Christian Folberth profile picture

Christian Folberth

Senior Research Scholar

Agriculture, Forestry, and Ecosystem Services Research Group

Biodiversity and Natural Resources Program

Biography

Chris Folberth is a senior researcher in the Agriculture, Forestry, and Ecosystem Services Research Group of the IIASA Biodiversity and Natural Resources Program. The main focus of his research is process-based agro-ecosystem modeling to improve the understanding of the interactions between crop and soil management, climate impacts, and adaptation potentials. He also serves as the group’s liaison to community networks such as the Intersectoral Impact Model Intercomparison Project (ISIMIP) and the Global Gridded Crop Model Intercomparison (GGCMI) initiative.

Besides process-based modeling, he leads initiatives on data-driven and hybrid modeling to harness the combined advantages of both process-based and machine-learning methods. He also engages in transdisciplinary research that aligns methods and tools with stakeholder needs.

Prior to joining IIASA in 2013, he was a research assistant at the Swiss Federal Institute of Aquatic Science and Technology (EAWAG) and the Federal Institute of Technology Zurich (ETH Zurich), where he also acquired a doctoral degree. In the years 2015-2016, he completed a research fellowship at Ludwig Maximilian University Munich, focusing on geographies of substance flows in the agro-food system.


Last update: 21 MAY 2024

Publications

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Oberleitner, T., Skalský, R. , Balkovič, J. , & Folberth, C. (2024). AI4SoilHealth: Supporting Europe’s Soil Deal using AI Technology and
Predictive Services. In: Mission Soil.at, September 17, 2024, Vienna.

Ermolieva, T., Havlik, P. , Derci Augustynczik, A.L., Frank, S. , Balkovič, J. , Skalský, R. , Deppermann, A., Nakhavali, A., Komendantova, N. , Kahil, T. , Wang, G., Folberth, C. , & Knopov, P.S. (2024). Tracking the Dynamics and Uncertainties of Soil Organic Carbon in Agricultural Soils Based on a Novel Robust Meta-Model Framework Using Multisource Data. Sustainability 16 (16) e6849. 10.3390/su16166849.

Orlov, A., Jägermeyr, J., Müller, C., Daloz, A.S., Zabel, F., Minoli, S., Liu, W., Lin, T.-S., Jain, A.K., Folberth, C. , Okada, M., Poschlod, B., Smerald, A., Schneider, J.M., & Sillmann, J. (2024). Human heat stress could offset potential economic benefits of CO2 fertilization in crop production under a high-emissions scenario. One Earth 7 (7) 1250-1265. 10.1016/j.oneear.2024.06.012.

Folberth, C. , Baklanov, A. , Khabarov, N. , Oberleitner, T., Balkovič, J. , & Skalský, R. (2024). CROMES - A fast and efficient machine learning emulator pipeline for gridded crop models. DOI:10.5194/egusphere-egu24-5852. In: EGU General Assembly 2024, 14-19 April 2024, Vienna.

Müller, C., Jägermeyr, J., Franke, J.A., Ruane, A.C., Balkovič, J. , Ciais, P., Dury, M., Falloon, P., Folberth, C. , Hank, T., Hoffmann, M., Izaurralde, R.C., Jacquemin, I., Khabarov, N. , Liu, W., Olin, S., Pugh, T.A.M., Wang, X., Williams, K., Zabel, F., & Elliott, J.W. (2024). Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality‐Based Model Evaluation. Earth's Future 12 (3) e2023EF003773. 10.1029/2023EF003773.