土壤与作物2026，Vol.15Issue(2)：191-201,11.DOI:10.11689/sc.2025071101
基于机器学习与SHAP算法的胶东半岛土壤酸化影响因素分析
Analysis of factors influencing soil acidification in the Jiaodong Peninsula based on machine learning and the SHAP algorithm
摘要
Abstract
Soil acidification has become a critical issue constraining the sustainable agricultural development of the Jiaodong Peninsula,yet the driving factors of its spatial differentiation remain unclear.To identify the key factors influencing soil acidifica-tion and clarify their roles,144 farmland topsoil samples were collected from the Jiaodong Peninsula in May 2021.A Random Forest(RF)model combined with the SHAP(SHapley Additive exPlanations)algorithm was employed to systematically analyze soil pH and its 13 influencing factors.The results indicate that soil pH in the Jiaodong Peninsula exhibites a spatial differentiation pattern,decreasing from the northwest to the southeast,with prominent acidification issues in the southeastern region.The RF model achieves an accurate prediction of soil pH(R2=0.999 9),while SHAP analysis further quantifies the contribution of each influencing factor.The primary drivers of soil pH variation are identified as nitrate-nitrogen(NO3--N)and the soil's intrinsic buffering capacity(calcium and magnesium base cations).Notably,the contribution of NO3--N(84.0%)is significantly higher than that of exchange-able Ca2+,Mg2+,and cation exchange capacity(CEC).Furthermore,a strong positive interaction is observed between NO3--N and Ca2+,along with a positive synergistic effect among exchangeable Ca2+,Mg2+,and CEC.A significant positive correlation between soil pH and NO3--N(P<0.001)is found,and the spatial distributions of soil NO3--N,Ca2+,Mg2+,and CEC values are highly consis-tent with that of soil pH.It is revealed that the synergistic retention effect of soils with high buffering capacity on nitrate and base cations effectively inhibits the decline in soil pH in the northwestern Jiaodong Peninsula.Conversely,the severe acidification in the southeastern region is primarily caused by nitrogen input into soils with low intrinsic buffering capacity.These findings provide a critical scientific basis for the prevention and control of soil acidification in the region.关键词
土壤pH值/机器学习/SHAP算法/硝态氮/酸化土壤Key words
soil pH/machine learning/SHAP algorithm/nitrate nitrogen/acidic soil分类
农业科技引用本文复制引用
崔瑾,刘凯,冯欣玉,孙琳,杨庆润,李明月,刘爱菊,许玉芝..基于机器学习与SHAP算法的胶东半岛土壤酸化影响因素分析[J].土壤与作物,2026,15(2):191-201,11.基金项目
山东省自然科学基金青年项目(ZR2021QD073) （ZR2021QD073）
山东省自然科学基金重大基础研究项目(ZR2020ZD19) （ZR2020ZD19）
淄博市重点研发计划(市内校城融合)项目(2021SNPT0012). （市内校城融合）
