With the support of
AgML aspires to
Close collaboration with other AgMIP activities (i.e. the Global Gridded Crop Model Intercomparison, GGCMI) will facilitate the creation of agricultural model datasets for use in cutting-edge ML research.
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Future climate impacts
on yields
By measuring the skill of machine learning models in emulating existing process-based crop models under climate change scenarios, we can evaluate and intercompare the ability of data-driven approaches to generalise outside of the training distribution.
More on the Future Crop Challenge, our benchmark dataset to evaluate ML models that estimate agricultural impacts of climate change.
Regional yield forecasting
Sub-national yield forecasting is often approached differently in terms both of available predictors and evaluation strategies. In this task, we aim to harmonize and intercompare machine learning models for forecasting crop yields in different environments and for different crops.
More about CYbench, the AgML benchmark dataset for subnational crop yield forecasting to intercompare ML models.
New tasks
Interested to propose or organize a new task?
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On January 22-24, 2024, the first AgML workshop was hosted in Wageningen, the Netherlands.
During the workshop the AgML teams further developed our first two benchmarks with the aim of launching the first intercomparison studies.
Wageningen University and Research published a news article on the AgML workshop.
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