ISIMIP3b Simulation Data from the Agriculture Sector

Please note that a newer version of this DOI is available: https://doi.org/10.48364/ISIMIP.723340.1

Cite as

Jonas Jägermeyr, Sam Rabin, Juraj Balkovic, Thiago Berton Ferreira, Joshua W. Elliott, Christian Folberth, Gerrit Hoogenboom, Toshichika Iizumi, Takahashi Kiyoshi, Wenfeng Liu, Okada Masashi, Oleksandr Mialyk, Christoph Müller, Andrew Smerald, Tommaso Stella, Hong Yang, Yi Yang, Florian Zabel, Katja Frieler (2023): ISIMIP3b Simulation Data from the Agriculture Sector (v1.0). ISIMIP Repository. https://doi.org/10.48364/ISIMIP.723340

Metadata

DOI:
https://doi.org/10.48364/ISIMIP.723340
Title:
ISIMIP3b Simulation Data from the Agriculture Sector
Version:
1.0
Creators:
Contact person:

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

Abstract:

This dataset contains ISIMIP3b (www.isimip.org, Frieler et al. 2023, in prep.) simulation data from the following agriculture models: ACEA (Jägermeyr et al. 2021, Mialyk et al. 2022), CROVER (Jägermeyr et al. 2021, Okada et al. 2018), CYGMA1p74, DSSAT-Pythia (https://dssat.net) DayCent (Yang et al. 2020, Zhang et al. 2020, Del Grosso et al 2010, Parton et al. 2002), EPIC-IIASA (Balkovič et al 2014), LDNDC (Jägermeyr et al. 2022, Haas et al. 2012), LPJmL (von Bloh et al. 2018, Lutz et al. 2019), LPJ-GUESS (Smith et al. 2003, Lindeskog et al. 2013, Olin et al. 2015), pDSSAT (Jägermeyr et al. 2021), PEPIC (Lui et al. 2016a/b), and PROMET (Mauser et al. 2015).

The outputs are based on simulations according to the ISIMIP3b protocol (https://protocol.isimip.org) A more detailed description of the models are available on https://www.isimip.org.

Publication date:
Aug. 30, 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

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
  • Del Grosso S, Ogle S, Parton W, Breidt F et al. Estimating uncertainty in N2O emissions from U.S. cropland soils. Global Biogeochemical Cycles, 24, n/a-n/a, 2010. https://doi.org/10.1029/2009gb003544
  • 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
  • Iizumi T et al. Responses of crop yield growth to global temperature and socioeconomic changes. Scientific Reports, 7, 7800, 2017. https://doi.org/10.1038/s41598-017-08214-4
  • 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
  • Lindeskog M, Arneth A, Bondeau A, Waha K, Seaquist J, Olin S, Smith B et al. Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa. Earth System Dynamics, 4, 385-407, 2013. https://doi.org/10.5194/esd-4-385-2013
  • 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
  • Mialyk O, Schyns J, Booij M, Hogeboom R et al. Historical simulation of maize water footprints with a new global gridded crop model ACEA. Hydrology and Earth System Sciences, 26, 923-940, 2022. https://doi.org/10.5194/hess-26-923-2022
  • 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
  • Olin S, Schurgers G, Lindeskog M, Wårlind D, Smith B, Bodin P, Holmér J, Arneth A et al. Modelling the response of yields and tissue C : N to changes in atmospheric CO<sub>2</sub> and N management in the main wheat regions of western Europe. Biogeosciences, 12, 2489-2515, 2015. https://doi.org/10.5194/bg-12-2489-2015
  • Parton W, Hartman M, Ojima D, Schimel D et al. DAYCENT and its land surface submodel: description and testing. Global and Planetary Change, 19, 35-48, 2002. https://doi.org/10.1016/s0921-8181(98)00040-x
  • Smith B, Prentice I, Sykes M et al. Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Global Ecology and Biogeography, 10, 621-637, 2003. https://doi.org/10.1046/j.1466-822x.2001.t01-1-00256.x
  • 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
  • Yang Y, Ogle S, Del Grosso S, Mueller N, Spencer S, Ray D et al. Regionalizing crop types to enhance global ecosystem modeling of maize production. Environmental Research Letters, 17, 014013, 2021. https://doi.org/10.1088/1748-9326/ac3f06
  • Zhang Y, Gurung R, Marx E, Williams S, Ogle S, Paustian K et al. DayCent Model Predictions of NPP and Grain Yields for Agricultural Lands in the Contiguous U.S.. Journal of Geophysical Research: Biogeosciences, 125, 2020. https://doi.org/10.1029/2020jg005750

Additional documentation

Other references

This DOI is the previous version of

  • Jägermeyr, Jonas; Rabin, Sam; Balkovic, Juraj; Berton Ferreira, Thiago; Elliott, Joshua W.; Faye, Babacar; Folberth, Christian; Hoogenboom, Gerrit; Iizumi, Toshichika; Kiyoshi, Takahashi; Liu, Wenfeng; Masashi, Okada; Mialyk, Oleksandr; Müller, Christoph; Smerald, Andrew; Stella, Tommaso; Wang, Chenzhi; Webber, Heidi; Yang, Hong; Yang, Yi; Zabel, Florian; Frieler, Katja (2023): ISIMIP3b Simulation Data from the Agriculture Sector. Version 1.1. ISIMIP Repository. (Dataset). https://doi.org/10.48364/ISIMIP.723340.1.

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