Artículos científicos

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

Examinar

Mostrando 1 - 20 de 977

An ecological modelling approach to support Peru wildlife conservation planning based on geospatial datasets and remote sensing information
(John Wiley & Sons Ltd., 2026-05-05) Cotrina Sanchez, Alexander; Rojas Briceño, Nilton; Guzman Valqui, Betty Karina; Valentini, Riccardo; Vaglio Laurin, Gaia
Peru, a megadiverse country, has developed conservation plans for some threatened wildlife species. This study produced spatially explicit data integrating Species Distribution Models (SDMs) into a geospatial analysis of connectivity within the protected areas (PAs) network. In addition, a deforestation analysis around selected PAs was performed evaluating the related conservation implications. The use of lidar-derived vegetation vertical structure metrics from the spaceborne Global Ecosystem Dynamics Investigation (GEDI) mission was tested as an innovative data source to support ecological modelling. This country-level analysis is a useful approach to support conservation in high-biodiversity areas. Location: Peru. Methods: Occurrence data of seven threatened wildlife species were used to compute SDMs in MaxEnt using three variable sets: (i) bioclimatic and topographic, (ii) GEDI vegetation structure metrics joined with Normalized Difference Vegetation Index (NDVI), and (iii) a combination of both. MaxEnt was explicitly calibrated by testing 126 candidate models per species across feature-class and regularization multiplier combinations. SDMs combined with auxiliary data were used to identify core areas, then connected through main ecological corridors (ECs) using geospatial analysis. Deforestation rates were computed in the buffer zones (BZ) of Protected Natural Areas (PNAs) identified as core areas. GEDI lidar-derived data were also used to compare forest degradation between two PNAs and their BZ. Results: This ecological modelling effort identified several core conservation areas, as well as the main ecological corridors interconnecting them. The study showed that highly suitable habitats are currently poorly represented by the present Peru protected areas network, particularly for primates. Test Area Under Curve (AUC) values ranged from 0.867 to 0.995; the Biotopveg set, integrating bioclimatic, topographic, GEDI, and NDVI variables was optimal for three species and the bioclimatic-topographic set for four, suggesting a species-specific contribution of vegetation structural data. GEDI data were used to detect forest degradation gradients, in accordance with known anthropogenic impacts. Deforestation analysis showed that even if indirect use protected areas resulted in less affected by deforestation in their surroundings, notable exceptions occur, calling for additional measures to support human-wildlife coexistence. Main Conclusions: Ecological modelling based on SDMs and spatial analyses can support species conservation plans and landscape connectivity at broader planning scales. GEDI provides valuable data as input in SDMs and supports detecting forest degradation.
Phenotypic variability and yield component analysis of lima bean (Phaseolus lunatus L.) genotypes under coastal conditions of Peru
(Agricultural Research Communication Centre (ARCC), 2026-05-05) Camargo Cobeñas, Marcos Antonio; Almidon Ramirez, Karen Karina; Rojas Meza, María Elena; Terán Rojas, José Alfonso
Background: Lima bean (Phaseolus lunatus L.) is an important grain legume cultivated in tropical and subtropical regions and contributes significantly to food security and local agriculture, particularly in Peru. However, information on the agronomic variability of lima bean germplasm under coastal conditions of Peru remains limited. This study aimed to evaluate the phenotypic variability and yield-related traits of lima bean genotypes from the INIA Germplasm Bank. Methods: Seven genotypes were evaluated under field conditions in Pisco, Ica (Peru), using a randomized complete block design with four replications. Twelve agronomic and morphological traits related to pod, seed and yield components were recorded. Data were analyzed using analysis of variance, Pearson correlation, principal component analysis and hierarchical clustering. Result: Significant differences among genotypes were observed for most traits, indicating considerable phenotypic variability. Grain yield per plant showed positive correlations with the total number of pods per plant and the number of marketable pods. The first two principal components explained 51.14% of the total variation and separated genotypes mainly according to yield components and morphological traits. Cluster analysis grouped the genotypes into three clusters, identifying genotype Ac4 as the most promising material due to its favorable association with yield-related traits and lower incidence of pest-infested pods.
Peruvian national current research information system: implementation status and interoperability between PeruCRIS and institutional CRIS/RIM systems
(Taylor & Francis, 2026-05-09) Alhuay Quispe, Joel; Brañes Gutiérrez, Vanessa; Velarde Gutierrez, Renato; Zulueta Rafael, Hilda; Peña Pineda, Karla Mónica
PeruCRIS, the national Current Research Information System (CRIS) of Peru, represents a significant step toward consolidating scientific information management and enhancing research visibility across the country. In South American countries that have implemented open access legislation and institutional repositories, the development of integrated research information systems remains limited. This study offers a first comprehensive overview of PeruCRIS implementation status, focusing on its technical architecture, institutional adoption, and interoperability mechanisms. The study reports that 120 institutions are currently sending data to the national CRIS, either through manual uploads or via interoperability mechanisms. A sample of 60 CRIS/RIM cases were examined from May 2019 to December 2025, exploring the interaction between national and institutional CRIS platforms, addressing the practical challenges in aligning them. Findings reveal disparities in adoption levels, reliance on proprietary technologies, and limited interoperability coverage. The study also highlights the critical role of research managers and information professionals in sustaining CRIS systems. Finally, it outlines lessons learned and provides recommendations for similar national initiatives in developing countries.
Critical edaphic and altitudinal factors influencing cation exchange capacity in coffee-growing soils of northeastern Peru: implications for sustainable fertility management
(Frontiers Media SA, 2026-05-05) Díaz Chuquizuta, Henry; Manrique Gonzales, Luis Fernando; Sánchez Ojanasta, Martín; Cuevas Giménez, Juan Pablo; Carbajal Llosa, Carlos Miguel; Cuellar Condori, Néstor Edwin; Martínez Zapata, Boris Guillermo; Vallejos Torres, Geomar
Introduction: Effective cation exchange capacity (ECEC) is a key indicator of soil fertility and sustainable soil management assessment in coffee-growing systems. Methods: This study aimed to identify the principal edaphic and altitudinal factors explaining ECEC variability in 69 soil samples collected from coffee farms in northeastern Peru. Results: ECEC results exhibited substantial variation, ranging from 0.14 to 55.49 cmol(+)·kg⁻¹ (mean = 15.21; SD = 12.47), and were significantly correlated with organic matter (r = 0.71), clay content (r = 0.62), exchangeable acidity (r = -0.63), and altitude (r = 0.33). Principal component analysis accounted for 64.3% of the edaphic variability, identifying Ca²⁺, pH, Mg²⁺, and exchangeable acidity as the most influential variables. The Random Forest model demonstrated high predictive accuracy (R² = 0.93; root mean square error (RMSE) = 2.1 cmol(+)·kg⁻¹), outperforming the generalized additive model (GAM) and identifying Ca²⁺ as the most important predictor (IncMSE% = 3177.37). A functional altitudinal gradient was also evident: areas above 1150 m.a.s.l. showed higher acidity and aluminium content, whereas areas below 900 m.a.s.l. exhibited greater base saturation and higher ECEC. Discussion: These findings support the development of site-specific fertilization strategies and soil–climate zoning, emphasizing the value of integrating multivariate analyses with machine-learning models as key tools for optimizing fertility management and coffee crop productivity in tropical mountain ecosystems; where soil texture represents a key factor influencing coffee sustainability, as greater nutrient retention capacity and improved nutritional balance are associated with enhanced potential for sustainable production and reduced environmental impact.
Desempeño agronómico de líneas de frijol común (Phaseolus vulgaris L.) bajo abonamiento orgánico en condiciones de casa malla, Chincha, Perú
(Decanato de Agronomía, Universidad Centroccidental Lisandro Alvarado (UCLA), Venezuela, 2026-05-01) Camargo Cobeñas, Marcos Antonio; Almidon Ramirez, Karen Karina; Rojas Meza, María Elena; Aybar Peve, Leandro Joel; Terán Rojas, José Alfonso
El frijol común (Phaseolus vulgaris L.) es un cultivo estratégico por su valor nutricional y su asociación con microorganismos fijadores de nitrógeno, aunque en regiones como Chincha (Perú) los rendimientos permanecen bajos, lo que demanda alternativas de manejo. Se evaluó la respuesta agronómica de las líneas Larán Mejorado y Waf 78/20 a la aplicación de compost y BlackSoil en dosis de 8 y 16 % (p/p), bajo un Diseño en Bloques Completos al Azar (DBCA) con arreglo factorial 2 × 2 × 2 y dos testigos, con cuatro bloques en condiciones de casa malla. Se evaluaron 12 variables agromorfológicas y la densidad aparente del sustrato, analizadas mediante efectos principales e interacciones, pruebas paramétricas y no paramétricas, coeficientes de correlación de Pearson y análisis de componentes principales (ACP). Se obtuvo que el factor genético fue la principal fuente de variación. Larán Mejorado destacó por una respuesta más uniforme, mayor precocidad y mejores componentes de rendimiento, favorecidos por la aplicación de compost; mientras que Waf 78/20 se asoció con mayor crecimiento vegetativo y mayor sensibilidad a cambios en la densidad aparente del sustrato; en esta línea, la aplicación de BlackSoil se relacionó con ciclos fenológicos más prolongados y menor peso de semilla. Los hallazgos evidencian que la genética determina en mayor medida la respuesta al abonamiento orgánico, resaltando la importancia del genotipo en prácticas de manejo; sin embargo, estos resultados deben validarse en condiciones de campo y múltiples ambientes.
Agronomic variables outperform multispectral indices for individual plant yield prediction in Andean quinoa
(Elsevier B.V., 2026-04-18) Pizarro Carcausto, Samuel Edwin; García Seguil, Erika Janina; Gavino, Esthefany; Requena Rojas, Edilson Jimmy; Ortega Quispe, Kevin Abner; Cccopi Trucios, Dennis
Accurate pre-harvest yield estimation is essential for decision-making in high-altitude agriculture. This study evaluated agronomic and multispectral UAV variables for near-harvest prediction of individual quinoa grain weight, with data collected across six phenological stages to identify when predictors achieve reliable performance, under Andean conditions. A total of 374 plants were monitored across six phenological stages at Santa Ana Experimental Station (Huancayo, Peru, 3280 m a.s.l.) during 2024. OLS, Random Forest, Support Vector Machine, and Neural Network models were trained using agronomic-only (AGRO), spectral-only (IND), and combined (COMP) predictor sets, evaluated through 5-fold cross-validation reporting mean ± standard deviation. Agronomic and combined models achieved moderate performance (R² = 0.22–0.25, RPD = 1.10–1.15), suitable for relative plant ranking in breeding programs, while spectral-only models failed across all algorithms (R² ≤ 0.044, CCC ≤ 0.080), constrained by saturation, phenological decoupling, and canopy heterogeneity. Variable importance analysis confirmed that late-season structural traits dominated predictions, while spectral indices contributed marginally despite including red-edge bands. These results challenge spectral-only approaches for individual plant phenotyping in heterogeneous canopies, demonstrating that integrating simple ground measurements with UAV spectral data is essential for reliable quinoa yield estimation.
Electrophoretic profiles of alpaca seminal plasma proteins and their association with sperm quality parameters during cryopreservation processes
(John Wiley & Sons Ltd., 2026-03-24) Guillen Palomino, Crissthel Yverlin; Mujica Lengua, Fidel Rodolfo; Contreras Huamaní, Mijail; Carretero , María Ignacia; Rueda Alfonso, Fabian Leonardo; Orellana Berrocal, Harumi
The aim of the study was to characterize the electrophoretic profiles of alpaca seminal plasma (SP) proteins and establish their association with sperm quality parameters at different cryopreservation stages. Sperm quality was assessed in raw, cooled, and thawed semen from 128 ejaculates collected from 16 Huacaya alpacas, and SP proteins were analysed by SDS-PAGE in raw samples. Statistical associations were determined using Spearman's rank correlation (p ≤ 0.05). Twenty-three protein bands were identified: 21 bands ranging from 9.23 to 138.38 kDa, and 2 below 6.5 kDa. Notably, the 21.03 kDa protein was absent in six males, five of whom also lacked the 18.88 kDa band. These individuals exhibited superior post-thaw sperm quality, particularly higher motility. The 21.03 kDa protein showed a negative correlation (p ≤ 0.05) with sperm motility and membrane function in raw, cooled, and thawed semen, and a positive correlation with acrosome integrity in thawed semen. Similarly, the 18.88 kDa protein showed a negative correlation with sperm motility and membrane function, but a positive correlation with acrosome integrity in thawed semen (p ≤ 0.05). In conclusion, these findings suggest that specific SP proteins may serve as potential biomarkers for sperm quality and cryotolerance in alpacas, reflecting individual variability in response to cryopreservation.
Parámetros alométricos y contenido de betalaínas en accesiones de ayrampo (Airampoa soehrensii) del banco de germoplasma del INIA, Perú
(Universidad Centroccidental Lisandro Alvarado (UCLA), 2026-05-01) Dadther Huaman, Hans Adams; Hilari Hilari, Mari Carmen; Gonzales Hancco, Haydee; Jimenez Paye, Abraham; Calla Cornejo, Nancy Vanessa; Pacheco Lizarraga, Gonzalo Antonio
El ayrampo es una especie nativa altoandina con alto valor cultural, nutricional y potencial agroindustrial por su contenido de compuestos funcionales, como las betalaínas; no obstante, a pesar de su relevancia, existe escasa información científica sobre la caracterización físico-química de sus frutos. Se evaluaron parámetros alométricos, colorimétricos y contenido de betalaínas en 13 accesiones de ayrampo conservadas en el Banco de Germoplasma del INIA, en el Centro Experimental Santa Rita (Arequipa), durante la campaña agrícola 2024-2025. Se analizaron el peso de fruto, el peso fresco de pulpa y semilla, el peso seco de pulpa y semilla, la materia seca, coordenadas de color (L*, a*, b*) y la concentración de betacianinas y betaxantinas mediante espectrofotometría UV-VIS. Los datos se sometieron a análisis de varianza, y comparaciones múltiples mediante la prueba de Tukey, análisis de correlación de Pearson, componentes principales y agrupamiento jerárquico. Se evidenció una alta variabilidad fenotípica entre accesiones. El PER018775 presentó el mayor peso seco de pulpa y semilla (2,99 g); el PER018759 destacó en materia seca (38,94%); el PER018762 mostró los valores más altos de los componentes a* y b*; y el PER018767 tuvo la mayor concentración de betalaínas (158,78 mg·100 g⁻¹ de materia seca). El análisis de agrupamiento jerárquico clasificó a las accesiones en tres clústeres diferenciados por rendimiento, colorimetría y contenido de betalaínas. El ayrampo mostró un alto potencial como fuente de pigmentos naturales, especialmente betalaínas, con potencial aplicación en la industria alimentaria y la farmacéutica.
Vis-NIR spectroscopy and machine learning for prediction of soil fertility indicators and fertilizer recommendation in Andean highland and rainforest agroecosystems
(MDPI, 2026-04-26) Pizarro Carcausto, Samuel Edwin; Ccopi Trucios, Dennis; Ortega Quispe, Kevin Abner; Contreras Pino, Duglas Lenin; Ñaupari, Javier; Cano, Deyvis; Patricio Rosales , Solanch Rosy; Loayza, Hildo; Apolo Apolo, Orly Enrique
This study evaluated the use of visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning (ML) algorithms to predict soil fertility-related properties in two contrasting agroecological regions of Peru: the Highlands and the Rainforest. A total of 297 soil samples were analyzed using portable spectroradiometers covering a spectral range of 350–2500 nm, applying transformations such as Savitzky–Golay smoothing, first derivative, and band depth. Predictive models were developed using PLSR, Random Forest, Support Vector Machines, and neural networks. Results show variable predictive performance across soil properties and ecosystems. Organic matter in Highland soils and calcium in Rainforest soils achieved the strongest test-set accuracy (R2 > 0.70), while pH and texture fractions showed moderate performance (R2 = 0.42–0.67), and mobile nutrients including phosphorus, potassium, and sodium showed limited predictive accuracy due to their weak spectral expression. Spectral predictions were further integrated into a structured nutrient balance framework to assess agronomic reliability. Nitrogen fertilizer recommendations showed the strongest agreement between observed and predicted values across both ecosystems, whereas K2O and CaO recommendations in Highland soils were substantially underestimated, demonstrating that property-level statistical performance does not guarantee agronomic reliability. These findings confirm that Vis-NIR spectroscopy combined with ML represents a fast, cost-effective, and sustainable alternative to conventional soil analysis, especially in rural areas with limited laboratory infrastructure. Expanding regional calibration datasets and exploring mid-infrared FTIR spectroscopy as a complementary technology are identified as priority directions for improving predictions of agronomically critical nutrients.
Indirect monitoring of heterogeneous tropical agroforestry systems using active and passive remote sensing
(Elsevier B.V., 2026-03-11) Sánchez Fuentes, Teiser; Gómez Fernández, Darwin; Fernandez Jibaja, Jorge Antonio; Oblitas Troyes, Jhon Franklin; Chuquibala Checan, Beimer; Tafur Culqui, Josué; Quichua Baldeon, Rosalia; Taboada Mitma, Víctor Hugo; Tineo Flores, Daniel; Goñas Goñas, Malluri; Atalaya Marin, Nilton
Monitoring agroforestry systems remains challenging due to canopy heterogeneity and the coexistence of species with contrasting dynamics. While field-based methods offer high accuracy, they are inefficient for rapid and multitemporal structural assessments. This study integrated LiDAR and multispectral data collected using a Matrice 350 RTK equipped with a Zenmuse L2 sensor and a RedEdge-P camera. Raw LiDAR data were processed in DJI Terra v4.1 and subsequently pre-processed and corrected in TerraSolid v23.011, whereas multispectral products were generated in Agisoft Metashape Professional v2.2.1. The derived metrics indicated greater growth in System A, driven by fast-growing species, whereas System B showed an overall reduction with slight increases in the upper percentiles. In addition, MSAVI and MTVI2 were sensitive to canopy structure, while GNDVI and NDRE responded to foliage content. The agreement analysis revealed a slight bias (0.09 m) toward height overestimation by LiDAR compared to the hypsometer, with no apparent proportional error. This approach provides a replicable framework for multitemporal monitoring of structural and physiological changes in tropical vegetation, with potential for regional scaling and application in sustainable forest system management.
Soil spatial variability in high-yield Peruvian Amazon coffee: a geostatistical approach for precision fertilization
(Frontiers Media SA, 2025-12-18) Mejía Maita, Sharon Yahaira; Quispe Matos, Kenyi Rolando; Díaz Chuquizuta, Henry; Rengifo Sánchez, Raihil Rabindranath; Mercado Chinchay, Ruth Lizbeth; Cuevas Gimenez, Juan Pablo; Solórzano Acosta, Richard Andi
Fertilization practices in coffee plantations often overlook the spatial variability of soils, particularly in mountainous regions with acidic conditions. Although geostatistics has been used to map nutrient distributions, its integration with multivariate analysis to identify differentiated fertilization zones in coffee systems remains limited. This study evaluated the influence of soil properties, altitude, and crop age on coffee yield by combining principal component analysis (PCA) and ordinary kriging to design site-specific fertilization strategies. A total of 70 soil samples were collected from three districts of the Peruvian high jungle (San Martín and Amazonas), measuring physical and chemical properties, altitude, and crop age. The following analyses were applied: (1) Spearman correlations to assess associations with yield, (2) PCA to identify fertility gradients, and (3) geostatistical models with cross-validation. The PCA identified two main gradients: PC1 (32.41% of variance) associated with cation exchange capacity (CEC) and organic matter, and PC2 (17.88%) associated with the availability of K and P and crop age. Cross-validation confirmed high accuracy in the spatial prediction of available P and K across the three study areas. Kriging maps revealed zones with high available K (>150 mg kg⁻¹) and P (>20 mg kg⁻¹) associated with yields >1.5 t ha⁻¹. The integration of PCA and geostatistics enabled the delineation of management zones with differentiated nutrient requirements, reducing fertilization needs by up to 30% in areas with high fertility potential (e.g., Alto Saposoa). Overall, the results provide a solid methodological basis for implementing precision fertilization strategies in tropical coffee systems, promoting more efficient nutrient use and greater production sustainability.
Antagonistic interaction between zinc and cadmium in cocoa (Theobroma cacao L. var. CCN-51) seedlings amended with rock phosphate
(Frontiers Media SA, 2026-02-12) Díaz Chuquizuta, Henry; Malca Quezada, María Esmilda; Vallejos Torres, Geomar; Cuevas Gimenez, Juan Pablo; Huamaní Yupanqui , Hugo Alfredo; Sánchez Ojanasta, Martín; Solórzano Acosta, Richard Andi; Martínez Zapata, Boris Guillermo
Introduction: In the San Martın region, several studies have reported Cd concentrations in surface soils approaching the upper limit (UL), with mean values ranging from 0.27 to 1.351 mg·kg- ¹. Methods: Cadmium (Cd) transfer to Theobroma cacao (CCN-51) seedlings was evaluated under 12 factorial combinations of phosphate rock (RFP) and foliar zinc sulphate (ZnSO4) applications, using relative uptake (foliar Cd/soil Cd) as the primary response variable. Results: The treatment showing the highest Cd uptake was T4, defined as RFP = 0 mg·kg-1 and ZnSO4 = 527.80 mg·plant-1, with a value of 53.12. The observed range in relative uptake was 33.08 units, indicating substantial variation among management combinations. At the factor-level analysis, the high RFP treatment (114.55 mg·kg- ¹) was associated with an average reduction of approximately 26.5% in relative uptake and lower within-group variability compared to the 0 mg·kg- ¹ level. Interaction plots indicated that the effect of ZnSO4 on nutrient uptake depended on RFP level, with a descending response profile at high RFP concentrations. In parallel, soil correlation analyses identified available phosphorus and pH as the principal modulators of Cd transfer from soil to plant. Leaf-level principal component analysis showed that Zn and K were projected in the opposite direction to P2O5 and Cd, consistent with an ionic balance mechanism regulating Cd accumulation, and achieved an overall classification accuracy of approximately 81%, thereby confirming multivariate separability among treatments. Discussion: Collectively, these integrated results support identifying T4 as the treatment with the highest Cd uptake within the evaluated set. Accordingly, the presence of Zn²+–Cd²+ antagonism can be asserted; however, its expression is strongly influenced by soil pH and, most critically, by the availability of phosphorus derived from RFP.
Calidad fisiológica de las semillas de Vasconcellea pubescens A. DC en función del contenido de humedad y tratamientos pregerminativos
(Universidad Central del Ecuador (UCE) — Facultad de Ciencias Agrícolas, 2026-04-17) Lorenzo Quispe, Jhojana Marilia; Carrillo Castillo, Fredesvinda; Rucabado Miranda, Ana Laura; Borjas Ventura, Ricardo
Conocer la calidad fisiológica de las semillas y el control de la germinación contribuye significativamente en el uso y conservación de especies nativas. Vasconcellea pubescens A. DC, es importante por su valor nutricional en Perú; sin embargo, uno de los problemas que presenta esta especie es la latencia de su semilla. El objetivo de este estudio fue evaluar la influencia del contenido de humedad de la semilla y siete tratamientos pregerminativos, sobre la calidad fisiológica de las semillas almacenadas durante seis años a 10 °C y 50% de humedad relativa. El estudio se realizó en un diseño completamente al azar con un arreglo factorial, evaluando dos contenidos de humedad de semilla (8 y 9%) y siete tratamientos pregerminativos (GA3 200 ppm 24 h⁻¹, GA3 400 ppm 24 h⁻¹, KNO3 2,5% 24 h⁻¹, KNO3 1,5% 24 h⁻¹, escarificación mecánica, agua caliente a 70 °C 5 minutos⁻¹, y testigo). Los ANOVA se efectuaron a un nivel de significancia del 5% empleando la prueba de medias de Tukey (α = 0,05). Los resultados mostraron que los factores "Contenido de humedad" y "Tratamientos pregerminativos" afectaron significativamente la germinación, siendo la escarificación mecánica y GA3 a 200 ppm los que promovieron la mayor germinación (hasta 80%). En contraste, el KNO3 y el agua caliente presentaron altos porcentajes de semillas no germinadas, además que, la aplicación de GA3 a 400 ppm presentó oxidación de los cotiledones. En este sentido, el almacenamiento de semillas de V. pubescens a una humedad de 9% mantiene su viabilidad y mejora su respuesta a los tratamientos pregerminativos, como una alternativa para la conservación ex situ, asegurando la preservación y utilidad sostenible de esta especie.
Evaluación del rendimiento de diez variedades de trigo harinero en cultivo de secano en dos ambientes de Cusco - Perú, 2019-2020
(Asociación Latinoamericana para el Avance de la Ciencia (ALAC), 2026-04-14) Cuba Mellano, Gloria; Álvarez Cáceres, Aquilino; Céspedes Flores, Elizabeth; Estrada Zuñiga, Rigoberto
La investigación evaluó la influencia del secano y la variabilidad climática en el rendimiento de diez variedades de trigo harinero (Triticum aestivum L.) en los distritos de Taray y Zurite, Cusco, empleando un diseño de bloques completos al azar durante la campaña 2019-2020. El análisis climatológico identificó un déficit hídrico del 15.6% y un descenso significativo de las temperaturas mínimas en Zurite (p = 0.0006), con una reducción de la temperatura de 2.58 °C respecto al promedio histórico durante el ciclo del cultivo. En estas condiciones de estrés térmico, Zurite alcanzó un rendimiento promedio superior (6.24 t/ha) frente a Taray (4.08 t/ha), confirmándose una interacción genotipo-ambiente altamente significativa (p = 0.0005). La variedad INIA 434 Espiga Misha destacó como la más productiva, alcanzando 7.197 t/ha en Zurite, mientras que INIA 418 El Nazareno mostró mayor estabilidad en el ambiente restrictivo de Taray con 4.86 t/ha. El rendimiento de grano presentó correlaciones positivas y altamente significativas con los días a madurez (r = 0.62) y la altura de planta (r = 0.44). Se concluye que el uso de genotipos con ciclos de desarrollo prolongados permite maximizar la productividad al capitalizar los recursos disponibles ante la variabilidad térmica e hídrica de la región.
The effects of the inoculation of bacterial microorganisms (Pseudomonas sp. and Bacillus sp.) on soil quality, aerial biomass and nutritional quality of native grasses under field conditions in the Peruvian highlands
(Soil Science Society of Poland, 2026-04-15) Arias Arredondo, Alberto Gilmer; Pizarro Carcausto, Samuel Edwin; Requena Rojas, Edilson Jimmy; Verástegui Martínez, Patricia; Cruz Luis, Juancarlos Alejandro; Solórzano Acosta, Richard Andi
Peruvian highland ecosystems cover approximately 22 million hectares and provide key ecosystem services that support human well-being and food security. Soil functioning in these ecosystems largely depends on the activity of microbial communities. This study evaluated the effects of Pseudomonas sp. and Bacillus sp. inoculation on soil chemical properties, aerial biomass production, and nutritional quality of Festuca dolichophylla, Jarava ichu and Cinnagrostis vicunarum. A field experiment was conducted at 4379 m a.s.l. in the central Peruvian highlands. Bacterial inoculation increased soil organic matter and nitrogen availability in plots dominated by J. ichu and F. dolichophylla inoculated with Bacillus sp., compared to non-inoculated controls. Higher soil phosphorus content was observed in C. vicunarum pastures inoculated with Pseudomonas sp. In terms of biomass production, significant increases were recorded in C. vicunarum under both bacterial inoculations and in F. dolichophylla associated with Bacillus sp., while J. ichu showed higher yields with Pseudomonas sp. In addition, bacterial inoculation improved forage nutritional quality, particularly total protein, calcium, and phosphorus contents in J. ichu, highlighting species-specific plant–microorganism interactions. Overall, the inoculation of beneficial bacteria represents a promising and environmentally sustainable strategy to improve soil quality, forage productivity, and nutritional value in native highland grasslands, contributing to more resilient rangeland systems and the conservation of ecosystem services.
Mapping current and future coffee suitability in Peru under climate change: implications for restoration and deforestation-free development
(Frontiers Media S.A, 2026-04-20) Zabaleta Santisteban, Jhon A.; Rojas Briceño, Nilton B.; Silva López, Jhonsy O.; Medina Medina, Angel J.; Tuesta Trauco, Katerin M.; Rivera Fernandez, Abner S.; Silva Melendez, Teodoro B.; Grandez Alberca, Marlen A.; Puscan Rojas, Julio; Salas López, Rolando; Oliva Cruz, Manuel; Cotrina Sanchez, Alexander; Gómez Fernández, Darwin; Barboza, Elgar
Coffee cultivation is central to rural livelihoods and Andean–Amazonian landscapes in Peru; however, it faces increasing pressure from climate change and land-use restrictions. This study aimed to assess the current and future ecological suitability of Coffea arabica at the national scale. A Maximum Entropy (MaxEnt) modeling framework was applied, integrating high-resolution bioclimatic, topographic, and edaphic variables. Model performance was robust (mean AUC = 0.858), and variable importance was evaluated using jackknife tests and contribution metrics. Elevation, precipitation of the driest quarter (bio17), soil nitrogen content, and bulk density were identified as the main determinants of habitat suitability. Under current climatic conditions, highly suitable areas cover 42,322.95 km2 (3.3% of Peru), mainly along the eastern Andean slopes. Spatial exclusion scenarios revealed a pronounced funnel effect in effective land availability, with reductions exceeding 80% when forest-cover constraints were applied. Approximately 39.8% of highly suitable areas overlap with degraded lands, highlighting opportunities for productive restoration through agroforestry systems. Future projections under SSP1–2.6 to SSP5–8.5 scenarios indicate consistent contractions of highly suitable areas (–23% to –42%) and an upslope shift toward higher elevations, while unsuitable areas expand by 4%–5% nationally. These findings provide spatially explicit evidence to support climate-smart territorial planning, restoration prioritization, and sustainable coffee development under accelerating climate change.
Spatial analysis of soil acidity and available phosphorus in coffee-growing areas of Pichanaqui: Implications for liming and site-specific fertilization
(MDPI, 2025-07-28) Quispe Matos, Kenyi Rolando; Hermoza Ayme, Nilton Alexander; Mejia Maita, Sharon Yahaira; Romero Chávez, Lorena Estefani; Ottos Díaz, Elvis; Arce, Andrés; Solórzano Acosta, Richard
Soil acidity is one of the main limiting factors for coffee production in Peruvian rainforests. The objective of this study is to predict the spatial acidity variability for recommending site-specific liming and phosphorus fertilization treatments. We analyzed thirty-six edaphoclimatic variables, eight methods for estimating liming doses, and three geospatial variables from 552 soil samples in the Pichanaqui district of Peru. Multivariate statistics, nonparametric comparison, and geostatistical analysis with Ordinary Kriging interpolation were used for data analysis. The results showed low coffee yields (0.70 ± 0.16 t ha⁻¹) due to soil acidification. The interquartile ranges (IQR) were found to be 3.80–5.10 for pH, 0.21–0.87 cmol Kg⁻¹ for Al⁺³, and 2.55–6.53 mg Kg⁻¹ for available P, which are limiting soil conditions for coffee plantations. Moreover, pH, Al⁺³, Ca⁺², and organic matter (OM) were the variables with the highest accuracy and quality in the spatial prediction of soil acidity (R² between 0.77 and 0.85). The estimation method of liming requirements, MPM (integration of pH and organic material method), obtained the highest correlation with soil acidity-modulating variables and had a high spatial predictability (R² = 0.79), estimating doses between 1.50 and 3.01 t ha⁻¹ in soils with organic matter (OM) > 4.00%. The MAC (potential acidity method) method (R² = 0.59) estimated liming doses between 0.51 and 0.88 t ha⁻¹ in soils with OM < 4.00% and potential acidity greater than 0.71 cmol Kg⁻¹. Regarding phosphorus fertilization (DAP), the results showed high requirements (median = 137.21 kg ha⁻¹, IQR = 8.28 kg ha⁻¹), with high spatial predictability (R² = 0.74). However, coffee plantations on Ferralsols, with Paleogene parental material, mainly in dry forests, had the lowest predicted fertilization requirements (between 6.92 and 77.55 kg ha⁻¹ of DAP). This research shows a moderate spatial variation of acidity, the need to optimize phosphorus fertilization, and an optimal prediction of liming requirements using the MPM and MAC methods, which indicate high requirements in the southwest of the Pichanaqui district.
Morphometric variation and production constraints of Criollo sheep in the high Andes of southern Peru
(MDPI, 2025-08-31) Estrada Cañari, Richard; Guelac Mori, Elias; Pedemonte Cruz, Cristian Wilmer; Chiqui Condori, Katherine Milagros; Montero Pacherres, Javier Klinsmann; Cerdan Ramos, Dilser Alberto; Zúñiga Aranibar, Dayana Milagros
This study aimed to characterize the morphometric traits and production systems of Criollo sheep in the highlands of Caylloma, Arequipa, Peru. A total of 455 sheep were evaluated using a stratified proportional sampling method across the districts of Tisco, San Antonio de Chuca, and Yanque. Morphometric data were collected under standardized conditions, and nine zoometric indices were calculated to assess functional conformation and productive aptitude. Additionally, 52 sheep producers were surveyed to contextualize herd management practices. Results revealed low levels of formal education and limited technical assistance among producers. Sheep farming was primarily sustained by family tradition, with declining flock sizes attributed to pasture scarcity and climatic challenges. Campaign-based sales strategies and rudimentary reproductive management were prevalent. Health practices showed widespread deworming but limited preventive care. Multivariate analysis indicated significant morphometric variation linked to sex, biotype, and dental stage. This integrative approach highlights both the adaptive potential and production constraints of Criollo sheep in high-altitude environments, providing a basis for developing breeding strategies based on morphometric indices.
Predictive modeling of honey yield in rural apiaries: insight from Chachapoyas, Amazonas, Peru
(MDPI, 2025-11-18) Briceño Mendoza, Yander Mavila; Saucedo Uriarte, José Américo; Quiñones Huatangari, Lenin; Gaslac Gomez, Jhoyd B.; Quispe Ccasa, Hurley Abel; Cayo Colca, I.S.
Honey production is influenced by multiple factors, including climatic conditions, hive management practices, and harvest scheduling. This study evaluated the predictive capacity of statistical modeling techniques using data mining algorithms (MARS, CHAID, CART, and Exhaustive) and artificial neural network algorithms (Multilayer Perceptron, MLP) to estimate honey yields in apiaries located in northeastern Peru. A structured survey was conducted with sixty-nine beekeepers across nineteen districts in the Chachapoyas province. Variables included beekeeper experience, instruction, hive count, visit frequency, harvest frequency, additional income-generating activities, and geographic location. Descriptive statistics, non-parametric tests, Spearman correlations, and exploratory factor analysis were applied to identify latent structures. A linear mixed-effects model was used to assess the combined influence of predictors on honey production, with district included as a random effect. Results indicated that hive number, beekeeping experience, harvest frequency, and exclusive engagement in apiculture were statistically associated with increased honey yields. The model explained a substantial proportion of variance, supporting the integration of technical and socio-demographic variables in production forecasting. These findings demonstrate the utility of predictive modeling for informing hive management strategies and improving the operational efficiency of small-scale beekeeping systems in Andean regions.
Motility performance of thawed spermatozoa of bulls from the tropics throughout the year
(Animals, 2025-08-21) Poclín Rojas, Annie Yoselin; Arbaiza Barnechea, Martín Daniel; Segura Portocarrero, Gleni Tatiana; Ampuero Trigoso, Gustavo; Bernilla Carrillo, Diana; Depaz Hizo, Benjamín Alberto; Vásquez Tarrillo, Ronald Will; Diaz Quevedo, Clavel; Quispe Ccasa, Hurley Abel
Under tropical conditions, seasonal variations may also influence the sperm characteristics of Bos indicus. The objective was to evaluate the motility of thawed sperm of bulls from the Peruvian tropics throughout the year. Over 24 months, 129 ejaculates were evaluated based on semen quality and subjected to slow horizontal freezing in 0.5 mL straws. After thawing, the individual, seasonal period, and season effect on motility and kinetic parameters were analyzed using a Sperm Class Analyzer® (Microptic S.L.U., Barcelona, Spain). There was an individual effect on volume, motility, fresh concentration, and kinetic parameters when thawed. In the dry period, higher straight-line velocity (VSL) (p < 0.05) and beat cross frequency (BCF) were found than in the rainy period (p < 0.01). In summer and autumn, there was greater total motility, fast, circular routes, curvilinear velocity, average path velocity, VSL, amplitude of lateral displacement of the head, and BCF (p < 0.01). Greater volume and motility were found in winter and spring, but in summer and autumn, greater speed and vigor of movement were obtained in thawed sperm. The variation in annual climate patterns influences the seminal quality of bulls, and its effect needs to be assessed to propose adaptation strategies to climate change in tropical areas.

Examinar

Envíos recientes