Examinando por Autor "Taboada Mitma, Víctor Hugo"

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Agricultural research and extension: trends and challenges in Peru and around the world (2015–2025)
(Frontiers Media S.A., 2025-12-17) Olano Camán, Yadhira Milagros; Cruz Luis, Juancarlos Alejandro; Alvarez Robledo, Yeltsin Abel; Chuquibala Checan, Beimer; Arce Inga, Marielita; Tafur Culqui, Josué; Fernandez Zárate, Franklin Hitler; García Frias, Larry Dustin; Taboada Mitma, Víctor Hugo; Ganoza Roncal, Jorge Juan; Tineo Flores, Daniel; Goñas Goñas, Malluri
Agricultural extension, understood as producer-oriented guidance, is vital for driving technology adoption and sector development. This systematic review (2015–2025) examines global and national trends in agricultural research and extension, witha particular focus on Peru's policy landscape. Internationally, the U.S.A., China, and Europe are identified as leaders in scientific innovation, while Brazil excels regionally through its integrated approach to research, innovation, and extension. Agricultural research is still centralized in Lima on a national level, but expanding capability in areas like Amazonas, Junín, and Puno emphasizes how urgently decentralization is needed. By comparing national research output with MIDAGRI’s Agrarian Development Plan 2021–2030 and relevant Sustainable Development Goals (SDGs), the study reveals strong thematic alignment in innovation, food security, and climate adaptation. Crucially, however, significant shortcomings are identified in governance and the effective inclusion of small-scale producers. Based on these findings, this study recommends strengthening decentralized R&D investment, fostering interregional research networks, and promoting strategic collaboration among universities, local governments, and rural communities. Ensuring the active participation of producers in the innovation cycle is essential for building a more inclusive, resilient, and effective agricultural extension system aligned with sustainable development goals.
Comparación de métodos no destructivos para estimar el área foliar de Cinchona officinalis L. mediante procesamiento digital de imágenes
(Universidad de Pinar del Río “Hermanos Saíz Montes de Oca”, Cuba, 2025-09-11) Fernández Zarate, Franklin Hitler; Huaccha Castillo, Annick Estefany; Vaca Marquina, Segundo Primitivo; Seminario Cunya, Alejandro; Taboada Mitma, Víctor Hugo; Pérez Delgado, Luis Jhoseph; Coronel Bustamante, David; Quiñones Huatangari, Lenin
Cinchona officinalis es una importante especie vegetal, fue el único tratamiento para la malaria durante más de tres siglos. El objetivo de este estudio fue comparar la precisión de cuatro métodos no destructivos de procesamiento digital de imágenes (LeafArea y tres algoritmos de ImageJ) para estimar el área foliar de plantaciones jóvenes de C. officinalis en dos condiciones de establecimiento: macizo forestal y franjas de enriquecimiento. Se fotografiaron hojas a 8 cm de distancia utilizando un smartphone de 24 MP y se procesaron con los métodos evaluados. El análisis estadístico incluyó diagramas de caja y bigotes, correlación de Pearson y prueba de Friedman. Los resultados mostraron que los métodos M3 y M4 de ImageJ presentaron la mayor precisión (r = 0,99), sin diferencias significativas entre ellos, y con sobreestimaciones detectadas en M1 y M2. Se concluye que M3 y M4 son opciones rápidas, de bajo costo y alta precisión para el monitoreo foliar de C. officinalis en campo.
Desempenho e valor nutricional da forragem hidropônica de cevada verde na alimentação de porquinhos-da-índia
(Revista Acadêmica Ciência Animal, 2025-02-03) Taboada Mitma, Víctor Hugo; Sarria Bardales, José Antonio; Cruz Luis, Juancarlos Alejandro; Huamaní Bedoya, Daniel; Buendía Molina, Marilyn Aurora
O objetivo do estudo foi avaliar o desempenho e o valor nutricional da forragem verde hidropônica de cevada (FVH) na alimentação de cobaias, além de comparar diferentes sistemas de alimentação para determinar seu efeito no crescimento, conversão alimentar, mortalidade e desempenho de carcaça de porquinhos-da-índia. Utilizou-se delineamento inteiramente casualizado e teste de Duncan a 5%. Utilizaramse 100 cobaias machos com 14 ± 5 dias, distribuídos em cinco tratamentos (T1: concentrado; T2: FVH; T3: chala; T4: concentrado + FVH; T5: concentrado + chala), com cinco repetições. O peso, ganho de peso, consumo de ração, conversão alimentar e percentagem de carcaça foram avaliados durante 49 dias. O ganho de peso por dia em T4 (16,11 g), T1 (15,95 g) e T5 (15,65 g) foi significativamente maior do que o obtido em T2 (5,51 g) e T3 (4,95 g) (p < 0,05). T5 e T4 apresentaram os maiores consumos de ração em base seca (3538,9 e 3239,9 g, respectivamente; p < 0,05), seguidos por T1 (2973,4 g). As melhores conversões alimentares foram apresentadas em T1 (3,81), T4 (4,11) e T5 (4,63), e as cobaias de T5 (74,79%), T4 (74,26%) e T1 (73,66%) obtiveram as maiores porcentagens de carcaça (p < 0,05). Em conclusão, o sistema de alimentação integral e misto tem uma influência positiva sobre os parâmetros produtivos.
Estimation of aboveground biomass and carbon sequestration in a cocoa agroforestry system using UAV-LiDAR in northwestern Peru
(Elsevier B.V., 2025-10-08) Atalaya Marin, Nilton; Sanchez Fuentes, Teiser; Goñas Goñas, Malluri; Tineo Flores, Daniel; Taboada Mitma, Víctor Hugo; Cabrera Hoyos, Héctor Antonio; Cruz Luis, Juancarlos Alejandro; Ganoza Roncal, Jorge Juan; Gómez Fernández, Darwin
Accurate estimation of biomass and carbon in agroforestry systems is essential to assess their contribution to climate change mitigation and to improve their management. In this context, UAV-mounted LiDAR technology emerges as a fast, accurate, and non-destructive alternative for the structural characterization of cocoa agroforestry systems. This study aimed to estimate and analyze structural parameters, mainly tree height and diameter at breast height (DBH), as well as to calculate aboveground biomass and carbon sequestration in a cocoa agroforestry system, using LiDAR data obtained with a DJI Matrice 350 RTK UAV equipped with a Zenmuse L2 sensor, complemented by automatic tree segmentation in LiDAR360 and the application of species-specific allometric equations. The results showed a 93 % segmentation efficiency, with accuracies of 0.93 and 0.99 for DBH and height estimations, respectively. The evaluated plot, located at the Yanayacu Experimental Center (Jaén, Peru) and covering an area of 0.58 ha, had stored 15,492.5 kg of aboveground biomass and 7746.25 kg of aboveground carbon, with Mangifera indica and Cocos nucifera contributing more than 80 %. Consequently, this approach demonstrates the potential of UAV-based LiDAR to generate accurate and detailed information on system structure, enabling optimized management of high-biomass species and the development of more efficient and sustainable management strategies.
Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.)
(Elsevier, 2025-07-15) Fernandez Jibaja, Jorge Antonio; Atalaya Marin, Nilton; Álvarez Robledo, Yeltsin Abel; Taboada Mitma, Víctor Hugo; Cruz Luis, Juancarlos Alejandro; Tineo Flores, Daniel; Goñas Goñas, Malluri; Gómez Fernández, Darwin
Rice (Oryza sativa L.) is a staple crop for sustaining global food security and is particularly important in tropical and subtropical regions. In this context, precision agriculture enables more efficient crop management to increase productivity and sustainability. This study proposes an integrated framework for monitoring the phenological development and estimating the yield of O. sativa by combining agronomic variables, vegetation indices (VIs), and meteorological data. Six rice varieties (Victoria, Esperanza, Bellavista, Puntilla, Capoteña, and Valor) were evaluated across six phenological stages using field data, 20 VIs and meteorological parameters. Field data revealed greater tillering of the Puntilla and Valor varieties (9–28 tillers), with Esperanza having the most stable chlorophyll values (21.5–38.7, σ = 10.46) during ripening. The temporal dynamics of the VIs consistently increased from the seedling to inflorescence emergence stage, followed by a decrease during flowering and ripening, which aligns with known physiological transitions in rice; however, significant differences in the NDVI index were detected during ripening (p > 0.05). For yield estimation, feature selection was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) to increase model efficiency and interpretability. Among the regression algorithms tested, support vector regression (SVR) demonstrated the highest predictive accuracy (R² = 0.81) for the Bellavista variety at the maximum tillering stage. Furthermore, the Valor variety presented the highest grain yield (13.70 t/ha). These results underscore the potential of integrating multisource data with machine learning techniques for high-resolution phenological monitoring and varietal performance assessment.
New record for Peru of the invasive Asian beetle Xylopsocus capucinus (Fabricius, 1781) (Coleoptera: Bostrichidae: Bostrichinae), with biological and ecological notes
(Magnolia Press, 2025-11-06) Chuquibala Checan, Beimer; Tineo Flores, Daniel; Goñas Goñas, Malluri; Taboada Mitma, Víctor Hugo; Hernández May, Manuel A.
As part of our study of saproxylic fauna in cocoa (Theobroma cacao Linnaeus, 1753) sites in northern Peru, we present a new record of Xylopsocus capucinus (Fabricius, 1781) in the country. The species X. capucinus is considered an invasive beetle in the Bostrichidae family to Central America. Our report includes biological and ecological data from this site, as well as detailed taxonomic information and images to facilitate identification.
Sustainable rice–fish farming systems: a systematic review and meta‐analysis
(John Wiley & Sons Ltd., 2025-06-12) Fernández Zatrate, Franklin Hitler; Pérez Delgado, Luis Jhoseph; Coronel Bustamante, David; Huanca Silva, Leisy; Taboada Mitma, Víctor Hugo; Quispe Carhuapoma, Mariela Judith; Oliva Alvarez, Yashira Steffani; Ramirez Antaurco, Maximo Fabricio; Anchayhua Torres, Janella Jelyn; Huaccha Castillo, Annick Estefany; Seminario Cunya, Alejandro; Tineo Flores, Daniel; Gomez Fernandez, Darwin; Goñas Goñas, Malluri; Cruz Luis, Juancarlos Alejandro
The rice–fish farming system is an efficient ecological model with economic, ecological, and social benefits, reduces environmental impacts and optimizes the use of resources. The objective of the research was to explore and analyze scientific publications through a systematic review and meta-analysis related to rice–fish intercropping. A review of publications hosted in the Scopus and PubMed database from January 2000 to April 2025 was conducted. Research articles were selected, excluding review articles, com-mentaries, book chapters, and letters, and only documents published in English were analyzed. The analysis shows that the countries with the highest number of publications were China and Bangladesh, with a proportion of 48% and 24% respectively, followed by Thailand with 10% and Pakistan, Indonesia, Malaysia, and India with 5% each. The fish species used in rice–fish systems were reported to be Cyprinus carpio (37%), Oreochromis niloticus (29%), Barbonymus gonionotus, Micropterus salmoides and Pelteobagrus fulvidraco (8%), Amblypharyngodon mola (5%), and Labeo rohita and Monopterus albus (3%). On average, fish settle in the rice–fish system 27 days after rice planting, with a density of 13,390 fish/ha. Between rice planting and harvesting 132 days pass, obtaining an average yield of 4397 kg of rice/ha and 1383 kg of fish/ha. It is recommended to prioritize integrated research on unstudied fish species, optimal densities, fertilization, culture models, and emerging technologies in rice–fish systems, considering regional variations to improve sustainability, productivity, and food security at a global level.
Territorial zoning as a strategy for sustainable natural resource management in Cajamarca, Northwestern Peru
(Elsevier B.V., 2025-09-25) Gómez Fernández, Darwin; Atalaya Marin, Nilton; Arce Inga, Marielita; Tineo Flores, Daniel; Fernandez Jibaja, Jorge Antonio; Taboada Mitma, Víctor Hugo; Cabrera Hoyos, Héctor Antonio; Cruz Luis, Juancarlos Alejandro; Goñas Goñas, Malluri
Generating agricultural suitability analyses that are objective, consistent, and accessible through digital platforms remains a technical and methodological challenge, creating an information gap for certain stakeholders. To address this issue, we assessed the territorial suitability of the Cajamarca region for coffee and cocoa cultivation using 18 subcriteria grouped into climatic, edaphological, topographic, and socioeconomic categories. To reduce subjectivity and improve consistency in variable comparisons, we applied multicriteria evaluation techniques, including the analytical hierarchy process (AHP) and Shannon entropy method. On the basis of the resulting weights, suitability models were generated using two approaches: one based on threshold reclassification and another using continuous suitability functions. Both approaches were validated using 3886 presence points for coffee and 671 for cocoa. The continuous approach demonstrated a greater ability to capture internal variability and spatial transitions, with greater dispersion and significant differences between classes. The most influential subcriteria for coffee were annual mean temperature, soil texture, elevation, and land use/land cover (LULC); for cocoa, they were annual mean temperature, soil pH, elevation, and LULC. In key districts, up to 59.8 % of the territory was classified as highly suitable, highlighting localized production potential. Finally, the results were integrated into the Suitability Watch Cajamarca application, developed in the Google Earth Engine, enabling interactive inspection of spatial suitability. This tool aims to support evidence-based agricultural planning and is intended for national scaling to other strategic crops.