Examinando por Autor "Lobato Galvez, Roiser Honorio"

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Tecnologías de precisión y su relación con la gestión de insumos agrícolas en el contexto de la sostenibilidad en regiones vulnerables
(Universidad Nacional de Trujillo, 2025-08-18) Santillán Gonzáles, Manuel Dante; Ganoza Roncal, Jorge Juan; Lobato Galvez, Roiser Honorio; Oliva Cruz, Carlos Alberto; Arratea Pillco, David
Esta investigación examina la relación entre el uso de tecnologías de precisión con la gestión de insumos agrícolas dentro de un contexto de sostenibilidad. Se aplicaron encuestas estructuradas a 120 productores que describieron su acceso a tecnología, capacitación, integración tecnológica, productividad y barreras; se procesó la información mediante regresión lineal múltiple, correlación de Spearman y análisis de clúster para descubrir los factores que propician una gestión eficiente responsable con el ambiente y viable en lo socioeconómico de los insumos agrícolas. Se evidencia que la dimensión productividad y sostenibilidad impacta de manera positiva mientras que las barreras y desafíos lo hacen negativamente; además el modelo enfocado en la salud del suelo (R² ajustado = 0,304) demuestra que dichas tecnologías pueden elevar el rendimiento y disminuir los impactos ambientales. Sin embargo, los altos costos, la falta de infraestructura y la escasa capacitación, frenan su adopción; por lo que se propone impulsar políticas públicas, formación técnica y acompañamiento, para alcanzar sistemas agrícolas más sostenibles resilientes e inclusivos en línea con los Objetivos de Desarrollo Sostenible.
Tillage Systems Modify the Soil Properties and Cassava Physiology During Drought
(MDPI, 2024-12-13) Ocaña Reyes, Jimmy Alcides; Paredes Espinoza, Richard; Quispe Tomas, Astrid; Díaz Chuquizuta, Henry; Ore Aquino, Zoila Luz; Agurto Piñarreta, Alex; Paz Monge, W. Michel; Lobato Galvez, Roiser Honorio; Ruiz Reyes, José G.; Zavala Solórzano, José W.; Huamani Yupanqui, Hugo Alfredo; Egoávil Jump, Gianfranco; Lao Olivares, Celia P.
Soils are highly sensitive to the type of tillage practices used, as these practices influence soil properties and affect crops, the environment, and society. However, research on cassava production under different tillage systems during drought conditions in the Peruvian Amazon has not been reported. The objective of this study was to compare soil properties, cassava physiology, and yield under conservation agriculture (CA) and traditional agriculture (TA) practices, with and without mulch, in a water-scarce environment. Soil moisture, earthworm population (Ew), stomatal conductance, leaf area index, and commercial yield under CA were 5.26% (~105.2 m³ ha⁻¹), 83%, 1.2 times, 1.14 times, and 7.3 t ha⁻¹, respectively, higher than under TA. Hydraulic conductivity (Ks) in TA was 2.1 times higher than that in CA. However, Ks, bulk density, and Ew over time showed a gradual recovery under CA. The mulch factor only affected Ew, which was higher without mulch than with mulch. The results indicate that CA practices were superior to TA practices, improving soil properties, cassava physiology, and yield, and, therefore, offer significant benefits in resource conservation and higher production and profitability in a drought-prone environment.
Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru
(MDPI, 2024-08-20) Goigochea Pinchi, Diego; Justino Pinedo, Maikol; Vega Herrera, Sergio Sebastian; Sanchez Ojanasta, Martín; Lobato Galvez, Roiser Honorio; Santillan Gonzales, Manuel Dante; Ganoza Roncal, Jorge Juan; Ore Aquino, Zoila Luz; Agurto Piñarreta, Alex Iván
Rice is cataloged as one of the most widely cultivated crops globally, providing food for a large proportion of the global population. Integrating Geographic Information Systems (GISs), such as unmanned aerial vehicles (UAVs), into agricultural practices offers numerous benefits. UAVs, equipped with imaging sensors and geolocation technology, enable precise crop monitoring and management, enhancing yield and efficiency. However, Peru lacks sufficient experience with the application of these technologies, making them somewhat unfamiliar in the context of modern agriculture. In this study, we conducted experiments involving four distinct rice varieties (n = 24) at various stages of growth to predict yield using vegetation indices (VIs). A total of nine VIs (NDVI, GNDVI, ReCL, CIgreen, MCARI, SAVI, CVI, LCI, and EVI) were assessed across four dates: 88, 103, 116, and 130 days after sowing (DAS). Pearson correlation analysis, principal component analysis (PCA), and multiple linear regression were used to build prediction models. The results showed a general prediction model (including all the varieties) with the best performance at 130 days after sowing (DAS) using NDVI, EVI, and SAVI, with a coefficient of determination (adjusted-R2 = 0.43). The prediction models by variety showed the best performance for Esperanza at 88 DAS (adjusted-R2 = 0.94) using EVI as the vegetation index. The other varieties showed their best performance using different indices at different times: Capirona (LCI and CIgreen, 130 DAS, adjusted-R2 = 0.62); Conquista Certificada (MCARI, 116 DAS, R2 = 0.52); and Conquista Registrada (CVI and LCI, 116 DAS, adjusted-R2 = 0.79). These results provide critical information for optimizing rice crop management and support the use of unmanned aerial vehicles (UAVs) to inform timely decision making and mitigate yield losses in Peruvian agriculture.