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<p>Biochar is an environmentally friendly soil amendment and is widely used for improving soil properties. Especially the Cation Exchange Capacity (CEC) of soil, which is the main criterion for assessing soil nutrients. Therefore, this study proposes a method for predicting the cation exchange capacity of soil, which is of great significance for the precise application of biochar and improving soil amendment efficiency. This study collects and organizes experimental data from published literature on biochar-amended soils to construct a dataset that includes biochar properties (feedstock type, pyrolysis temperature, specific surface area, cation exchange capacity) and soil properties. The dataset is divided into seven groups based on the properties of biochar to investigate the impact of biochar properties on the model’s prediction results. Using four machine learning algorithms—Light Gradient Boosting Machine (LightGBM), Deep Neural Network (DNN), Categorical gradient Boosting (CatBoost), and Random Forest (RF)—a predictive model for soil CEC after biochar was established. The results show that the CatBoost model performed best, with a coefficient of determination (R<sup>2</sup>) of 0.963, a Mean Absolute Error (MAE) of 1.346, and a Root Mean Square Error (RMSE) of 2.238, indicating that it is effective in predicting soil CEC after the addition of biochar. Shapley Additive Explanations (SHAP) analysis and Partial Dependence Plot (PDP) results indicate that the pyrolysis temperature of biochar promotes the predicted values of soil CEC, while biochar with high CEC reduces the predicted values of soil CEC. The reason for this counterintuitive result may be that biochar with a high CEC competes for cations in the soil solution. Choosing biochar produced at high pyrolysis temperatures and with a specific surface area (SSA) below 50 m<sup>2</sup>/g can achieve a good improvement effect within the studied conditions. This study develops a promising model for predicting soil CEC, which can better optimize actual soil improvement, and provides valuable insights into the mechanism of the impact of biochar on soil CEC.</p>

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Prediction of the effect of biochar on soil CEC improvement based on machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月30日

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  • Hang Yang,
  • Chenxi Zhao,
  • Hongyu He,
  • Xinran Lv,
  • Peiwen Wang,
  • Mei Zhi,
  • Biao Ma,
  • Aihui Chen

摘要

Biochar is an environmentally friendly soil amendment and is widely used for improving soil properties. Especially the Cation Exchange Capacity (CEC) of soil, which is the main criterion for assessing soil nutrients. Therefore, this study proposes a method for predicting the cation exchange capacity of soil, which is of great significance for the precise application of biochar and improving soil amendment efficiency. This study collects and organizes experimental data from published literature on biochar-amended soils to construct a dataset that includes biochar properties (feedstock type, pyrolysis temperature, specific surface area, cation exchange capacity) and soil properties. The dataset is divided into seven groups based on the properties of biochar to investigate the impact of biochar properties on the model’s prediction results. Using four machine learning algorithms—Light Gradient Boosting Machine (LightGBM), Deep Neural Network (DNN), Categorical gradient Boosting (CatBoost), and Random Forest (RF)—a predictive model for soil CEC after biochar was established. The results show that the CatBoost model performed best, with a coefficient of determination (R2) of 0.963, a Mean Absolute Error (MAE) of 1.346, and a Root Mean Square Error (RMSE) of 2.238, indicating that it is effective in predicting soil CEC after the addition of biochar. Shapley Additive Explanations (SHAP) analysis and Partial Dependence Plot (PDP) results indicate that the pyrolysis temperature of biochar promotes the predicted values of soil CEC, while biochar with high CEC reduces the predicted values of soil CEC. The reason for this counterintuitive result may be that biochar with a high CEC competes for cations in the soil solution. Choosing biochar produced at high pyrolysis temperatures and with a specific surface area (SSA) below 50 m2/g can achieve a good improvement effect within the studied conditions. This study develops a promising model for predicting soil CEC, which can better optimize actual soil improvement, and provides valuable insights into the mechanism of the impact of biochar on soil CEC.