Soil erosion: An important indicator for the assessment of land degradation neutrality in Russia

• Main Authors: Petr Tsymbarovich, German Kust, Mikhail Kumani, Valentin Golosov, Olga Andreeva
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2020-12-01
• Series: International Soil and Water Conservation Research
• Subjects: Russian Federation; Soil erosion; Land degradation neutrality
• Online Access: http://www.sciencedirect.com/science/article/pii/S209563392030040X

This review of soil erosion (SE) studies in Russia focuses on two main tasks: (i) ensuring the completeness and reliability of SE data in Russia, a large country (17.1 million km2) with a variety of natural and socio-economic causes of land degradation, (ii) assessing the possibility of including a SE indicator among the indicators of land degradation neutrality (LDN). A wide range of statistical, remote sensing, mathematical modeling data, the results of scientific and field studies obtained at different levels were analyzed. It is asserted that in Russia the total area of eroded lands and those under erosion risk occupy more than 50% of all agricultural lands, whereas soil fertility of croplands decreased in Soviet time (from 1950s to 1980s) by 30–60% only due to water erosion. However, recent scientific studies indicate a decrease in erosion rate and in the area of eroded land during the last 30–40 years as a result of abandonment of arable land and subsequent overgrown with natural vegetation. The climate change resulting in decrease of the depth of soil freezing, flow of spring runoff also adds to the decrease of soil erosion. The SE indicator was suggested as an important complement to three global LDN indicators. At national and subnational level, it can be interpreted through such indices as “Rate of soil loss” (ton ha-1 yr-1) and “Total soil loss” (1000 tons, in certain area during selected time period). At local level the set of indices can be wider and site-specific, including those obtained through remote sensing data by using the classifier of thematic applications of remote sensing technologies; the example was tested at the local site.