Comparison of terrain-based drift models to improve the quality of soil predictive mapping at a field scale.pdf Introduction Spatial variability of soil properties is an important indicator of soil quality, and it is important in ecological modeling, environmental prediction, precision agriculture, and natural resource management [1]. Revealing the characteristics of spatial patterns will provide the basis for evaluating soil fertility, and assist in the development of sound agricultural management policies. So, there is a need for adequate information about spatio-temporal behavior of soil properties over a region and accurate interpolation at unsampled locations is needed for better planning and management. In general, there are two major approaches to predict soil properties at unsampled location. Methods of "classic" statistics use linear and non-linear regression models to predict dependent variable using auxiliary data. Remote sensing data, topographic and morphologic attributes, climate, land-use and geology are auxiliary parameters commonly used for the calibration of predictive models. For example, Rodriquez-Lado and Martinez-Cortizas used multiple linear regression, e.g. principal component regression and partial least squares, for modeling and mapping organic carbon content of topsoil using climatic and geological data as independent variables [2]. The second approach is geostatistics, which has been rapidly developing for last decades [3; 4]. Geostatistics is an efficient method for studying spatial allocation of soil characteristics and their inconsistency and reducing the variance of assessment error and execution costs [5]. Geostatistical methods model the local uncertainty about the attribute value at any particular location through the set of possible realizations of the random variable at that location [6]. Earlier researchers, who applied geospatial techniques to evaluate geographical changeability of soil characteristics, reported that ordinary kriging in most cases was the best method for prediction of the spatial distribution of soil properties [7; 8]. And there is the third, hybrid approach that uses advantages of the first two. And the typical example is regression kriging (RK) that uses regression models to explain deterministic part of spatial variation using auxiliary data and kriging technique to interpolate the residuary, stochastic part of spatial variation. In RK, the deterministic part can be explained using various statistical techniques. Many authors suggest the relative accuracy advantage of the RK compared to OK, and this prediction performance depends on the relationship between the target variable and the explanatory co-variables [9; 10]. The present study was undertaken to compare the accuracy of various approaches to model the deterministic part of regression kriging. Materials and methods 2.1. Study area and sampling design The study was carried out in the national crop testing field (CTF) that is located in the southeastern part of the Republic of Tatarstan (Russian Federation, 55°05'56.0"N 52°02'24.0"E). The relief of the field is flat in the northern part and changes to the gentle slope in the southern and south-eastern parts. The soil cover is represented by leached, silt loamy chernozems with varying rates of erosion. The most eroded chernozem is located in the eastern and south-eastern parts of the field. The soft eroded soils are located in the northern part of the CTF. Parent rocks are represented by fine loamy and clayey calcareous deluvium, underlined by the ancient alluvial deposits in the eastern part. Particle size distribution, according to the Russian classification by NA Kachinsky, is fine loamy [11]. The soil sampling was performed according to an agrochemical sampling design: the field was divided into 60 sections; within each section 12-15 sampling points were taken using a hand auger at the depth of 10-20 cm to produce one mixed sample (Fig. 1). For geostatistical analysis the mixed samples were georeferenced into centers of the corresponding sections. The following soil properties were measured: humus content by the Tyurin method and the particle size distribution by the Kachinsky-Robinson-Kehl pipet method [12]. As an indicator of the particle size distribution, the sum of particles

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