unchained-angel/Botswana_Crop_Planning_System

Data-driven agricultural planning tool for Botswana. Analyses weather and soil data to recommend crops, planting cycles, and paddock rotations across three climate regions. # ITNA v2.0 — Regional Crop Suitability & Rotation Planning ITNA is a data-driven agricultural planning tool that analyses historical weather data to recommend the best crops, planting cycles, and paddock rotations across Botswana's three distinct climate zones. ## The Three Regions - **Region A (Hot Semi-Arid / BSh):** The eastern arable heartland (Serowe, Gaborone, Francistown). Supports 12 crops including Sorghum, Millet, and Bambara Groundnut. - **Region B (Hot Desert / BWh):** The Kalahari southwest (Tsabong, Hukuntsi). Extreme conditions support only 5 ultra-hardy crops. - **Region C (Tropical Savanna / Aw):** The wetter north (Maun, Kasane). Supports 8 crops including Maize, Tiger Nuts, and Sunflower. ## Features - **Region-Based Analysis:** Automatically fetches weather and filters crops based on the selected climate zone. - **14-Crop Database:** Sourced from FAO Ecocrop and ICRISAT, covering Cereals, Pulses, Oilseeds, and Tubers. - **4 Agricultural Cycles:** Groups recommendations into Summer (Nov-Feb), Autumn (Mar-May), Winter (Jun-Aug), and Spring (Sep-Oct). - **Paddock Rotation Planning:** Generates a 3-year rotation plan to maintain soil health and prevent nutrient depletion. - **Drought-Aware Scoring:** Applies dynamic penalties based on each crop's specific drought tolerance rating. ## Usage ### 1. Fetch Weather for a Region Fetches 365 days of historical data from Open-Meteo for the region's representative coordinates. python -m src.ingest.weather --region A --year 2025 ### 2. Generate Crop & Rotation Report Scores all viable crops and outputs the best options for each cycle, plus a paddock rotation plan. python -m src.scoring.score_crops --region A python -m src.scoring.score_crops --region B python -m src.scoring.score_crops --region C ## Adding Custom Crops Edit `data/crop_rules.csv`. The columns are: `crop_name,crop_category,min_temp,optimal_min,optimal_max,max_temp,drought_tolerance,suitable_regions,source` Then reload the database: …