Machine learning for agricultural modelling

AgML

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About AgML

AgML is the AgMIP transdisciplinary community of agricultural and machine learning modellers

AgML aspires to

  • identify key research gaps and opportunities at the intersection of agricultural modelling and machine learning research,
  • support enhanced collaboration and engagement between experts in these disciplines, and
  • conduct and publish protocol-based studies to establish best practices for robust machine learning use in agricultural modelling.

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.

Interested to join us? Join the AgML mailing list!

With the support of

Activities

AgML team is organized around model intercomparison activities.
Currently we have launched the first two tasks, and we are open to more!

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.

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.

New tasks

Interested to propose or organize a new task?
Join us!

First AgML Workshop

Our first milestone: Wageningen, January 22-24, 2024

On January 22-24, 2024, the first AgML workshop will be hosted in Wageningen, the Netherlands.

During the workshop the AgML teams will further develop our first two benchmarks with the aim of launching the first intercomparison studies.

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Interested in AgML activities?