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Spatial variations of organic matter concentration in cultivated land topsoil in North China based on updated soil databases
Soil and Tillage Research ( IF 6.1 ) Pub Date : 2025-01-08 , DOI: 10.1016/j.still.2024.106445
Dongheng Yao, Enyi Xie, Ruqian Zhang, Bingbo Gao, Liang Li, Zhenting Zhao, Wencai Zhang, Yubo Liao, Ming Lei, Xiangbin Kong
Soil and Tillage Research ( IF 6.1 ) Pub Date : 2025-01-08 , DOI: 10.1016/j.still.2024.106445
Dongheng Yao, Enyi Xie, Ruqian Zhang, Bingbo Gao, Liang Li, Zhenting Zhao, Wencai Zhang, Yubo Liao, Ming Lei, Xiangbin Kong
Accurate knowledge of spatial variations in organic matter concentration of cultivated land topsoil (CTSOM) is crucial for the effective use and management of cultivated land. However, this knowledge remains largely uncertain owing to outdated and imprecise soil databases. Therefore, in 2020, this study meticulously collected 918 samples of cultivated land topsoil (0–30 cm) in Hebei Province of North China, and a Random Forest (RF) model was used to delineate the spatial variability of CTSOM. Results indicated the robust performance of the RF model containing 21 predictors, with an R2 of 0.77, and soil total nitrogen (TN) emerging as the most important predictor. The current mean CTSOM level in the study area stood at 16.47 ± 3.94 g kg−1 , displaying a spatial pattern with higher CTSOM levels in the western and northern mountainous areas, and lower levels in the eastern plain areas. A comparison with the second national soil survey data revealed that the overall regional level of CTSOM has increased by 4.28 g kg−1 over the last 40 years. However, a significant decline in CTSOM was observed in the northern part of the study area, where straw return and fertilization can be key contributing factors. This study provides updated knowledge on the spatial variations of CTSOM in North China, which is valuable for agricultural ecosystem management worldwide and for carbon accounting in terrestrial ecosystems.
中文翻译:
基于更新土壤数据库的华北耕地表土有机质浓度空间变化
准确了解耕地表土有机质浓度 (CTSOM) 的空间变化对于有效利用和管理耕地至关重要。然而,由于土壤数据库过时和不精确,这些知识在很大程度上仍然不确定。因此,本研究在 2020 年精心收集了华北河北省 918 份耕地表土 (0–30 cm) 样本,并使用随机森林 (RF) 模型来描绘 CTSOM 的空间变异性。结果表明,包含 21 个预测因子的 RF 模型具有稳健性能,R2 为 0.77,土壤全氮 (TN) 成为最重要的预测因子。研究区目前的平均 CTSOM 水平为 16.47 ± 3.94 g kg-1,表现出西部和北部山区 CTSOM 水平较高,东部平原地区 CTSOM 水平较低的空间格局。与第二次全国土壤调查数据的比较显示,在过去 40 年中,CTSOM 的总体区域水平增加了 4.28 g kg-1。然而,在研究区的北部观察到 CTSOM 显着下降,那里的秸秆还田和施肥可能是关键的促成因素。本研究为华北地区 CTSOM 的空间变化提供了最新的知识,对全球农业生态系统管理和陆地生态系统的碳核算具有重要价值。
更新日期:2025-01-08
中文翻译:
![](https://scdn.x-mol.com/jcss/images/paperTranslation.png)
基于更新土壤数据库的华北耕地表土有机质浓度空间变化
准确了解耕地表土有机质浓度 (CTSOM) 的空间变化对于有效利用和管理耕地至关重要。然而,由于土壤数据库过时和不精确,这些知识在很大程度上仍然不确定。因此,本研究在 2020 年精心收集了华北河北省 918 份耕地表土 (0–30 cm) 样本,并使用随机森林 (RF) 模型来描绘 CTSOM 的空间变异性。结果表明,包含 21 个预测因子的 RF 模型具有稳健性能,R2 为 0.77,土壤全氮 (TN) 成为最重要的预测因子。研究区目前的平均 CTSOM 水平为 16.47 ± 3.94 g kg-1,表现出西部和北部山区 CTSOM 水平较高,东部平原地区 CTSOM 水平较低的空间格局。与第二次全国土壤调查数据的比较显示,在过去 40 年中,CTSOM 的总体区域水平增加了 4.28 g kg-1。然而,在研究区的北部观察到 CTSOM 显着下降,那里的秸秆还田和施肥可能是关键的促成因素。本研究为华北地区 CTSOM 的空间变化提供了最新的知识,对全球农业生态系统管理和陆地生态系统的碳核算具有重要价值。