IDEAS home Printed from https://ideas.repec.org/a/spr/climat/v128y2015i3p215-227.html
   My bibliography  Save this article

Interactively modelling land profitability to estimate European agricultural and forest land use under future scenarios of climate, socio-economics and adaptation

Author

Listed:
  • Eric Audsley
  • Mirek Trnka
  • Santiago Sabaté
  • Joan Maspons
  • Anabel Sanchez
  • Daniel Sandars
  • Jan Balek
  • Kerry Pearn

Abstract

Studies of climate change impacts on agricultural land use generally consider sets of climates combined with fixed socio-economic scenarios, making it impossible to compare the impact of specific factors within these scenario sets. Analysis of the impact of specific scenario factors is extremely difficult due to prohibitively long run-times of the complex models. This study produces and combines metamodels of crop and forest yields and farm profit, derived from previously developed very complex models, to enable prediction of European land use under any set of climate and socio-economic data. Land use is predicted based on the profitability of the alternatives on every soil within every 10’ grid across the EU. A clustering procedure reduces 23,871 grids with 20+ soils per grid to 6,714 clusters of common soil and climate. Combined these reduce runtime 100 thousand-fold. Profit thresholds define land as intensive agriculture (arable or grassland), extensive agriculture or managed forest, or finally unmanaged forest or abandoned land. The demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. An iteration adjusts prices to meet these constraints. A range of measures are derived at 10’ grid-level such as diversity as well as overall EU production. There are many ways to utilise this ability to do rapid What-If analysis of both impact and adaptations. The paper illustrates using two of the 5 different GCMs (CSMK3, HADGEM with contrasting precipitation and temperature) and two of the 4 different socio-economic scenarios (“We are the world”, “Should I stay or should I go” which have contrasting demands for land), exploring these using two of the 13 scenario parameters (crop breeding for yield and population) . In the first scenario, population can be increased by a large amount showing that food security is far from vulnerable. In the second scenario increasing crop yield shows that it improves the food security problem. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Eric Audsley & Mirek Trnka & Santiago Sabaté & Joan Maspons & Anabel Sanchez & Daniel Sandars & Jan Balek & Kerry Pearn, 2015. "Interactively modelling land profitability to estimate European agricultural and forest land use under future scenarios of climate, socio-economics and adaptation," Climatic Change, Springer, vol. 128(3), pages 215-227, February.
    • Handle: RePEc:spr:climat:v:128:y:2015:i:3:p:215-227
    DOI: 10.1007/s10584-014-1164-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10584-014-1164-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10584-014-1164-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lehtonen, Heikki & Peltola, Jukka & Sinkkonen, Marko, 2006. "Co-effects of climate policy and agricultural policy on regional agricultural viability in Finland," Agricultural Systems, Elsevier, vol. 88(2-3), pages 472-493, June.
    2. J E Annetts & E Audsley, 2002. "Multiple objective linear programming for environmental farm planning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(9), pages 933-943, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kipling, Richard P. & Bannink, André & Bellocchi, Gianni & Dalgaard, Tommy & Fox, Naomi J. & Hutchings, Nicholas J. & Kjeldsen, Chris & Lacetera, Nicola & Sinabell, Franz & Topp, Cairistiona F.E. & va, 2016. "Modeling European ruminant production systems: Facing the challenges of climate change," Agricultural Systems, Elsevier, vol. 147(C), pages 24-37.
    2. Grundy, Michael J. & Bryan, Brett A. & Nolan, Martin & Battaglia, Michael & Hatfield-Dodds, Steve & Connor, Jeffery D. & Keating, Brian A., 2016. "Scenarios for Australian agricultural production and land use to 2050," Agricultural Systems, Elsevier, vol. 142(C), pages 70-83.
    3. Holman, I.P. & Brown, C & Janes, V & Sandars, D, 2017. "Can we be certain about future land use change in Europe? A multi-scenario, integrated-assessment analysis," Agricultural Systems, Elsevier, vol. 151(C), pages 126-135.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Janssen, Sander & van Ittersum, Martin K., 2007. "Assessing farm innovations and responses to policies: A review of bio-economic farm models," Agricultural Systems, Elsevier, vol. 94(3), pages 622-636, June.
    2. Soraya Tanure & Carlos Nabinger & João Luiz Becker, 2015. "Bioeconomic Model of Decision Support System for Farm Management: Proposal of a Mathematical Model," Systems Research and Behavioral Science, Wiley Blackwell, vol. 32(6), pages 658-671, November.
    3. Shunyu Yao & Neng Fan & Clark Seavert & Trent Teegerstrom, 2023. "Demand-Driven Harvest Planning and Machinery Scheduling for Guayule," SN Operations Research Forum, Springer, vol. 4(1), pages 1-25, March.
    4. Anderson, Kym & Yao, Shunli, 2001. "China, GMOs and World Trade in Agricultural and Textile Products," Conference papers 330922, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    5. Groot, Jeroen C.J. & Rossing, Walter a.H. & Tichit, Muriel & Turpin, Nadine & Jellema, André & Baudry, Jacques & Verburg, Peter & Doyen, Luc & van de Ven, Gerrie, 2009. "On the contribution of modelling to multifunctional agriculture: learning from comparisons," MPRA Paper 65467, University Library of Munich, Germany.
    6. Tanure, Soraya & Nabinger, Carlos & Becker, João Luiz, 2013. "Bioeconomic model of decision support system for farm management. Part I: Systemic conceptual modeling," Agricultural Systems, Elsevier, vol. 115(C), pages 104-116.
    7. Babic, Zoran & Peric, Tunjo, 2011. "Optimization of livestock feed blend by use of goal programming," International Journal of Production Economics, Elsevier, vol. 130(2), pages 218-223, April.
    8. Hediger, Werner, 2009. "The non-permanence of optimal soil carbon sequestration," 83rd Annual Conference, March 30 - April 1, 2009, Dublin, Ireland 51057, Agricultural Economics Society.
    9. Happe, K. & Hutchings, N.J. & Dalgaard, T. & Kellerman, K., 2011. "Modelling the interactions between regional farming structure, nitrogen losses and environmental regulation," Agricultural Systems, Elsevier, vol. 104(3), pages 281-291, March.
    10. Carravilla, M. A. & Oliveira, J. F., 2013. "Operations Research in Agriculture: Better Decisions for a Scarce and Uncertain World," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 5(2), pages 1-10, June.
    11. Fekete-Farkas, Maria & Rounsevell, Mark & Audsley, Eric, 2005. "Socio-economic Scenarios of Agricultural Land Use Change in Central and Eastern European Countries," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24640, European Association of Agricultural Economists.
    12. Kusumastuti, Ratih Dyah & Donk, Dirk Pieter van & Teunter, Ruud, 2016. "Crop-related harvesting and processing planning: a review," International Journal of Production Economics, Elsevier, vol. 174(C), pages 76-92.
    13. M J López-Baldovin & C Gutiérrez-Martin & J Berbel, 2006. "Multicriteria and multiperiod programming for scenario analysis in Guadalquivir river irrigated farming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(5), pages 499-509, May.
    14. Aurbacher, Joachim & Dabbert, Stephan, 2011. "Generating crop sequences in land-use models using maximum entropy and Markov chains," Agricultural Systems, Elsevier, vol. 104(6), pages 470-479, July.
    15. Angelo Aliano Filho & Helenice Oliveira Florentino & Margarida Vaz Pato & Sônia Cristina Poltroniere & João Fernando Silva Costa, 2022. "Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation," Annals of Operations Research, Springer, vol. 314(2), pages 347-376, July.
    16. Marius Rădulescu & Constanta Rădulescu & Gheorghiţă Zbăganu, 2014. "A portfolio theory approach to crop planning under environmental constraints," Annals of Operations Research, Springer, vol. 219(1), pages 243-264, August.
    17. A J Higgins & C J Miller & A A Archer & T Ton & C S Fletcher & R R J McAllister, 2010. "Challenges of operations research practice in agricultural value chains," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(6), pages 964-973, June.
    18. Glithero, N.J. & Wilson, P. & Ramsden, S.J., 2015. "Optimal combinable and dedicated energy crop scenarios for marginal land," Applied Energy, Elsevier, vol. 147(C), pages 82-91.
    19. Salassi, Michael E. & Deliberto, Michael A. & Guidry, Kurt M., 2013. "Economically optimal crop sequences using risk-adjusted network flows: Modeling cotton crop rotations in the southeastern United States," Agricultural Systems, Elsevier, vol. 118(C), pages 33-40.
    20. Heidari, Mohammad Davoud & Turner, Ian & Ardestani-Jaafari, Amir & Pelletier, Nathan, 2021. "Operations research for environmental assessment of crop-livestock production systems," Agricultural Systems, Elsevier, vol. 193(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:climat:v:128:y:2015:i:3:p:215-227. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.