Jadonsofficiall/Nigerian-Post-harvest-loss-Analysis

Post harvest loss Analysis in Nigeria (A Statistical documentation) by Jahbuikem Anderson — Data Community Africa. A 6-week Data Analytics Project. Tools used includes; Microsoft Excel (EDA), Descriptive Statistics, Visual Analytics. # Nigerian-Post-harvest-loss-Analysis Post harvest loss Analysis in Nigeria (A Statistical documentation) by Jahbuikem Anderson — Data Community Africa. A 6-week Data Analytics Project. Tools used: Microsoft Excel (EDA), Descriptive Statistics, Visual Analytics. ### Introduction Nigeria continues to experience substantial agricultural losses after harvest, driven by inadequate storage, poor logistics, and environmental factors. This report statistically analyzes post-harvest loss (PHL) patterns across crops, regions, and influencing factors such as technology use, training, and market access. The analysis covers six major crops — Cassava, Maize, Yam, Rice, Tomato, and Pepper — across six Nigerian geopolitical zones. ### Methodology - Data Source: Nigerian post-harvest dataset (aggregated from survey-based estimates) from Data Community Africa. - Analytical Tools: Descriptive statistics, percentage analysis, and comparative analysis. - Key Variables: PHL_Percent (post-harvest loss rate per crop and region), Revenue Loss (₦), Storage Method, Market Access, Training, and Technology Adoption. ### Descriptive Statistics & Results - Production & Spoilage | Metric | Observation | Interpretation | :--- | :------: | ----: | | Highest PHL% Crop | Tomato (48.33%) | Indicates high perishability and poor preservation | | Average Spoilage (KG) | 38.61 kg | Moderate spoilage across crops | | Regional Variation | North East highest spoilage | Due to poor infrastructure and climatic instability | | Storage Method Effect | No significant variation | Spoilage levels similar across storage types | - Youth Involvement and Training | Factor | Observation | Statistical Inference | :--- | :------: | ----: | | Training vs Spoilage | Trained farmers recorded slightly higher spoilage | Indicates low training effectiveness | | Technology use | 19.05% PHL (tech users) vs. 19.38% (Non-users) | Technology has mild but positive effect | | Youth involvement | No significant im …