Quantitative assessment of effectiveness of soil conservation measures using a combination of 137Cs radioactive tracer and conventional techniques

Abstract

Abstract Quantitative assessment of soil redistribution in landscapes remains a challenging task. In this study we used radioactive soil redistribution tracer 137Cs together with soil morphological characteristics and empirically-based modeling for quantitative assessment of long-term soil conservation effectiveness. Three pairs of arable slopes were selected, all located within the territory of the Novosil experimental station (the Orel Region, central European Russia). One slope in each pair undergone creation of artificial terraces with forest shelter belts located parallel to topography contour lines and spaced at approximately 100 m from each other. Preliminary results have shown that slopes with soil-protective measures are characterized by a 11–80% reduction of average soil redistribution rates, as shown by soil profile morphology and 137Cs methods. Discrepancy in values obtained can be attributed to differences in temporal resolution of methods as well as possible influence of individual extreme events on results yielded by the 137Cs method. On the other hand, more significant decrease in average soil degradation rates on slopes with soil conservation (62–75% for each pair of slopes) was predicted by the model. The 137Cs method overestimates gross and net soil redistribution rates, as a result of the influence of extreme erosion prior to tillage mixing of a fresh fallout isotope, not accounted for by calibration models used. Another shortcoming of the estimations obtained is that sediment redeposition directly within forest belts was not taken into account. Therefore, net erosion rates obtained for slopes with forest belts should be regarded as overestimation. Nevertheless, it can be generally concluded that the multi-technical approach has allowed acquiring much more detailed information on temporal and spatial variability of soil redistribution rates than single method-based studies.