D3-C2

Adapting to climate change with AI and data science

The WUR Research Investment Theme D3-C2 aims to advance Wageningen climate solutions by exploring the potential of AI for data-driven discovery. We aim to better understand the effects of climate change and develop new solutions to adapt to a changing climate.

Together, we create climate smart systems and map them to nature-based solutions. In doing so, we increase climate resilience and work together across disciplines. In addition, bringing together the vast amounts of data available at WUR, and from our partners, is of great importance. In doing so, we are especially looking at the effects of climate change on different socio-ecological systems.

Our vision is that AI methodology will enable us to identify patterns in data to better understand the impacts of climate change, develop new solutions for adapting to a changing climate and prepare for climate action. Data-driven discoveries can be the key element in accelerating the identification, development and acceptance of evidence-based solutions for adapting to a changing climate.

D3-C2 funded more than 25 internal seed projects to accelerate data-intensive research collaborations that go beyond disciplinary boundaries, and include (big) data infrastructures, new sensing technologies and AI algorithms for adapting to climate change.

For more, see the project webpage D3-C2.

Related publications:

• O.G. Younis, L. Corinzia, I. N. Athanasiadis, A. Krause, J. M. Buhmann, M. Turchetta, Breeding Programs Optimization with Reinforcement Learning, NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning, 2023, doi:10.48550/arXiv.2406.03932.
• D. Marcos, R. van de Vlasakker, I. N. Athanasiadis, P. Bonnet, H. Goeau, A. Joly, W. D. Kissling, C. Leblanc, A. S. J. van Proosdij, K. P. Panousis, Fully automatic extraction of morphological traits from the Web: utopia or reality?, Applications in Plant Sciences, 5:1-12, 2025, doi:10.1002/aps3.70005.
• O. G. Younis, M. Turchetta, D. A. Suarez, S. Yates, B. Studer, I. N. Athanasiadis, A. Krause, J. M. Buhmann, L. Corinzia, ChromaX: a fast and scalable breeding program simulator, Bioinformatics, 39(12):btad691, 2023, doi:10.1093/bioinformatics/btad691.