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

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

doi:10.1016/j.landusepol.2010.11.003

Cited by 387 publications

(246 citation statements)

References 65 publications

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“…Three different approaches in studies of driving forces of landscape change can be observed: Case studies conducted at the local/regional scale (Baumann et al 2011;Bürgi et al 2015), large-scale analyses (Terres et al 2015), and meta-analyses (Munteanu et al 2014;van Vliet et al 2015;Plieninger et al 2016). Whereas local case studies on landscape changes aim at grasping the specifics of a place and its development, which is essential for a systematic understanding of local changes, large-scale analyses search for main trends and processes over large areas.…”

Section: Introductionmentioning

confidence: 99%

Processes and driving forces in changing cultural landscapes across Europe

et al. 2017

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Context Cultural landscapes evolve over time. However, the rate and direction of change might not be in line with societal needs and more information on the forces driving these changes are therefore needed. Objectives Filling the gap between single case studies and meta-analyses, we present a comparative study of landscape changes and their driving forces based in six regions across Europe conducted using a consistent method. Methods A LULC analysis based on historical and contemporary maps from the nineteenth and twentieth century was combined with oral history interviews to learn more about perceived landscape changes, and remembered driving forces. Land cover and landscape changes were analysed regarding change, conversions and processes. For all case study areas, narratives on mapped land cover change, perceived landscape changes and driving forces were compiled. Results Despite a very high diversity in extent, direction and rates of change, a few dominant processes and widespread factors driving the changes could be identified in the six case study areas, i.e. access and infrastructure, political shifts, labor market, 123Landscape Ecol (2017) 32:2097-2112 DOI 10.1007 technological innovations, and for the more recent period climate change. Conclusions Grasping peoples' perception supplements the analyses of mapped land use and land cover changes and allows to address perceived landscape changes. The list of driving forces determined to be most relevant shows clear limits in predictability: Whereas changes triggered by infrastructural developments might be comparatively easy to model, political developments cannot be foreseen but might, nevertheless, leave major marks in the landscape.

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Section: Introductionmentioning

confidence: 99%

Processes and driving forces in changing cultural landscapes across Europe

et al. 2017

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“…We used a subtractive model for visualization, where the primary factors are illustrated with a pure color and the overlapping areas of influence are depicted by the intersection of colors. Although the studies have discussed the impact of climatic variation on land surface changes [33,55,56], very few of them have focused on the impact of environmental factors on land surface trends in Ukraine as opposed to management and land tenure factors [1][2][3][4][5][6][7][8]. Our results show that the changes in land surface can be partially explained by the combination of thermal and water conditions.…”

Section: Factors Of Land Surface Dynamics In Ukrainementioning

confidence: 74%

“…In addition, the scale compatibility was another issue, as the datasets might be not representative for an 8 km grid [53]. To overcome this, we compared our results with the results of the previous studies [1,[4][5][6][7][8][9] and investigated the validity of the results by local experts. …”

Section: Trends Of Ndvi Seriesmentioning

confidence: 99%

“…Although the additional datasets give insight about the processes that can affect the land surface changes, further analysis should consider the use of complementary factors and include datasets representing anthropogenic activity. Several studies discussed the factors that affect the land abandonment in Ukraine, and they depicted that factors such as soil type, yield decrease, unfavourable socio-economic conditions and population density demonstrate a significant association with land abandonment and cultivation rate [1,10]. The observed increasing and decreasing trends and their interruptions are only partially explained by temperature, precipitation and soil moisture.…”

Section: Factors Of Land Surface Dynamics In Ukrainementioning

confidence: 99%

“…Ukraine has experienced immense environmental and institutional changes during the last three decades due to both human-induced and environmental processes such as socio-economic transformation, ongoing urbanization, increased climate variability and frequency of hazardous events [1,2]. The drastic social, institutional and economic alterations at the end of 1991 as a result of Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

et al. 2016

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Ukraine has experienced immense environmental and institutional changes during the last three decades. We have conducted this study to analyze important land surface dynamics and to assess processes underlying the changes. This research was conducted in two consecutive steps. To analyze monotonic changes we first applied a Mann-Kendall trend analysis of the Normalized Difference Vegetation Index (NDVI3g) time series. Gradual and abrupt changes were studied by fitting a seasonal trend model and detecting the breakpoints. Secondly, essential environmental factors were used to quantify their possible relationships with land surface changes. These factors included soil moisture as well as gridded air temperature and precipitation data. This was done using partial rank correlation analysis based on annually aggregated time-series. Our results demonstrate that positive NDVI trends characterize approximately one-third of Ukraine's land surface, located in the northern and western areas of the country. Negative trends occurred less frequently, covering less than 2% of the area and are distributed irregularly across the country. Monotonic trends were rarely found; shifting trends were identified with a greater frequency. Trend shifts were seen to occur with an increased frequency following the period of the 2000s. We determined that land surface dynamics and climate variability are functionally interdependent; however, the relative influence of the drivers varies in different locations. Among the factors analyzed, the air temperature variable explains the largest portion of NDVI variability. High air temperature/NDVI correlation coefficients (r = 0.36´0.77) are observed over the entire country. The soil moisture content is of significant influence in the eastern portion of Ukraine (r = 0.68); precipitation (r = 0.65) was most influential in the central regions of the country. These results increase our understanding of ecosystem responses to climatic changes and anthropogenic activities.

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Integrated spatiotemporal modelling of bioenergy production potentials, agricultural land use, and related GHG balances; demonstrated for Ukraine

Hilst

Verstegen

Zheliezna³

et al. 2014

Biofuels Bioprod Bioref

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This study shows how bioenergy potential and total greenhouse gas (GHG) balances of land-use change and agricultural intensifi cation can be modeled in an integrated way. The modeling framework is demonstrated for fi rst-and second-generation ethanol production in Ukraine for the timeframe 2010-2030 for two scenarios: a business as usual (BAU) scenario in which current trends in agricultural productivity are continued; and a progressive scenario, which projects a convergence of yield levels in Ukraine with Western Europe. The spatiotemporal development in land for food production is analyzed making use of the PCRaster Land Use Change (PLUC) model. The land-use projections serve as input for the analysis of the CO 2 , N 2 O, and CH 4 emissions related to changes in land use and agricultural management, as well as the abatement of GHG emissions by replacing fossil fuels with bioethanol production from wheat and switchgrass. This results in annual maps (1 km 2 resolution) of the different GHG emissions for the modeled timeframe. In the BAU scenario, the GHG emissions increase over time, whereas in the progressive scenario, a total cumulative GHG emission reduction of 0.8 Gt CO 2 -eq for wheat and 3.8 Gt CO 2 -eq for switchgrass could be achieved in 2030. When the available land is used for the re-growth of natural vegetation, 3.5 Gt CO 2 -eq could be accumulated. These emission reductions could increase when appropriate measures are taken. The spatiotemporal PLUC model + GHG module allows for spatiotemporal and integrated modeling of total GHG emissions of bioenergy production and intensifi cation of the agricultural sector. Some studies have integrally assessed the impacts of agricultural and use and LUCs on GHG emissions. [17][18][19][20] Popp et al. 21 and De Wit et al. 22 assessed the GHG emissions of bioenergy production including the emissions from agricultural intensifi cation spatially explicitly. Due to the scope of these studies (global, European), the assessments were made on an aggregated spatial level, which partly disregard the high spatial heterogeneity biophysical factors. In addition, these assessments are not temporally explicit and therefore ignore the development of carbon sequestration and GHG emissions over time.In this study, a dynamic model is developed to assess the developments in CO 2 , N 2 O, and CH 4 emissions temporally and spatially explicitly for the period 2010-2030, taking into account the emissions related to agricultural intensifi cation and LUC. Th e GHG model is developed as an additional module of the PLUC model.15 Th e emissions are diff erentiated for biophysical factors such as land use, climate, and soil, making use of the best quality spatial data available. Th e model is demonstrated for a case study in Ukraine in which agricultural production is intensifi ed and the abandoned agricultural land is used for energy crops.Wheat and switchgrass are selected as typical fi rst-and second-generation bioethanol crops, respectively. As reand aff orestation could off er ...

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

Cited by 387 publications

References 65 publications

Processes and driving forces in changing cultural landscapes across Europe

Processes and driving forces in changing cultural landscapes across Europe

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

Integrated spatiotemporal modelling of bioenergy production potentials, agricultural land use, and related GHG balances; demonstrated for Ukraine

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