ITClaude/Topic2

This is results analysis of topic two: Impact of Climate Variability on Crop Selection and Rotation Practices in Semi-Arid Zones: Evidence from Integrated Farmer Surveys and AI-Based Panel Data Analysis in Bugesera, Rwanda # Impact of Climate Variability on Crop Selection and Rotation Practices ## Evidence from Bugesera District, Rwanda ## Project Overview This repository contains the dataset, Python scripts, generated figures, tables, and summary outputs for the study **“Impact of Climate Variability on Crop Selection and Rotation Practices in Semi-Arid Zones.”** The analysis is based on survey data collected from smallholder farmers in Bugesera District, Rwanda, and examines how climate variability shapes crop selection, crop rotation, and related adaptation practices. ## Study Objectives The project aims to: - assess the effect of climate variability on crop selection and crop rotation practices; - identify socioeconomic and environmental factors associated with farmer decision-making; - generate descriptive, inferential, and predictive outputs from the survey data; and - provide a clear and reproducible workflow for climate-smart agriculture research. ## Dataset The repository uses a cross-sectional farmer survey from Bugesera District, Rwanda. The dataset includes variables on: - household and demographic characteristics; - farm size and agricultural inputs; - perceived and geospatial climate risk; - yield and loss outcomes; and - adaptation responses such as crop rotation and advisory access. **Sample used for analysis:** 240 valid farmer records **Primary data file:** `data/FINAL Data collected in Bugesera District-Template (1).csv` ## Repository Structure ```text Final/ ├── README.md ├── requirements.txt ├── results_summary.md ├── .gitignore ├── data/ │ ├── FINAL Data collected in Bugesera District-Template (1).csv │ └── Topic 2-Research paper.docx ├── results/ │ ├── figures/ │ │ └── Figures/ │ └── tables/ └── src/ ├── 01_data_quality_and_cleaning.py ├── 02_descriptive_analysis.py └── 03_models.py ``` ## Analysis Workflow ### 1. Data cleaning and preparation `src/01_data_quality_and_cleaning.py` - removes blank or invalid records; - standardises text-based re …