Abstract
Decision support for developing practicable, resilient climate change adaptation strategies for the sustainable use of agro-landscapes encompasses a wide range of options and issues. So far, only a few suitable tools and methods have been available to farmers, regional planners and other stakeholders to support decision-making processes in this direction. The model-based interactive spatial information and decision support system, LandCaRe-DSS, closes this methodical gap. This system does not only support interactive scenario simulations and multi-ensemble and multi-model simulations at the regional level by providing information about the complex long-term impacts of climate change. It also helps different stakeholders to find suitable, sustainable agricultural adaptation strategies to climate change (crop rotation, soil tillage, fertilisation, irrigation, price and cost changes etc.) at the local or farm level. LandCaRe-DSS uses different ecological impact models, including for crop yield, erosion risk, regional evapotranspiration, total water flow-out and irrigation water demand. At the local level, a farm economy model is directly coupled with both the biophysical-based agro-ecosystem model MONICA and the statistical-based crop yield model YIELDSTAT to simulate the economic consequences of regional climate change and of proposed agricultural adaptation strategies. Due to the modular architecture and innovative design of LandCaRe-DSS, alternative or new impact models can easily be incorporated into the system. Scenario simulation runs can be realised in a reasonable amount of time. The interactive LandCaRe-DSS prototype offers a variety of data analysis and visualisation tools and an information system for climate adaptation in agriculture. This article describes the conceptual framework, the structure, the methodology and basic principles of operating LandCaRe-DSS. A number of selected examples demonstrate the versatility of LandCaRe-DSS applications. Using different scales and regions as examples, the impact of climate change is shown on: the ontogenesis of winter wheat for Müncheberg, Germany; the start, end and duration of the vegetation period in two German regions Uckermark (dry lowlands, 2600 km2) and Weisseritz (humid mountain area, 400 km2); irrigation water demand in Thuringia, Germany and the winter wheat yield in the Prenzlau region, Germany. Using LandCaRe-DSS up to 2075 for the Uckermark und Weisseritz regions, the effects and impacts of different agricultural adaption strategies were analysed taking into account irrigation, the absence of soil tillage and two different cropping ratios (actual cropping ratio vs. cropping ratio enriched with energy maize). Thanks to the modular structure of LandCaRe-DSS, little effort is required to adapt the whole system to geo-data valid for other regions or countries; incorporate other static or dynamic impact models; switch to other climate scenarios and implement other interface communication languages. The LandCaRe-DSS is constantly being developed, updated and adapted in different research projects such as the REGKLAM project for agricultural regions of Saxony, Germany, and the CARBIOCIAL project for regions within the Mato Grosso and Pará states of Brazil. It has already been used in a number of climate scenario studies for the Federal States of Thuringia, Brandenburg and Saxony. In the years ahead, international cooperative activities will be initiated with institutions from St. Petersburg, Russia, and Puławy, Poland, in order to use, adapt and advance this system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ascough JC, Shaffer MJ, Hoag DL, Mc Master GS, Dunn GH, Ahuja LR, Weltz MA (2001) GPFARM: an integrated decision support system for sustainable great plain agriculture. In: Scott DE, Mohtar RH, Steinhardt GC (eds) sustaining the global farm selected papers from the 10th international soil conservation organization meeting held from 24 to 29 May 1999 at Purdue University and the USDA-ARS National Soil Erosion Research Laboratory, pp 951–960
ATV-DVWK-Regelwerk (2002) Verdunstung in Bezug zu Landnutzung, Bewuchs und Boden. ATV-DVWK-Regelwerk: Merkblatt; M 504: 144 pp
Badenko VL, Garmanov, VV, Osipov GK (2003) National cadastral land register. 320 p (Гocyдapcтвeнный зeмeльный кaдacтp. CПб:Питep, 320 c)
Böhm U, Kücken M, Ahrens W, Block A, Hauffe D, Keuler K, Rockel B, Will A (2006) CLM—the climate version of LM: brief description and long-term applications. COSMO Newsl 6:225–235
Carbiocial (2014) Carbon sequestration, biodiversity and social structures in Southern Amazonia: models and implementation of carbon-optimized land management strategies. http://www.uni-goettingen.de/de/211024.html. Accessed 9 Mar 2015
Chmielewski F-M (2003) Phenology and Agriculture (chapter 7). In: Schwartz MD (ed) Phenology: an integrative environmental science. Kluwer Academic Publishers, Boston, Dordrecht, London, pp 505–522
Chmielewski F-M, Henniges Y (2007) Phänologische Modelle als Grundlage zur Abschätzung des Klimaimpakts. Berichte Meteorologisches Institut Freiburg 16:229–235
DIN 19708 (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, 25 p
Enke W, Schneider F, Deutschländer T (2005) A novel scheme to derive optimized circulation pattern classifications for downscaling and forecast purposes. Theoret Appl Climatol 82:51–63
Gerstengarbe F-W, Badeck F, Hattermann F, Krysanova V, Lahmer W, Lasch P, Stock M, Suckow F, Wechsung F, Werner PC (2003) Studie zur klimatischen Entwicklung im Land Brandenburg bis 2055 und deren Auswirkungen auf den Wasserhaushalt, die Forst- und Landwirtschaft sowie die Ableitung erster Perspektiven. PIK-Report No. 83, Potsdam, 79 p
Gömann H, Kreins P, Herrmann S, Wechsung F (2005) Impacts of global changes on agricultural land-use in the German Elbe region: results of an operational modelling tool for planning, monitoring and agri-environmental policy counselling [CD-ROM]. In: 21st European Regional Conference—ERC 2005—integrated land and water resources management: towards sustainable rural development, 15‒19 May 2005, Frankfurt (Oder), Germany and Slubice, Poland, p 11
Gruber S, Pekrun C, Möhring J, Claupein W (2012) Long-term yield and weed response to conservation and stubble tillage in SW Germany. Soil and Tillage Res 121:49–56
Henniges Y (2008) Entwicklung phänologischer Modelle für ausgewählte Pflanzen für die Naturräume 74 (Quillow-Einzugsgebiet) und 42 (Weißeritzkreis), Humboldt Universität Berlin, 9 pp
IPCC (2007) Impacts, adaptation and vulnerability. Contribution of working group II in the fourth assessment report of the intergovernmental panel on climate change, summary for policemakers. Cambridge University Press, Cambridge, pp 7–22
Ivanov AL, Kirjushin VI (2009) Global climate change and risk prediction in Russian agriculture. (Глoбaльныe измeнeния климaтa и пpoгнoз pиcкoв в ceльcкoм xoзяйcтвe Poccии). Russian Academy of Agricultural Science, Moscow, 517 p
Käding H, Keiser T, Werner A (2005) Model for calculating grassland yields and forage quality in North-East Germany on the basis of site and management characteristics. Arch Agron Soil Sci 51(4):417–431
Kahle P, Korn G, Pach F, Lehmann E (2012) Auch ohne Pflug gute Erträge. Landwirtschaft ohne Pflug 6/2012, pp 35-41
Köstner B, Berg M, Bernhofer Ch, Franke J, Gömann H, Kersebaum KC, Kuhnert M, Lindau R, Manderscheid T, Mengelkamp H-T, Mirschel W, Nendel C, Nozinski E, Pätzold A, Simmer S, Stonner R, Weigel H-J, Wenkel K-O, Wieland R (2008) Land, climate and resources (LandCaRe) 2020—foresight and potentials in rural areas under regional climate change. Ital J Agron/Riv Agron 3(3):743–744
Köstner B, Wenkel K-O, Bernhofer Ch (2012) New model-based information systems for climate impact scenarios in agriculture. Promet 38(1/2):42–52
KIT LandCaRe-DSS (2014) KMU-innovativ—joint project—LandCaRe-DSS: Model-based tools for strategic and operational irrigation measures under climate change. http://www.zalf.de/de/forschung/projekte/Seiten/detail.aspx?fpid=1543&tpid=832. Accessed 9 Mar 2015
LADSS (2005) Land Allocation Decision Support System. http://www.macaulay.ac.uk/LADSS. Accessed 9 Mar 2015
Mahammadzadeh M, Biebler H, Bardt H (2009) Klimaschutz und Anpassung an die Klimafolgen—Strategien, Maßnahmen und Anwendungsbeispiele. Institut der deutschen Wirtschaft Köln Medien GmbH, Köln, 310 p
Mirschel W (2010) ONTO—Modell zur Beschreibung der Pflanzenentwicklung (Ontogenese) von landwirtschaftlichen Fruchtarten. In: Wenkel K-O, Berg M, Wieland R, Mirschel W, Vorsorge und Gestaltungspotenziale in ländlichen Räumen unter regionalen Wetter- und Klimaänderungen (LandCaRe 2020): Modelle und Entscheidungsunterstützungssystem zur Klimafolgenabschätzung und Ableitung von Adaptationsstrategien der Landwirtschaft an veränderte Klimabedingungen (AGROKLIM-ADAPT); Müncheberg (Leibniz-Zentrum für Agrarlandschaftsforschung), pp A3/1–A3/9
Mirschel W, Wenkel KO (2007) Modelling soil-crop interactions with AGROSIM model family. In: Kersebaum KC, Hecker JM, Mirschel W, Wegehenkel M (eds) Modelling water and nutrient dynamics in soil-crop-systems: proceedings of the workshop on “Modelling water and nutrient dynamics in soil-crop systems” held from 14 to 16 June 2004 in Müncheberg, Germany. Springer, Dordrecht, pp 59–73
Mirschel, W. Wenkel K.-O, Wieland R, Albert E, Köstner B (2009) Klimawandel und Ertragsleistung: Auswirkungen des Klimawandels auf die Ertragsleistung ausgewählter landwirtschaftlicher Fruchtarten im Freistaat Sachsen - eine landesweite regionaldifferenzierte Abschätzung -. In: SÄCHSISCHES LANDESAMT FÜR LANDWIRTSCHAFT, UMWELT UND GEOLOGIE (eds) Schriftenreihe des Landesamtes für Umwelt, Landwirtschaft und Geologie (LfULG Sachsen), Issue 28/2009, 61 p, Dresden
Mirschel W, Wenkel K-O, Wieland R, Albert E, Köstner B (2010) Impact assessment of climate change on agricultural crop yields using the hybrid model YIELDSTAT—a case study for the Free State of Saxony, Germany. In: Materialy vserossiskoi konferencii “Matematiceskie modeli i informachionnye tekhnologii v sel´skokhozaistvennoi biologii: itogi i perspektivy” (14.-15.10.2010, ARI St. Petersburg, Russia), St. Petersburg, pp 200–203. http://www.agrophys.ru/upload/poluektov/sbornik.pdf. Accessed 9 Mar 2015
Mirschel W, Wieland R, Guddat C, Michel H, Wenkel K-O (2012) Crop yield estimation on arable land under climate change for the Free State of Thuringia, Germany, using the model YIELDSTAT. In: Tendencii razvitija agrofiziki v uslovijakh izmenjajushhegosja klimata (k 80-letiju Agrofiziceskogo NII): Materialy Mezhdunarodnoj konferencii, Sankt-Peterburg, 20-21 sentjabrja 2012.- SPb: Ljubovich, pp 169–180. http://www.agrophys.ru/Thesys-collection. Accessed 9 Mar 2015
Mirschel W, Wieland R, Gutzler C (2013a) Ertragsleitung. In: Gutzler C, Helming K (eds) Folgenabschätzung von Szenarien der landwirtschaftlichen Produktion in Brandenburg 2025: Feldberegnung und Energiemaisanbau. Leibniz-Zentrum für Agrarlandschaftsforschung, Müncheberg, pp 32–38
Mirschel W, Wieland R, Wenkel K-O, Guddat CH, Michel H (2013b) Modellgestützte Abschätzung der Auswirkungen des Klimawandels auf Ertrag und Zusatzwasserbedarf im Freistaat Thüringen bis 2050. In: Nguyen Xuan Thin (ed) Modellierung und Simulation von Ökosystemen: Workshop Kölpinsee 2012. (Berichte aus der Umweltinformatik), Shaker Verlag Aachen, pp 1–19
Mirschel W, Wieland R, Wenkel K-O, Nendel C, Guddat C (2014) YIELDSTAT—a spatial yield model for agricultural crops. Eur J Agron 52:33–46
Mueller L, Schindler U, Shepherd TG, Ball BC, Smolentseva E, Pachikin K, Hu C, Hennings V, Sheudshen AK, Behrendt A, Eulenstein F, Dannowski R (2014) The Muencheberg Soil Quality Rating for assessing the quality of global farmland. 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, Springer Cham Heidelberg New York Dordrecht London, pp 235–248
Münch T, Berg M, Mirschel W, Wieland R, Nendel C (2014) Considering cost accountancy items in crop production simulations under climate change. Eur J Agron 52:57–68
Nendel C, Berg M, Kersebaum KC, Mirschel W, Specka X, Wegehenkel M, Wenkel KO, Wieland R (2011) The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics. Ecol Model 222(9):1614–1625
Offermann F, Gömann H, Kleinhanß W, Kreins P, von Ledebur O, Osterburg B, Pelikan J, Salamon P, Sanders J (2010) vTI-Baseline 2009—2019: Agrarökonomische Projektionen für Deutschland. Landbauforschung vTI Agriculture and Forestry Research, Sonderheft 333, 88 pp. http://literatur.ti.bund.de/digbib_extern/dk043068.pdf. Accessed 9 Mar 2015
Poluektov RA, Terleev VV (2007) Crop simulation of the second and the third productivity levels. In: Kersebaum KC, Hecker JM, Mirschel W, Wegehenkel M (eds) Modelling water and nutrient dynamics in soil-crop-systems: proceedings of the workshop on “Modelling water and nutrient dynamics in soil-crop systems” held from 14 to 16 June 2004 in Müncheberg, Germany. Springer, Dordrecht, pp 75–89
Poluektov RA, Terleev VV (2010) Computer model of nitrogen dynamics in the rooting zone. Agrokhimiya 10:68–74
Poluektov RA, Fintushal SM, Oparina IV, Shatskikh DV, Terleev VV, Zakharova ET (2002) Agrotool—a system for crop simulation. Arch Agron Soil Sci 48(6):609–635
Poluektov RA, Topazh AG, Jakushev VP, Medvedev SA (2012): Usage of a dynamic agro-ecosystem model for impact assessment of climate change on crop yield productivity (theory and realization). (Иcпoльзoвaниe динaмичecкoй мoдeли aгpoeкocиcтeмы для oцeнки влияния климaтичecкиx измeнeний нa пpoдyктивнocть пoceвoв (тeopия и peaлизaция). Vestnik Russian Acad Agric Sci 2:7–12)
Prased JR, Prasad RS, Kulkami UV (2008) A decision support system for agriculture using natural language processing (ADSS). In: Proceedings of the international multiconference of engineers and computer scientists (IMECS 2008, 19–21 March 2008, Hong Kong), vol. I, pp 1–5
REGKLAM (2013) REGKLAM – Regionales Klimaanpassungsprogramm Modellregion Dresden, http://www.regklam.de
REGKLAM-KONSORTIUM (2013) Integriertes Regionales Klimaanpassungsprogramm für die Region Dresden: Grundlagen, Ziele und Maßnahmen, REGKLAM-Publikationsreihe 7, Rhombus-Verlag Berlin, 344 p
Roeckner E, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kornblueh L, Manzini E, Schlese U, Schulzweida U (2004) The atmosphere general circulation model ECHAM5. Part 2: Sensitivity of simulated climate to horizontal and vertical resolution. MPI-Report 354, Hamburg: Max Planck Institute for Meteorology, 64 p. http://www.mpimet.mpg.de/fileadmin/publikationen/Reports/max_scirep_354.pdf. Accessed 9 Mar 2015
Roth D (1993) Richtwerte für den Zusatzwasserbedarf in der Feldberegnung. In: Schriftenreihe LUFA Thüringen, Issue 6 (Richtwerte Pflanzenproduktion), pp 53–86
Schwärzel H (2000) Untersuchungen zu Kriterien der Standort- und Gehölzauswahl bei extensiven Anpflanzungen von Obstbäumen. Dissertation, Humboldt University Berlin, 86 p
Shishov LL, Tonkonogov VD, Lebedeva II, Gerasimova MI (2004) Classification and diagnostic of Russian soils. (Клaccификaция и диaгнocтикa пoчв Poccии. Cмoлeнcк: Oйкyмeнa)
Terleev VV, Mirschel W, Schindler U, Wenkel KO (2010) Estimation of soil water retention curve using some agrophysical characteristics and Voronin’s empirical dependence. Int Agrophysics 24(4):381–387
Terleev VV, Narbut MA, Topazh AG, Mirschel W. (2014): Modelling hydrophysical properties of soils…) (Moдeлиpoвaниe гидpoфизичecкиx cвoйcтв пoчвы кaк кaпилляpнo-пopиcтoгo тeлa и ycoвepшeнcтвoвaниe мeтoдa Myaлeмa-Baн Гeнyxтeнa: тeopия). Agrofisica (Aгpoфизикa) St. Petersburg 2(14):35–44) (in Russian)
TGL (1990) Verfahren der Pflanzenproduktion, Beregnung – Ermittlung des Zusatzwasserbedarfs. TGL 46200/03, 30 pp
Topaj AG, Mirschel W, Künkel KJ, Hoffmann J (2000) Methodik zur Identifikation von Klimatrends aus langjährigen meteorologischen Datenreihen. Meteorol Z 9(6):339–350
Tubiello FN, Soussana JF, Howden MS (2007) Crop and pasture response to climate change. Proc Nath Acad Sci USA 104:19686–19690
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: Environmental modelling, software and decision support: state of the art and new perspective, Amsterdam (Elsevier), pp 285–295
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 127:168–183
Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses—a guide to conservation planning. Agriculture Handbook, vol. 537, Washington D.C., U.S. Department of Agriculture, 60 pp
Acknowledgements
This article was funded by the German Federal Ministry of Education and Research (BMBF) within the klimazwei research programme (grant: 01 LS 05109). The authors would like to acknowledge the support they received from the German Federal Ministry of Food and Agriculture, the Brandenburg Ministry of Sciences, Research and Cultural Affairs (Germany) and the Russian Academy of Agricultural Sciences.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Mirschel, W. et al. (2016). A Spatial Model-Based Decision Support System for Evaluating Agricultural Landscapes Under the Aspect of Climate Change. In: Mueller, L., Sheudshen, A., Eulenstein, F. (eds) Novel Methods for Monitoring and Managing Land and Water Resources in Siberia. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-319-24409-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-24409-9_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24407-5
Online ISBN: 978-3-319-24409-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)