- Updated the job structure to include `anmod_id`, allowing for multiple mathematical models to be processed for each field and disease combination.
- Enhanced the batch processing logic to discover and execute jobs for `(field, disease, anmod_id)` instead of just `(field, disease)`.
- Modified the output file naming convention to include `anmod_id`, ensuring unique output for each model.
- Adjusted database interactions to accommodate the new primary key structure based on `pathogen_model_id`.
- Updated relevant documentation in `README.md` to reflect changes in job discovery, execution, and output generation.
- Enhanced unit tests to validate the new functionality and ensure correct behavior across various scenarios.
- Enhanced `.env.example` to include `SQL_APP_DATABASE` for managing additional advisory-related tables.
- Refactored `config.yaml` to remove the `crops` block, shifting to a database-driven approach for job discovery.
- Updated `README.md` to clarify the new database structure and job discovery process, emphasizing the role of the `AI_agrosupport_an_pathogen_models` and `AI_agrosupport_agro_models` tables.
- Modified SQL queries across multiple files to accommodate the new database schema, ensuring correct table references and improving overall data handling.
- Adjusted `pipeline` logic to utilize the new configuration, enhancing the efficiency of the advisory generation process.
