Skip to main content

MONICA: A Simulation Model for Nitrogen and Carbon Dynamics in Agro-Ecosystems

  • Chapter
  • First Online:

Part of the book series: Environmental Science and Engineering ((ENVSCIENCE))

Abstract

Process-based simulation models that predict crop growth, evapotranspiration, nitrate leaching or other environmental variables are commonly applied for impact assessment on agricultural crop production or the environment. Algorithms of the dynamic, process-based simulation model MONICA are presented, which was developed for demonstrating the climate and management impact on crop yields and environmental variables on the plot scale and in smaller regions in Central Europe. A legal successor of the HERMES model, it maintains the simple and robust philosophy of its progenitor and adds a full carbon cycle model to it, including the feedback relations between atmospheric CO2 concentration and other environmental variables on crop growth and water use efficiency. MONICA is the central part of a web-based decision support system that helps farmers and other stakeholders in Germany identifying management options to mitigate the impact of the expected climate change on their business. MONICA has the potential to assess the impacts of climate change and land management on crop yields, carbon balance and nitrogen efficiency in Central Asia.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Abrahamsen P, Hansen S (2000) Daisy: an open soil-crop-atmosphere system model. Environ Mod Softw 15:313–330

    Article  Google Scholar 

  • Ad-hoc-AG B (2005) Bodenkundliche Kartieranleitung. E. Schweizerbartsche Verlagsbuchhandlung, Hannover, p 438

    Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Roma

    Google Scholar 

  • Alvarez R (2009) Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. Eur J Agron 30(2):70–77

    Article  Google Scholar 

  • Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn P, Rötter RP, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal PK, Angulo C, Bertuzzi P, Biernath C, Challinor A, Doltra J, Gayler S, Goldberg RA, Grant R, Heng LK, Hooker J, Hunt T, Ingwersen J, Izaurralde RC, Kersebaum KC, Müller C, Maresh Kumar S, Nendel C, O’Leary G, Olesen JE, Osborne T, Palosuo T, Priesack E, Ripoche D, Semenov MA, Shcherbak I, Steduto P, Stöckle CO, Stratonovitch P, Streck T, Supit I, Tao FL, Travasso M, Waha K, Wallach D, White JW, Williams JR, Wolf J (2013) Uncertainty in simulating wheat yields under climate change. Nature Climate Change [online]. doi:10.1038/nclimate1916

  • Bachinger J, Reining E (2009) An empirical statistical model for predicting the yield of herbage from legume-grass swards within organic crop rotations based on cumulative water balances. Grass Forage Sci 64(2):144–159

    Article  Google Scholar 

  • Bardossy A, Haberlandt U, Krysanova V (2003) Automatic fuzzy-rule assessment and its application to the modelling of nitrogen leaching for large regions. Soft Comput 7(6):370–385

    Article  Google Scholar 

  • Chatskihk D, Nendel C, Hagemann U, Specka X, Augustin J, Sommer M, van Oost, K (2012) An approach to assess NEE and C costs associated with an energy crop production at different erosion-induced transient states in a typical North-Eastern Germany landscape using process-based agroecosystem modelling. Geophys Res Abstr 14 (EGU2012-13610). http://publ.ext.zalf.de/publications/c3de3ee5-aa90-46cc-aab6-68883c946e44.pdf

  • Christensen JH, Carter TR, Rummukainen M, Amanatidis G (2007) Evaluating the performance and utility of regional climate models: the PRUDENCE project. Clim Change 81:1–6

    Article  Google Scholar 

  • Cossani CM, Savin R, Slafer GA (2007) Contrasting performance of barley and wheat in a wide range of conditions in Mediterranean Catalonia (Spain). Ann Apl Biol 151:167–173

    Article  Google Scholar 

  • Déqué M, Rowell DP, Luthi D, Giorgi F, Christensen JH, Rockel B, Jacob D, Kjellstrom E, de Castro M, van den Hurk B (2007) An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim Change 81:53–70

    Article  Google Scholar 

  • Enke W, Spekat A (1997) Downscaling climate model outputs into local and regional weather elements by classification and regression. Clim Res 8(3):195–207

    Article  Google Scholar 

  • Falloon P, Betts R (2010) Climate impacts on European agriculture and water management in the context of adaptation and mitigation—The importance of an integrated approach. Sci Total Environ 408:5667–5687

    Article  Google Scholar 

  • Farquhar, G.D., von Caemmerer, S. (1982) Modelling of photosynthetic response to environmental conditions. In: Lange, O.L., Nobel, P.S., Osmond, C.B., Ziegler, H. (Eds.), Encyclopedia of plant physiology. New series. Volume 12B. Physiological plant ecology. II. Water relations and carbon assimilation. Springer, Berlin, pp. 549–587

    Google Scholar 

  • Feller C, Fink M, Laber H, Maync A, Paschold PJ, Scharpf HC, Schlaghecken J, Strohmeyer K, Weier U, Ziegler J (2007) Fertilization of field vegetables—a comprehensive database. Institute of Vegetable and Ornamental Crops, Großbeeren, p 266

    Google Scholar 

  • Goudriaan J, van Laar HH (1978) Relations between leaf resistance, CO2 concentration and CO2 assimilation in maize, beans, lalang grass and sunflower. Photosynthetica 12(3):241–249

    Google Scholar 

  • Greenwood DJ, Stone DA, Draycott A (1990) Weather, nitrogen supply and growth rate of field vegetables. Plant Soil 124(2):297–301

    Article  Google Scholar 

  • Hansen S, Jensen HE, Nielsen NE, Svendsen H (1991) Simulation of nitrogen dynamics and biomass production in winter-wheat using the Danish simulation-model DAISY. Fert Res 27(2–3):245–259

    Article  Google Scholar 

  • IPCC (2007) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

    Google Scholar 

  • Jansson PE (1991) The SOIL model—Users manual. Swedish University of Agricultural Sciences, Uppsala

    Google Scholar 

  • Jensen LS, Salo T, Palmason F, Breland TA, Henriksen TM, Stenberg B, Pedersen A, Lundström C, Esala M (2005) Influence of biochemical quality on C and N mineralisation from a broad variety of plant materials in soil. Plant Soil 273:307–326

    Article  Google Scholar 

  • Kersebaum KC (1989) Die Simulation der Stickstoff-Dynamik von Ackerböden. PhD Thesis, University of Hanover, pp 141

    Google Scholar 

  • Kersebaum KC (1995) Application of a simple management model to simulate water and nitrogen dynamics. Ecol Mod 85:145–156

    Article  Google Scholar 

  • Kersebaum KC (2007) Modelling nitrogen dynamics in soil-crop systems with HERMES. Nutr Cycl Agroecosys 77(1):39–52

    Article  Google Scholar 

  • Kersebaum KC, Hecker J-M, Mirschel W, Wegehenkel M (2007) Modelling water and nutrient dynamics in soil-crop systems: a comparison of simulation models applied on common data sets. In: Kersebaum KC, Hecker J-M, Mirschel W, Wegehenkel M (eds) Modelling water and nutrient dynamics in soil crop systems. Springer, Stuttgart, pp 1–17

    Chapter  Google Scholar 

  • Kersebaum KC, Wurbs A, De Jong R, Campbell CA, Yang J, Zentner RP (2008) Long term simulation of soil-crop interactions in semiarid southwestern Saskatchewan, Canada. Eur J Agron 29:1–12

    Article  Google Scholar 

  • Lasch P, Badeck FW, Lindner M, Suckow F (2002) Sensitivity of simulated forest growth to changes in climate and atmospheric CO2. Forstwissenschaftliches Centralblatt 121:155–171

    Google Scholar 

  • Lobell DB, Burke MB, Tebaldi C, Mastrandrea MD, Falcon WP, Naylor RL (2008) Prioritizing climate change adaptation needs for food security in 2030. Science 319:607–610

    Article  Google Scholar 

  • Long SP (1991) Modification of the response of photosynthetic productivity to rising temperature by atmospheric CO2 concentrations—Has its importance been underestimated. Plant, Cell Environ 14(8):729–739

    Article  Google Scholar 

  • Lucarini V, Calmanti S, Dell’Aquila A, Ruti PM, Speranza A (2007) Intercomparison of the northern hemisphere winter mid-latitude atmospheric variability of the IPCC models. Clim Dynam 28(7–8):829–848

    Article  Google Scholar 

  • Mitchell RAC, Lawlor DW, Mitchell VJ, Gibbard CL, White EM, Porter JR (1995) Effects of elevated CO2 concentration and increased temperature on winter-wheat—Test of ARCWHEAT1 simulation model. Plant, Cell Environ 18(7):736–748

    Article  Google Scholar 

  • Motovilov YG, Gottschalk L, Engeland K, Belokurov A (1999) ECOMAG: regional model of hydrological cycle—Application to the NOPEX region. University of Oslo, Department of Geophysics Report Series 105, Oslo. http://folk.uio.no/kolbjoen/nygen/ECOMAG-REPORT.pdf

  • Münch T, Berg M, Mirschel W, Wieland R, Nendel C (2013) Considering cost accountancy items in crop production simulations under climate change. Eur J Agron. http://dx.doi.org/10.1016/j.eja.2013.01.005

  • Nendel C (2009) Evaluation of Best Management Practises for N fertilisation in regional field vegetable production with a small scale simulation model. Eur J Agron 30:110–118

    Article  Google Scholar 

  • 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 Mod 222:1614–1625

    Article  Google Scholar 

  • Nendel C, Kersebaum KC, Mirschel W, Manderscheid R, Weigel HJ, Wenkel K-O (2009) Testing different CO2 response algorithms against a FACE crop rotation experiment. NJAS—Wageningen J Life Sci 57(1):17–25

    Article  Google Scholar 

  • Nendel C, Kersebaum KC, Mirschel W, Wenkel KO (2012) Testing farm management options as climate change adaptation strategies using the MONICA model. Eur J Agron, http://dx.doi.org/10.1016/j.eja.2012.09.005

  • Nendel C, Wieland R, Mirschel W, Specka X, Kersebaum KC (2013) Simulating winter wheat yields using input data with different spatial resolutions. Field Crop Res 145:67–77. http://dx.doi.org/10.1016/j.fcr.2013.02.014

  • Neusypina TA (1979) Rascet teplovo rezima pocvi v modeli formirovanija urozaja., Teoreticeskij osnovy i kolicestvennye metody programmirovanija urozaev. Leningrad, pp 53–62

    Google Scholar 

  • Olsen PA, Haugen LE (1997) Jordas termiske egenskaper. 8, Ås

    Google Scholar 

  • Paeth H, Capo-Chichi A, Endlicher W (2008) Climate change and food security in tropical West Africa—A dynamic-statistical modelling approach. Erdkunde 62(2):101–115

    Article  Google Scholar 

  • Pedersen A, Zhang KF, Thorup-Kristensen K, Jensen LS (2010) Modelling diverse root density dynamics and deep nitrogen uptake—A simple approach. Plant Soil 326(1–2):493–510

    Article  Google Scholar 

  • Potgieter AB, Hammer GL, Doherty A, de Voil P (2005) A simple regional-scale model for forecasting sorghum yield across North-Eastern Australia. Agric For Meteorol 132(1–2):143–153

    Article  Google Scholar 

  • Rahn CR, Zang K, Lillywhite RD, Ramos C, de Paz JM, Doltra J, Riley H, Fink M, Nendel C, Thorup-Kristensen K, Pedersen A, Piro F, Venezia A, Firth C, Schmutz U, Rayns F, Strohmeyer K (2010) EU-Rotate_N —a European decision support system—to predict environmental and economic consequences of the management of nitrogen fertiliser in crop rotations. Eur J Hort Sci 75(1):20–32

    Google Scholar 

  • Reidsma P, Ewert F, Lansink AO, Leemans R (2010) Adaptation to climate change and climate variability in European agriculture: the importance of farm level responses. Eur J Agron 32(1):91–102

    Article  Google Scholar 

  • Riley H, Bonesmo H (2005) Modelling of snow and freeze-thaw cycles in the EU-Rotate_N decision support system. Grønn Kunnskap 9(112):0

    Google Scholar 

  • Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn P, Antle JM, Nelson G, Porter CH, Janssen S, Asseng S, Basso B., Ewert F, Wallach D, Baigorria G, Winter JM (2012) The agricultural model intercomparison and improvment project (AgMIP): protocols and pilot studies. Agric For Meteorol, http://dx.doi.org/10.1016/j.agroformet.2012.09.011

  • Rötter RP, Palosuo T, Kersebaum KC, Angulo C, Bindi M, Ewert F, Ferrise R, Hravlinka P, Moriondo M, Nendel C, Olesen JE, Patil R, Ruget F, Tacáè J, Trnka M (2012) Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models. Field Crop Res 133:23–36

    Article  Google Scholar 

  • Sadeghi AM, McInnes KJ, Kissel DE, Cabrera ML, Koelliker JK, Kanemasu ET (1988) Mechanistic model for predicting ammonia volatilization from urea. In: Bock BR, Kissel DE (eds) Ammonia volatilization from urea fertilizers. National Fertilizer Development Centre, Tennessee Valley Authority, Muscle Shoals, Alabama, pp 67–92

    Google Scholar 

  • Shrestha RR, Bardossy A, Rode M (2007) A hybrid deterministic-fuzzy rule based model for catchment scale nitrate dynamics. J Hydrol 342(1–2):143–156

    Article  Google Scholar 

  • Søgaard HT, Sommer SG, Hutchings NJ, Huijsmans JFM, Bussink DW, Nicholson F (2002) Ammonia volatilization from field-applied animal slurry—the ALFAM model. Atmos Environ 36(20):3309–3319

    Article  Google Scholar 

  • Trnka M, Dubrovský M, Žalud Z (2004) Climate change impacts and adaptation strategies in spring barley production in the Czech Republic. Clim Change 64(1–2):227–255

    Article  Google Scholar 

  • van Keulen H, Penning de Vries FWT, Drees EM (1982) A summary model for crop growth. In: Penning de Vries FWT and van Laar HH (eds) Simulation of plant growth and crop production. PUDOC, Wageningen, pp 87–97

    Google Scholar 

  • Wegehenkel M (2000) Test of a modelling system for simulating water balances and plant growth using various different complex approaches. Ecol Mod 129(1):39–64

    Article  Google Scholar 

  • Wehrmann J, Scharpf HC (1979) Mineral nitrogen concentration of soil as a gauge of need for nitrogen-fertilizer (N-min method). Plant Soil 52(1):109–126

    Article  Google Scholar 

  • Wenkel KO, 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. http://dx.doi.org/10.1016/j.jenvman.2013.02.051

  • Wessolek G, Asseng S (2006) Trade-off between wheat yield and drainage under current and climate change conditions in northeast Germany. Eur J Agron 24(4):333–342

    Article  Google Scholar 

  • Wessolek G, Kaupenjohann M, Renger M (2009) Bodenphysikalische Kennwerte und Berechnungsverfahren für die. Praxis 40:1–80

    Google Scholar 

  • Wieland R, Mirschel W (2008) Adaptive fuzzy modeling versus artificial neural networks. Environ Mod Softw 23:215–224

    Article  Google Scholar 

  • Williams JR, Jones CA, Dyke PT (1984) A modeling approach to determining the relationship between erosion and soil productivity. Trans ASAE 27(1):129–144

    Google Scholar 

  • Willmott CJ, Wicks DE (1980) An empirical method for the spatial interpolation of monthly precipitation within California. Phys Geogr 1:59–73

    Google Scholar 

  • Yu Q, Goudriaan J, Wang TD (2001) Modelling diurnal courses of photosynthesis and transpiration of leaves on the basis of stomatal and non-stomatal responses, including photoinhibition. Photosynthetica 39(1):43–51

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Claas Nendel .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Nendel, C. (2014). MONICA: A Simulation Model for Nitrogen and Carbon Dynamics in Agro-Ecosystems. 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, Cham. https://doi.org/10.1007/978-3-319-01017-5_23

Download citation

Publish with us

Policies and ethics