ISIMIP2a Simulation Data from the Agriculture Sector

Cite as

Christoph Müller, Joshua W. Elliott, Almut Arneth, Juraj Balkovic, James Chryssanthacopoulos, Philippe Ciais, Delphine Deryng, Christian Folberth, Michael Glotter, Toshichika Iizumi, Roberto César Izaurralde, Andrew D. Jones, Jonas Jägermeyr, Nikolay Khabarov, Peter Lawrence, Junguo Liu, Wenfeng Liu, Fei Lun, Ganquan Mao, Hermine Mitter, Stefan Olin, Thomas Pugh, Ashwan D. Reddy, Gen Sakurai, Erwin Schmid, Xuhui Wang, Rachel F. Warren, Xiuchen Wu, Hong Yang, Shouchun Yi, Allard de Wit, Katja Frieler (2023): ISIMIP2a Simulation Data from the Agriculture Sector (v2.0). ISIMIP Repository. https://doi.org/10.48364/ISIMIP.729341

Metadata

DOI:
https://doi.org/10.48364/ISIMIP.729341
Title:
ISIMIP2a Simulation Data from the Agriculture Sector
Version:
2.0
Creators:
Contact person:

For inquiries concerning this dataset, please contact info@isimip.org.

Abstract:

This dataset contains ISIMIP2a (https://www.isimip.org, Schewe et al. 2019) simulation data from 24 agriculture models: CGMS-WOFOST (de Wit et al. 2019), CLM-Crop (Drewniak et al. 2012), EPIC-BOKU, EPIC-BOKU-BR, EPIC-BOKU-HG, EPIC-BOKU-P, EPIC-BOKU-PT (Kiniry et al. 2011, Izaurralde et al. 2005), EPIC-IIASA (Balkovic et al. 2014), EPIC-TAMU (Kiniry et al. 2011, Izaurralde et al. 2005), GEPIC, GEPIC-BR, GEPIC-P, GEPIC-PM, GEPIC-PT (Folberth et al. 2012, Izaurralde et al. 2005, Williams et al. 1989, Liu et al . 2007), LPJ-GUESS (Smith et al. 2014), LPJmL (Sitch et al. 2003, Schaphoff et al. 2013, Rost et al. 2008), ORCHIDEE-Crop (Wu et al. 2016), pAPSIM, pAPSIM-PM (Elliott et al. 2014, Keating et al. 2003, Holzworth et al. 2014), pDSSAT, pDSSAT-PT (Morgan et al. 2003), PEGASUS (Deryng et al. 2014, Deryng et al. 2006), PEPIC (Lui et al. 2016, Izaurralde et al. 2005, Williams et al. 1989), and PRYSBI2 (Sakurai et al. 2014, Okada et al. 2015).

With version 2.0 of this dataset, the DOI page has been moved to the new ISIMIP repository.

Methods:

The ISIMIP2a agriculture outputs are based on simulations from agriculture models according to the ISIMIP2a protocol (https://www.isimip.org/protocol/2a/) A more detailed description of the models and model-specific amendments of the protocol are available at https://www.isimip.org/impactmodels.

Publication date:
March 9, 2023
Publisher:
ISIMIP Repository
Contributors:

Here we list the persons and organizations, who are responsible for the collection, the management, and the publication of this dataset.

Rights

Attribution 4.0 International
Attribution 4.0 International (CC BY 4.0)
When using ISIMIP data for your research, please appropriately credit the data providers, e.g. either by citing the DOI for the dataset, or by appropriate acknowledgment. We strongly encourage to offer co-authorship to at least a representative of the data providers. Further information can be found in our terms of use.

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References

  • Allard de Wit, Hendrik Boogaard, Davide Fumagalli, Sander Janssen, Rob Knapen, Daniel van Kraalingen, Iwan Supit, Raymond van der Wijngaart, Kees van Diepen, et al. 25 years of the WOFOST cropping systems model. Agricultural Systems, 168, 154-167, 2019. https://doi.org/10.1016/j.agsy.2018.06.018
  • B.A Keating, P.S Carberry, G.L Hammer, M.E Probert, M.J Robertson, D Holzworth, N.I Huth, J.N.G Hargreaves, H Meinke, Z Hochman, G McLean, K Verburg, V Snow, J.P Dimes, M Silburn, E Wang, S Brown, K.L Bristow, S Asseng, S Chapman, R.L McCown, D.M Freebairn, C.J Smith, An overview of APSIM, a model designed for farming systems simulation, European Journal of Agronomy, Volume 18, Issues 3–4, 2003, Pages 267-288, ISSN 1161-0301, https://doi.org/10.1016/S1161-0301(02)00108-9.
  • Balkovič J, van der Velde M, Skalský R, Xiong W, Folberth C, Khabarov N, Smirnov A, Mueller N, Obersteiner M et al. Global wheat production potentials and management flexibility under the representative concentration pathways. Global and Planetary Change,122,107-121,2014. https://doi.org/10.5194/gmdd-5-4137-2012
  • Bondeau A., Smith P., Zaehle S., Schaphoff S., Lucht W., Cramer W., Gerten D., Lotze-Campen H., Müller C., Reichstein M., Smith B. et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology, 13, 679-706, 2007. https://doi.org/10.1111/j.1365-2486.2006.01305.x
  • Dean P. Holzworth, Neil I. Huth, Peter G. deVoil, Eric J. Zurcher, Neville I. Herrmann, Greg McLean, Karine Chenu, Erik J. van Oosterom, Val Snow, Chris Murphy, Andrew D. Moore, Hamish Brown, Jeremy P.M. Whish, Shaun Verrall, Justin Fainges, Lindsay W. Bell, Allan S. Peake, Perry L. Poulton, Zvi Hochman, Peter J. Thorburn, Donald S. Gaydon, Neal P. Dalgliesh, Daniel Rodriguez, Howard Cox, Scott Chapman, Alastair Doherty, Edmar Teixeira, Joanna Sharp, Rogerio Cichota, Iris Vogeler, Frank Y. Li, Enli Wang, Graeme L. Hammer, Michael J. Robertson, John P. Dimes, Anthony M. Whitbread, James Hunt, Harm van Rees, Tim McClelland, Peter S. Carberry, John N.G. Hargreaves, Neil MacLeod, Cam McDonald, Justin Harsdorf, Sara Wedgwood, Brian A. Keating, APSIM – Evolution towards a new generation of agricultural systems simulation, Environmental Modelling & Software, Volume 62, 2014, Pages 327-350, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2014.07.009.
  • Delphine Deryng et al.: Global crop yield response to extreme heat stress under multiple climate change futures. 2014 Environ. Res. Lett. 9 034011. https://doi.org/10.1088/1748-9326/9/3/034011
  • Deryng, D., Sacks, W. J., Barford, C. C., and Ramankutty, N. (2011), Simulating the effects of climate and agricultural management practices on global crop yield, Global Biogeochem. Cycles, 25, GB2006, https://doi.org/10.1029/2009GB003765
  • Drewniak, B. et al. Modeling agriculture in the Community Land Model. Geoscientific Model Development Discussions,5,4137-4185,2012. https://doi.org/10.5194/gmdd-5-4137-2012
  • Folberth C, Gaiser T, Abbaspour K, Schulin R, Yang H et al. Regionalization of a large-scale crop growth model for sub-Saharan Africa: Model setup, evaluation, and estimation of maize yields. Agriculture, Ecosystems & Environment, 151, 21-33, 2012. https://doi.org/10.1016/j.agee.2012.01.026
  • Izaurralde R, Williams J, McGill W, Rosenberg N, Jakas M et al. Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecological Modelling, 192, 362-384, 2005. https://doi.org/10.1016/j.ecolmodel.2005.07.010
  • J. R. Williams, C. A. Jones, J. R. Kiniry, D. A. Spanel et al. et al. The EPIC Crop Growth Model. Transactions of the ASAE, 32, 0497-0511, 1989. https://doi.org/10.13031/2013.31032
  • Joshua Elliott, David Kelly, James Chryssanthacopoulos, Michael Glotter, Kanika Jhunjhnuwala, Neil Best, Michael Wilde, Ian Foster, The parallel system for integrating impact models and sectors (pSIMS), Environmental Modelling & Software, Volume 62, 2014, Pages 509-516, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2014.04.008.
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  • Kiniry, J. et al. EPIC model parameters for cereal, oilseed, and forage crops in the northern Great Plains region. Canadian Journal of Plant Science, 75, 679-688, 2011. https://doi.org/10.4141/cjps95-114
  • Liu J, Williams J, Zehnder A, Yang H et al. GEPIC – modelling wheat yield and crop water productivity with high resolution on a global scale. Agricultural Systems, 94, 478-493, 2007. https://doi.org/10.1016/j.agsy.2006.11.019
  • Müller C, Robertson R et al. Projecting future crop productivity for global economic modeling. Agricultural Economics, 45, 37-50, 2013. https://doi.org/10.1111/agec.12088
  • Okada M, Iizumi T, Sakurai G, Hanasaki N, Sakai T, Okamoto K, Yokozawa M et al. Modeling irrigation-based climate change adaptation in agriculture: Model development and evaluation in Northeast China. Journal of Advances in Modeling Earth Systems, 7 , 1409-1424, 2015. https://doi.org/10.1002/2014ms000402
  • Sakurai G, Iizumi T, Nishimori M, Yokozawa M et al. How much has the increase in atmospheric CO2 directly affected past soybean production?. Scientific Reports, 4, 2014. https://doi.org/10.1038/srep04978
  • Schewe, J., Gosling, S.N., Reyer, C. et al. State-of-the-art global models underestimate impacts from climate extremes. Nat Commun 10, 1005 (2019). https://doi.org/10.1038/s41467-019-08745-6
  • Smith et al. et al. Implications of incorporating N-cycling and {N} limitations on primary production in an individual-based dynamic vegetation model. 2014. https://doi.org/10.5194/bg-11-2027-2014
  • W. Liu, H. Yang, C. Folberth, X. Wang, Q. Luo, R. Schulin et al. Global investigation of impacts of PET methods on simulating crop-water relations for maize. Agricultural and Forest Meteorology,221,164-175. https://doi.org/10.1016/j.agrformet.2016.02.017
  • Wu, X., Vuichard, N., Ciais, P., Viovy, N., de Noblet-Ducoudré, N., Wang, X., Magliulo, V., Wattenbach, M., Vitale, L., Di Tommasi, P., Moors, E. J., Jans, W., Elbers, J., Ceschia, E., Tallec, T., Bernhofer, C., Grünwald, T., Moureaux, C., Manise, T., Ligne, A., Cellier, P., Loubet, B., Larmanou, E., and Ripoche, D.: ORCHIDEE-CROP (v0), a new process-based agro-land surface model: model description and evaluation over Europe, Geosci. Model Dev., 9, 857–873, https://doi.org/10.5194/gmd-9-857-2016, 2016. https://doi.org/10.5194/gmd-9-857-2016

Additional documentation

This DOI is a new version of

  • Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; de Wit, Allard; Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Glotter, Michael; Iizumi, Toshichika; Izaurralde, Roberto C.; Jones, Andrew D.; Khabarov, Nikolay; Lawrence, Peter; Liu, Wenfeng; Mitter, Hermine; Müller, Christoph; Olin, Stefan; Pugh, Thomas A. M.; Reddy, Ashwan D.; Sakurai, Gen; Schmid, Erwin; Wang, Xuhui; Wu, Xiuchen; Yang, Hong; Büchner, Matthias (2017): ISIMIP2a Simulation Data from Agricultural Sector. GFZ Data Services. https://doi.org/10.5880/PIK.2017.006

Version History

GCMD Keywords

  • EARTH SCIENCE > AGRICULTURE > AGRICULTURAL PLANT SCIENCE > CROP/PLANT YIELDS
  • EARTH SCIENCE > AGRICULTURE > AGRICULTURAL PLANT SCIENCE > IRRIGATION
  • EARTH SCIENCE > AGRICULTURE > PLANT COMMODITIES > FIELD CROP PRODUCTS
  • EARTH SCIENCE > AGRICULTURE > SOILS > NITROGEN
  • EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WATER VAPOR > EVAPOTRANSPIRATION
  • EARTH SCIENCE > CLIMATE INDICATORS > LAND SURFACE/AGRICULTURE INDICATORS > LENGTH OF GROWING SEASON
  • EARTH SCIENCE > HUMAN DIMENSIONS > ECONOMIC RESOURCES > AGRICULTURE PRODUCTION
  • EARTH SCIENCE > HUMAN DIMENSIONS > ENVIRONMENTAL GOVERNANCE/MANAGEMENT > ENVIRONMENTAL ASSESSMENTS
  • EARTH SCIENCE > HUMAN DIMENSIONS > HABITAT CONVERSION/FRAGMENTATION > IRRIGATION
  • EARTH SCIENCE > HUMAN DIMENSIONS > NATURAL HAZARDS > FAMINE
  • EARTH SCIENCE > HUMAN DIMENSIONS > SUSTAINABILITY > ENVIRONMENTAL SUSTAINABILITY
  • EARTH SCIENCE > HUMAN DIMENSIONS > SUSTAINABILITY > SUSTAINABLE DEVELOPMENT
  • EARTH SCIENCE > LAND SURFACE > SOILS > NITROGEN
  • EARTH SCIENCE > LAND SURFACE > SOILS > SOIL MOISTURE/WATER CONTENT
  • Global Change Master Directory (GCMD) Keywords are a hierarchical set of controlled vocabularies maintained by NASA (more information).

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