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Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations

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  • Christian Folberth

    (Ecosystem Services and Management Program, International Institute for Applied Systems Analysis
    Ludwig Maximilian University)

  • Rastislav Skalský

    (Ecosystem Services and Management Program, International Institute for Applied Systems Analysis
    Soil Science and Conservation Research Institute, National Agricultural and Food Centre)

  • Elena Moltchanova

    (Ecosystem Services and Management Program, International Institute for Applied Systems Analysis
    School of Mathematics and Statistics, University of Canterbury)

  • Juraj Balkovič

    (Ecosystem Services and Management Program, International Institute for Applied Systems Analysis
    Faculty of Natural Sciences, Comenius University)

  • Ligia B. Azevedo

    (Ecosystem Services and Management Program, International Institute for Applied Systems Analysis)

  • Michael Obersteiner

    (Ecosystem Services and Management Program, International Institute for Applied Systems Analysis)

  • Marijn van der Velde

    (European Commission, Joint Research Centre)

Abstract

Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations.

Suggested Citation

  • Christian Folberth & Rastislav Skalský & Elena Moltchanova & Juraj Balkovič & Ligia B. Azevedo & Michael Obersteiner & Marijn van der Velde, 2016. "Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations," Nature Communications, Nature, vol. 7(1), pages 1-13, September.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms11872
    DOI: 10.1038/ncomms11872
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    Cited by:

    1. Palosuo, Taru & Hoffmann, Munir P. & Rötter, Reimund P. & Lehtonen, Heikki S., 2021. "Sustainable intensification of crop production under alternative future changes in climate and technology: The case of the North Savo region," Agricultural Systems, Elsevier, vol. 190(C).
    2. Amirhossein Hassani & Adisa Azapagic & Nima Shokri, 2021. "Global predictions of primary soil salinization under changing climate in the 21st century," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
    3. Chapagain, Ranju & Huth, Neil & Remenyi, Tomas A. & Mohammed, Caroline L. & Ojeda, Jonathan J., 2023. "Assessing the effect of using different APSIM model configurations on model outputs," Ecological Modelling, Elsevier, vol. 483(C).
    4. Ojeda, Jonathan J. & Huth, Neil & Holzworth, Dean & Raymundo, Rubí & Zyskowski, Robert F. & Sinton, Sarah M. & Michel, Alexandre J. & Brown, Hamish E., 2021. "Assessing errors during simulation configuration in crop models – A global case study using APSIM-Potato," Ecological Modelling, Elsevier, vol. 458(C).
    5. Bandaru, Varaprasad, 2022. "Climate data induced uncertainties in simulated carbon fluxes under corn and soybean systems," Agricultural Systems, Elsevier, vol. 196(C).
    6. Wang, Zhaozhi & Zhang, T.Q. & Tan, C.S. & Xue, Lulin & Bukovsky, Melissa & Qi, Z.M., 2021. "Modeling impacts of climate change on crop yield and phosphorus loss in a subsurface drained field of Lake Erie region, Canada," Agricultural Systems, Elsevier, vol. 190(C).
    7. Stephen Whitfield & Sarah Chapman & Marcelin Tonye Mahop & Chetan Deva & Kennedy Masamba & Andekelile Mwamahonje, 2021. "Exploring assumptions in crop breeding for climate resilience: opportunities and principles for integrating climate model projections," Climatic Change, Springer, vol. 164(3), pages 1-18, February.
    8. Cai, Liping & Wang, Hui & Liu, Yanxu & Fan, Donglin & Li, Xiaoxiao, 2022. "Is potential cultivated land expanding or shrinking in the dryland of China? Spatiotemporal evaluation based on remote sensing and SVM," Land Use Policy, Elsevier, vol. 112(C).
    9. Stefan Hochrainer-Stigler & Juraj Balkovič & Kadri Silm & Anna Timonina-Farkas, 2019. "Large scale extreme risk assessment using copulas: an application to drought events under climate change for Austria," Computational Management Science, Springer, vol. 16(4), pages 651-669, October.
    10. van Zelm, Rosalie & van der Velde, Marijn & Balkovic, Juraj & Čengić, Mirza & Elshout, Pieter M.F. & Koellner, Thomas & Núñez, Montserrat & Obersteiner, Michael & Schmid, Erwin & Huijbregts, Mark , 2018. "Spatially explicit life cycle impact assessment for soil erosion from global crop production," Ecosystem Services, Elsevier, vol. 30(PB), pages 220-227.
    11. Eranga M. Wimalasiri & Ebrahim Jahanshiri & Tengku Adhwa Syaherah Tengku Mohd Suhairi & Hasika Udayangani & Ranjith B. Mapa & Asha S. Karunaratne & Lal P. Vidhanarachchi & Sayed N. Azam-Ali, 2020. "Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    12. van der Velde, M. & Nisini, L., 2019. "Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015," Agricultural Systems, Elsevier, vol. 168(C), pages 203-212.
    13. Asante, Paulina A. & Rozendaal, Danaё M.A. & Rahn, Eric & Zuidema, Pieter A. & Quaye, Amos K. & Asare, Richard & Läderach, Peter & Anten, Niels P.R., 2021. "Unravelling drivers of high variability of on-farm cocoa yields across environmental gradients in Ghana," Agricultural Systems, Elsevier, vol. 193(C).

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