Soil quality indicators are measurable soil attributes that reveal the soil productivity response or soil-environment functionality that are used to know whether soil quality is improving, remain constant, or declining. These characteristics could be assessed by different indices such as sustainability index approach (SI) based on the threshold levels of soil indicators and cumulative rating approach (CR) based on crop production limitations, which show the sustainability of soil ecosystem in terms of soil degradation. Since Iran is situated in arid and semi-arid climatic conditions, this research was conducted in agriculture fields of southeast of Mashhad, Iran for comparing these two approaches. Sixty three soil samples (0-30 cm) were collected and nine soil properties such as pH, electrical conductivity (EC), soil organic carbon (SOC), soil particle-size distribution, available water holding capacity (AWHC), bulk density (BD), air capacity (AC), relative field capacity (RFC) and sodium adsorption ratio (SAR) were measured. All these measurements were considered as total data set (TDS). Principal component analysis (PCA) was used to select more effective indicators to conform the minimum data set (MDS). There was a strong correlation between SI and CR (R 2 =0.69, p <0.05). Only six soil indicators selected as MDS (pH, SOC, AWC, BD and SAR) were correlated (p<0.01) significantly with SI and CR. These SI and CR results showed more promising effects on soil sustainability. PCA was found a suitable method for selecting the more effective indicators having R 2 = 0.77 (p <0.05) (CR-MDS versus CR-TDS) comparable with R 2 = 0.80 (p <0.05) (CR-MDS versus SI) to use less soil data input in assessing soil quality in arid zone.
A b s t r a c t. This study was planned to examine the use of LandSat ETM + images to develop a model for monitoring spatial variability of soil cation exchange capacity in a semi-arid area of Neyshaboor. 300 field data were collected from specific GPS registered points, 277 of which were error free, to be analysed in the soil laboratory. The statistical analysis showed that there was a small R-Squared value, 0.17, when we used the whole data set. Visual interpretation of the graphs showed a trend among some of the data in the data set. Forty points were filtered based on the trends, and the statistical analysis was repeated for those data. It was discovered that the 40 series were more or less in the same environmental conditions; most of them were located in disturbed soils or abandoned lands with sparse vegetation cover. The soil was classified into high and medium salinity, with variable carbon (1.0 to 1.6%), heavy textured and with high silt and clay. Finally it was concluded that two different models could be fitted in the data based on their spatial dependency. The current models are able to explain spatial variability in almost 45 to 65% of the cases.K e y w o r d s: soil cation exchange capacity, remote sensing, soil properties, soil spatial variability
A b s t r a c t. Quantifying soil quality is important for assessing soil management practices effects on spatial and temporal variability of soil quality at the field scale. We studied the possibility of defining a simple and practical fuzzy soil quality index based on biological, chemical and physical indicators for assessing quality variations of soil irrigated with well water and treated urban wastewater during two experimental years. In this study 6 properties considered as minimum data set were selected out of 18 soil properties as total data set using the principal component analysis. Treated urban wastewater use had greater impact on biological and chemical quality. The results showed that the studied minimum data set could be a suitable representative of total data set. Significant correlation between the fuzzy soil quality index and crop yield (R 2 = 0.72) indicated the index had high biological significance for studied area. Fuzzy soil quality index approach (R 2 = 0.99) could be effectively utilized as a tool leading to better understanding soil quality changes. This is a first trial of creation of a universal index of soil quality undertaken.K e y w o r d s: fuzzy membership functions, principal component analysis, soil quality, treated urban wastewater
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