Quantitative assessment of soil quality in different land uses and slope gradients at Babolrood watershed, south of Mazandaran province

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Aghalari Fatemeh; Chavoshi Elham; Chavoshi Borujeni Sattar; Honarjoo Naser; Jalalian Ahmad

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Journal Paper

Paper Information

Journal: APPLIED SOIL RESEARCH Year:2023 | Volume:11 | Issue:2 Page(s): 95-112

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Title

Quantitative assessment of soil quality in different land uses and slope gradients at Babolrood watershed, south of Mazandaran province

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Keywords

weighted additive soil qualityQ4

additive soil qualityQ4

Minimum data setQ4

principal components analysisQ4

Abstract

Soil quality is affected by land use and slope gradient, and soil quality assessment is important in determining sustainable land-use and soil-management practices. This study was conducted to assess soil quality in different land uses and slope gradients at Babolrood watershed, south of Mazandaran province. The studied factors included five levels of land uses (natural forest, rangeland, paddy field, citrus orchard and farmlands) and five levels of slope classes (0-2%, 2-5%, 5-8%, 8-12% and above 12 %). A total of 89 soil samples were collected in the upper layer of topsoil (depth 0-20 cm). The seventeen soil properties were used in a total data set (TDS). These properties included pH, EC, organic carbon (OC), clay, silt, sand, water dispersible clay (WDC), soil bulk density (BD), soil porosity (Pr), mean weight diameter (MWD), geometric mean diameter (GMD), macro aggregates, micro aggregates, total neutralizing value (TNV), organic carbon pool (OCpool), particulate organic carbon (POC), and mineral-associated organic carbon (MOC). Using the principal components analysis (PCA) method, five soil properties (OCpool, macro aggregates, EC, silt and clay) were selected for the Minimum data set (MDS). The soil quality was evaluated by additive soil quality index (SQIa) and weighted additive soil quality index (SQIw) in two data sets of soil properties including MDS and TDS. The eight soil quality indices showed that the forest has the best soil quality with a good and very good grade and the paddy field has the poorest soil quality. Also, among the evaluated SQIs, only SQIw-TDS-NL showed a decrease in soil quality with increasing slope gradients. Moreover, the correlation coefficients were 82% and 89% between the two sets of MDS and TDS in SQIa and SQIw using nonlinear scoring methods, respectively. Because of the good correlation between TDS and MDS, the MDS set is better to determine soil quality indices in the study area. The correlation coefficient between SQIw-MDS-NLwith OCpool was higher (0.85) than all SQIs. So, SQIw-MDS-NL is suggested to evaluate soil quality in the study area.

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