Predicting Biochar‐Induced Changes in Soil Organic Carbon With Ensemble Machine Learning.
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| Title: | Predicting Biochar‐Induced Changes in Soil Organic Carbon With Ensemble Machine Learning. |
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| Authors: | Ray, Avedananda1 (AUTHOR), Li, Xin2 (AUTHOR), Chen, Yujuan3 (AUTHOR), Yang, Xinyao4 (AUTHOR), Zhang, Wenju2 (AUTHOR), Hui, Dafeng1 (AUTHOR) dhui@tnstate.edu |
| Source: | GCB Bioenergy. May2026, Vol. 18 Issue 5, p1-14. 14p. |
| Subject Terms: | *Biochar, *Ensemble learning, *Prediction models, *Soil amendments, *Carbon sequestration, *Machine learning, *Histosols, *Sustainable agriculture |
| Abstract: | Biochar is a promising soil amendment for enhancing soil organic carbon (SOC), but accurately predicting its effect under diverse environmental conditions remains challenging due to complex, nonlinear interactions among biochar properties, soil characteristics, climate, and management practices. To address this research gap, we developed an ensemble machine learning (ML) framework, combining Extremely Randomized Trees (ExtraTrees), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) regressors, to model SOC responses to biochar application using a globally curated dataset of 800 field observations. The ensemble model showed strong predictive performance (R2 = 0.86, RMSE = 0.11) and generalized well across a wide range of conditions. Shapley Additive exPlanations (SHAP) analysis identified biochar addition rates, crop types, soil type, and soil pH were the most influential predictors of SOC changes. The most effective biochar application rate was about 40 t/ha, and the saturation point was 121.7 t/ha. Partial dependence plots revealed nonlinear and threshold effects of pyrolysis temperature, initial SOC levels, and nitrogen content. To facilitate practical application, we also developed a user‐friendly graphical interface for SOC prediction under various biochar‐soil‐climate scenarios. This work highlights the predictive power and interpretability of ML tools in digital soil carbon modeling and supports data‐driven strategies for optimizing biochar use in climate‐smart agriculture. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Biochar is a promising soil amendment for enhancing soil organic carbon (SOC), but accurately predicting its effect under diverse environmental conditions remains challenging due to complex, nonlinear interactions among biochar properties, soil characteristics, climate, and management practices. To address this research gap, we developed an ensemble machine learning (ML) framework, combining Extremely Randomized Trees (ExtraTrees), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) regressors, to model SOC responses to biochar application using a globally curated dataset of 800 field observations. The ensemble model showed strong predictive performance (R2 = 0.86, RMSE = 0.11) and generalized well across a wide range of conditions. Shapley Additive exPlanations (SHAP) analysis identified biochar addition rates, crop types, soil type, and soil pH were the most influential predictors of SOC changes. The most effective biochar application rate was about 40 t/ha, and the saturation point was 121.7 t/ha. Partial dependence plots revealed nonlinear and threshold effects of pyrolysis temperature, initial SOC levels, and nitrogen content. To facilitate practical application, we also developed a user‐friendly graphical interface for SOC prediction under various biochar‐soil‐climate scenarios. This work highlights the predictive power and interpretability of ML tools in digital soil carbon modeling and supports data‐driven strategies for optimizing biochar use in climate‐smart agriculture. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 17571693 |
| DOI: | 10.1111/gcbb.70112 |
