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.
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References
Antrop M, Van Eetvelde V (2017) Landscape perspectives: the holistic nature of landscape. Landscape series, vol 23. Springer Science+Business Media, p 436
Bouma J (2002) Land quality indicators os sustainable land management across scales. Agr Ecosyst Environ 88(2):129–136
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
Chmielewski FM (2003) Phenology and agriculture. In: Schwartz MD (ed) Phenology: an integrative environmental science. Kluwer Academic Publishers, Boston/Dordrecht/London, pp 505–522
Chmielewski FM, Hennings Y (2007) Phänologische Modelle als Grundlage zur Abschätzung des Klimaimpact. Berichte Meteorologisches Institut Freiberg (6. Fachtagung BIOMET) 16:229–235
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 25
Endlicher W, Gerstengarbe F-W (eds) (2007) Der Klimawandel – Einblicke. Rückblicke und Ausblicke, Potsdam-Institut für Klimafolgenforschung, p 134
GE (2019) Getreideeinheit (GE) – Ausführliche Definition. https://wirtschaftslexikon.gabler.de/definition/getreideeinheit-ge-35840/version-259314. Last accessed 13 March 2019
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 70:1–10
Haase G, Barsch H, Schmidt R (1991) Zur Einleitung: Landschaft, Naturraum und Landnutzung. Beiträge zur Geographie 34:19–25
Helming K, Diehl K, Geneletti D, Wiggering H (2013) Mainstreaming ecosystem services in European policy impact assessment. Environ Impact Assess Rev 40:82–87
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 96:150–165
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 S
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 52:22–32
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 68:5–16
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 1:19–25
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 49–59
Mirschel W, Schultz A, Wenkel K-O (1997) Agroökosystemmodelle als Bestandteile von Landschaftsmodellen. Arch Nat Conserv Landsc Res 35:209–225
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):329–343
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 1–19
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)
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):33–46
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_6
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/2009057
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):339–354
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 92:209–223
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.2019.01.001
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/agriculture9010006
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
Spekat A, Kreienkamp F, Enke W (2010) An impact-oriented classification method for atmospheric patterns. Phys Chem Earth 35:352–359
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
United Nations (2015) Agenda 2030: sustainable development goals. 17 goals to transform our world. https://www.un.org/sustainabledevelopment/. Last accessed 19 Dec 2018
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
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
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–S183
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.)
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 285–295
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−A6/7
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):1–5
Wieland R, Mirschel W (2017) Combining expert knowledge with machine learning on the basis of fuzzy training. Ecol Inform 38:26–30
Wieland R, Voss M, Holtmann X, Mirschel W, Ajibefun IA (2006) Spatial analysis and modeling tool (SAMT): 1. Struct Possibilities Ecol Inform 1(2006):67–76
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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.
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Mirschel, W. et al. (2020). Modelling and Simulation of Agricultural Landscapes. In: Mirschel, W., Terleev, V., Wenkel, KO. (eds) Landscape Modelling and Decision Support. Innovations in Landscape Research. Springer, Cham. https://doi.org/10.1007/978-3-030-37421-1_1
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