Document Type : Applicable
Authors
1 Former MSc.student, Department of Soil Sciences , Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Assistant professor, Department of Soil Sciences, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 Associate Professor, Department of Soil Sciences, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
4 Professor, Department of Soil Sciences, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Abstract
Introduction: Today, the concept of soil quality (SQ) has been widely used to know the capacity and limitations of soils in different environmental systems. The degree of suitability of land is determined by its capacity to provide services and its flexibility against external conditions. Production of plant biomass is one of the most important functions of soil in relation to food security. The share of dry land in Iran's agricultural production, especially wheat, is very significant. So that in terms of area, about half of the total area of agricultural lands, in terms of volume of production, about 10% of all agricultural products and about 30% of the country's wheat production are related to these lands. Therefore, maintaining the soil quality of these lands is very important. The main goal of this research is to model and quantify the soil quality of part of the rainfed agricultural lands of Dezpart city using integrated multivariate analysis and also to determine the minimum effective data set.
Materials and methods: This study was carried out in a part of the rainfed agricultural area of Dezpart County. First, 119 soil samples were prepared using the composite method from the soil depth of 0-30 cm. Soil sampling was done in a stratified random manner to include all the different geomorphological units. The geographic location of the sampling points was also recorded. The samples were transferred to the laboratory and their chemical-fertility and physical characteristics include reaction (pH), electrical conductivity (EC), organic matter (OM), total nitrogen, available potassium, absorbable phosphorus, calcium carbonate equivalent (CCE), texture, bulk density, mean weight diameter (MWD) of soil aggregates, soil gravel content and cation exchange capacity (CEC) were measured. Then the soil quality was determined using two datasets of total (TDS) and minimum (MDS), and multivariate analysis method. In this method, by using appropriate scoring functions, a score between zero and one was considered for each member of the data set. Also, a weight coefficient was calculated for each member, and finally, the soil quality index, which indicates its degree of desirability, was obtained by three indices including Nemero (NQI), cumulative weighted index (IQI) and simple cumulative index (AQI). Finally, a spatial variation map of soil quality was prepared using the Inverse Distance Weighting (IDW) method in geographic information system (GIS) software.
Results and Discussion: The results of the principal component analysis (PCA) test indicated that there are three main components that cover 78% of the total variance changes. The first component alone accounts for about 41% and the second and third components account for 25% and 12% of the total data variance, respectively. Based on the correlation analysis between soil components and characteristics, five characteristics including organic matter (OM), silt content, gravel, pH and EC were selected as MDS members. Became in the TDS collection, the highest weights related to silt and sand (0.093 and 0.095, respectively) and the lowest weight with 0.050 was assigned to bulk density (BD). In the MDS set, the highest weight was related to organic matter and silt and the lowest weight was related to pH. The soil quality of the region was generally classified as medium based on the two indexes of AQI and WQI. However, the NQI method indicated that the soil quality was low. Among the three selected indices with different functions and data sets, the weighted soil quality index with the minimum data set and nonlinear function (WQI_MDS_NL) was chosen as the superior model due to having a higher sensitivity index (or a larger standard deviation). The spatial soil quality map, which was prepared for this study, showed that approximately 50% of the lands in the region had an average soil quality and 50% had a low soil quality.
Conclusion: Organic matter, silt, pH, gravel and EC are the main characteristics to determine the soil quality of the region. In addition, stability of soil aggregates, bulk density and lime are the most important limiting factors of soil quality in the region. Therefore, it is suggested to use appropriate management practices such as conservation tillage and use of organic fertilizers to improve these characteristics.
Conclusion: Organic matter, silt, pH, gravel and EC are the main characteristics to determine the soil quality of the region. In addition, stability of soil aggregates, bulk density and lime are the most important limiting factors of soil quality in the region. Therefore, it is suggested to use appropriate management practices such as conservation tillage and use of organic fertilizers to improve these characteristics.
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