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Modelling and Simulation of Agricultural Landscapes

  • Wilfried MirschelEmail author
  • Michael Berg-Mohnicke
  • Ralf Wieland
  • Karl-Otto Wenkel
  • Vitaly V. Terleev
  • Alex Topaj
  • Lothar Mueller
Chapter
  • 47 Downloads
Part of the Innovations in Landscape Research book series (ILR)

Abstract

An agricultural landscape is a section of a region shaped by its natural landscape features primarily involving agricultural land use and land management. Intensive anthropogenic activities have left a permanent mark on agricultural landscapes, which have been developed over hundreds of years. Agricultural landscapes constitute a spatiotemporal structure. Hence they represent a complex system in which a large number of processes occur continuously that, due to their temporal dynamics, lead to constant changes in the state of the system. Traditional experiments are inappropriate for the impact assessment of anthropogenic and naturally occurring changes in agricultural landscapes. The only option here is to conduct virtual landscape experiments at the computer level. To this end, a relevant set of spatial, quantifiable landscape indicators is defined that can be used to map the landscape on the computer. Building on the extensive expertise in agricultural landscape research, indicators can be mapped using validated and robust models and their dynamics described involving temporal aspects. Various model types can be used in this process. Special simulation environments involving the use of spatial data and accounting for possible land use and global changes enable forward-looking scenario simulations to provide answers to the question of the sustainability of agricultural land use systems. Decision support systems (DSS) that exploit the latest possibilities offered by information technology, statistics and artificial intelligence provide the framework for integrating models, spatial data concerning the state of the landscape and scenario data, simulation techniques as well as tools for interpreting and visualising results. Such DSS are also the basis for quantifying the complex impact of site conditions, changes in land use or management, and of potential climate change on individual landscape parameters or landscape indicators. A number of examples show how indicator-based models of different types can be used to assess the impact and sustainability of land use systems on a landscape scale. As a prerequisite for the development and validation of integrated dynamic landscape models, more long-term ecological studies and monitoring systems are required. This also means that more resources are necessary to support these activities. The use of models and virtual simulation experiments within a DSS framework at the computer is a very promising way of finding suitable site-specific complex measures for the adaptation of agriculture to climate change.

Keywords

Agricultural landscape Modelling Scenario simulation Landscape indicator Decision support system Climate change Growing season Ontogenesis Yield Additional water demand Water erosion risk Cultivation strategy 

Notes

Acknowledgements

This study was funded by the Ministry of Science, Research and Culture of the Federal State of Brandenburg and the Federal Ministry of Food, Agriculture and Consumer Protection.

References

  1. Antrop M, Van Eetvelde V (2017) Landscape perspectives: the holistic nature of landscape. Landscape series, vol 23. Springer Science+Business Media, p 436Google Scholar
  2. Bouma J (2002) Land quality indicators os sustainable land management across scales. Agr Ecosyst Environ 88(2):129–136CrossRefGoogle Scholar
  3. Buckwell A, Heissenhuber A, Blum WEH (2014) The sustainable intensification of european agriculture: a review. Sponsored by the rise foundation, 96 pp. http://www.risefoundation.eu/images/files/2014/2014_%20SI_RISE_FULL_EN.pdf. Accessed 19 Dec 2018
  4. Chmielewski FM (2003) Phenology and agriculture. In: Schwartz MD (ed) Phenology: an integrative environmental science. Kluwer Academic Publishers, Boston/Dordrecht/London, pp 505–522CrossRefGoogle Scholar
  5. Chmielewski FM, Hennings Y (2007) Phänologische Modelle als Grundlage zur Abschätzung des Klimaimpact. Berichte Meteorologisches Institut Freiberg (6. Fachtagung BIOMET) 16:229–235Google Scholar
  6. DIN19708 (2005) Bodenbeschaffenheit – Ermittlung der Erosionsgefährdung von Böden durch Wasser mit Hilfe der ABAG (Soil quality – Predicting soil erosion by water by means of ABAG). DIN 19708:2005–02, Normenausschuss Wasserwesen (NAW) im DIN, p 25Google Scholar
  7. Endlicher W, Gerstengarbe F-W (eds) (2007) Der Klimawandel – Einblicke. Rückblicke und Ausblicke, Potsdam-Institut für Klimafolgenforschung, p 134Google Scholar
  8. GE (2019) Getreideeinheit (GE) – Ausführliche Definition. https://wirtschaftslexikon.gabler.de/definition/getreideeinheit-ge-35840/version-259314. Last accessed 13 March 2019
  9. Graß R, Thies B, Kersebaum KC, Wachendorf M (2015) Simulating dry matter yield of two cropping systems with the simulation model HERMES to evaluate impact of future climate change. Eur J Agonomy 70:1–10CrossRefGoogle Scholar
  10. Haase G, Barsch H, Schmidt R (1991) Zur Einleitung: Landschaft, Naturraum und Landnutzung. Beiträge zur Geographie 34:19–25Google Scholar
  11. Helming K, Diehl K, Geneletti D, Wiggering H (2013) Mainstreaming ecosystem services in European policy impact assessment. Environ Impact Assess Rev 40:82–87CrossRefGoogle Scholar
  12. van Ittersum M, Ewert F, Hechelei T, Wery J, Olsson JA, Andersen E, Bezlepkina I, Brouwer F, Donatelli M, Flichmann G, Olsson L, Rizzoli AE, van der Wal T, Wien JE, Wolf J (2008) Integrated assessment of agricultural systems—a component-based framework fort the European Union (SEAMLESS). Agric Syst 96:150–165CrossRefGoogle Scholar
  13. IPCC (2013) Climate Change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 SGoogle Scholar
  14. Kersebaum KC, Nendel C (2014) Site-specific impacts of climate change on wheat production across regions of Germany using different CO2 response functions. Eur J Agronomy 52:22–32CrossRefGoogle Scholar
  15. Lischeid G, Kalettka T, Holländer M, Steidl J, Merz C, Dannowski R, Hohenbrink T, Lehr C, Onandia G, Reverey F, Pätzig M (2018) Natural ponds in an agricultural landscape: external drivers, internal processes, and the role of the terrestrial-aquatic interface. Limnologica 68:5–16CrossRefGoogle Scholar
  16. Lutze G, Schultz A, Wenkel K-O (1993) Vom Populationsmodell zum Landschaftsmodell – Neue Herausforderungen und Wege zur Nutzung von Modellen in der Agrarlandschaftsforschung. Zeitschrift für Agrarinformatik 1:19–25Google Scholar
  17. Mirschel W, Lutze G, Schultz A (2006) Luzi K (2006) Klima und Wetter in der Agrarlandschaft Chorin—gestern, heute, morgen. In: Lutze G, Schultz A, Wenkel K-O (eds) Landschaften beobachten, nutzen und schützen – Landschaftsökologische Langzeit-Studie in der Agrarlandschaft Chorin 1992–2006. G.B.TeubnerVerlag, Wiesbaden, pp 49–59Google Scholar
  18. Mirschel W, Schultz A, Wenkel K-O (1997) Agroökosystemmodelle als Bestandteile von Landschaftsmodellen. Arch Nat Conserv Landsc Res 35:209–225Google Scholar
  19. Mirschel W, Schultz A, Wenkel K-O, Wieland R, Poluektov RA (2004) Crop growth modelling on different spatial scales—a wide spectrum of approaches. Arch Agronomy Soil Sci 50(3):329–343CrossRefGoogle Scholar
  20. Mirschel W, Wieland R, Gutzler C, Helming K (2016) Luzi K (2016) Auswirkungen landwirtschaftlicher Anbauszenarien auf Ertrag und Zusatzwasserbedarf im Land Brandenburg im Jahr 2025. In: Nguyen XT (ed) Modelling and simulation of ecosystems: workshop Kölpinsee 2015. Rhombos-Verlag, Berlin, pp 1–19Google Scholar
  21. Mirschel W, Wieland R, Luzi K, Groth K (2019) Model-based estimation of irrigation water demand for different agricultural crops under climate change, presented for the Federal State of Brandenburg, Germany (in this book)Google Scholar
  22. Mirschel W, Wieland R, Wenkel K-O, Nendel C, Guddat C (2014) YIELDSTAT—a spatial yield model for agricultural crops. Eur J Agron 52(2014):33–46CrossRefGoogle Scholar
  23. Mueller L, Schindler U, Ball BC, Smolentseva E, Sychev VG, Shepherd TG, Qadir M, Helming K, Behrendt A, Eulenstein F (2014) Productivity potentials of the global land resource for cropping and grazing. In: Mueller L, Saparov A, Lischeid G (eds) Novel measurement and assessment tools for monitoring and management of land and water resources in agricultural landscapes of Central Asia. Environmental science and engineering. Springer International Publishing, Cham, pp 115–142.  https://doi.org/10.1007/978-3-319-01017-5_6Google Scholar
  24. Mueller L, Schindler U, Mirschel W, Shepherd TG, Ball B, Helming K, Rogasik J, Eulenstein F, Wiggering H (2010) Assessing the productivity function of soils: a review. Agron Sustain Dev 30(3):601–614.  https://doi.org/10.1051/agro/2009057CrossRefGoogle Scholar
  25. Murgue C, Therond O, Leenhardt D (2016) Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape. Land Use Policy 54(2016):339–354CrossRefGoogle Scholar
  26. Orlowsky B, Gerstengarbe F-W, Werner PC (2008) A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM. Theoret Appl Climatol 92:209–223CrossRefGoogle Scholar
  27. Schmitt M, Liescheid G, Nendel C (2019a) Microclimate and matter dynamics in transition zones for forest to arable land. Agriculture and Forest Meteorology 268:1–10.  https://doi.org/10.1016/j.agrformet.2019.01.001CrossRefGoogle Scholar
  28. Schmitt M, Nendel C, Funk R, Mitchel MGE (2019b) Lischeid G (2019b) Modeling yields response to shading in the field-to-forest transition zones in heterogeneous landscapes. Agriculture 9(1):6.  https://doi.org/10.3390/agriculture9010006CrossRefGoogle Scholar
  29. SEDAC (2016) Last of the Wild (Version Two). Socioeconomic date and application center (SEDAC) in NASA’s earth observing system data an information system (EOSDIS), hosted by CIESIN at the Columbia University. http://sedac.ciesin.columbia.edu/data/collection/wildareas-v2. Last accessed 15 Jan 2019
  30. Spekat A, Kreienkamp F, Enke W (2010) An impact-oriented classification method for atmospheric patterns. Phys Chem Earth 35:352–359CrossRefGoogle Scholar
  31. Topaj A, Badenko V, Medvedev S, Terleev V (2018) Dynamically adjusted forecasting of agro-landscape productivity using massive computations of crop model in GIS environment. In: Sychev VG, Mueller L (eds) Novel methods and results of landscape research in Europe, Central Asia and Siberia. Monograph in 5 Volumes. Vol III, Landscape monitoring and modelling © «FSBI VNII Agrochemistry» 2018, pp 253–257.  https://doi.org/10.25680/3309.2018.28.99.246, http://vniia-pr.ru/monografii/pdf/tom3-53.pdf. Last accessed 15 Jan 2019
  32. United Nations (2015) Agenda 2030: sustainable development goals. 17 goals to transform our world. https://www.un.org/sustainabledevelopment/. Last accessed 19 Dec 2018
  33. Van Huylenbroeck G, Vandermeulen V, Mettepenningen E, Verspech A (2007) Multifunctionality of agriculture. A review of definitions, evidence and instruments. Liv Rev Landsc Res 1:3. http://www.livingreviews.org/lrlr-2007–3. Last accessed 19 Dec 2018
  34. WBG (2018) Agriculture and rural development—agricultural land (% of land area). World Bank open data—free and open access to global development data. https://data.worldbank.org/topic/agriculture-and-rural-development. Last accessed 15 Jan 2019
  35. Wenkel K-O, Berg M, Mirschel W, Wieland R, Nendel C, Köstner B (2013) LandCaRe DSS—an interactive decision support system for climate change impact assessment and the analysis of potential agricultural land use adaptation strategies. J Environ Manage 127(Supplement):S168–S183CrossRefGoogle Scholar
  36. Wenkel K-O, Berg M, Wieland R, Mirschel W (2010) Modelle und Entscheidungsunterstützungssystem zur Klimafolgenabschätzung und Ableitung von Adaptionsstrategien der Landwirtschaft an veränderte Klimabedingungen (AGROKLIM-ADAPT)—Decision Support System (DSS). Forschungs-Abschlußbericht: BMBF 01 LS 05104, Leibniz-Zentrum für Agrarlandschaftsforschung Müncheberg, 51 pp, 14 Annexes (133 pp.)Google Scholar
  37. Wenkel K-O, Wieland R, Mirschel W, Schultz A, Kampichler C, Kirilenko A, Voinov A (2008) Regional models of intermediate complexity (REMICs)—a new direction in integrated landscape modelling. In: Jackeman A, Voinov AA, Rizzoli AE, Chen SH (eds) Environmental modelling, software and decision support-state of the art and new perspectives−developments in integrated environmental assessment, vol 3. Elsevier, Amsterdam, pp 285–295Google Scholar
  38. Wieland R (2010) EROSION—Modell zur Berechnung der potentiellen Erosionsgefährdung. In: Wenkel K-O, Berg M, Wieland R, Mirschel W Modelle und Entscheidungsunterstützungssystem zur Klimafolgenabschätzung und Ableitung von Adaptionsstrategien der Landwirtschaft an veränderte Klimabedingungen (AGROKLIM-ADAPT)—Decision Support System (DSS). Forschungs−Abschlußbericht: BMBF 01 LS 05104, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Müncheberg, pp A6/1−A6/7Google Scholar
  39. Wieland R, Groth K, Linde F, Mirschel W (2015) Spatial analysis and modeling tool version 2 (SAMT2), a spatial modeling tool kit written in Python. Ecol Inform 30(2015):1–5CrossRefGoogle Scholar
  40. Wieland R, Mirschel W (2017) Combining expert knowledge with machine learning on the basis of fuzzy training. Ecol Inform 38:26–30CrossRefGoogle Scholar
  41. Wieland R, Voss M, Holtmann X, Mirschel W, Ajibefun IA (2006) Spatial analysis and modeling tool (SAMT): 1. Struct Possibilities Ecol Inform 1(2006):67–76CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wilfried Mirschel
    • 1
    Email author
  • Michael Berg-Mohnicke
    • 1
  • Ralf Wieland
    • 1
  • Karl-Otto Wenkel
    • 1
  • Vitaly V. Terleev
    • 2
  • Alex Topaj
    • 3
  • Lothar Mueller
    • 1
  1. 1.Leibniz-Centre for Agricultural Landscape Research (ZALF) e.V.MünchebergGermany
  2. 2.Peter the Great St. Petersburg Polytechnik UniversitySt. PetersburgRussia
  3. 3.Agrophysical Research InstituteSt. PetersburgRussia

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