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
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.
Regional yield forecasting
Regional 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.
Interested to propose or organize a new task?