ISIMIP3a Simulation Data from the Agriculture Sector

Cite as

Jonas Jägermeyr, Tzu-Shun Lin, Sam Rabin, Juraj Balkovic, Joshua W. Elliott, Babacar Faye, Christian Folberth, Toshichika Iizumi, Atul Jain, Takahashi Kiyoshi, Wenfeng Liu, Okada Masashi, Oleksandr Mialyk, Christoph Müller, Tommaso Stella, Chenzhi Wang, Heidi Webber, Hong Yang, Florian Zabel, Katja Frieler (2024): ISIMIP3a Simulation Data from the Agriculture Sector (v1.1). ISIMIP Repository. https://doi.org/10.48364/ISIMIP.370868.1

Metadata

DOI:
https://doi.org/10.48364/ISIMIP.370868.1
Title:
ISIMIP3a Simulation Data from the Agriculture Sector
Version:
1.1
Creators:
Contact person:

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

Abstract:

This dataset contains ISIMIP3a (www.isimip.org, Frieler et al. 2023, in prep.) simulation data from 11 agriculture models: ACEA, CROVER (Jägermeyr et al. 2021, Okada et al. 2018), CYGMA1p74, EPIC-IIASA (Balkovič et al. 2014), ISAM, LDNDC (Jägermeyr et al. 2023, Haas et al. 2012), LPJmL (von Bloh et al. 2018, Lutz et al. 2019), pDSSAT (Jägermeyr et al. 2021), PEPIC (Liu et al. 2016a,b), PROMET (Mauser et al. 2015), SIMPLACE-LINTUL5.

The outputs are based on simulations according to the ISIMIP3a protocol (https://protocol.isimip.org) A more detailed description of the models are available on https://www.isimip.org.

Version 1.1 adds the files for ISAM.

Publication date:
April 10, 2024
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

CC0 1.0 Universal Public Domain Dedication
CC0 1.0 Universal Public Domain Dedication (CC0 1.0)
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

  • 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
  • Haas E, Klatt S, Fröhlich A, Kraft P, Werner C, Kiese R, Grote R, Breuer L, Butterbach-Bahl K et al. LandscapeDNDC: a process model for simulation of biosphere–atmosphere–hydrosphere exchange processes at site and regional scale. Landscape Ecology, 28, 615-636, 2012, https://doi.org/10.1007/s10980-012-9772-x
  • Jägermeyr J et al. Climate change signal in global agriculture emerges earlier in new generation of climate and crop models. Nature Food, 2, 873-885, 2022. https://doi.org/10.5194/egusphere-egu22-3011
  • Jägermeyr, J., Müller, C., Ruane, A.C. et al. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nature Food, 2, 873–885, 2021. https://doi.org/10.1038/s43016-021-00400-y
  • Liu W, Yang H, Liu J, Azevedo L, Wang X, Xu Z, Abbaspour K, Schulin R et al. Global assessment of nitrogen losses and trade-offs with yields from major crop cultivations. Science of The Total Environment, 572, 526-537, 2016. https://doi.org/10.1016/j.scitotenv.2016.08.093
  • Lutz F, Herzfeld T, Heinke J, Rolinski S, Schaphoff S, von Bloh W, Stoorvogel JJ, and Müller C et al. Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage). Geoscientific Model Development, 12, 2419-2440, 2019. https://doi.org/10.5194/gmd-12-2419-2019
  • Mauser, W., Klepper, G., Zabel, F., Delzeit, R., Hank, T. Putzenlechner, B., Cazadilla, A. et al. Global biomass production potentials exceed expected future demand without the need for cropland expansion. Nature Communications, 6, 11, 2015. https://doi.org/10.1038/ncomms9946
  • Okada M et al. Varying Benefits of Irrigation Expansion for Crop Production Under a Changing Climate and Competitive Water Use Among Crops. Earth's Future, 6, 1207-1220, 2018. https://doi.org/10.1029/2017ef000763
  • von Bloh W, Schaphoff S, Müller C, Rolinski S, Waha K, Zaehle S et al. Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0). Geoscientific Model Development, 11, 2789-2812, 2018. https://doi.org/10.5194/gmd-11-2789-2018
  • 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

Additional documentation

Other references

This DOI is a new version of

  • Jägermeyr, Jonas; Rabin, Sam; Balkovic, Juraj; Elliott, Joshua W.; Faye, Babacar; Folberth, Christian; Iizumi, Toshichika; Jain, Atul; Kiyoshi, Takahashi; Lin, Tzu-Shun; Liu, Wenfeng; Masashi, Okada; Mialyk, Oleksandr; Müller, Christoph; Stella, Tommaso; Wang, Chenzhi; Webber, Heidi; Yang, Hong; Zabel, Florian; Frieler, Katja (2023): ISIMIP3a Simulation Data from the Agriculture Sector. Version 1.0. ISIMIP Repository. (Dataset). https://doi.org/10.48364/ISIMIP.370868.

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