KILIMO SHWARI is an Enterprise Breeding System (EBS) designed to aid crop breeding programs and provide valuable insights to resource-poor farmers in Africa. Leveraging comprehensive datasets and advanced analysis techniques, this project aims to enhance agricultural productivity by delivering precise recommendations on crop suitability, soil health, and climate adaptability.

• Integrate Comprehensive Data Sources: Combine soil, climate, crop, water resources, and socioeconomic data to create a robust decision-support system.
• Crop Suitability Analysis: Use detailed datasets to evaluate the suitability of different regions for various crops.
• Machine Learning Models: Develop and deploy machine learning models to predict crop yields and identify optimal breeding strategies.
• Climate Analysis: Analyze historical and projected climate data to understand its impact on crop productivity.
• Interactive Web Application: Develop a user-friendly web application that allows farmers and breeders to access and interact with the data.

1. Soil Data

   • Comprehensive soil profiles including pH, nutrient levels, texture, and organic matter content.
   • Geographic distribution of different soil types.

2. Climate Data

   • Historical and projected weather data.
   • Temperature, rainfall, and humidity levels.

link to the data: https://drive.google.com/drive/folders/1L42_-eatg7yANp_47wW0Ue7PL_9fBL_j?usp=sharing

3. Crop Data

   • Genotypic and phenotypic information of various crop varieties.
   • Yield data for different regions.

4. Water Resources Data

   • Information on surface and groundwater availability.
   • Details on existing irrigation infrastructure.

link to the data source: https://africangroundwateratlas.org/

5. Socioeconomic Data
   • Information on farmer demographics and farming practices.
   • Market access and economic conditions.