Examinando por Materia "Bosque aleatorio"

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Agronomic variables outperform multispectral indices for individual plant yield prediction in Andean quinoa
(Elsevier B.V., 2026-04-18) Pizarro Carcausto, Samuel Edwin; García Seguil, Erika Janina; Gavino Lulo, Esthefany Irene; Requena Rojas, Edilson Jimmy; Ortega Quispe, Kevin Abner; Cccopi Trucios, Dennis
Accurate pre-harvest yield estimation is essential for decision-making in high-altitude agriculture. This study evaluated agronomic and multispectral UAV variables for near-harvest prediction of individual quinoa grain weight, with data collected across six phenological stages to identify when predictors achieve reliable performance, under Andean conditions. A total of 374 plants were monitored across six phenological stages at Santa Ana Experimental Station (Huancayo, Peru, 3280 m a.s.l.) during 2024. OLS, Random Forest, Support Vector Machine, and Neural Network models were trained using agronomic-only (AGRO), spectral-only (IND), and combined (COMP) predictor sets, evaluated through 5-fold cross-validation reporting mean ± standard deviation. Agronomic and combined models achieved moderate performance (R² = 0.22–0.25, RPD = 1.10–1.15), suitable for relative plant ranking in breeding programs, while spectral-only models failed across all algorithms (R² ≤ 0.044, CCC ≤ 0.080), constrained by saturation, phenological decoupling, and canopy heterogeneity. Variable importance analysis confirmed that late-season structural traits dominated predictions, while spectral indices contributed marginally despite including red-edge bands. These results challenge spectral-only approaches for individual plant phenotyping in heterogeneous canopies, demonstrating that integrating simple ground measurements with UAV spectral data is essential for reliable quinoa yield estimation.
Critical edaphic and altitudinal factors influencing cation exchange capacity in coffee-growing soils of northeastern Peru: implications for sustainable fertility management
(Frontiers Media SA, 2026-05-05) Díaz Chuquizuta, Henry; Manrique Gonzales, Luis Fernando; Sánchez Ojanasta, Martín; Cuevas Giménez, Juan Pablo; Carbajal Llosa, Carlos Miguel; Cuellar Condori, Néstor Edwin; Martínez Zapata, Boris Guillermo; Vallejos Torres, Geomar
Introduction: Effective cation exchange capacity (ECEC) is a key indicator of soil fertility and sustainable soil management assessment in coffee-growing systems. Methods: This study aimed to identify the principal edaphic and altitudinal factors explaining ECEC variability in 69 soil samples collected from coffee farms in northeastern Peru. Results: ECEC results exhibited substantial variation, ranging from 0.14 to 55.49 cmol(+)·kg⁻¹ (mean = 15.21; SD = 12.47), and were significantly correlated with organic matter (r = 0.71), clay content (r = 0.62), exchangeable acidity (r = -0.63), and altitude (r = 0.33). Principal component analysis accounted for 64.3% of the edaphic variability, identifying Ca²⁺, pH, Mg²⁺, and exchangeable acidity as the most influential variables. The Random Forest model demonstrated high predictive accuracy (R² = 0.93; root mean square error (RMSE) = 2.1 cmol(+)·kg⁻¹), outperforming the generalized additive model (GAM) and identifying Ca²⁺ as the most important predictor (IncMSE% = 3177.37). A functional altitudinal gradient was also evident: areas above 1150 m.a.s.l. showed higher acidity and aluminium content, whereas areas below 900 m.a.s.l. exhibited greater base saturation and higher ECEC. Discussion: These findings support the development of site-specific fertilization strategies and soil–climate zoning, emphasizing the value of integrating multivariate analyses with machine-learning models as key tools for optimizing fertility management and coffee crop productivity in tropical mountain ecosystems; where soil texture represents a key factor influencing coffee sustainability, as greater nutrient retention capacity and improved nutritional balance are associated with enhanced potential for sustainable production and reduced environmental impact.
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.
Variability in Fruit Production of Carapa Guianensis Associated with Edaphoclimatic Factors in the Amazon
(Preprints.org (MDPI), 2025-12-17) Angulo Villacorta, Carlos Darwin; Silva da Conceição, Denilson; Chuchon Remon, Rodolfo Juan; Manigat, Donald; Antunez Jimenez, Lorena; de Toledo, José Julio
Carapa guianensis Aubl., widely distributed throughout the Amazon, is recognized for its ecological, economic, and social importance, and constitutes a key source of income for numerous extractive communities. However, fruit production exhibits marked spatial variation that may be influenced by soil properties and climatic factors. In this study, we assessed this variability using data from 21 studies conducted in the Brazilian Amazon, incorporating georeferenced information from each site on climate and soil characteristics. Environmental variables were evaluated using Random Forest models. Average fruit productivity showed a broad range (0.34 to 34.6 kg·tree⁻¹·year⁻¹), with higher values in várzea forests (16.5 kg·tree⁻¹·year⁻¹) and lower values in igapó forests (2.5 kg·tree⁻¹·year⁻¹). The model explained 42% of the observed variability (R² = 0.83 in cross-validation), identifying soil organic carbon, mean annual temperature, and clay content as the most influential predictors. These findings demonstrate that fruit production is shaped by the interaction between edaphic and climatic conditions, which determine the species' productivity patterns, and highlight the need to foster adaptive management strategies that ensure the sustainable use of andiroba across Amazonian ecosystems.