hannahandkush/Data_Management

Multi-Crop Database System for Variety Recommendation in Sub-Saharan Africa # Multi-Crop Database System for Variety Recommendation in Sub-Saharan Africa ## 1. Overview This project is a relational database and analytics platform designed to support evidence-based crop variety selection across Sub-Saharan Africa (SSA). The system integrates multi-year, multi-location trial data from five countries (Nigeria, Kenya, Ethiopia, Tanzania, and Ghana) for four major crops: Maize, Rice, Sorghum, and Cowpea. The goal is to help researchers, breeders, agronomists, and farmers make data-driven decisions by providing a centralized, structured, and queryable source of agricultural data. ## 2. Key Features * **Centralized Data**: Consolidates multi-environment trial (MET) data into a single, consistent database. * **Standardized Schema**: Implements a star schema to normalize heterogeneous data from different sources. * **Rich Datasets**: Includes phenotypic, environmental, and agronomic metadata. * **Analytical Support**: Enables complex queries to analyze variety performance, stability, and environmental response. * **Data Provenance**: Retains the original, unprocessed data in a staging table for lineage and auditing. ## 3. Project Structure The project is organized into the following directories: ``` . ├── Project/ │ ├── data_use_scripts/ # SQL scripts with example analytical queries. │ ├── database_dump/ # Full database dump for backup and restoration. │ ├── documentation/ # Detailed project documentation, including data dictionary and setup guides. │ ├── processed_data/ # Cleaned, consolidated CSV data used for populating the database. │ ├── raw_data/ # Original, unmodified data files from various sources. │ └── sql_scripts/ # Scripts for creating and populating the database schema. ├── Report/ # Project report. └── README.md # This file. ``` ## 4. Database Schema The database follows a star schema with a central fact table (`pheno …