Robust assessment of climatic risks to crop production

 

15.05.2025

 

In the face of intensifying climate extremes, agriculture urgently needs more robust tools to assess risks to crop production. In a new Nature Food commentary, an international research team, led by the Leibniz Centre for Agricultural Landscape Research (ZALF), outlines key research gaps that limit our ability to quantify the impacts of extreme weather events, such as heatwaves, droughts, floods, and other extreme events on cropping systems and project their associated risks on crops, farmers incomes, food security and the environment. The authors see solutions in the use of machine learning and close cooperation between the community of researchers and with farmers.

Current crop-climate risk assessments often rely on historical yield data and simplified weather models, which do not fully capture the range or intensity of extreme events. Lead author, Dr. Yean-Uk Kim of the Integrated Crop System Analysis group at ZALF states, “Our crop modelling community needs to leverage large ensembles of climate model simulations to better reflect future variability and uncertainty in weather conditions when assessing future risks.”

The article also points out that most existing crop models fail to account for critical stressors like waterlogging, frost, pests, and diseases—factors likely to intensify with climate change. To address this, the authors advocate for expanded data collection from farmers’ fields with the use of advanced sensing technologies and partnership models with farmers. Co-author Prof. Heidi Webber, head of the Integrated Crop System Analysis group at ZALF, reflects “There is great potential to make use of machine learning to understand the impact of multiple stressors in farmers fields. The Agricultural Model Intercomparison and Improvement Project (AgMIP) Machine Learning team, an international community of experts that we are working with, is making great contributions here. But we will likely need to integrate these data driven approaches with process-based modelling approaches for the novel combination of stressors which we expect in the future.” 

Importantly, the authors stress that risk assessments must also account for trade-offs across sustainability goals—such as yield, profitability, nitrogen losses, and greenhouse gas emissions, for example. Understanding these trade-offs is vital to support resilient and equitable food systems and ensure risk management supports and not jeopardize sustainability.

The authors conclude that advancing climate risk assessments in agriculture demands coordinated effort across the modeling community, such as that coordinated by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for climate change impact studies. This includes improving climate data inputs, expanding crop models to better represent extremes, and developing tools for understanding complex trade-offs. While technical challenges remain, the current moment presents an opportunity to align scientific progress with global climate finance and policy priorities, enabling smarter, more resilient investments in food systems. The article ultimately calls for the agricultural modeling community to step up—quickly and collaboratively—to meet the urgent need for robust, data-driven risk assessments that can guide transformative adaptation in agriculture.

The commentary was co-authored by an international team from ZALF, NASA Goddard Institute for Space Studies, ETHZ and BTU Cottbus. The work was funded by the Einstein Research Unit “Climate and Water under Change” (CliWaC) (grant number ERU-2020-609) from the Einstein Foundation Berlin and Berlin University Alliance, the NASA Earth Sciences Division via the NASA GISS Climate Impacts Group and the Leibniz Association Female Professorship (award number P102/2020).

Project partners:

• Leibniz Centre for Agricultural Landscape Research (ZALF)
• NASA Goddard Institute for Space Studies
• ETH Zürich
• BTU Cottbus

Further information:

https://www.nature.com/articles/s43016-025-01168-1

Note on the text:

This is a summary of the original text created with the help of artificial intelligence: Kim, YU., Ruane, A.C., Finger, R. et al. Rob​ust assessment of climatic risks to crop production. Nat Food (2025). https://doi.org/10.1038/s43016-025-01168-1.