Ensemble machine learning for digital mapping of soil pH and electrical conductivity in the Andean agroecosystem of Peru

• Autores: Carbajal Llosa, Carlos Miguel, Barja , Antony, Pizarro Carcausto, Samuel Edwin
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.inia.gob.pe:20.500.12955/2967
• Enlace del recurso: http://hdl.handle.net/20.500.12955/2967; https://doi.org/10.3389/fsoil.2025.1673628
• Nivel de acceso: acceso abierto
• Materia: Ensemble learning; Spatial machine learning; Digital soil mapping; Soil pH; Electrical conductivity; Aprendizaje conjunto; aprendizaje automático espacial; mapeo digital del suelo; pH del suelo; conductividad eléctrica.; https://purl.org/pe-repo/ocde/ford#4.01.04; Propiedad del suelo; Soil properties; Teledetección; Remote sensing; Modelo digital de superficie; Digital Surface models; Sistema de información geográfica; Geographic information systems; Análisis espacial; Spatial analysis; Perú; Peru.

In agricultural systems, soil pH and electrical conductivity (EC) are crucial chemical properties that directly affect nutrient availability and microbial activity, but the challenging environment of the Peruvian Andes has limited research on their estimation. This study aimed to develop an ensemble learning method to predict soil pH and EC in Andean agroecosystems using environmental predictors. By using simple and weighted averaging, we developed a heterogeneous ensemble learning approach that integrates machine learning (ML) algorithms, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The weighted ensemble assigns weights to models based on their predictive accuracy, measured by R² from spatial cross-validation. Spatial patterns are noticeable, and pH displays greater spatial clustering than EC. Elevation was the most important predictor in ML models for both parameters. Ensemble models significantly outperformed individual models, with the weighted ensemble achieving R² >0.93 and reducing RMSE by approximately 72%. Among standalone models, RF and XGBoost performed best for pH, while SVM performed the best for EC. ANN models were the least effective. Uncertainty analysis indicated high confidence in pH predictions but moderate to high uncertainty in EC predictions, suggesting that EC is more challenging to predict. Ensemble models with optimized weighting provide robust and accurate mapping of spatially autocorrelated soil properties. The high-confidence pH maps are reliable for soil management decisions, while EC predictions, though more uncertain, effectively identify priority areas for future sampling and investigation.