Machine learning-assisted model for predicting biochar efficiency in colloidal phosphorus immobilisation in agricultural soils

• Published in: Biochar (Online) Vol. 7; no. 1; pp. 1 - 20
• Main Authors: Eltohamy, Kamel M., Alashram, Mohamed Gaber, ElManawy, Ahmed Islam, Menezes-Blackburn, Daniel, Khan, Sangar, Jin, Junwei, Liang, Xinqiang
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 14.03.2025; Springer
• Subjects: Agricultural soils; Agriculture; Biochar; Ceramics; Colloidal phosphorus; Composites; Earth and Environmental Science; Environment; Environmental Engineering/Biotechnology; Fossil Fuels (incl. Carbon Capture); Glass; Immobilisation efficiency; Machine learning; Natural Materials; Original Research; Renewable and Green Energy; Soil Science & Conservation; Immobilisation efficiency; Agricultural soils; Colloidal phosphorus; Biochar; Machine learning
• ISSN: 2524-7867; 2524-7867
• DOI: 10.1007/s42773-025-00442-6

The loss of colloidal phosphorus (P coll ) from agricultural lands significantly contributes to nonpoint source nutrient pollution of receiving waters. This study aimed to develop an advanced machine learning (ML) model to predict the immobilisation efficiency of P coll (IE-P coll ) by biochar in agricultural soils. Six ML algorithms were evaluated using a dataset containing 18 biochar- and soil-related variables. The random forest (RF) algorithm outperformed the others (R 2  = 0.936–0.964, RMSE = 2.536–3.367), achieving superior test performance (R 2  = 0.971, RMSE = 2.276). Key biochar-related parameters, such as oxygen content, total phosphorus content, and application rate were found to be stronger drivers of IE-P coll than most soil parameters. Soil Olsen-P was found to be a more reliable predictor of IE-P coll than the other soil-related parameters. Feature selection techniques narrowed down the original 18 features to the most critical ones, enhancing the performance of the model. A graphical user interface based on the optimised model was developed to provide practical field-based predictions of IE-P coll under varying conditions. This study highlights the strong potential of using biochar as a sustainable soil amendment to enhance P coll immobilisation, thereby reducing non-point source nutrient pollution from agricultural soils. Graphical Abstract Highlights Biochar effectively immobilized P coll in soils, reducing its loss to surrounding waters. Machine learning can predict IE-P coll with high performance, offering a cheaper alternative to chemical assessments. The RF algorithm outperformed five others in predicting IE-P coll , thereby achieving superior performance. Feature selection refined the RF-based model to 11 key features, simplifying and enhancing its performance.