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Selected Trade-Offs and Risks Associated with Land Use Transitions in Central Germany

  • Joerg A. PriessEmail author
  • Christian Hoyer
  • Greta Jäckel
  • Eva Lang
  • Sebastian Pomm
  • Christian Schweitzer
Chapter

Abstract

Future uncertainties and risks for socio-environmental systems are often addressed in the form of scenarios. This study aims to identify the biggest future risks and uncertainties for the study region Central Germany and the question which land use changes and impacts on selected ecosystem services related to agricultural production can be expected in the coming decades.

For this purpose, we co-developed scenario storylines along the largest uncertainties, how the region may change with different stakeholders and used environmental models to simulate land-use changes and impacts on selected ecosystem services related to agricultural production.

The study revealed that Climate change may have beneficial (e.g. maize, sugar beet) or adverse effects (e.g. barley, wheat) on crop yield levels, depending on crop type and level of climate change. In the scenarios crop production is additionally influenced by different levels of regional preferences influencing crop land extent (e.g., afforestation), crop management (e.g., organic production), and crop types used for food or bioenergy production. As driving factors such as climate change, land availability, and land management all influence agriculture, integrated studies like this are needed to assess future crop production. However, sustainability objectives may prefer other than the most productive agricultural pathways providing additional benefits such as regulating or cultural services.

Keywords

Agriculture Integrated modelling Land-use change Participatory scenarios Provisioning services 

Notes

Acknowledgements

This paper was funded by the Helmholtz research program POF2/3 (CH, EL, GJ, JAP, SP) and by the EU FP7 project OpenNESS (project EC-308428) (CS).

References

  1. 1.
    Liu J, Dietz T, Carpenter SR, Alberti M, Folke C, Moran E, et al. Complexity of coupled human and natural systems. Science. 2007;317:1513–6.CrossRefGoogle Scholar
  2. 2.
    Hauck J, Winkler KJ, Priess JA. Reviewing drivers of ecosystem change as input for environmental modelling. Sustain Water Qual Ecol. 2015;5:9–30.CrossRefGoogle Scholar
  3. 3.
    Alcamo J, Kok K, Busch G, Priess J. Searching for the future of land: scenarios from the local to global scale. Dev Integr Environ Assess. 2008;2:67–103.CrossRefGoogle Scholar
  4. 4.
    Priess JA, Hauck J. Integrative Scenario Development. Ecol Soc. 2014;19(1):12.CrossRefGoogle Scholar
  5. 5.
    DESTATIS. GENESIS online database of the federal statistical office of Germany. https://www-genesis.destatis.de/genesis/online. Accessed 13 Oct 2017.
  6. 6.
    Schweitzer C, Priess JA, Das S. A generic framework for land-use modelling. Environ Model Softw. 2011;26(8):1052–5.CrossRefGoogle Scholar
  7. 7.
    Wochele-Marx S, Lang E, Pomm S, Das S, Priess JA. Central Germany GIS dataset. 2015.  https://doi.org/10.6084/m9.figshare.1318765. Accessed Jan 2015.
  8. 8.
    Niedertscheider M, Kuemmerle T, Müller D, Erb K-H. Exploring the effects of drastic institutional and socio-economic changes on land system dynamics in Germany between 1883 and 2007. Glob Environ Chang. 2014;28:98–108.CrossRefGoogle Scholar
  9. 9.
    SMUL - Sächsisches Staatsministerium für Umwelt und Landwirtschaft. Richtlinie Agrarumweltmaßnahmen und Waldmehrung. 2007. http://www.smul.sachsen.de/foerderung/94.htm. Accessed Mar 2015.
  10. 10.
    BMJV - Bundesministerium der Justiz und Verbraucherschutz. Gesetz für den Ausbau erneuerbarer Energien (Erneuerbare-Energien-Gesetz – EEG 2014). 2014. Berlin. www.bmjv.de. Accessed Dec 2014.
  11. 11.
    European Commission. Renewable Energy Directive 2009/28/EC. 2009. Brussels. http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32009L0028. Accessed Mar 2015.
  12. 12.
    Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci U S A. 2014;111(9):3268–73.CrossRefGoogle Scholar
  13. 13.
    Trnka M, Olesen JE, Kersebaum KC, Skjelvåg AO, Eitzinger J, Seguin B, et al. Agroclimatic conditions in Europe under climate change. Glob Chang Biol. 2011;17(7):2298–318.CrossRefGoogle Scholar
  14. 14.
    Seppelt R, Manceur AM, Liu J, Fenichel EP, Klotz S. Synchronized peak-rate years of global resources use. Ecol Soc. 2014;19(4):50.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Joerg A. Priess
    • 1
    Email author
  • Christian Hoyer
    • 2
  • Greta Jäckel
    • 3
    • 4
  • Eva Lang
    • 1
  • Sebastian Pomm
    • 5
  • Christian Schweitzer
    • 6
  1. 1.Department Computational Landscape EcologyHelmholtz Centre for Environmental Research–UFZLeipzigGermany
  2. 2.German Environment AgencySection National and International Environmental ReportingDessau-RosslauGermany
  3. 3.Department of Aquatic Ecosystem AnalysisHelmholtz Centre for Environmental Research–UFZLeipzigGermany
  4. 4.Department of Ecological ModellingHelmholtz Centre for Environmental Research–UFZLeipzigGermany
  5. 5.Annalinde gGmbHLeipzigGermany
  6. 6.Fachgebiet Umweltinformationssysteme und -dienste, Satellitenfernerkundung, DateninfrastrukturGerman Environment AgencyDessau-RosslauGermany

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