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Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on water quality. Traditional modeling approaches, such as linear regression, may fail to represent the intricate relationships that exist between soil qualities and environmental influences. Therefore, in this study, we investigated the major determinants of CP release from different land use/types such as farmland, desert, forest soils, and rivers. The study utilizes the structural equation model (SEM), multiple linear regression (MLR), and three machine learning (ML) models (Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost)) to predict the release of CP from different soils by using soil iron (Fe), aluminum (Al), calcium (Ca), pH, total organic carbon (TOC) and precipitation as independent variables. Results show that colloidal-cations (Fe, Al, Ca) and colloidal-TOC strongly influence CP release, while bioclimatic variables (precipitation) and pH have weaker effects. XGBoost outperforms the other models with an R 2 of 0.94 and RMSE of 0.09. SHapley Additive Explanations described the outcomes since XGBoost is accurate. The relative relevance ranking indicated that colloidal TOC had the highest ranking in predicting CP. This was supported by the analysis of partial dependence plots, which showed that an increase in colloidal TOC increased soil CP release. According to our research, the SHAP XGBoost model provides significant information that can help determine the variables that considerably influence CP contents as compared to RF, SVM, and MLR., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Soil colloids have been shown to play a critical role in soil phosphorus (P) mobility and transport. However, identifying the potential mechanisms behind colloidal P (P coll) release and the key influencing factors remains a blind spot. Herein, a machine learning approach (random forest (RF) coupled with partial dependence plot analyses) was applied to determine the effects of different soil physicochemical parameters on P coll content in three colloidal subfractions (i.e., nano- (NC): 1–20 nm, fine- (FC): 20–220 nm and medium-sized colloids (MC): 220–450 nm) based on a regional dataset of 12 farmlands in Zhejiang Province, China. RF successfully predicted P coll content (R2 = 0.98). Results showed that colloidal- organic carbon (OC coll) and minerals were the major determinants of total P coll content (1–450 nm); their critical values for increasing P coll release were 87.0 mg L−1 for OC coll , 11.0 mg L−1 for iron (Fe coll) or aluminium (Al coll), 2.6 mg L−1 for calcium (Ca coll), 9.0 mg L−1 for magnesium (Mg coll), 2.5 mg L−1 for silicon (Si coll), and 1.4 mg L−1 for manganese (Mn coll). Among three colloidal subfractions, the major factors determining P coll were soil Olsen-P (P Olsen ; 125.0 mg kg−1), Ca coll (2.5 mg L−1), and colloidal P saturation (21.0%) in NC; Mn coll (1.5 mg L−1), Mg coll (6.8 mg L−1), and P Olsen (135.0 mg kg−1) in FC; while Mn coll (1.5 mg L−1), Al coll (2.5 mg L−1), and Fe coll (3.8 mg L−1) in MC, respectively. OC coll had a considerable effect in the three fractions, with critical values of 80.0 mg L−1 in NC or FC, and 50.0 mg L−1 in MC. Our study concluded that the information gleaned using the RF model can be used as crucial evidence to identify the key determinants of different size fractionated P coll contents. However, we still need to discover one or more easy-to-measure parameters that can help us better predict P coll. [Display omitted] • Effects of different soil physicochemical factors on colloidal P content were investigated. • Random Forest showed good performance to predict colloidal P content in agricultural soils. • Colloidal organic carbon and minerals were the key determinants of colloidal P content. [ABSTRACT FROM AUTHOR]