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Inappropriate land use and soil mismanagement produced wide-scale soil and environmental degradation to the short-grass steppe ecosystem in the semiarid region of central east Kazakhstan. A limitation for determining the impacts of land use changes on soil organic carbon (SOC) is the dearth of information on SOC stocks under the predominant land uses in the region. Here we used the Environmental Policy Integrated Climate (EPIC) model to study long-term impacts of land use changes and soil management on SOC to a depth of 50 cm during 1955–2030, in degraded agricultural lands of central east Kazakhstan. Simulated land uses were: native rangeland vegetation, wheat (Triticum aestivum L.), wheatgrass (Agropyron cristatum L.), and abandoned croplands. The EPIC model was initialized with soil properties obtained from a soil map of the study area. Data on crop management, fertilizer application and tillage practices were gathered from local expert knowledge. Simulations were performed for each polygon on a land use classification map, resulting in 4661 simulations. Our results showed that simulated SOC explained 50% of the variation in measured SOC. Of the 1.38 million hectares in the study area, 78% were under native vegetation, 3% cultivated to wheat, 8% on wheatgrass, and 11% were abandoned croplands in 2005. If land use remained constant, total stock of SOC would decrease at an annual rate of 723 kg C ha−1. However, if best management practices are implemented, resulting in reallocation of land use according to the land capability with abandoned croplands being converted to reduced-tillage wheat or wheatgrass, total stock of SOC would increase to an equivalent of 4700 kg C ha−1 yr−1. Combining land use classification and soil maps with EPIC, proved valid for studying impacts of land use changes and management practices on SOC; an important aspect of this approach is the ability to scale up site-specific SOC to the region. With the available data, EPIC produced relatively accurate results but more data on spatial and temporal variation in SOC are needed to improve model calibration and validation.
