IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i11p1029-d661120.html
   My bibliography  Save this article

Effects of Bias-Corrected Regional Climate Projections and Their Spatial Resolutions on Crop Model Results under Different Climatic and Soil Conditions in Austria

Author

Listed:
  • Sabina Thaler

    (Institute of Meteorology and Climatology, University of Natural Resources and Life Sciences (BOKU), Gregor-Mendel-Straße 33, 1180 Vienna, Austria
    CzechGlobe—Global Change Research Institute CAS, Belidla 986/4a, 603 00 Brno, Czech Republic)

  • Herbert Formayer

    (Institute of Meteorology and Climatology, University of Natural Resources and Life Sciences (BOKU), Gregor-Mendel-Straße 33, 1180 Vienna, Austria)

  • Gerhard Kubu

    (Institute of Meteorology and Climatology, University of Natural Resources and Life Sciences (BOKU), Gregor-Mendel-Straße 33, 1180 Vienna, Austria)

  • Miroslav Trnka

    (CzechGlobe—Global Change Research Institute CAS, Belidla 986/4a, 603 00 Brno, Czech Republic
    Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic)

  • Josef Eitzinger

    (Institute of Meteorology and Climatology, University of Natural Resources and Life Sciences (BOKU), Gregor-Mendel-Straße 33, 1180 Vienna, Austria)

Abstract

The quality, reliability, and uncertainty of Austrian climate projections (ÖKS15) and their impacts on the results of the crop model DSSAT for three different orographic and climatic agricultural regions in Austria were analyzed. Cultivar-specific grain yields of winter wheat, spring barley, and maize were simulated for different soil classes to address three main objectives. First, the uncertainties of simulated crop yields related to the ÖKS15 projections were analyzed under current climate conditions. The climate projections revealed that the case study regions with higher humidity levels generally had lower yield deviations than the drier regions (yield deviations from −19% to +15%). Regarding the simulated crop types, spring barley was found to be less sensitive to the climate projections than rainfed maize, and the response was greater in regions with a low soil water storage capacity. The second objective was to simulate crop yields for the same cultivars using future climate projections. Winter wheat and spring barley tended to show increased yields by the end of the century due to an assumed CO 2 -fertilization effect in the range of 3–23%, especially under RCP 8.5. However, rainfed and irrigated maize were associated with up to 17% yield reductions in all three study regions due to a shortened growth period caused by warming. The third objective addressed the effects of crop model weather input data with different spatial resolutions (1 vs. 5, 11, and 21 km) on simulated crop yields using the climate projections. Irrigated grain maize and rainfed spring barley had the lowest simulated yield deviations between the spatial scales applied due to their better water supply conditions. The ranges of uncertainty revealed by the different analyses suggest that impact models should be tested with site representative conditions before being applied to develop site-specific adaptation options for Austrian crop production.

Suggested Citation

  • Sabina Thaler & Herbert Formayer & Gerhard Kubu & Miroslav Trnka & Josef Eitzinger, 2021. "Effects of Bias-Corrected Regional Climate Projections and Their Spatial Resolutions on Crop Model Results under Different Climatic and Soil Conditions in Austria," Agriculture, MDPI, vol. 11(11), pages 1-39, October.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:11:p:1029-:d:661120
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/11/1029/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/11/1029/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pascalle Smith & Georg Heinrich & Martin Suklitsch & Andreas Gobiet & Markus Stoffel & Jürg Fuhrer, 2014. "Station-scale bias correction and uncertainty analysis for the estimation of irrigation water requirements in the Swiss Rhone catchment under climate change," Climatic Change, Springer, vol. 127(3), pages 521-534, December.
    2. Schönhart, Martin & Mitter, Hermine & Schmid, Erwin & Heinrich, Georg & Gobiet, Andreas, 2014. "Integrated Analysis of Climate Change Impacts and Adaptation Measures in Austrian Agriculture," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 63(03), pages 1-21, September.
    3. Lobell, David B. & Ortiz-Monasterio, J. Ivan & Falcon, Walter P., 2007. "Yield uncertainty at the field scale evaluated with multi-year satellite data," Agricultural Systems, Elsevier, vol. 92(1-3), pages 76-90, January.
    4. Uddameri, Venkatesh & Ghaseminejad, Ali & Hernandez, E. Annette, 2020. "A tiered stochastic framework for assessing crop yield loss risks due to water scarcity under different uncertainty levels," Agricultural Water Management, Elsevier, vol. 238(C).
    5. Franziska Strauss & Erwin Schmid & Elena Moltchanova & Herbert Formayer & Xiuying Wang, 2012. "Modeling climate change and biophysical impacts of crop production in the Austrian Marchfeld Region," Climatic Change, Springer, vol. 111(3), pages 641-664, April.
    6. Daniel Wallach & Linda O. Mearns & Alex C. Ruane & Reimund P. Rötter & Senthold Asseng, 2016. "Lessons from climate modeling on the design and use of ensembles for crop modeling," Climatic Change, Springer, vol. 139(3), pages 551-564, December.
    7. Detlef Vuuren & Jae Edmonds & Mikiko Kainuma & Keywan Riahi & Allison Thomson & Kathy Hibbard & George Hurtt & Tom Kram & Volker Krey & Jean-Francois Lamarque & Toshihiko Masui & Malte Meinshausen & N, 2011. "The representative concentration pathways: an overview," Climatic Change, Springer, vol. 109(1), pages 5-31, November.
    8. Manderscheid, Remy & Dier, Markus & Erbs, Martin & Sickora, Jan & Weigel, Hans-Joachim, 2018. "Nitrogen supply – A determinant in water use efficiency of winter wheat grown under free air CO2 enrichment," Agricultural Water Management, Elsevier, vol. 210(C), pages 70-77.
    9. Schönhart, Martin & Mitter, Hermine & Schmid, Erwin & Heinrich, Georg & Gobiet, Andreas, 2014. "Integrated Analysis of Climate Change Impacts and Adaptation Measures in Austrian Agriculture," Journal of International Agricultural Trade and Development, Journal of International Agricultural Trade and Development, vol. 63(3).
    10. Samuel S. Myers & Antonella Zanobetti & Itai Kloog & Peter Huybers & Andrew D. B. Leakey & Arnold J. Bloom & Eli Carlisle & Lee H. Dietterich & Glenn Fitzgerald & Toshihiro Hasegawa & N. Michele Holbr, 2014. "Increasing CO2 threatens human nutrition," Nature, Nature, vol. 510(7503), pages 139-142, June.
    11. Miroslav Trnka & Reimund P. Rötter & Margarita Ruiz-Ramos & Kurt Christian Kersebaum & Jørgen E. Olesen & Zdeněk Žalud & Mikhail A. Semenov, 2014. "Adverse weather conditions for European wheat production will become more frequent with climate change," Nature Climate Change, Nature, vol. 4(7), pages 637-643, July.
    12. A. J. Challinor & J. Watson & D. B. Lobell & S. M. Howden & D. R. Smith & N. Chhetri, 2014. "A meta-analysis of crop yield under climate change and adaptation," Nature Climate Change, Nature, vol. 4(4), pages 287-291, April.
    13. Richard H. Moss & Jae A. Edmonds & Kathy A. Hibbard & Martin R. Manning & Steven K. Rose & Detlef P. van Vuuren & Timothy R. Carter & Seita Emori & Mikiko Kainuma & Tom Kram & Gerald A. Meehl & John F, 2010. "The next generation of scenarios for climate change research and assessment," Nature, Nature, vol. 463(7282), pages 747-756, February.
    Full references (including those not matched with items on IDEAS)

    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. Dono, Gabriele & Cortignani, Raffaele & Dell'Unto, Davide & Deligios, Paola & Doro, Luca & Lacetera, Nicola & Mula, Laura & Pasqui, Massimiliano & Quaresima, Sara & Vitali, Andrea & Roggero, Pier Paol, 2016. "Winners and losers from climate change in agriculture: Insights from a case study in the Mediterranean basin," Agricultural Systems, Elsevier, vol. 147(C), pages 65-75.
    2. Nicole Costa Resende Ferreira & Jarbas Honorio Miranda, 2021. "Projected changes in corn crop productivity and profitability in Parana, Brazil," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 3236-3250, March.
    3. 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.
    4. Wiebe, Keith & Sulser, Timothy B & Dunston, Shahnila & Rosegrant, Mark W. & Fuglie, Keith & Willenbockel, Dirk & Nelson, Gerald C., 2020. "Modeling impacts of faster productivity growth to inform the CGIAR initiative on Crops to End Hunger," SocArXiv h2g6r, Center for Open Science.
    5. Nikolaos Karapetsas & Anne Gobin & George Bilas & Thomas M. Koutsos & Vasileios Pavlidis & Eleni Katragkou & Thomas K. Alexandridis, 2024. "Analysis of Land Suitability for Maize Production under Climate Change and Its Mitigation Potential through Crop Residue Management," Land, MDPI, vol. 13(1), pages 1-24, January.
    6. Kirchner, Mathias & Schmidt, Johannes & Kindermann, Georg & Kulmer, Veronika & Mitter, Hermine & Prettenthaler, Franz & Rüdisser, Johannes & Schauppenlehner, Thomas & Schönhart, Martin & Strauss, Fran, 2015. "Ecosystem services and economic development in Austrian agricultural landscapes — The impact of policy and climate change scenarios on trade-offs and synergies," Ecological Economics, Elsevier, vol. 109(C), pages 161-174.
    7. Kirchner, Mathias & Schönhart, Martin & Schmid, Erwin, 2016. "Spatial impacts of the CAP post-2013 and climate change scenarios on agricultural intensification and environment in Austria," Ecological Economics, Elsevier, vol. 123(C), pages 35-56.
    8. Ding, Yimin & Wang, Weiguang & Song, Ruiming & Shao, Quanxi & Jiao, Xiyun & Xing, Wanqiu, 2017. "Modeling spatial and temporal variability of the impact of climate change on rice irrigation water requirements in the middle and lower reaches of the Yangtze River, China," Agricultural Water Management, Elsevier, vol. 193(C), pages 89-101.
    9. Gerald Nelson & Jessica Bogard & Keith Lividini & Joanne Arsenault & Malcolm Riley & Timothy B. Sulser & Daniel Mason-D’Croz & Brendan Power & David Gustafson & Mario Herrero & Keith Wiebe & Karen Coo, 2018. "Income growth and climate change effects on global nutrition security to mid-century," Nature Sustainability, Nature, vol. 1(12), pages 773-781, December.
    10. Jaewon Kwak & Huiseong Noh & Soojun Kim & Vijay P. Singh & Seung Jin Hong & Duckgil Kim & Keonhaeng Lee & Narae Kang & Hung Soo Kim, 2014. "Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea," IJERPH, MDPI, vol. 11(10), pages 1-19, October.
    11. Henzler, Julia & Weise, Hanna & Enright, Neal J. & Zander, Susanne & Tietjen, Britta, 2018. "A squeeze in the suitable fire interval: Simulating the persistence of fire-killed plants in a Mediterranean-type ecosystem under drier conditions," Ecological Modelling, Elsevier, vol. 389(C), pages 41-49.
    12. Anna Yusa & Peter Berry & June J.Cheng & Nicholas Ogden & Barrie Bonsal & Ronald Stewart & Ruth Waldick, 2015. "Climate Change, Drought and Human Health in Canada," IJERPH, MDPI, vol. 12(7), pages 1-54, July.
    13. Abhiru Aryal & Albira Acharya & Ajay Kalra, 2022. "Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling," Forecasting, MDPI, vol. 4(3), pages 1-22, June.
    14. Kokou Amega & Yendoubé Laré & Ramchandra Bhandari & Yacouba Moumouni & Aklesso Y. G. Egbendewe & Windmanagda Sawadogo & Saidou Madougou, 2022. "Solar Energy Powered Decentralized Smart-Grid for Sustainable Energy Supply in Low-Income Countries: Analysis Considering Climate Change Influences in Togo," Energies, MDPI, vol. 15(24), pages 1-24, December.
    15. Mitter, Hermine & Schmid, Erwin, 2019. "Computing the economic value of climate information for water stress management exemplified by crop production in Austria," Agricultural Water Management, Elsevier, vol. 221(C), pages 430-448.
    16. Magalhães Filho, L.N.L. & Roebeling, P.C. & Costa, L.F.C. & de Lima, L.T., 2022. "Ecosystem services values at risk in the Atlantic coastal zone due to sea-level rise and socioeconomic development," Ecosystem Services, Elsevier, vol. 58(C).
    17. Américo S. Ribeiro & Maite deCastro & Liliana Rusu & Mariana Bernardino & João M. Dias & Moncho Gomez-Gesteira, 2020. "Evaluating the Future Efficiency of Wave Energy Converters along the NW Coast of the Iberian Peninsula," Energies, MDPI, vol. 13(14), pages 1-15, July.
    18. Qun'ou Jiang & Yuwei Cheng & Qiutong Jin & Xiangzheng Deng & Yuanjing Qi, 2015. "Simulation of Forestland Dynamics in a Typical Deforestation and Afforestation Area under Climate Scenarios," Energies, MDPI, vol. 8(10), pages 1-26, September.
    19. Miftakhova, Alena & Judd, Kenneth L. & Lontzek, Thomas S. & Schmedders, Karl, 2020. "Statistical approximation of high-dimensional climate models," Journal of Econometrics, Elsevier, vol. 214(1), pages 67-80.
    20. Pedro Pérez-Cutillas & Pedro Baños Páez & Isabel Banos-González, 2020. "Variability of Water Balance under Climate Change Scenarios. Implications for Sustainability in the Rhône River Basin," Sustainability, MDPI, vol. 12(16), pages 1-22, August.

    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:gam:jagris:v:11:y:2021:i:11:p:1029-:d:661120. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.