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Research article - Peer-reviewed, 2011

Patterns and drivers of post-socialist farmland abandonment in Western Ukraine

Baumann, Matthias; Kuemmerle, Tobias; Elbakidze, Marine; Ozdogan, Mutlu; Radeloff, Volker C.; Keuler, Nicholas S.; Prishchepov, Alexander V.; Kruhlov, Ivan; Hostert, Patrick


Farmland abandonment restructures rural landscapes in many regions worldwide in response to gradual industrialization and urbanization. In contrast, the political breakdown in Eastern Europe and the former Soviet Union triggered rapid and widespread farmland abandonment, but the spatial patterns of abandonment and its drivers are not well understood. Our goal was to map post-socialist farmland abandonment in Western Ukraine using Landsat images from 1986 to 2008, and to identify spatial determinants of abandonment using a combination of best-subsets linear regression models and hierarchical partitioning. Our results suggest that farmland abandonment was widespread in the study region, with abandonment rates of up to 56%. In total, 6600 km(2) (30%) of the farmland used during socialism was abandoned after 1991. Topography, soil type, and population variables were the most important predictors to explain substantial spatial variation in abandonment rates. However, many of our a priori hypotheses about the direction of variable influence were rejected. Most importantly, abandonment rates were higher in the plains and lower in marginal areas. The growing importance of subsistence farming in the transition period, as well as off-farm income and remittances likely explain these patterns. The breakdown of socialism appears to have resulted in fundamentally different abandonment patterns in the Western Ukraine, where abandonment was a result of the institutional and economic shock, compared to those in Europe's West, where abandonment resulted from long-term socio-economic transformation such as urbanization and industrialization. (C) 2010 Elsevier Ltd. All rights reserved.


Land-use transitions; Fallow fields; Carpathians; Remote sensing; Support Vector Machines; Regression analysis

Published in

Land Use Policy
2011, volume: 28, number: 3, pages: 552-562

Authors' information

Swedish University of Agricultural Sciences, School for Forest Management

Sustainable Development Goals

SDG11 Sustainable cities and communities

UKÄ Subject classification

Environmental Sciences

Publication Identifiers


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