Universität Bonn

INRES Pflanzenbau


Limpopo Living Landscapes - Understanding the dynamics of ecological and cultural landscapes, in the face of global change, in the northern Limpopo region of South Africa


Within the agricultural landscapes of Limpopo Province, the Limpopo Living Landscapes project aims to understand and predict the combined effects of land use and climate change processes on (i) rangeland vegetation (ii) unique biodiversity and (iii) rural livelihoods and to identify farm and policy level intervention strategies that support sustainable rural livelihoods and the natural resource base on which these people depend. The project will make detailed studies along two transects (Thohoyandou to Giyani and Turfloop to Phalabora) which provide a representative sample of climate, vegetation, soils, landuse and communities. The methodologies will include socio-economic assessment of the current and future livelihood options for rural communities, on-farm/on-station experimentation and measurement in agricultural systems with a focus on understanding crop and rangeland productivity under highly variable climates and the use of remote sensing and predictive modelling tools to upscale and extrapolate. By working within communities along these transects, scenarios will be developed participatively that explore better land management options under current and future climate scenarios. An important feature of this project is a strong collaboration between Universities in South Africa (Witwatersrand, Venda and Limpopo) and Germany (Göttingen, Frankfurt, Bonn and Cologne) and significant efforts to build capacity in the regions scientists.

Structure and workflow of Limpopo Living Landscapes [Digital Elevation Model (DEM); Land Use and Land Cover Change (LULCC); Geographic Information System (GIS); Dynamic Global Vegetation Model (DGVM)] © LAP

Subproject 3: This sub-project will focus on the state of rural rangelands, its ability to provide ecosystem services and vulnerability under future global change which includes anthropogenic land transformation. The dimension of the physical and biological environment will be captured by surveys, ground truth measurements and remote sensing, a technology that will allow coverage of spatio-temporal variation along climatic and pedological gradients. The coupling of dynamic growth models with remotely sensed information on vegetation will enable us to make predictions on current and future land use patterns, hot spots and episodic events of degradation and its response to a wider range of climate scenarios. The link between socio-economic and ecological aspects will be at the centre of our research, including feedbacks of management to vegetation state and productivity. Project activities on GIS and remote sensing will be coordinated with SP 1 for the benefit of common processing and interpretation of spatial data, integration of dynamic modeling of vegetation phenology and its incorporation of algorithms into the dynamic global vegetation model (DGVM).

Persons in charge

PD Dr. Jürgen Schellberg
PhD student: Dipl. Geogr. Andreas Tewes


2013 – 2017



Cooperating partners

  • Risk and Vulnerability Assessment Centre, University of Limpopo (UL-RVAC)
  • School of Animal, Plant and Environmental Science, University of the Witwatersrand, Johannesburg, South Africa (UWits-APES)
  • Institute for Physical Geography, University Frankfurt am Main (UFrank-IPG)
  • Range ecology and Range management group, Botanical Institute, University of Cologne (UCol-BI)
  • BiK-F, Senckenberg Natural History Museum and Research Institute, Frankfurt am Main
  • Department of Ecology and Resource Management, University of Venda (UVen-ERM)
  • Chair of Crop Production Systems in the Tropics, Department of Crop Sciences, Georg-August-Universität, Göttingen (UGOE)


Teixeira, E.I., G. Zhao, J.d. Ruiter, H. Brown, A.-G. Ausseil, E. Meenken, F. Ewert, in press. The interactions between genotype, management and environment in regional crop modelling. European Journal of Agronomy. DOI:10.1016/j.eja.2016.05.005.

Parplies, A., O. Dubovyk, A. Tewes, J.-P. Mund, J. Schellberg, 2016. Phenomapping of rangelands in South Africa using time series of RapidEye data. International Journal of Applied Earth Observation and Geoinformation 53, 90-102. DOI:10.1016/j.jag.2016.08.001.

Webber, H., T. Gaiser, R. Oomen, E. Teixeira, G. Zhao, D. Wallach, A. Zimmermann, F. Ewert, 2016. Uncertainty in future irrigation water demand and risk of crop failure for maize in Europe. Environmental Research Letters 11, 074007. DOI:10.1088/1748-9326/11/7/074007.

Zhao, G., H. Hoffmann, J. Yeluripati, S. Xenia, C. Nendel, E. Coucheney, M. Kuhnert, F. Tao, J. Constantin, H. Raynal, E. Teixeira, B. Grosz, L. Doro, R. Kiese, H. Eckersten, E. Haas, D. Cammarano, B. Kassie, M. Moriondo, G. Trombi, M. Bindi, C. Biernath, F. Heinlein, C. Klein, E. Priesack, E. Lewan, K.-C. Kersebaum, R. Rötter, P.P. Roggero, D. Wallach, S. Asseng, S. Siebert, T. Gaiser, F. Ewert, 2016. Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops. Environmental Modelling & Software 80, 100-112. DOI:10.1016/j.envsoft.2016.02.022.

Dubovyk, O., T. Landmann, B.F.N. Erasmus, A. Tewes, J. Schellberg, 2015. Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa. International Journal of Applied Earth Observation and Geoinformation 38, 175-183. DOI:10.1016/j.jag.2015.01.002.

Tewes, A., F. Thonfeld, M. Schmidt, R.J. Oomen, Xiaolin Zhu, O. Dubovyk, G. Menz, J. Schellberg, 2015. Using RapidEye and MODIS data fusion to monitor vegetation dynamics in semi-arid rangelands in South Africa. Remote Sensing 7, 6510-6534. DOI:10.3390/rs70606510.

Linstädter, A., J. Schellberg, K. Brüser, C.A. Moreno Garcia, R.J. Oomen, C.C. du Preez, J.C. Ruppert, F. Ewert, 2014. Are there consistent grazing indicators in drylands? Testing plant functional types of various complexity in South Africa's grassland and savanna biomes. PLoS One 9: e104672. DOI:10.1371/journal.pone.0104672.

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