| 陕西渭北旱塬区农田土壤有机质空间预测方法 |
| 摘要点击 2272 全文点击 612 投稿时间：2021-06-15 修订日期：2021-07-26 |
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| 中文关键词 土壤有机质(SOM) 空间预测 地理探测器 地理加权回归(GWR) 随机森林(RF) |
| 英文关键词 soil organic matter(SOM) spatial prediction geographic detector geographic weighted regression(GWR) random forest(RF) |
| 作者 | 单位 | E-mail | | 尉芳 | 西北农林科技大学资源环境学院, 杨凌 712100
农业部西北植物营养与农业环境重点实验室, 杨凌 712100 | weifang_97@163.com | | 刘京 | 西北农林科技大学资源环境学院, 杨凌 712100
农业部西北植物营养与农业环境重点实验室, 杨凌 712100 | linjing@nwauf.edu.cn | | 夏利恒 | 西北农林科技大学资源环境学院, 杨凌 712100
农业部西北植物营养与农业环境重点实验室, 杨凌 712100 | | | 徐仲炜 | 西北农林科技大学资源环境学院, 杨凌 712100
农业部西北植物营养与农业环境重点实验室, 杨凌 712100 | | | 龙小翠 | 西北农林科技大学资源环境学院, 杨凌 712100
农业部西北植物营养与农业环境重点实验室, 杨凌 712100 | |
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| 中文摘要 |
| 准确预测土壤有机质（SOM）含量的空间分布对于改善土壤质量、提高区域土壤管理水平具有重要意义.为探索预测陕西渭北旱塬区农田SOM含量的最优模型，借助地理探测器选取与SOM含量密切相关的影响因子作为建模的协变量，选用普通克里格方法（OK）、地理加权回归模型（GWR）、偏最小二乘回归模型（PLS）、地理加权回归扩展模型（GWRPLS）和随机森林模型（RF）这5种常用方法对训练集样本SOM含量的空间分布进行预测，并利用验证集样本对比分析了5种方法的预测精度.结果表明：①影响土壤有机质空间变异的主要因素分别为全氮、化肥施用量、速效钾、有效磷和海拔，且任意两因子间的交互作用对SOM的解释力均高于单因子；②农田ω（SOM）范围在2.25~30.23 g·kg-1之间，均值为15.14 g·kg-1，变异系数为30.00，5种方法在农田土壤有机质预测结果，虽然局部存在差异，但在整体的空间分布趋势基本一致，在研究区域内呈现北部、东北部地区含量低，西部、东南部含量高的空间分布趋势；③从5种方法的预测精度来看，RF的均方根误差（RMSE）与平均绝对误差（MAE）最小，GWRPLS的预测偏差（RPD）最大，相比于OK法，GWR、PLS、RF和GWRPLS的相关系数（r）分别升至0.907、0.836、0.968和0.972.综合分析结果，随机森林模型的预测精度最高. |
| 英文摘要 |
| Accurately predicting the spatial distribution of soil organic matter (SOM) content is of great significance for improving soil quality and improving the level of regional soil management. In order to explore the optimal model for predicting the SOM content of farmland in the Weibei Dryland of Shaanxi Province, the influence factors closely related to SOM content were selected as the modeling covariables, and a geographic detector, the ordinary kriging method (OK), geographic weighted regression model (GWR), partial least squares regression model (PLS), geographically weighted regression extended model (GWRPLS), and random forest model (RF) were used to predict the spatial distribution of SOM content in training samples. Additionally, the validation set samples were used to compare and analyze the prediction accuracy of the five methods. The results showed:① the main factors affecting the spatial variability of soil SOM were total nitrogen, fertilizer application, available potassium, available phosphorus, and altitude, and the interaction between any two factors was more explanatory for SOM than any single factor. ②ω(SOM) in farmland was between 2.25 and 30.23 g·kg-1, with an average value of 15.14 g·kg-1 and a coefficient of variation of 30.00. Although there were local differences in the prediction results of SOM by the five methods, the overall spatial distribution trend was basically the same. In the study area, the content of organic matter was low in the north and northeast and high in the west and southeast. ③ From the perspective of the prediction accuracy of the five methods, the root mean square error (RMSE) and mean absolute error (MAE) of RF were the smallest, and the prediction deviation (RPD) of GWRPLS was the largest. Compared with the OK method, the correlation coefficients (r) of GWR, PLS, RF, and GWRPLS increased to 0.907, 0.836, 0.968, and 0.972, respectively. Comprehensive analysis results showed that the random forest model had the highest prediction accuracy. |
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