Landscape Ecology

, 24:1195 | Cite as

Cropland change in southern Romania: a comparison of logistic regressions and artificial neural networks

  • Tobia LakesEmail author
  • Daniel Müller
  • Carsten Krüger
Research Paper


Changes in cropland have been the dominating land use changes in Central and Eastern Europe, with cropland abandonment frequently exceeding cropland expansion. However, surprisingly little is known about the rates, spatial patterns, and determinants of cropland change in Eastern Europe. We study cropland changes between 1995 and 2005 in Argeş County in Southern Romania with two distinct modeling techniques. We apply and compare spatially explicit logistic regressions with artificial neural networks (ANN) using an integrated socioeconomic and environmental dataset. The logistic regressions allow identifying the determinants of cropland changes, but cannot deal with non-linear and complex functional relationships nor with collinearity between variables. ANNs relax some of these rigorous assumptions inherent in conventional statistical modeling, but likewise have drawbacks such as the unknown contribution of the parameters to the outcome of interest. We compare the outcomes of both modeling techniques quantitatively using several goodness-of-fit statistics. The resulting spatial predictions serve to delineate hotspots of change that indicate areas that are under more eminent threat of future abandonment. The two modeling techniques address two controversial issues of concern for land-change scientists: (1) to identify the spatial determinants that conditioned the observed changes and (2) to deal with complex functional relationships between influencing variables and land use processes. The spatially explicit insights into patterns of cropland change and in particular into hotspots of change derived from multiple methods provide useful information for decision-makers.


Land use change Human-environment system Spatial analysis Logistic regression Neural network Eastern Europe Romania 



We are very grateful for the valuable comments of two reviewers and Eleanor Milne from the Office for Integration and Modeling that significantly improved the paper. We thank Tobias Kuemmerle and Patrick Griffiths for the remote sensing analysis of land cover changes. We also thank the Global Land Project and the guest editors for organizing this special issue. Funding of the data collection from the Deutsche Forschungsgemeinschaft (DFG) under the Emmy-Noether Programme is gratefully acknowledged.


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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Geomatics Lab, Geography DepartmentHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO)Halle (Saale)Germany

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