Evidence map›Paper›PMID 42594152›Full record

ArticlePloS one2026

Soil quality assessment based on the TDS and MDS methods in the Qinling mountains of China.

Weige Yang, Junwei Yue, Qi Liu, Shiquan Liang, Yuanyuan Ye

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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Weige YangCollege of Urban, Rural Planning and Architectural Engineering, Shangluo University, Shangluo, China.ORCID https://orcid.org/0000-0001-8616-9883
Junwei YueCollege of Urban, Rural Planning and Architectural Engineering, Shangluo University, Shangluo, China.
Qi LiuCollege of Urban, Rural Planning and Architectural Engineering, Shangluo University, Shangluo, China.
Shiquan LiangCollege of Urban, Rural Planning and Architectural Engineering, Shangluo University, Shangluo, China.
Yuanyuan YeCollege of chemical engineering and modern materials, Shangluo University, Shangluo, Shaanxi, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial forests can improve the function and quality of poor soil. Different artificial trees are grown in some areas worldwide. However, data on soil quality levels in different artificial forest soils in the Qingling Mountains of China are scarce. The purpose of this research was to estimate the level and spatial distribution characteristics of soil quality by applying the total dataset (TDS) and minimum dataset (MDS) methods and to verify the accuracy of the results using the MDS methods for different artificial forestland soils in the Qingling Mountains of China. Twenty-six soil samples were collected from the Walnut, Robinia pseudoacaci, Broussonetia papyrifera, Pinus psammophila, and Quercus palustris Münchh soil and 13 soil quality indicators comprising soil physical and chemical properties were analyzed. The values of the soil quality index (SQI) of the different artificial forest soils according to the TDS and MDS methods were in the following order: Walnut > Robinia pseudoacaci > Pinus psammophila > Broussonetia papyrifera> and Quercus palustris Münchh. The pivotal indicators for characterizing soil quality in different artificial forest soils in the Qingling Mountains were soil BD, pH, soil SOM, SP, and SWC, which were screened out by principal component analysis and the norm value. The values of the SQI ranged from 0.27 to 0.77, with an average value of 0.53, when the TDS method was used, and from 0.39 to 0.87, with an average value of 0.62, when the MDS method was used. The spatial distribution of the SQI was in the form of a strip. Moreover, a 1:1 line was used to test the accuracy of the MDS method. The coefficient of certainty was 0.778, and the coefficient of relative deviation was 0.037, which indicated that the cultivated soil quality in different artificial forestland areas in the Qingling Mountains of China could be assessed using the MDS method. Hence, to achieve the sustainable development of forestry, effective soil quality improvement measures, for instance, building the best ecological benefit of the stand and preventing the decline in soil fertility and the ecological environment through reasonable tending, should be implemented instead of current traditional forest management measures.

Indexed as

Environmental MonitoringSoilBroussonetiaChinaForestsPinusQuercusRobiniaTreesSoil

Identifiers

PMID42594152
PMCPMC13472376

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.