Abstract
Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.
Highlights
Nutrients are essential for soil fertility and stable plant growth [1]
Studying, modeling, and mapping the spatial distribution of soil properties is an important task for effective farming and sustainable land management [2]
digital soil mapping (DSM) methods are more cost-effective and allow the creation of maps with greater accuracy and higher spatial resolution [5]. These methods are especially relevant to digital mapping of soil nutrients, as laboratory analyses of soil nutrients are costly and time-consuming
Summary
Studying, modeling, and mapping the spatial distribution of soil properties is an important task for effective farming and sustainable land management [2]. Field soil surveys and further large-scale mapping (including updating old maps) are expensive and time-consuming processes. Digital soil mapping (DSM) methods have been actively used to study and map soils and their properties. DSM methods are more cost-effective and allow the creation of maps with greater accuracy and higher spatial resolution [5]. These methods are especially relevant to digital mapping of soil nutrients, as laboratory analyses of soil nutrients are costly and time-consuming
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