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生物炭施用下农田温室气体减排效应预测:基于XGBoost的Elastic Net模型

Prediction of the Mitigation Effect of Biochar on Greenhouse Gas Emissions from Farmland: An Elastic Net model Based on XGBoost

  • 摘要: 生物炭对农田甲烷(CH4)和氧化亚氮(N2O)的减排效果受土壤性质、生物炭性质及作物种类等多因素复合影响,导致其减排效应难以准确评估。为此,本文通过文献筛选构建生物炭作用下农田CH4和N2O排放数据库,采用极端梯度提升(XGBoost)与SHAP方法识别关键特征及其交互效应。在此基础上,以土壤pH为一级分层依据,利用分类与回归树(CART)算法确定各pH层内土壤全氮(TN)的最优分割阈值,进而建立pH−TN二级分层下的弹性网络(Elastic Net)显性预测模型。结果表明:土壤pH、生物炭含碳量、生物炭添加量及TN是关键影响特征,且土壤pH与TN在pH<6.5和pH>7.5区间内存在显著交互效应。碱性土壤中,CH4和N2O排放的TN调控阈值分别为0.95,0.80 g•kg−1;酸性土壤中则分别为1.99,1.86 g•kg−1。基于上述分层构建的Elastic Net预测模型中,酸性高TN分层的CH4模型预测精度最高(R2=0.71),酸性低TN层的N2O模型预测精度最高(R2=0.45)。本研究所构建的pH-TN二级分层预测模型,可为不同土壤条件下生物炭的温室气体减排策略制定提供参考。

     

    Abstract: The mitigation effects of biochar on methane (CH4) and nitrous oxide (N2O) emissions are subject to considerable variation depending on soil properties, biochar characteristics, and crop type, making accurate assessment of emission-reduction efficacy in cropland systems inherently difficult. In this study, a database of CH4 and N2O emissions under biochar amendment was compiled through systematic literature screening. Key features and their interaction effects were identified using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Soil pH was adopted as the primary stratification criterion, and the Classification and Regression Tree (CART) algorithm was applied to determine the optimal split threshold of soil total nitrogen (TN) within each pH stratum. On this basis, pH–TN two-level stratified Elastic Net explicit predictive models were constructed. The results indicate that soil pH, biochar carbon content, biochar application rate, and soil TN are the key influential features, with significant interaction effects between soil pH and TN observed in the pH<6.5 and pH>7.5 ranges. The TN regulatory thresholds for CH4 and N2O emissions are 0.95 and 0.80 g kg−1 in alkaline soils, and 1.99 and 1.86 g kg−1 in acidic soils, respectively. Among the stratified Elastic Net models constructed, the acidic high-TN stratum model achieves the highest predictive accuracy for CH4 emissions (R2=0.71), while the acidic low-TN stratum model performs best for N2O emissions (R2=0.45). The pH–TN stratified predictive framework developed in this study provides a reference for assessing biochar-induced greenhouse gas mitigation effects and informing application strategies under varying soil conditions.

     

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