| 黄土丘陵区土壤有机碳含量影响因素分析及空间估测 |
| 摘要点击 478 全文点击 56 投稿时间：2024-03-28 修订日期：2024-05-24 |
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| 中文关键词 黄土高原 土壤有机碳（SOC） 随机森林（RF） 空间估测 数字土壤制图（DSM） |
| 英文关键词 Loess Plateau soil organic carbon （SOC） random forest （RF） spatial estimation digital soil mapping（DSM） |
| 作者 | 单位 | E-mail | | 霍瑢彦町 | 西北农林科技大学资源环境学院, 杨凌 712100
农业农村部西北旱地农业绿色低碳重点实验室, 杨凌 712100 | huorong03150521@163.com | | 刘京 | 西北农林科技大学资源环境学院, 杨凌 712100
农业农村部西北旱地农业绿色低碳重点实验室, 杨凌 712100 | jingliu@nwafu.edu.cn | | 张锐 | 西北农林科技大学资源环境学院, 杨凌 712100
农业农村部西北旱地农业绿色低碳重点实验室, 杨凌 712100 | | | 林桂玲 | 西北农林科技大学资源环境学院, 杨凌 712100
农业农村部西北旱地农业绿色低碳重点实验室, 杨凌 712100 | | | 代睿 | 西北农林科技大学资源环境学院, 杨凌 712100
农业农村部西北旱地农业绿色低碳重点实验室, 杨凌 712100 | |
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| 中文摘要 |
| 土壤有机碳（SOC）是土壤肥力的重要评价指标,了解其空间分布特征和影响因素对促进农业可持续发展和国家粮食安全具有重要意义. 以陕西省延安市富县为研究对象,从地形、气候、植被及土壤这4种类型环境因子中选取与SOC形成相关的22个环境变量,使用随机森林（RF）、支持向量机（SVM）和地理加权回归（GWR）这3种数字土壤制图方法建立SOC含量估测模型,分析了研究区全域、园地、耕地和林地0~20 cm土壤深度的SOC含量影响因素及其空间分布特征. 结果表明：①富县全域ω（SOC）的平均值为8.54 g·kg-1,园地ω（SOC）的平均值为6.44 g·kg-1,耕地ω（SOC）的平均值为7.49 g·kg-1,林地ω（SOC）的平均值为10.22 g·kg-1；变异系数分别为36.90%、19.24%、29.88%和32.56%,均属于中等变异程度；②富县全域中,地形、气候、植被和土壤因子均显著影响SOC分布,且对SOC的作用方向差异较大；林地中,坡度（SLP）、年平均温度（MAT）和土壤容重（BD）对SOC有显著的负向作用,年平均降水量（MAP）和全氮（TN）对SOC有显著的正向作用；园地中,pH 、TN和全钾（TK）均对SOC有显著的正向作用；耕地中,MAP 对SOC有显著的负向作用,TN对SOC有显著的正向作用；③对比不同估测模型的性能, RF估测模型的决定系数（R2）最高、均方根误差（RMSE）和平均绝对误差（MAE）最低,模型估测误差最小；在实测值和估测值线性拟合结果中,RF的拟合精度R2达到了0.85以上,模型的估测性能最好；④使用RF模型对富县的SOC含量进行空间估测,结果显示全域SOC含量呈现出东低西高的空间分布特征. 研究结果可为黄土丘陵区用地结构优化调整提供决策参考以及对SOC的准确估测提供技术支持. |
| 英文摘要 |
| Soil organic carbon （SOC） is a crucial indicator for assessing soil fertility. Understanding its spatial distribution patterns and influencing factors is essential for enhancing agricultural sustainability and securing national food security. This study focused on the Fuxian County, Yan'an City, and Shaanxi Province, selecting 22 environmental variables related to SOC formation from four types of environmental factors： topography, climate, vegetation, and soil. Three digital soil mapping methods, random forest （RF）, support vector machine （SVM）, and geographically weighted regression （GWR）, were employed to establish SOC content estimation models. The influencing factors and spatial distribution characteristics of SOC content at 0-20 cm soil depth for the entire study area, garden land, cultivated land, and forest land were analyzed. The results showed that： ① The average ω（SOC） across the entire region of the Fuxian County was 8.54 g·kg-1, with garden land at 6.44 g·kg-1, cultivated land at 7.49 g·kg-1, and forest land at 10.22 g·kg-1. The coefficients of variation were 36.90%, 19.24%, 29.88%, and 32.56%, respectively, all of which fall into the moderate degree of variation. ② In the entire region of the Fuxian County, topography, climate, vegetation, and soil factors all significantly affected the distribution of SOC, with notable differences in their effects on SOC. In forest land, slope （SLP）, mean annual temperature （MAT）, and bulk density （BD） had significant negative effects on SOC, while mean annual precipitation （MAP） and total nitrogen （TN） had significant positive effects on SOC. In garden land, pH, TN, and total potassium （TK） all had significant positive effects on SOC. In cultivated land, MAP had a significant negative effect on SOC, while TN had a significant positive effect. ③ Comparing the performance of different estimation models, the RF estimation model used in this study had the highest R2, the lowest root mean square error （RMSE） and mean absolute error （MAE） values, and the smallest model prediction error. In the linear fitting between measured and estimated values, the RF model's fitting accuracy R2 reached above 0.85, demonstrating the best estimation performance among the models. ④ Utilizing the RF model for spatial estimation of SOC content in the Fuxian County revealed a distribution pattern of lower concentrations in the east and higher in the west. The results can provide decision-making reference for the optimization and adjustment of land-use structure in hilly areas of the Loess Plateau and offer technical support for the accurate estimation of SOC. |
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