OlafimihanBamidele/Nigeria-Climate-Livestock-Analysis

Power BI dashboard analyzing the relationship between climate variables and livestock production in Nigeria (2000-2020) using FAO and NASA datasets # Climate Impact on Livestock Production in Nigeria (2000–2020) ## Project Overview This project examines whether climate variables — specifically temperature and rainfall — significantly influence livestock production in Nigeria over a 20-year period (2000–2020). Using Microsoft Power BI, I built an interactive multi-page dashboard combining livestock production data from the FAO Global Database with climate data from NASA's POWER dataset, covering three major livestock types: Cattle, Goat, and Sheep. ## Objectives - Analyze 20 years of temperature and rainfall trends in Nigeria - Determine whether temperature and rainfall correlate with total livestock production - Compare production trends across Cattle, Goat, and Sheep - Identify seasonal patterns in rainfall and temperature - Communicate findings through an interactive, question-driven dashboard designed for both technical and non-technical audiences ## Data Sources | Dataset | Source | Period | |---------|--------|--------| | Livestock Production | FAO Global Livestock Database | 2000–2020 | | Climate Data (Temperature & Rainfall) | NASA POWER Dataset | 2000–2020 | ## Methodology - Data cleaning and transformation using Power Query - DAX measures created for Month-over-Month comparisons and aggregated yearly averages - Scatter/bubble visualization to visually explore the relationship between total production, temperature, and rainfall across all 20 years, with one bubble representing each year - Area chart to compare production trends across the three livestock types over time - Line chart to track average temperature and rainfall trends year over year - Bar charts to compare seasonal rainfall and temperature (Dry vs Rainy season) - Slicers for Year and Livestock Type to allow interactive filtering across the whole report - Correlation between climate variables and production was assessed visually through scatter plot bubble size and positioning, rather than through formal statistical testi …