Assessment of soil quality indices using multivariate analysis in different land uses (case study: Tootkabon, Guilan province)

 Soil quality index is used as a quantitative tool for assessing the impact of land use and management practices on soil condition. Soil quality is a sensitive indicator for revealing the dynamics of soil conditions, and it may vary with different land use and ecological restoration measures. The land use affects the physical and chemical properties, biological processes, and land productivity, which lead to the change in soil quality. Land use change and agricultural development can lead to degradation, erosion and reduction of surface and subsurface soil quality. In most of the conducted studies, the surface soil quality has been evaluated; but these studies provide incomplete information because subsurface soil have the greatest impact on soil function and crop. In spite of various soil quality assessment methods developed in former researches, there are fewer attempts for selecting suitable and sensitive soil quality index, especially in different land uses. In this study, soil quality indicators were evaluated using multivariate analysis in three different land uses to select the most suitable and appropriate soil quality index in Tootkabon area of Guilan province.

The study area is located in Tootkabon in Guilan province (latitude 36º 53' 21" N, longitude 49º 33' 44" E). Parent material is limestone and geomorphologic units that are comprised of hill land and plateau. In order to achieve the objectives of the research, 20 composite soil samples were taken from two depths of 0 to 15 and 15 to 30 cm from each of the land use, including forest, cropland and rangeland (60 soil samples in total) with the same parent material. The three land uses were located next to each other and at a close distance. In this research, using the principal component analysis (PCA) method, among 12 physical, chemical and biological soil indicators as total data set (TDS), clay percent, mean weight diameter, organic matter and available phosphorus were determined as the minimum data set. Then, the soil quality was evaluated by integrated quality index (IQI) and Nemoro quality index (NQI) using two linear and non-linear scoring methods (LS and NLS) and two soil indicator selection approaches, a total data set (TDS) and a minimum data set (MDS). Finally, to prioritize the soil quality indices based on sensitivity index (SI) and efficiency ratio (ER), the ranks of both criteria were summed and then made appropriate decision. All soil parameters were tested using one-way analysis of variance and the differences among means were analyzed using Duncan's significant difference test at the probability level of 0.05.

The results of the present study showed that some soil properties including clay percentage, mean weight diameter, organic matter and available phosphorus had the greatest effect on soil quality in the study area. Most of the soil properties in rangeland and forest had a higher stratification ratio compared to cropland. The soil quality indices calculated using linear function for MDS indicated soil quality of forest and cropland were higher than rangeland. Maximum SI belonged to IQI-LS-TDS and IQI-LS-MDS with values of 1.56 and 1.40, respectively. Efficiency ratios (ER) were calculated to specify the power of each soil quality index being as representative index for whole soil parameters set. IQI-LS-MDS and IQI-NLS-MDS have the highest value of ER (75.0 %), it is obviously deducted that these developed soil quality indices correlate with much indicators than other indices. It has more efficiency ratio and therefore represents the soil overall condition highly. Finally prioritizing according to ranks of SI and ER showed that IQI-LS-MDS is the most suitable approach in soil quality assessment of study area.

Minimum data set selection using principal component analysis as a multivariate statistical method could adequately represent total data set method. Therefore, it seems to be an appropriate approach for choosing more effective indicators with respect to saving time and money in the developing countries The linear soil quality indices showed higher capability than non-linear indices to differentiate soil quality among different land uses. Overall results of the prioritization soil quality indices imply that the IQI-LS-MDS has the most efficiency and sensitivity for variation in land uses, so it is suggested to use this quality index for further and comprehensive soil quality assessments plans.