluó los niveles de glomalina y carbono en función de los agregados del suelo en estos ambientes. Se estudiaron tres tipos de ecosistemas: i) ecosistema de bosque, ii) agroecosistema de silvopastura y iii) agroecosistema de café. En cada uno se establecieron nueve parcelas de 100 m2 y se extrajeron muestras de suelos a una profundidad de 0-20 cm. Se evaluó el carbono orgánico del suelo (COS) así como la glomalina extraíble (GE) y total (GT), en cuatro diferentes rangos del tamaño de los agregados (>2 mm, 2-1 mm, 1-0,25 mm y 0,25 mm). El efecto del tipo de ecosistema y agregados sobre el COS y glomalina se analizó mediante un ANOVA, así como con una prueba de regresión. Los ecosistemas estudiados influyeron significativamente en la disponibilidad de COS y glomalina; se obtuvo mayor COS en el agroecosistema de café, seguido del ecosistema de bosque con 101,08 y 80,17 t·ha-1. El bosque presentó mayor concentración de GE y GT respecto al cafetal y las silvopasturas. El tamaño de los agregados influyó significativamente en la disponibilidad de COS, GE y GT; cuando fueron de menores dimensiones, los contenidos de COS y glomalina disminuyeron. El tipo del sistema y los agregados del suelo influyeron significativamente en la disponibilidad de COS y glomalina en los tres ambientes estudiados.
Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation
(Elsevier, 2024-07-28) Gómez Fernández, Darwin; Salas López, Rolando; Zabaleta Santisteban, Jhon A.; Medina Medina, Angel J.; Goñas Goñas, Malluri; Silva López, Jhonsy O.; Oliva Cruz, Manuel; Rojas Briceño, Nilton B.
Monitoring and evaluation of landscape fragmentation is important in numerous research areas, such as natural resource protection and management, sustainable development, and climate change. One of the main challenges in image classification is the intricate selection of parameters, as the optimal combination significantly affects the accuracy and reliability of the final results. This research aimed to analyze landscape change and fragmentation in northwestern Peru. We utilized accurate land cover and land use (LULC) maps derived from Landsat imagery using Google Earth Engine (GEE) and ArcGIS software. For this, we identified the best dataset based on its highest overall accuracy, and kappa index; then we performed an analysis of variance (ANOVA) to assess the differences in accuracies among the datasets, finally, we obtained the LULC and fragmentation maps and analyzed them. We generated 31 datasets resulting from the combination of spectral bands, indices of vegetation, water, soil and clusters. Our analysis revealed that dataset 19, incorporating spectral bands along with water and soil indices, emerged as the optimal choice. Regarding the number of trees utilized in classification, we determined that using between 10 and 400 decision trees in Random Forest classification doesn't significantly affect overall accuracy or the Kappa index, but we observed a slight cumulative increase in accuracy metrics when using 100 decision trees. Additionally, between 1989 and 2023, the categories Artificial surfaces, Agricultural areas, and Scrub/ Herbaceous vegetation exhibit a positive rate of change, while the categories Forest and Open spaces with little or no vegetation display a decreasing trend. Consequently, the areas of patches and perforated have expanded in terms of area units, contributing to a reduction in forested areas (Core 3) due to fragmentation. As a result, forested areas smaller than 500 acres (Core 1 and 2) have increased. Finally, our research provides a methodological framework for image classification and assessment of landscape change and fragmentation, crucial information for decision makers in a current agricultural zone of northwestern Peru.
From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a Peruvian Andean highlands: a machine learning modeling approach
(Springer, 2024-09-09) Carbajal, Mariella; Ramirez, David A.; Turin Canchaya, Cecilia Claudia; Schaeffer, Sean M.; Konkel, Julie; Ninanya, Johan; Rinza, Javier; De Mendiburu, Felipe; Zorogastua, Percy; Villaorduña, Liliana; Quiroz, Roberto
Andean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotope composition of SOC (d13CSOC)—using machine learning (ML) algorithms in the Central Andean Highlands of Peru, where grasslands and wetlands (‘‘bofedales’’) dominate the landscape surrounded by Junin National Reserve. A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variablesusing remote sensing data, land-use and landcover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and d13CSOC prediction, whereas ANN was the best to model RSOC. ‘‘Bofedales’’ showed 2–3 times greater SOC (11.2 ± 1.60%) and RSOC (1.10 ± 0.23%) and more depleted d13CSOC (- 27.0 ± 0.44 &) than other LULC, which reflects high C persistent, turnover rates, and plant productivity. This highlights the importance of ‘‘bofedales’’ as SOC reservoirs. LULC and vegetation indices close to the near-infrared bands were the most critical environmental predictors to model C variables SOC and d13CSOC. In contrast, climatic indices were more important environmental predictors for RSOC. This study’s outcomes suggest the potential of ML methods, with a particular emphasis on RF, for mapping SOC and its fractions in the Andean highlands.
Season and breed effect in fresh semen parameters at a bull semen production center in Peru
(Unique Scientific Publishers, 2024-07-14) Dipaz Berrocal, Deysi; León Trinidad, Silvia Eliana; Figueroa Venegas, Deyanira Antonella; Mamani Chullo, Rolando; Ramirez Hernandez, Juan; Alvarez García, Wuesley Yusmein; Estrada Cañari, Richard; Quilcate Pairazaman, Carlos Enrique
In the present study, the effect of breed, season, and their interaction on semen volume, sperm concentration, and motility was tested. Nine bulls were tested: three Braunvieh, three Simmental (B. taurus), and three Gyr (B. indicus). These bulls were managed equally in a cold semi-arid climate, divided into hot (from November to April) and cold (from May to October) for maximum and minimum temperature and relative humidity. Semen was collected twice a week for 24 consecutive months. MANOVA by ranks was used to determine significant effects; breed had a significant effect (P<0.001) on semen parameters, and seasonal variations were not significant (P>0.05). However, there was a significant effect (P<0.001) for the interaction between breed and season. To evaluate differences between breeds, we performed Tukey's post-hoc test; all pairwise comparisons (Braunvieh-Gyr, Braunvieh-Simmental and Gyr-Simmental) showed significant differences (P<0.001). Nonparametric one-way analysis showed significantly higher B. taurus breeds’ sperm concentration and B. indicus breed’s semen volume. For interaction between breed and season, Gyr reported higher sperm volume than B. taurus breeds in both seasons (P>0.05), but B. taurus breeds had higher sperm concentration in the cold season. The findings will help management make better decisions in semen collection, especially during peak season.

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