Journal of Environment and Earth Science www.iiste.org ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online) Vol.4, No.15, 2014 17 Using OK and IDW Methods for Prediction the Spatial Variability of A Horizon Depth and OM in Soils of Shahrekord, Iran Abbas Almasi 1 Ahmad Jalalian 2 Norair Toomanian 3* 1.PhD Student, Azad Islamic University, Khorasgan branch, Iran 2.Soil professor, Azad Islamic University, Khorasgan branch, Iran. 3.Assistant professor, Soil Science, Isfahan Agricultural Research Center, Isfahan, Iran Abstract This study attempts to evaluate some interpolation techniques for mapping spatial distribution of A horizon depth and OM in Shahrekord, Iran. 15000 hectares of South West Shahrekord soils were studied in which totally 92 soil profiles were excavated and classified according to USDA. The performance of methods was evaluated by RMSE, ME and R 2 . Calculated RMSE for depth of A horizon were 0.01074, 0.19670 and 0.19858, respectively by IDW and OK (with Spherical and Exponential models). The RMSE for surface horizon OM were obtained 0.05593, 0.12121 and 0.05078, respectively by IDW and OK (with Spherical and Exponential models). The results showed that IDW could estimate the variability of A horizon depth and Ok (with Exponential semivariogram) could estimate the variability of depth of A horizon more better than other methods. The weakness of kriging in prediction of spatial continuity of depth of A horizon is due to effects of variability of soil forming factors in evolution of soils evolved in different landforms of study area which could take out the stationary assumptions. Keywords: Ordinary Kriging, Inverse Distance Weighting, Evaluation of models Introduction Hilinski (2001);Soil organic matter (SOM) is the fraction of the soil that consists of plant or animal tissue in various stages of decomposition. Most of the productive agricultural soils have organic matter between 3 and 6%percentages. SOM exerts numerous positive effects on soil physical and chemical properties, as well as the soil’s capacity to provide regulatory ecosystem services. The vertical distribution of SOM in mineral soils is a general decrease of OM content with depth. The vertical decreasing of OM is non-linear and frequently modeled by an exponential function. Based on published results there appear to be distinct differences between the distribution of SOC in topsoil and the subsoil section depending on land use. Jobbagy and Jackson (2000); At a global scale not only the amount of OC but also the specific characteristics of the exponential relationship of OC with depth in the profile were found to vary strongly with vegetation type. Post et al (1982); Climatic conditions seem to be the dominant factor determining SOC for the upper soil layer while for deeper soils clay content becomes increasingly influential. In reality SOC increases with increasing precipitation and decreases with increasing temperature. The major conditions influencing SOC independently of climatic conditions are; Land use / cover, SOC content, Soil depth and Clay content of soils. Shrub lands and arable lands have the lowest rate of decrease of SOC with depth. Soils with high SOC show less of a decrease in OC with depth than soils low in OC. In shallow soils SOC decreases more rapidly with depth than in deeper soils. For deep soils clay content is more closely related to SOC than for shallow soils. Goovaerts (1998); Soils are characterized by high degree of spatial variability due to the combined effect of physical, chemical and biological processes that operate with different intensities and at different scales. Knowledge on spatial variation of soil properties is important in several disciplines, including agricultural field trial research and precision farming. Goovaerts (1998); Corwin (2003); Godwin and Miller (2003); Vrindts et al (2005); Reports have shown that there is large variability in soil, crop, disease, weed and yield not only in large- sized fields, Mouazen et al (2003); but also in small-sized fields. In recent years, geostatistics such as Webster (1994); Zhang et al (1998); Zhang et al (2000); Webster and Oliver (2001); Corwin et al (2003); Mueller et al (2003); Sun et al (2009);has been proven to effectively assess the variability of soil properties. Geostatistics provides a set of statistical tools for analyzing spatial variability and spatial interpolation. These techniques produce not only prediction surfaces but also error or uncertainty surfaces. Cressie (1993); Spatial prediction techniques, also known as spatial interpolation techniques, differ from classical modeling approaches by the way they incorporate information on the geographic position of the sample data points. The most common interpolation techniques calculate the estimate for a property at any given location by a weighted average of nearby data. Salder et al (1998); A number of factors affect map quality including the nature of the soil variability, intensity of sampling and methods of interpolation. Availability of a variety of interpolation methods has posed questions to the users as to which is the most appropriate method in different contexts and has stimulated several comparative studies of relative accuracy. Deutsch (2002); Nalder and Wein (1998); Among