Wang Youfu, Qiang Fangfang, Liu Guangquan, Liu Changhai, Gao Jie, Ai Ning
College of Life Sciences, Key Laboratory of Applied Ecology on the Loess Plateau of Shaanxi Higher Education Institutions, Yan'an University, Yan'an, 716000, China.
China Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
Sci Rep. 2025 Aug 20;15(1):30496. doi: 10.1038/s41598-025-13113-0.
Evaluating the effects of different vegetation patterns on soil quality and clarifying the soil quality drivers in the loess area of northern Shaanxi are highly important for the future land use and vegetation optimization in this area. In this work, eight typical vegetation patterns in the loess area of northern Shaanxi were used as research objects, and the main soil physicochemical indicators were screened out using principal component analysis, the soil quality evaluation model was established using fuzzy comprehensive evaluation method, and the soil quality (SQ) was comprehensively evaluated. Finally, structural equation modeling (SEM) was used to explore the key factors affecting soil quality. The results revealed that (1) the maximum water holding capacity (MWHC), capillary water holding capacity (CWHC), total porosity (TCP), soil organic carbon (SOC), quick acting phosphorus (AP), C/P, and C/N of the PTF sample were significantly greater than those of the other vegetation models, and the maximum water holding capacity (MWHC), capillary water holding capacity (CWHC), soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) of the MAF sample site were significantly lower than those of the other sample sites, while soil bulk density (BD) was significantly higher than other sample sites. (2) According to the principal component analysis of the 16 physical and chemical indicators, the eigenvalues of the first four principal components were 1, and the cumulative contribution rate reached 77.482%, which effectively included the information of the original variables. (3) The soil qualities of the different vegetation types in the loess area of northern Shaanxi were ranked as follows: PTF (0.534) > SLP (0.494) > SF (0.462) > MF1 (0.430) > HPF (0.423) > BLF (0.420) MF2 > (0.415) > MAF (0.389). (4) SEM revealed that soil quality drivers varied among vegetation patterns, but soil organic carbon (SOC), as the main influencing factor, positively affected all vegetation. Therefore, this study suggests that in the future, when forestry ecological construction is carried out in similar areas, vegetation such as Pinus tabuliformissmall-leaf poplars, and sea buckthorn can be created to improve soil quality and ecological benefits.
评估不同植被模式对土壤质量的影响并阐明陕北黄土区土壤质量驱动因素，对于该地区未来的土地利用和植被优化至关重要。本研究以陕北黄土区8种典型植被模式为研究对象，通过主成分分析筛选出主要土壤理化指标，采用模糊综合评价法建立土壤质量评价模型，对土壤质量（SQ）进行综合评价。最后，利用结构方程模型（SEM）探究影响土壤质量的关键因素。结果表明：（1）PTF样地的最大持水量（MWHC）、毛管持水量（CWHC）、总孔隙度（TCP）、土壤有机碳（SOC）、速效磷（AP）、C/P和C/N显著高于其他植被模式，MAF样地的最大持水量（MWHC）、毛管持水量（CWHC）、土壤有机碳（SOC）、全氮（TN）和全磷（TP）显著低于其他样地，而土壤容重（BD）显著高于其他样地。（2）对16项理化指标进行主成分分析，前四个主成分的特征值均为1，累计贡献率达到77.482%，有效包含了原始变量的信息。（3）陕北黄土区不同植被类型土壤质量排序为：PTF（0.534）＞SLP（0.494）＞SF（0.462）＞MF1（0.430）＞HPF（0.423）＞BLF（0.420）＞MF2（0.415）＞MAF（0.389）。（4）结构方程模型（SEM）表明，不同植被模式下土壤质量驱动因素存在差异，但土壤有机碳（SOC）作为主要影响因素，对所有植被均有正向影响。因此，本研究建议未来在类似地区进行林业生态建设时，可营造油松、小叶杨、沙棘等植被，以提高土壤质量和生态效益。
