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.
Mapping of coffee cultivation based on land use/land cover in the tropics of Cajamarca (Peru): An integration of Sentinel data, machine learning, and Google Earth Engine
(Elsevier B.V., 2026-08-12) Barboza, Elgar; Aragon, Amanda; Pizarro, Samuel; Roman Peña, Alcides; Adams, Brandon; Delgado, Ellen; Cotrina Sanchez, Alexander; Tariq, Aqil; Medina Medina, Angel; Tuesta Trauco, Katerin; Rivera Fernandez, Abner; Zabaleta Santisteban, Jhon; Ocaña, Candy; Madden, Marguerite
Coffee cultivation is a strategic economic activity in the Andean-Amazonian region of Peru, characterized by its diversity of production systems and complex landscape. In this study, a methodological approach based on multitemporal mapping of land cover and land use (LULC) was developed to identify coffee crops by integrating optical satellite imagery (Sentinel-2) and radar data (Sentinel-1), as well as topographic variables, processed on the Google Earth Engine (GEE) platform, with supervised classification based on the Random Forest (RF) algorithm. The study area covered the provinces of Jaen and San Ignacio (Cajamarca), the main coffee-growing areas in the country's north. A time series of satellite data from 2019 to 2024 was used, considering two climatic seasons (dry and wet). The 69 spectral, textural, and topographic variables were evaluated but subsequently reduced to 22 using the Variance Inflation Factor (VIF). The classification distinguished 11 LULC classes, including shade-grown and non-shade-grown coffee, achieving an overall accuracy of over 84% and a Kappa index of 0.90. The binary coffee maps made it possible to quantify and analyze the spatial dynamics of the crop in both provinces, showing a growing trend toward shade-grown coffee, especially in San Ignacio. The proposed approach proved to be operational for mountainous tropical areas with high cloud cover.
Predicting Pregnancy After Fixed-Time Artificial Insemination in Goats: Comparative Performance of Multivariable Logistic Regression and Random Forest Models
(MDPI, 2026-08-17) Rodríguez Vargas, Aníbal Raúl; Rivas Flores, Josselin Kelly; Ruiz Chamorro, José Antonio; Galván Cavero, Gerardo Antonio; Mendoza Ordoñez, Gilmar; Ortiz Morera, Narda Cecilia; Cruz Luis, Juancarlos Alejandro
Accurate prediction of pregnancy following fixed-time artificial insemination (FTAI) remains challenging under commercial goat production conditions. This study compared multivariable logistic regression and Random Forest models for pregnancy prediction and used SHAP analysis to characterize predictor contributions. Data from 920 goats subjected to FTAI across four regions of Peru were analyzed using reproductive, hormonal, animal-level, and management variables. Logistic regression was developed after univariable screening, whereas Random Forest models used 500 trees with mtry values of 2, 3, and 4. Models were compared using stratified 10-fold cross-validation with identical folds. The mtry = 4 configuration yielded the highest Random Forest AUC. Logistic regression identified semen type, buck and doe breed, insemination site, and cervical mucus score as independent predictors. Chilled semen was associated with higher pregnancy odds, whereas superficial cervical insemination was associated with lower odds. Logistic regression outperformed Random Forest in discrimination (AUC, 0.761 vs. 0.654), accuracy (0.679 vs. 0.644), and specificity (0.882 vs. 0.782), whereas Random Forest showed higher sensitivity (0.369 vs. 0.278). SHAP analysis identified eCG dose, cervical mucus score, semen type, and insemination site as the most influential predictors. Overall, logistic regression provided superior and more consistent predictive performance, whereas Random Forest offered complementary sensitivity and captured nonlinear predictor contributions. These findings support logistic regression as the more effective predictive approach for pregnancy following FTAI under the conditions studied, with explainable machine learning providing complementary insight into prediction patterns.
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.
Random Forest-based spatial distribution from heavy metals and associated with ecological and health risk assessment in agricultural soils of the Mantaro Valley, Peru
(Elsevier B.V., 2026-07-31) Orellana Mendoza, Edith; Cosme de la Cruz, Roberto Carlos; Quispe Coquil, Violeta; Mendoza Delgado, Katia; Valero Maraví, Sheila; Rojas León, Ángeles; Ccopi Trucios, Dennis; Pizarro Carcausto, Samuel Edwin
Heavy metal (HM) contamination of agricultural soils in Andean valleys poses a critical threat to ecological integrity, food safety, and human health. This study assessed the spatial distribution, contamination levels, ecological and health risk (non-carcinogenic and carcinogenic) associated with Zn, Pb, Cu, Cd, and As in agricultural soils of the Mantaro Valley in the central Andes of Peru. A total of 306 topsoil samples were analyzed. Random Forest (RF) was used to model the spatial distribution of HM concentrations, integrating spectral, climatic, topographic, edaphic, and distance-related covariates. The RF models showed consistent but element-specific predictive performance. The values of R² ranged from 0.76 to 0.89, with the highest values for Pb and As and the lowest for Cu. Spatial predictions should be interpreted with consideration of the irregular sampling design and inherent uncertainties in spatial extrapolation. Mean concentrations of Zn, Pb, Cu, As, and Cd reached 1347.87, 439.91, 201.71, 90.69, and 4.27 mg kg⁻¹, respectively, exceeding both regulatory thresholds and local geochemical background values. Contamination indices revealed marked enrichment, particularly for As and Cd. Ecological risk was primarily driven by Cd and As, with 70.10% and 61.41% of samples classified as very high ecological risk for these elements, respectively. The comprehensive potential ecological risk index indicated a critical scenario with 71.7% of samples exceeding the very high-risk threshold. Human health risk assessment identified As as the main contributor to both non-carcinogenic and carcinogenic risks, with children showing higher vulnerability than adults. These findings delineate priority agricultural areas where ecological risk and arsenic-related carcinogenic risk overlap, underscoring the urgent need for targeted soil monitoring, exposure reduction, and locally adapted risk management strategies in the Mantaro Valley.
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.