عنوان مقاله [English]
Introduction: Understanding soil biology and ecology is increasingly important for renewing and sustainability of ecosystems. In all ecosystems, soil microbes play an important role in organic matter turnover, nutrient cycling and availability of nutrients for plants. Different scenarios of land use may affect soil biological properties. Advanced information technologies in modern software tools such as spatial geostatistics and geographical information system (GIS) enable the integration of large and complex databases, models, tools and techniques, and are proposed to improve the process of soil quality and sustainability. Spatial distribution of chemical and biological properties under three scenarios of land use was assessed.
Materials and Methods: This study was carried out in Mirabad area located in the western part of Souldoz plain surrounded by Urmieh, Miandoab, Piranshahr and Naghadeh cities in the west Azerbaijan province with latitude and longitude of 36°59′N and 45°18′E, respectively. The altitude varies from 1310 to 1345 with average of 1325 m above sea level. The monthly average temperature ranges from -1.4 °C in January to 24.6 °C in July and monthly precipitation ranges from 0.9 mm in July to 106.6 mm in March. Apple orchard, crop production field and rich pasture are three selected scenarios in this research work. Soil samples were systematically collected at 65 sampling points (0-30 cm) on mid July 2010. Soil chemical and biological properties i.e. microbial community, organic carbon and calcium carbonate equivalent were determined. The ArcGIS Geostatistical Analyst tool was applied for assessing and mapping the spatial variability of measured properties. The experimental design was randomized complete blocks design (RCBD) with five replications. Two widely applied methods i.e. Kriging and Inverse Distance Weighed (IDW) were employed for interpolation. According to the ratio of nugget variance to sill of the best variogram model three following spatial dependence conditions for the soil properties can be considered: (I) if this ratio is less than 25%, then the variable has strong spatial dependence; (II) if the ratio is between 25% and 75%, the variable has moderate spatial dependence; and (III) otherwise, the variable has weak spatial dependence. Data were also integrated with GIS for creating digital soil biological maps after testing analysis and interpolating the mentioned properties.
Results and Discussion: Spherical model was the best isotropic model fitted to variograms of all examined properties. The value of statistics (R2 and reduced sum of squares (RSS)) revealed that IDW method estimated calcium carbonate equivalent more reliably while organic carbon and microbial community was estimated more accurately by Kriging method. The minimum effective range (6110 m) was found for microbial community which had the strong spatial dependence [(Co/Co+C)