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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.