Artículos científicos

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

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Assessment of soil fertility variability for maize production in highland agroecosystems of Peru
(Polish Society of Ecological Engineering (PTIE), 2026-04-01) Garcia Seguil, Erika Janina; Ccopi Trucios, Dennis; Requena Rojas, Edilson Jimmy; Sanabria Quispe, Samuel; Arias Arredondo, Alberto Gilmer; Gavino Lulo, Esthefany Irene; Azabache, Andres; Pizarro Carcausto, Samuel Edwin
Maize (Zea mays L.) is central to food, feed, and rural livelihoods, yet the yields in Peru’s highlands remain modest, underscoring the need for spatially explicit soil diagnostics. This study aimed to characterize the spatial variability of soil fertility in a highland maize production area of the southern Mantaro Valley and translate those patterns into site-specific management zones. The authors sampled the arable layer (0–30 cm) at 100 plots and analyzed pH, electrical conductivity, exchangeable acidity, texture, organic matter (OM), total nitrogen (N), available phosphorus (P), available potassium (K), exchangeable cations (Ca, Mg, Na, K), and calcium carbonate (CaCO₃). Laboratory data were integrated with environmental covariates using geostatistics, Random Forest, and GIS to generate high-resolution maps. Results showed uneven distributions in key attributes about 25% of the area with P deficiency, 15% with localized K shortages, and ~20% with OM < 2% while pH and CEC were comparatively stable. Random Forest achieved strong predictive performance for relatively stable properties (e.g., OM, pH, exchangeable cations), whereas mobile nutrients (available P, exchangeable K) were less predictable. The resulting products constitute the first high-resolution soil-fertility baseline for maize in the southern Mantaro Valley. The maps delineate fertilization management zones and provide a practical basis for preliminary rate recommendations that target constraints while avoiding surpluses. Future work will refine these zoned recommendations through yield-response trials, seasonal monitoring of mobile nutrients, and farmercentered decision-support tools, with the goal of improving nutrient-use efficiency, sustaining maize productivity, and reducing environmental risks across the valley.
Moderate deficit irrigation improves agronomic performance of quinoa (Chenopodium quinoa Willd.) compared to full irrigation in the central highlands of Peru
(Learning Gate, 2026-03-19) Gavino Lulo, Esthefany Irene; Ccopi Trucios, Dennis; Garcia Seguil, Erika Janina; Requena Rojas, Edilson Jimmy; Contreras, Jose; Solórzano Acosta, Richard Andi; Betega, S.D.; Yaranga, Raúl M.; Pizarro Carcausto, Samuel Edwin
This study evaluated the agronomic performance and water productivity of quinoa (Chenopodium quinoa Willd. cv. INIA 433) under three irrigation regimes in the central highlands of Peru: optimal irrigation (Ks = 1.00), moderate deficit (Ks = 0.66), and severe deficit (Ks = 0.49). The experiment combined constant water table lysimeters and field plots, integrating crop coefficient estimation, water balance analysis, and multispectral monitoring (NDVI, NDRE, SRWI) using UAV imagery and ground spectroradiometry. Moderate water stress (Ks = 0.66) significantly improved reproductive performance, producing approximately 8,000 grains per plant compared with ~3,900 grains per plant under optimal irrigation. Grain protein content increased from 4.8% to 6.0%, while evapotranspiration decreased by 37% (from 374.5 to 234.4 mm), markedly improving water use efficiency. In contrast, optimal irrigation promoted maximum vegetative growth (plant height ~110 cm; NDVI 0.7–0.8) but lower reproductive output, whereas severe stress (Ks = 0.49) reduced yield to 4,400 grains per plant and accelerated senescence. Multispectral indices effectively distinguished water stress levels: NDVI reflected canopy vigor, NDRE detected chlorophyll variation, and SRWI captured plant water status. The results demonstrate that regulated deficit irrigation enhances water productivity and grain quality in quinoa. Maintaining Ks values around 0.65–0.70 appears to optimize yield and resource use efficiency in water-limited Andean agroecosystems.
Geographic information system applications in bee research
(MDPI, 2026-05-29) Rojas Briceño, Nilton B.; Silva López, Jhonsy O.; Guzman Valqui, Betty Karina; Ix Balam, Manuel A.; Ramos Tejeda, José L.; Oliva Cruz, Manuel; Veneros, Jaris; García, Ligia
Bees play crucial ecological, economic, and environmental roles, and research on them increasingly includes a spatial dimension. Geographic Information Systems (GISs) enable the acquisition, storage, analysis, management, and visualization of spatial data. However, GIS applications in bee research have expanded while remaining dispersed across topics, tools, taxa, and methodological approaches. This study provides a comprehensive and updated review of GIS applications in bee research by integrating bibliometric analysis with a structured synthesis of GIS purposes and techniques. A total of 228 publications were analyzed to assess publication trends, co-authorship patterns, keyword themes, study areas, taxonomic coverage, GIS application themes, and methodological tools. GIS was used to select suitable apiary sites, map floral resources, analyze bee behavior, assess diseases and pests, monitor bee products, evaluate urban and landscape contexts, and predict climate change effects. The main GIS-related approaches included multicriteria decision analysis (MCDA), remote sensing, species distribution models (SDMs), spatial interpolation, WebGIS platforms, and emerging machine-learning applications. The review also identified underrepresented taxa, especially wild bees, stingless bees, and other Apis species. Future advances should integrate MCDA with data-driven models, improve floral-resource mapping with remote sensing, and strengthen reproducibility through standardized spatial data and workflows.
Genomics and reproductive biotechnologies in goat production systems in Peru
(2026-06-01) Romero Avila, Yolanda Madelein; Sessarego Davila, Emmanuel Alexander; Pinazo Herencia, René Alfredo; Cruz Luis, Juancarlos Alejandro
Goat production in Peru is primarily carried out under extensive systems shaped by climatic variability, forage seasonality, infrastructure limitations, and persistent sanitary pressure. In this context, Creole goats represent a strategic animal genetic resource due to their capacity to adapt to arid and high-Andean environments. This review integrates the available evidence on production typologies in the main goat-producing regions of the country, the major sanitary and structural bottlenecks, and the state of the art of genomic, multi-omics, and reproductive biotechnology tools applicable to goats. It discusses how the transition from traditional markers to SNP genotyping, together with functional approaches such as microbiome analysis, transcriptomics, and proteomics, can contribute to understanding the biological basis of complex traits related to resilience, feed efficiency, and reproductive performance. Likewise, the potential of precision livestock farming to generate longitudinal phenotypes and strengthen genetic improvement programs in low-input systems is highlighted. Finally, priorities and considerations are outlined to advance the integration of phenotyping, genomics, and reproductive biotechnologies in extensive contexts, with emphasis on the generation of systematic data, interinstitutional coordination, and technology transfer aimed at the sustainability and conservation of goat resources. These insights may also inform genetic improvement strategies in other developing countries facing similar environmental and structural constraints in low-input goat production systems, particularly in arid and semi-arid regions.
Integrated assessment of water quality and trophic status in High Altitude Andean lagoons: a multi-index and multivariate approach for sustainable fish farming management
(Elsevier Inc., 2026-05-27) Custodio, María; Huarcaya, Javier; Ccopi Trucios, Dennis; Alvarez, Daniel; Pizarro Carcausto, Samuel Edwin; Ortega Quispe, Kevin Abner
High-altitude Andean lagoons are ecologically sensitive systems whose water quality is under increasing pressure from the expansion of aquaculture and other human activities. An integrated assessment of water quality and trophic status was carried out in three lagoons located in the highlands of central Peru, using a multi-index and multivariate approach that combined physicochemical parameters, trophic indices (TRIX, TSI, molar N:P ratio), and heavy-metal indices, evaluated against Peruvian EQS, USEPA, and CCME water-quality standards. Principal component analysis explained 60.1% of the total variability, with the first axis (40.4%) structured by an organic loading and nutrient gradient that consistently separated Tipicocha from the other lagoons. Total phosphorus in Tipicocha reached concentrations well above the Peruvian EQS threshold, and TRIX values classified all lagoons as eutrophic to hypertrophic (5.46–6.21). TSI (TP) classified Tipicocha and Tranca Grande as hypereutrophic, whereas TSI (Chl-a) remained within the eutrophic range (53–60), a pattern consistent with additional environmental constraints on phytoplankton biomass. Molar N:P ratios indicated strong nitrogen limitation in Tipicocha and Tranca Grande, whereas Pomacocha showed a seasonal shift from co-limitation during the dry season to potential nitrogen limitation during the rainy season. Metal contamination was low according to EQS and USEPA criteria, whereas CCME thresholds suggested moderate to high contamination (HPI up to 105.1), with systematic exceedances of Cd, Cu, and Zn at all sites. Taken together, the results point to strong trophic enrichment associated with fish-farming activity as the main pressure on water quality in these ecosystems and show that the choice of regulatory framework has a decisive influence on metal-risk classification.
Spatial assessment of heavy metal contamination in agricultural soils of the lower Chancay–Huaral valley, Peru
(Wiley, 2026-05-29) Samaniego Vivanco, Tomás Daniel; Ramirez Aparicio, Jorge Adrian; Solórzano Acosta, Richard Andi
Soil contamination by heavy metals (HMs) poses a potential threat to agricultural productivity and food security, particularly in rapidly developing coastal regions. This study evaluates the spatial distribution and contamination levels of cadmium (Cd), copper (Cu), lead (Pb), zinc (Zn), and manganese (Mn) in agricultural soils from the Huaral, Chancay, and Aucallama districts of the Lima region, Peru, an area undergoing urban expansion, mining concessions, and the establishment of a new international port. A total of 88 soil samples were analyzed for metal concentrations using microwave plasma atomic emission spectrophotometry (MP-AES), along with key physicochemical properties. Geostatistical interpolation techniques, including ordinary kriging (OK) and cokriging (CK), were applied to generate spatial prediction maps for each element. The geo-accumulation index (Igeo) and contamination factor (CF) were used to determine the contamination status. Mean Igeo values ranged from −0.59 to −0.46, while mean CF values ranged from 1.09 to 1.21, indicating generally unpolluted to moderately polluted conditions. Although maximum values reached 1.41 for Igeo and 4.21 for CF, these were spatially localized. Results revealed that most metal concentrations remained below the Peruvian Environmental Quality Standards for agricultural soils, suggesting predominantly natural geochemical origins. However, a small proportion of samples showed slight and localized exceedances (4.54% for Cd and 3.41% for Mn). For elements not regulated under national standards, international guidelines were considered, further supporting the absence of widespread anthropogenic contamination. Higher concentrations of Cd, Cu, Zn, Pb, and Mn were spatially clustered in the north-central sector of the study area, suggesting responses to soil properties such as pH, texture, and moisture. Notably, areas of higher concentration did not coincide with zones of higher contamination indices, indicating limited anthropogenic influence. However, proximity to urban expansion, mining activities, and port infrastructure highlights the need for continuous soil monitoring to prevent accumulation and ensure agricultural sustainability.
Ecological risk associated with potentially toxic elements in agricultural soils across coastal and highland valleys
(Elsevier Ltd., 2026-05-06) Pérez Porras, Wendy Elizabeth; Ccopi Trucios, Dennis; Flores Marquez, Ricardo; Carbajal Llosa, Carlos Miguel; Pizarro Carcausto, Samuel Edwin
Soil elemental composition in heterogeneous agroecosystems is shaped by interacting environmental and anthropogenic controls. This study evaluated the spatial variability of potentially toxic elements (PTEs: Cu, Cr, Fe, Mn, Mo, Ni, Pb, V, Zn, As, and Cd) across coastal (Chancay and Pativilca) and highland (Mantaro and Tarma) agricultural valleys of Peru. A stratified sampling design was combined with multivariate analyses (PCA, PERMANOVA, PERMDISP, and variance partitioning) and ecological risk assessment using integrated indices (PLI, mCd, SRI, and Nemerow index). The first two principal components explained 50.2% of total variance (PC1 = 36.8%; PC2 = 13.4%), reflecting distinct soil–geochemical and climatic–spatial gradients. The Valley × Zone interaction significantly structured elemental composition (R² = 0.049, p = 0.011), whereas crop type showed no significant effect (p = 0.838). Variance partitioning indicated that soil physicochemical, climatic, and spatial/topographic predictors jointly explained 60% of total variation (adjusted R² = 0.597), with the three-way shared fraction accounting for 28%, highlighting strong coupling among pedogenic, climatic, and topographic drivers. Ecological risk indices revealed clear spatial differentiation between systems. Highland valleys exhibited greater contamination intensity, spatial heterogeneity, and more frequent high-risk categories according to PLI, mCd, and SRI. In contrast, coastal valleys showed more homogeneous and diffuse accumulation patterns associated with long-term agricultural intensification. These findings underscore the need for regionally adapted soil monitoring frameworks that incorporate environmental gradients in the assessment and management of PTE-related ecological risk in agricultural landscapes.
Mathematical modeling of the germination and growth of Leucaena leucocephala under different substrates and nursery conditions
(Polish Society of Agricultural Engineering, 2026-05-28) Sauceo Uriarte, José Américo; Milla Pino, Manuel Emilio; Quispe Ccasa, Hurley Abel; Segura Portocarrero, Gleni Tatiana; Vásquez Pérez, Héctor Vladimir; Gongora Bardales, Deiner Jhonel; Maicelo Quintana, Jorge Luis
Livestock production in tropical regions is predominantly extensive and relies heavily on native or monoculture pastures, which often prove insufficient for ruminant nutrition. The incorporation of Leucaena leucocephala into silvopastoral systems represents a promising strategy due to its high forage quality; however, information on its early establishment under nursery conditions remains limited. This study aimed to model the germination dynamics and early seedling growth of L. leucocephala under different substrate compositions during the nursery phase. Germination percentage and daily plant height were recorded over a 30-day period. Treatment effects were evaluated using analysis of variance (ANOVA) and growth dynamics were described using non-linear sigmoidal models (Gompertz, Logistic, von Bertalanffy, and Brody). Significant differences in germination rate among substrates were detected (p<0.05), whereas no significant effect of substrate on plant height was observed during the evaluation period (p>0.05). Among the evaluated models, von Bertalanffy, Gompertz, and Logistic functions provided the best fit for plant height based on R² and AIC criteria. Although some models showed high R² values for germination, elevated AIC values suggest limited biological adequacy. These findings highlight the usefulness of predictive modeling to support nursery management decisions, optimize substrate selection, and facilitate the establishment of L. leucocephala in sustainable silvopastoral systems.
Metabarcoding reveals rhizosphere microbiome differences in healthy and basal rot-affected dragon fruit plants
(Elsevier B.V., 2026-02-18) Guelac Santillan, Marly; Fernandez Castro, Paul; Huaman Pilco, Angel F.; Estrada Cañari, Richard; Rodríguez Grados, Pedro; Arbizu, Carlos I.
The rhizosphere microbiome plays a crucial role in plant health, yet its dynamics in Selenicereus megalanthus (yellow dragon fruit) remain poorly understood. This study employed high-throughput sequencing to characterize the bacterial and fungal communities in the rhizosphere of healthy and basal rot-affected plants across four commercial production sites in Amazonas department from Peru. Amplicon sequencing Metagenomics Sequencing (WOBI) targeting to 16S rRNA (for bacteria) and ITS (for fungi) gene regions show differences in microbial community structure associated with plant health status. Multivariate analyses revealed a clear disease-driven reassembly of the bacterial microbiome, marked by the loss of health-associated taxa (Xanthobacteraceae, Geminicoccaceae, Nocardioidaceae) and enrichment of oligotrophic and stress-tolerant groups (Nitrososphaeraceae, Acidobacteriaceae Subgroup 1). In contrast, fungal assemblages displayed structural inertia, responding primarily through pathogen-associated increases in Nectriaceae. Soil physicochemistry particularly pH, exchangeable aluminum, and nutrient levels modulated the strength of bacterial differentiation, highlighting the role of edaphic filters in microbiome resilience. Our findings provide evidence of a bacterial-centered dysbiosis associated with basal stem rot in S. megalanthus, while positioning fungal communities as structurally resilient components of the holobiont. Together, these results outline a framework in which disease is linked to altered plant microbe soil feedbacks rather than pathogen presence alone, and suggest that bacterial assemblages could inform the development of microbiome-based early-warning indicators and soil health strategies for sustainable dragon fruit management.
Forest structure and fragmentation dynamics in cacao-producing landscapes of Amazonas, Peru, revealed by multi-temporal land-use change and spaceborne LiDAR
(Springer Science+Business Media, LLC, part of Springer Nature, 2026-05-27) Cotrina Sanchez, Alexander; Barboza, Elgar; Veneros, Jaris; Huaman Pilco, Angel Fernando; García, Ligia; Guzman Valqui, Betty Karina; Oliva, Manuel; Rojas Briceño, Nilton B.; Torresani, Michele
The ongoing loss and degradation of tropical forests poses a significant threat to biodiversity, carbon storage, and ecosystem services throughout the Amazon Basin. Agroforestry systems such as cacao cultivation can help balance production and conservation, yet integrated analyses combining spatial and structural forest data remain limited. This study integrates multi-temporal land-use/land-cover (LULC) data, fragmentation metrics, and canopy indicators from the Global Ecosystem Dynamics Investigation (GEDI) mission to assess forest transformation across two contrasting cacao-producing landscapes in the Amazonas region of Peru. LULC dynamics (1985–2020) were derived from the 30m Global Land Cover Change Dataset (GLC_FCS30D), with 2020 used as a baseline consistent with the European Union Deforestation Regulation (EUDR). The 2020 forest/non-forest map was compared with the 10m Global Forest Cover 2020 product to quantify fragmentation across multiple grid sizes. GEDI L2A and L2B data provided structural metrics, including relative height (RH25–RH98), plant area index (PAI), foliage height diversity (FHD), and canopy cover, which were linked to fragmentation indicators. In the indigenous territories of Condorcanqui, cacao landscapes maintained stable forest cover, while rural areas in Bagua and Utcubamba showed greater forest loss and landscape modification. Fine-scale (10m) data revealed localised zones of conservation and degradation, particularly in lowland cacao areas. Taller, more structurally complex canopies were associated with less fragmented forests, whereas shorter and more heterogeneous structures reflected long-term disturbance. Integrating spaceborne LiDAR with multi-scale fragmentation metrics provides robust indicators of forest integrity, supporting sustainable cacao agroforestry management and conservation plannin.
Variabilidad fenotípica de accesiones de olivo (Olea europaea L.) del Banco de Germoplasma del INIA
(Universidad Nacional de Colombia, Facultad de Ciencias, Departamento de Biología, Sede Bogotá, 2026-04-28) León Mendoza, Luis Humberto; Torres Huall, Dayanha Beatriz; Condori Cuno, Esther; Huatuco Coaquira, Janet Libertad; Casanova Núñez Melgar, David Pavel
El olivo (Olea europaea) ocupa una extensa superficie agrícola en el departamento de Tacna, Perú. En este contexto, la Subdirección de Recursos Genéticos del Instituto Nacional de Innovación Agraria (INIA) del Perú, implementó en 2019 una colección con 30 accesiones para su conservación y estudio. El objetivo fue evaluar la variabilidad fenotípica e identificar accesiones promisorias mediante descriptores morfológicos y parámetros del fruto. El análisis de varianza y prueba de Tukey mostraron diferencias significativas entre accesiones, aunque no entre sus respectivas plantas madre. El análisis de diversidad fenotípica, mediante los índices de Shannon-Weaver y Simpson, indicó que los descriptores cualitativos más discriminantes fueron los relacionados con fruto y endocarpo; de forma concordante, el análisis de componentes principales en descriptores cuantitativos confirmó la alta influencia de estas variables. El análisis de Pearson evidenció correlaciones positivas entre (i) el índice de madurez y sólidos solubles, (ii) el rendimiento de aceite y sólidos solubles, y (iii) correlación negativa entre el rendimiento de aceite y diámetro del fruto. Por último, la prueba de concordancia de atributos y el análisis de variación permitieron identificar 16 descriptores y 12 accesiones constantes. Entre estas, Arbequina destacó como promisoria para la producción de aceite, Cabaret para aceituna de mesa, y Farga como material de doble propósito.
Morphological and genome characterization of Alternaria alternata causing blueberry (Vaccinium corymbosum L.) leaf spot in Peru
(SCIEPublish, 2026-05-20) Velasquez Ochoa, Edwin Ricardo; Osorio, Valentina; Leiva, Ana María; Pardo, Juan Manuel; Gil Ordoñez, Alejandra; Bartolini, Ida; Cuellar, Wilmer J
Blueberries (Vaccinium corymbosum L.), valued for their nutritional benefits and economic significance, have become Peru's leading agro-export crop. However, intensive cultivation can lead to phytosanitary problems if not addressed promptly, posing a serious threat to blueberry production. This study aimed to isolate and identify the causal agent of leaf spot symptoms initially observed in blueberries cultivated in Peru, marking the first formal documentation of its presence in the country. In 2022, leaf spot symptoms were recorded on V. corymbosum cv. Biloxi, in the north of Lima, Peru. Field observations revealed necrotic, sunken spots on leaves and fruits, with 4.84% of leaves diseased and 1.28% of fruits affected. Pathogen isolation and microscopic studies identified Alternaria alternata as the primary causal agent, which was confirmed by genome sequencing using Oxford Nanopore Technology. Pathogenicity tests demonstrated the fungus' ability to reproduce symptoms identical to those observed in the field, fulfilling Koch's postulates. Under experimental conditions, disease severity increased over time, with the affected leaf area ranging from 9.35% to 25.61% between 7 and 14 days post-inoculation. This study establishes A. alternata as a pathogen of blueberries in Peru and provides essential insights for future research and strategies to mitigate its impact on the industry.
Phenotypic variability of Calycophyllum spruceanum (Benth.) Hook. f. ex K. Schum. in the peruvian amazonia
(Revista Mexicana de Ciencias Forestales, 2026-04-21) Flores Castillo, Gorky; Mamani Mariaca, Yicelia Maura; Hilares Vargas, Sharmely
Calycophyllum spruceanum, commonly known as "capirona", is a tree native to the Peruvian Amazonia, with ecological, cultural and economic importance due to its diverse uses. However, gaps remain in understanding of the morphological traits that significantly contribute to its genetic diversity. This study evaluated 18 C. spruceanum individuals in situ using 34 qualitative and quantitative morphological descriptors (leaf, flower, fruit and seed) and 6 forest descriptors in two forest types in the Tambopata province, Madre de Dios. The results confirmed high variability in forest characteristics (CV > 35% for height and DBH), regardless of forest type (p > 0.05). Multiple correspondence analysis revealed a close association between leaf and flower descriptors (41.9% variability). Simultaneously, principal component analysis explained 44.6% of the total variance using two axes associated with leaf and reproductive morphology, allowing the grouping of individuals into three distinct morphological groups: one with a vegetative emphasis and two with favorable reproductive potential. Strong correlations (r ≥ 0.7) between leaf and reproductive traits support this classification. These findings validate the use of these descriptors as a baseline for identifying promising phenotypes and constitute an essential contribution to establishing ex situ germplasm banks aimed at the conservation and genetic improvement of the species in the Peruvian Amazonia.
Prediction of biomass and nutritional quality of tropical pastures using multispectral analysis and machine learning models
(Elsevier B.V., 2026-05-17) Tafur Culqui, Josué; Atalaya Marin, Nilton; Gómez Fernandez, Darwin; Taboada Mitma, Víctor Hugo; Cruz Luis, Juancarlos Alejandro; Neyra, Henri; Anchayhua Torres, Janella Jelin; Quichua Baldeon, Rosalía; Sánchez Fuentes, Teiser; Olano Camán, Yadhira Milagros; Barrazueta Campos, Mauro Adel; Tineo Flores, Daniel; Goñas Goñas, Malluri
Determining pasture productivity and nutritional value through non-destructive approaches aimed at optimizing forage resource management and improving efficiency in livestock systems has become an urgent priority. In this context, the objective of this study was to evaluate the performance of machine learning models in predicting biomass production and the nutritional contribution of different pasture species, as well as to assess the role of vegetation indices (VIs) in these predictions. To this end, a multispectral sensor mounted on a DJI Matrice 350 RTK platform was used, together with agronomic, yield, and nutritional variables. The curated dataset was subsequently analyzed using linear and polynomial models, as well as tree-based algorithms and support vector machines. Model validation was performed using a group-constrained random partitioning scheme (Group Shuffle Split), with species considered as the grouping variable. Model interpretability was addressed through the SHAP (SHapley Additive Explanations) framework. The results indicated better predictive performance for yield-related variables compared to nutritional attributes. In particular, the Extra Trees model achieved the highest coefficients of determination (R²). SHAP analysis revealed that the Visible Atmospherically Resistant Index (VARI) contributed more strongly to yield-related predictions, whereas the Normalized Difference Red Edge (NDRE) showed a more consistent contribution to nutritional variables. In conclusion, these findings highlight the potential of integrating vegetation indices and machine learning models as effective tools for forage management, supporting informed decision-making in livestock production systems.
Species-specific allometric models for aboveground biomass estimation in two cinchona species in the peruvian andes
(IIETA (International Information and Engineering Technology Association, 2026-02-28) Fernández Zarate, Franklin Hitler; Mejía, Marly; Neyra, Fiorella; Juárez Alarcón, Luis Mariano; Núñez García, Elio Rossel; Ocupa Campos, Lindeley; Espiritu Natividad, Jimmy Edward; Taboada Mitma, Víctor Hugo; Tantalean Martínez, Jerson; Sanchez Santillan, Tito; Seminario Cunya, Alejandro; Cruz Luis , Juancarlos Alejandro; Huaccha Castillo, Annick Estefany
Accurate estimation of aboveground biomass is an essential component for assessing carbon sequestration and ecological dynamics of forest ecosystems. This study aims to determine the aboveground biomass content using specific allometric models in two species of the genus Cinchona (C. micrantha and C. pubescens) in the Peruvian Andes. A total of 51 individuals of C. micrantha and 60 individuals of C. pubescens (diameter at breast height (DBH) > 5 cm) were sampled non-destructively. For each species, 25 combinations resulting from applying five mathematical forms (linear, exponential, logarithmic, polynomial, and power) to five independent variables (DBH, H, DBH × H, DBH² × H, DBH × H²) were evaluated. Second-order polynomial models with the composite variable DBH² × H presented the best predictive performance with an R² = 0.95 for C. micrantha and 0.97 for C. pubescens, along with low errors (RMSE < 4.35 for C. micrantha and < 9.02 for C. pubescens) and reduced Akaike information criterion (AIC) values. The results reveal morpho-functional differences between species, highlighting the importance of fitting specific models to optimize the precision of the estimates. Furthermore, the effectiveness of non-destructive sampling in conservation contexts is confirmed. This study provides robust quantitative tools for forest monitoring and ecological restoration in areas of high ecological vulnerability.
Teledetección del rendimiento del arroz mediante el índice SAVI obtenido con drones y modelos de aprendizaje automático supervisado en zonas bajas tropicales
(Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo, 2026-04-27) Ysuiza Perez, Alfredo; Perez Tello, Mónica; Goicochea Pinchi, Diego; Vega Herrera, Sergio Sebastián; Rios Rios, Raúl Martín; Dominguez Yap, Percy; García, Leonela; Barrera Torres, Cicerón; Oliva Cruz, Carlos Alberto; Santillán Gonzáles, Manuel; Arratea Pillco, David; Alejos Patiño, Italo W.
La estimación de la productividad del arroz dentro de una misma parcela es un desafío en los agroecosistemas tropicales, por la alta variabilidad espacial y limitaciones en los métodos tradicionales de monitoreo. El objetivo del estudio fue evaluar la capacidad del índice de vegetación ajustado al suelo (SAVI, soil adjusted vegetation index) derivado de imágenes multiespectrales obtenidas mediante vehículos aéreos no tripulados (UAV, unmanned aerial vehicles) para diferenciar las zonas productivas de las que no lo son en parcelas arroceras de selva baja tropical, en la región San Martín, Perú. Se usó un diseño de bloques completos al azar en dos localidades, con tres variedades de arroz, y se tomaron imágenes multiespectrales usando plataformas UAV. El rendimiento real de campo se midió con muestreo destructivo georreferenciado, ajustando el peso del grano a una humedad estándar y expresándolo en toneladas por hectárea. Con esos datos, las parcelas se clasificaron en zonas productivas y no productivas según criterios de umbral obtenidos de las mediciones directas. Después se extrajo los valores de SAVI y se usaron como variable de entrada en varios modelos de clasificación supervisada: regresión logística, máquinas de soporte vectorial (SVM), k vecinos más cercanos (KNN), bosque aleatorio y árbol de decisión. Los resultados mostraron que los valores de SAVI entre 0,50 y 0,70 se relacionaban con las zonas productivas, mientras que los que estaban entre 0,30 y 0,50 correspondían a las no productivas. La regresiónlogística y el SVM fueron los que mejor rindieron, con una exactitud global del 88,9%, valores de F1 por encima del 92% y un balance adecuado entre sensibilidad y especificidad. Esto demuestra que el SAVI con aprendizaje automático supervisado es una estrategia para discriminar espacialmente la productividad del arroz, con potencial para apoyar en el monitoreo dentro de la parcela y en las decisiones agronómicas en sistemas arroceros tropicales.
Modelado espacial de propiedades fisicoquímicas y fertilidad del suelo en sistemas agrícolas tropicales bajo distinta heterogeneidad estructural mediante UAV multiespectral y geoestadística
(Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo, 2026-04-27) Vega Herrera, Sergio Sebastián; Ysuiza Perez, Alfredo; Perez Tello, Mónica; Goicochea Pinchi, Diego; Rios Rios, Raúl Martín; Dominguez Yap, Percy; García, Leonela; Barrera Torres, Cicerón; Oliva Cruz, Carlos Alberto; Santillán Gonzáles, Manuel Dante; Arratea Pillco, David; Alejos Patiño, Italo
La variabilidad espacial del suelo condiciona la eficiencia productiva, la gestión de nutrientes y la sostenibilidad de los sistemas agrícolas tropicales, especialmente en contextos donde la heterogeneidad limita la implementación de estrategias de manejo sitio-específico. En este estudio se comparó el desempeño de un flujo analítico basado en imágenes UAV multiespectrales, regresión lineal múltiple (MLR) e interpolación geoestadística en dos sistemas agrícolas con distinta heterogeneidad, un sistema multicultivo a escala de estación y un sistema arrocero con diferentes densidades de siembra, ambos ubicados en la Estación Experimental Agraria El Porvenir (San Martín, Perú). Se analizaron 60 muestras en el componente multicultivo y 27 en el sistema arrocero, georreferenciadas a 30 cm de profundidad, evaluando pH, conductividad eléctrica, nitrógeno, fósforo, potasio, carbono orgánico del suelo y textura. Se aplicó un flujo analítico homogéneo en ambos sistemas (correlación de Spearman, MLR stepwise y kriging ordinario). Los resultados evidenciaron diferencias marcadas en el desempeño predictivo, en el sistema arrocero se alcanzaron valores de R² de prueba de 0,93 para nitrógeno y 0,88 para fósforo, mientras que en el sistema multicultivo los mayores R² fueron 0,42 para conductividad eléctrica y 0,37 para limo. Asimismo, los índices espectrales basados en NIR y red edge mostraron mayor asociación con los atributos edáficos evaluados. Los resultados demuestran que el desempeño depende de la heterogeneidad estructural del sistema, donde entornos más homogéneos favorecen la predicción puntual, mientras que sistemas más heterogéneos potencian la zonificación y delimitación de unidades de manejo
Soil application of zinc for potato biofortification in the central Andes of Peru
(Springer, 2026-02-11) Chung Montoya, Fernando; Melendres Herrera, Victorio; Pilatarzi Vilchez, Vanessa; Sinche Ambrosio, Angely; Vera Vilchez , Jesús Emilio; Vega Ravello, Ruby; García Bendezú, Sady
Zinc is essential for human health, yet dietary deficiencies persist in many regions. This study evaluated the effectiveness of soil-applied zinc to enhance zinc content in potato tubers grown in Peru's central Andes, as an agronomic biofortification strategy. Field trials were conducted over two seasons in four Andean sites using five Zn rates (0-32 kg ha-1). One variety was tested in 2016-2017, and four in 2017-2018. Yield, Zn content, accumulation, partial balance, and dietary contribution were assessed. In both seasons, Zn fertilization did not significantly affect tuber yield. In 2016-2017, Zn content in tubers increased by up to 86% and accumulation by 74% at 16 kg Zn ha-1. The estimated dietary contribution rose by 79%, with Achoscuyo showing the highest response and Lucma the lowest. Site differences were more evident at intermediate and high doses. In 2017-2018, Zn accumulation in shoots exceeded that in tubers by up to 2.8-fold, and Zn content in the peel was twice that of the flesh. Maximum Zn content and accumulation varied among varieties and doses. Canchan and Perricholi showed high Zn content and accumulation at 16 kg Zn ha-1. Principal component analysis revealed that Zn dose was positively associated with Zn content and negatively with tuber yield. The response to Zn fertilization depended on site, dose, and genotype. Soil-applied Zn increased Zn content in potatoes without compromising yield. Selecting varieties with high tuber Zn accumulation improved nutritional outcomes and fertilizer use efficiency.
Genetic diversity and population structure of Myrciaria dubia from the Peruvian Amazon: implications for germplasm conservation and crop improvement
(Springer Nature, 2026-03-27) Mejía de Loayza, Eduardo; Estivals, Guillain; Castro Ruiz, Diana; Chota Macuyama, Werner; Angulo Chávez, Carlos; Corazon Guivin, Mike; Rodríguez del Castillo, Ángel Martín; Alvarado Reategui, Jhon; Angulo Villacorta, Carlos; Mejía, Kember; Del Castillo Torres, Dennis; García Dávila, Carmen
Myrciaria dubia (camu-camu) is a shrubby fruit tree native to the continental Amazon whose fruits have been intensively harvested from wild stands, potentially reducing effective population sizes. We quantified genetic diversity and population structure across seven wild Peruvian Amazon populations and delineated river-basin genetic units to guide provenance-aware germplasm conservation and breeding. We genotyped 254 individuals from the Napo, Ucayali, Nanay, Tahuayo, Putumayo, Tigre, and Curaray basins using six polymorphic microsatellite loci. Overall, 48 alleles were detected. Observed heterozygosity (0.149–0.483) was generally lower than expected heterozygosity (0.220–0.531), and population-level inbreeding coefficients (FIS=−0.038– 0.560) indicated significant heterozygote deficits in Napo, Curaray, and Tahuayo. The Putumayo population harbored nine private alleles, representing a unique genetic reservoir. Pairwise differentiation was substantial (FST=0.093–0.660; Nei’s distance=0.068–1.734), with the strongest divergence between Tigre and Ucayali. Neighbor-joining, Bayesian assignment, and Discriminant Analysis of Principal Components (DAPC) initially supported three major genetic units and highlighted Putumayo as genetically isolated; additionally, hierarchical STRU CTURE analyses resolved eight clusters, and DAPC distinguished seven population-specific groups. Analysis of molecular variance attributed 56.5% of the variation within individuals and 34.7% among populations. Mantel tests supported isolation by distance based on straight-line geographic distances (r=0.53– 0.56; p≤0.017), whereas river-network distances were not significant. Overall, the data indicate a geographically structured genetic architecture shaped by dispersal limitation and basin-scale differentiation, supporting three provenance units for germplasm banking and breeding: (i) Napo–Ucayali–Nanay– Tahuayo, (ii) Tigre–Curaray, and (iii) Putumayo.
Efecto del ácido abscísico sobre el rendimiento y la calidad del arroz (Oryza sativa L) en tres localidades del nororiente del Perú
(Universidad Centroccidental Lisandro Alvarado (UCLA), 2026-05-01) Cardoza Sánchez, Alan Mike; Díaz Sánchez, Alan; Aguilar Anccota, Rene; Velásquez Guerrero, Julián; Facundo Meza, Rosany
El arroz es un cultivo de gran importancia económica en el Perú; sin embargo, el rendimiento se mantiene estable en los últimos años, incluso bajo sistemas de manejo intensivo. En este contexto, el ácido abscísico ha mostrado efectos favorables sobre la regulación del llenado de grano y la mejora de la eficiencia productiva del cultivo. El objetivo del estudio fue evaluar su efecto en el rendimiento y calidad del cultivo de arroz en tres localidades de la provincia de Rioja, región San Martín, Perú. Se emplearon tres dosis (0, 30 y 60 g·ha⁻¹) del producto comercial InGrain® (20 % de ácido S-Abscísico) en un diseño de bloques completamente al azar en cada localidad con tres repeticiones. Posteriormente, se realizó un análisis combinado con el ambiente como factor fijo, evaluándose los efectos de las dosis, el ambiente y su interacción. La dosis de 60 g·ha⁻¹, incrementó significativamente el rendimiento en Yuracyacu (12.135,00 kg·ha⁻¹; 14 % humedad) y en Rioja (11.723,33 kg·ha⁻¹; 19 % humedad), además que redujo en 38 % los granos vanos en esta última localidad. Asimismo, aumentó el rendimiento de pila en 2 % y el porcentaje de granos enteros en 5 %. La variación entre localidades evidenció la influencia del ambiente en la magnitud del efecto, destacando la importancia de la interacción genotipo por ambiente en la expresión productiva. Se concluye que el ácido abscísico puede mejorar el desempeño del cultivo bajo condiciones de campo, aunque su eficacia depende de las condiciones agroclimáticas locales.

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