Evidence map›Paper›PMID 37968306›Full record

ArticleScientific reports2023

Assessment of soil quality in an arid and barren mountainous of Shandong province, China.

Lu Wang, Jianyao Guo, Xiumei Liu, Kun Li, Liang Ma, Yehan Tian, Jinming Wang, Qingdong Zhang, Yaozhen Tian, Chuanrong Li and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.5field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 1 country.

Lu WangLandscape Architecture Research Center, Shandong Jianzhu University, Jinan, 250101, Shandong, China.
Jianyao GuoShandong Forestry Protection and Development Service Center, Jinan, 250000, Shandong, China.
Xiumei LiuState Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, Tai'an, 271018, Shandong, China.
Kun LiState Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, Tai'an, 271018, Shandong, China.
Liang MaState Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, Tai'an, 271018, Shandong, China.
Yehan TianState Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, Tai'an, 271018, Shandong, China.
Jinming WangState Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, Tai'an, 271018, Shandong, China.
Qingdong ZhangState Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, Tai'an, 271018, Shandong, China.
Yaozhen TianState Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, Tai'an, 271018, Shandong, China.
Chuanrong LiState Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, Tai'an, 271018, Shandong, China. lcrsdau@163.com.
Min LuLandscape Architecture Research Center, Shandong Jianzhu University, Jinan, 250101, Shandong, China. 465819510@qq.com.
State Forestry and Grassland Administration · CNShandong Jianzhu University · CNShandong Academy of Forestry · CNShanghai University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Forest soils are important components of forest ecosystems, and soil quality assessment as a decision-making tool to understand forest soil quality and maintain soil productivity is essential. Various methods of soil quality assessment have been developed, which have occasionally generated inconsistent assessment results between soil types. We assessed the soil quality of five communities (herb, shrub, Quercus acutissima, Pinus thunbergii, and Q. acutissima-P. thunbergii mixed plantation) using two common methods of dry and barren mountains in the Yimeng Mountain area, China. Sixteen soil physical, chemical and biological properties were analysed. The soil quality index was determined using the established minimum data set based on the selection results of principal component analysis and Pearson analysis. Silt, soil total phosphorus (P), soil total nitrogen (N), L-leucine aminopeptidase, acid phosphatase and vector length were identified as the most representative indicators for the minimum data set. Linear regression analysis showed that the minimum data set can adequately represent the total data set to quantify the impact of different communities on soil quality (P < 0.001). The results of linear and non-linear methods of soil quality assessment showed that the higher soil quality index was Pinus forest (0.59 and 0.54), and the soil quality index of mixed plantation (0.41 and 0.45) was lower, which was similar to the herb community (0.37 and 0.44). Soil quality was mostly affected by soil chemical properties and extracellular enzyme activities of different communities, and the different reasons for the low soil quality of mixed plantations were affected by soil organic carbon (C) and total C. Overall, we demonstrate that the soil quality index based on the minimum data set method could be a useful tool to indicate the soil quality of forest systems. Mixed plantations can improve soil quality by increasing soil C, which is crucial in ecosystem balance.

Indexed as

EcosystemPinusCarbonChinaForestsNitrogenSoilCarbonNitrogenSoil

Identifiers

PMID37968306
PMCPMC10652006
OpenAlexW4388700016

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.