A multi-data approach to evaluate progress towards land degradation neutrality in Central Asia

Abstract

Central Asia hosts one of the largest continuous grassland areas on our planet, that is of vital importance for food security, biodiversity and carbon sequestration. However, this region is also subjected to some of the most intense land degradation processes, related to large scale land use change and climate change.To combat land degradation and pursue sustainable land management the concept of land degradation neutrality (LDN) has been proposed. LDN assessments are recommended to be based on global data in regions that lack sufficient regional data such as Central Asia. However, it remains unclear how the selection of datasets influences the estimated extent of land degradation. We followed the LDN framework and first calculated changes to three LDN indicators: land productivity, land cover, and Soil Organic Carbon (SOC). We then evaluated the impact of using different regional and global land cover data on the extent of land degradation in the region. Finally, we calculated a regionally specific integrated indicator on soil quality that we used to assess the “like-for-like” principle which enables counterbalancing losses and gains.Our results indicate that particularly the selection of land cover data has a significant impact on the overall assessment of degradation state. The extent of land between 2000 and 2019 varies considerably depending on the data and ranges between 15 and 34 % of the whole Central Asian region. Our findings reveal that the area of degraded land on high quality soils is twice as high as the area of high quality soils where the land condition has improved (12 % versus 6 %). In areas with low soil quality, 13 % was subject to degradation, and 10 % improved.Whilst the degradation extent varies according to the selected land cover datasets, our results demonstrates that Central Asia is undergoing land degradation affecting high quality soils. Sensitivity analysis of multiple datasets can reduce the risk of misjudgement in degradation extent assessments, especially in regions with mosaic ecosystems, such as grasslands.