Comprehensive Evaluation of Soil Substrate Improvement Based on the Minimum Data Set Method

Abstract

Long-term transitional grazing on the northern slopes of the Tianshan Mountains in Xinjiang has led to severe vegetation degradation, loss of self-renewal capacity and regional ecological degradation in the region. This study was conducted to improve the soil quality and vegetation restoration efficiency in the foreland zone of the northern slope of the Tianshan Mountains (Xiangyataizi slope) using xanthic acid, bentonite, a green plant growth regulator (GGR) and high amounts of mulch as improvement materials, and eight sets of experiments were conducted. Fifteen physical and chemical indicators were selected as the total data set (TDS), and the minimum data set (MDS) was constructed using principal component analysis (PCA) combined with norm values to evaluate the soils in the study area by nonlinear (NL) and linear (L) evaluation methods. The results showed that the soil quality evaluation indexes of the MDS included effective phosphorus, organic matter, percentage of powder, total potassium and total salt for the Xiangyataizi slope of the Tianshan Mountains. The SQI was (p < 0.05). The Ⅵ treatment significantly improved soil quality; that is, plastic mulch applied to soil with 250 g of fulvic acid, 1000 g of bentonite and 15 g of GGR (mixed with 100 kg of water) was the best treatment. Additionally, since the nonlinear soil quality evaluation method (SQI-NL) had a smaller variation interval and coefficient of variation of the soil quality index compared with linear soil quality evaluation method (SQI-L), the coefficient of determination between the MDS and TDS was 0.873 and 0.811 under the SQI-NL and SQI-L evaluation methods, respectively. The nonlinear soil quality evaluation method had better applicability in this region, and the minimum data set was more accurate for soil quality evaluation.

Soil Quality Evaluation Based on a Minimum Data Set (MDS)—A Case Study of Tieling County, Northeast China

“…There were significant differences between the maximum and minimum contents of soil elements in Tieling County, with the coefficient of variation between 0.03-1.41. Generally, coefficient of variation (CV) ≤ 0.1 is considered to indicate a weak variation, 0.1 < CV < 1.0 is considered to indicate a moderate variation, and CV ≥ 1.0 is considered to indicate a strong variation [42]. In conclusion, the pH, bulk density, silt, and total potassium of the soil in Tieling County showed weak variation, and the factors were relatively stable with a small variation range.…”

Soil Quality Evaluation Based on A Minimum Data Set (MDS)—A Case Study of Tieling County, Northeast China

“…There were significant differences between the maximum and minimum contents of soil elements in Tieling County, with the coefficient of variation between 0.03-1.41. Generally, coefficient of variation (CV) ≤0.1 is considered as indicating a weak variation, 0.1 < CV < 1.0 is considered as indicating a moderate variation, and CV≥1.0 is considered as indicating a strong variation [45]. In conclusion, the pH, bulk density, silt, and total potassium of the soil in Tieling County showed weak variation, and the factors were relatively stable with a small variation range.…”

“…Due to the correlation among the influencing factors, it is necessary to select indicators with high acquisition, strong stability, a large influence degree, and indicators that can reflect the difference in soil quality to carry out the relevant evaluation. [39]. In this study, SPSS 26.0 software was used for the correlation analysis, so as to preliminarily determine whether the primary indicators are suitable for inclusion in the soil quality evaluation indicator system.…”

Development of a Quality Index to Evaluate the Impact of Abiotic Stress in Saline Soils in the Geothermal Zone of Los Negritos, Michoacán, Mexico

“…The process seeks to obtain a minimum dataset (MDS) that adequately represents the total dataset (TDS) on quality and that contributes to reducing the cost of evaluation. The factorial analysis usually involves (1) the selection of a TDS of the soil properties in relation to a specific function focused on within the objective of the study, (2) the choice and interpretation of an MDS, and (3) the integration of the scores in an index [8,14,15].…”