Determinants of agricultural land abandonment in post-Soviet European Russia
Prishchepov, A.V., Müller, D., Dubinin, M., Baumann, M., V.C. Radeloff. 2013. Determinants of agricultural land abandonment in post-Soviet European Russia. Land Use Policy, 30:873-884.
The breakdown of socialismcausedmassive socio-economic and institutional changes thatled to substantial agricultural land abandonment. The goal of our study was to identify the determinants of agricultural land abandonment in post-Soviet Russia during the ﬁrst decade of transition from a state-controlled economy to a market-driven economy (1990–2000). We analyzed the determinants of agricultural land abandonment for approximately 150,550 km2 of land area in the provinces (oblasts) of Kaluga, Rjazan, Smolensk, Tula and Vladimir in European Russia. Based on the economic assumptions of proﬁt maximization, we integrated maps of abandoned agricultural land from ﬁve ∼185 km× 185 km Landsat TM/ETM+ footprints with socio-economic, environmental and geographic variables, and we estimated logistic regressions at the pixel level to identify the determinants of agricultural land abandonment. Our results showed that a higher likelihood of agricultural land abandonment was signiﬁcantly associated with lower average grain yields in the late 1980s and with higher distances from the nearest settlements, municipality centers, and settlements withmore than 500 citizens. Hierarchical partitioning showed that the average grain yields in the late 1980s had the greatest power to explain agricultural land abandonment in our models, followed by the locational attributes of the agricultural land. We hypothesize that the termination of 90% of state subsidies for agriculture from 1990 to 2000 was an important underlying cause for the decrease of cultivation in economically and environmentally marginal agriculture areas. Thus, whereas the spatial patterns corresponded to the land rent theory of von Thünen, it was primarily the macro-scale driving forces that fostered agricultural abandonment. Our study highlighted the value of spatially explicit statistical models for studying the determinants of land-use and land-cover change in large areas.