Machine Learning-Driven Assessment of Soil Carbon Sequestration and Emission Reduction Potential in Tea Plantations

Robust quantification of greenhouse gas (GHG) balances in tea plantations is critical for evaluating their contribution to agricultural carbon neutrality. This study aimed to develop data-driven models to quantify soil organic carbon (SOC) sequestration and N2O emissions in Chinese tea plantations, evaluate their net GHG balance at the national scale, and assess the mitigation potential under alternative nitrogen management scenarios. Using a comprehensive national dataset, we compared multiple machine learning (ML) approaches with a conventional multiple linear regression (MLR) model to simulate N2O emissions and SOC changes in Chinese tea plantations. All ML models substantially outperformed the MLR model, with the Random Forest (RF) algorithm achieving the highest predictive accuracy. The RF models yielded R2 values of 0.68 for N2O emissions and 0.67 for SOC changes, with no significant prediction bias. Variable importance and marginal effect analyses revealed strong non-linear controls. Mineral N fertilizer input was the dominant driver of N2O emissions, followed by organic N input, soil clay content, and SOC. In contrast, SOC dynamics were primarily regulated by organic carbon inputs, tea plantation age, climate variables, and soil pH. National-scale simulations indicated an average N2O emission intensity of 9.03 kg N2O ha−1 yr−1 and a mean SOC sequestration rate of 0.88 t C ha−1 yr−1. Overall, SOC sequestration offset N2O emissions, rendering Chinese tea plantations a net GHG sink (−2525 Gg CO2-eq yr−1). Scenario analyses showed that mineral N reduction increased net GHG uptake by 1804 Gg CO2-eq, while organic fertilizer substitution achieved a substantially larger mitigation potential of 5961 Gg CO2-eq. By integrating SOC sequestration and N2O emissions within a unified modeling framework and applying machine-learning-based national-scale simulations, this study provides a more comprehensive and data-driven quantification of GHG balances in tea ecosystems, offering a scientific basis for evaluating their role in agricultural carbon neutrality strategies.