AgML is the AgMIP transdisciplinary community of agricultural and machine learning modelers.
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.
The objective of AgML Crop Yield Forecasting task is to create a benchmark to compare models for crop yield forecasting across countries and crops. The models and forecasts can be used for food security planning or famine early warning.
Thank you for your interest in contributing to AgML Crop Yield Forecasting.
If you have not yet joined the AgML team, please send an email to [email protected].
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For any changes or enhancements, check if a related issue already exists. If not, open a new issue.
Clone the repository. Create a working branch to make changes. The branch can be named something like
<username>/<short-summary-of-issue>.
After making necessary changes and testing them, commit your changes (add the issue number in the commit message).
When you have finished making changes, create a pull request and request review from at least one reviewer. After the reviewers approve your changes, your branch can be merged with main.
For more information please visit the AgML website.