Examinando por Autor "Álvarez Robledo, Yeltsin Abel"

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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.
The Germination and Subsequent Development of Cocoa Seedlings (Theobroma cacao L.) Are Subject to Influence From the Pregerminative Methods Employed
(John Wiley & Sons Ltd., 2025-12-20) Chuquibala Checan, Beimer; Torres de la Cruz, Magdiel; Mendoza Merino, Jani Elisabet; Tineo Flores, Daniel; Arce Inga, Marielita; Álvarez Robledo, Yeltsin Abel; Atalaya Marin, Nilton; Gómez Fernández, Darwin; Goñas Goñas, Malluri
In Peru, cocoa production has increased significantly, thereby establishing a prominent position for this crop in the country's agricultural landscape. The effect of different pregerminative methods on seed germination of cocoa ecotypes and their influence on seedling development in nursery were evaluated. Three cocoa ecotypes were used INDES 24, INDES 31, and INDES 67, and five pregerminative methods were applied to the seeds with mucilage, mucilage removal, tegument removal, water immersion, and immersion in Trichoderma harzianum solution. Parameters such as germination percentage GP, mean germination time GT50, mean germination rate GR50, and plant growth characteristics in terms of cotyledon height, plant height, number of leaves per plant, and stem diameter were evaluated. The results revealed that the treatments INDES 24 removal tegument, INDES 67 removal tegument, INDES 67 water immersion, and INDES 67 immersion in T. harzianum showed the best GPs of 100% at 72 h. Additionally, the INDES 67 tegument removal showed the best mean germination time and rate GT50 and GR50 with 24 h and 8 seeds/24 h, respectively. For growth parameters, INDES 67 mucilage removal showed the highest values for cotyledon height and the number of leaves per plant with 4 cm and 13.33 leaves/plant; for plant height and stem diameter, the treatments INDES 31 tegument removal and INDES 24 mucilage removal obtained the highest average values with 30.21 cm and 6.65 mm, respectively. These findings demonstrate that pregerminative methods significantly impact the germination and growth of cocoa plants. This insight can enhance cocoa propagation practices and improve the success rate of their establishment in the field.