Examinando por Autor "Vega Herrera, Sergio Sebastian"

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Arbuscular mycorrhizal fungal community shifts driven by heavy metals in cocoa agroecosystems at different crop age
(Taylor & Francis Group, 2026-06-09) Del Aguila Torres, Cecil Eduardo; Pezo Torres, Maria Nelly; Vega Herrera, Sergio Sebastian; Verde Girbau, Carlos; Meseth Macchiavello, Enrique; Corazon Guivin, Mike Anderson; Paz Urrelo, Jorge Luis
Cocoa (Theobroma cacao L.) is an economically important tropical crop, but its production is increasingly constrained by strict regulations related to heavy metal accumulation in soils. Arbuscular mycorrhizal fungi (AMF) are a key biological component of cocoa agroecosystems due to their role in soil processes and plant nutrition. However, information on AMF diversity and their response to heavy metals remains limited. In this study, AMF communities were evaluated in cocoa plantations of different ages established under contrasting heavy metal conditions. A total of 53 AMF taxa were identified, indicating high fungal richness across all plantations. No significant differences were found in α-diversity indices or community composition among plantation ages. In contrast, multivariate analyses showed that variation in AMF community structure was primarily associated with soil heavy metal gradients, particularly cadmium (Cd) and zinc (Zn). Species-level models revealed contrasting responses among taxa, with both positive and negative associations along Cd and Zn gradients, suggesting differential tolerance strategies. Overall, these results indicate that edaphic factors, especially heavy metal concentrations, play a more important role than plantation age in shaping AMF community composition. These findings provide a basis for identifying AMF taxa adapted to metal-enriched soils and for developing management strategies that support sustainable cocoa production.
Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield
(2025-09-26) Goigochea Pinchi, Diego; Vega Herrera, Sergio Sebastian; Torres Chavez, Edson Esmith; Archentti Reategui, Fernando; Barrera Torres, Ciceron; Dominguez Yap, Percy Luis; Ysuiza Perez, Alfredo; Perez Tello, Monica; Rios Rios, Raúl; Santillan Gonzáles, Manuel Dante; Ganoza Roncal, Jorge Juan; Ruiz Reyes, Jose Guillermo; Agurto Piñarreta, Alex Ivan
Rice is a globally important crop and a staple in the diet of a large part of the world's population. This underscores the need for hybridization and improvement of rice genotypes to meet food demand in an environmentally sustainable manner. Geographic Information Systems (GIS) have proven to be valuable tools for the morphometric phenotyping of different genotypes. In this study, seven different rice genotypes were evaluated with the objective of selecting those with high yield. Multispectral imagery was used to develop prediction models based on supervised learning algorithms, including Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Elastic Net (EN), and Neural Networks (NN). The variables studied were plant height, number of panicles, number of tillers, and yield. The results showed the following performances: R² = 0.44 for plant height using Random Forest, R² = 0.92 for number of panicles with Neural Networks, R² = 0.44 for number of tillers with SVM, and R² = 0.31 for yield with SVM. This technology significantly supports traditional selection methodologies for hybridization and improvement by providing a spatial approach that enhances and facilitates selection criteria.
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.