Abstract
Soil quality evaluation is an effective pathway to understanding the status of soil function and ecosystem productivity. Numerous studies have been made in managed ecosystems and land cover to quantify its effects on soil quality. However, little is coincident regarding soil quality assessment methods and its compatibility in highly heterogeneous soil. This paper used the soil survey database of Taihang Mountains as a case study to: (i) Examine the feasibility of soil quality evaluation with two different indicator methods: Total data set (TDS) and minimum data set (MDS); and (ii) analyze the controlling factors of regional soil quality. Principal component analysis (PCA) and the entropy method were used to calculate soil quality index (SQI). SQI values assessed from the TDS and MDS methods were both significantly correlated with normalized difference vegetation index (p < 0.001), suggesting that both indices were effective to describe soil quality and reflect vegetation growth status. However, the TDS method represented a slightly more accurate assessment than MDS in terms of variance explanation. Boosted regression trees (BRT) models and path analysis showed that soil type and land cover were the most important controlling factors of soil quality, within which soil type had the greatest direct effect and land cover had the most indirect effect. Compared to MDS, TDS is a more sensitive method for assessing regional soil quality, especially in heterogeneous mountains. Soil type is the fundamental factor to determining soil quality. Vegetation and land cover indirectly modulate soil properties and soil quality.
Highlights
Soil quality has been a key issue in agrology, agronomy, and environmental science with the increasing awareness of its importance in soil management and ecosystem sustainability [1]
The results from the Taihang Mountain showed that soil quality assessment from both the total data set (TDS) and minimum data set (MDS) method had a significant correlation with normalized difference vegetation index (NDVI), indicating that soil quality indices can reflect vegetation growth status and both methods are effective for soil quality assessment
MDS was proven to be an effective method of assessing soil quality in the study area
Summary
Soil quality has been a key issue in agrology, agronomy, and environmental science with the increasing awareness of its importance in soil management and ecosystem sustainability [1]. In most of the previous studies [8,18,19], soil organic matter is regarded as a critical indicator for soil quality evaluation Other soil properties such as nitrogen, phosphorous, potassium, soil texture, cation exchange capacity, bulk density, and potential of hydrogen (pH) value are believed to have a considerable impact on soil quality. This has raised concerns about the selection of essential indicators for soil quality
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