Abstract:
This thesis deal with the assessing the effect of climate variation on tea production. The study focused on the Western Province of Rwanda, analyzing data from eight tea factories in the region that have at least five years of productivity records. The analysis utilized data spanning 38 years, from 1983 to 2021. This included secondary data on rainfall and maximum and minimum temperatures from the Rwanda Meteorological Agency, as well as secondary data on tea productivity from the Rwanda National Agriculture Export Development Board. Variations an in tea production, rainfall, and both minimum and maximum temperatures over time were analyzed using graphs created in Excel. Pearson correlation was used to identify the strength and direction of the relationship between tea production and climate variables. The multiple regression model was used to quantify the relationship between tea production and climate variables. It was employed to identify which climate variables significantly affect tea production and to assess how well these climate variables explain the variability in tea production. To ensure accuracy, Principal Component Analysis (PCA) was employed to identify the key directions of variability and to visualize the principal components that capture the most variance, thereby highlighting the contribution of each variable to these components. The result of every site has been differently to the another accordingly to it location. The Pearson correlation analysis revealed a moderate positive correlation between both minimum and maximum temperatures and tea production, while a weak positive correlation was observed between tea production and rainfall. Examination of correlation circle plots across various sites indicated consistent similarities, with minimum temperature showing a positive correlation with Principal Component One (PC1) across all locations, suggesting its general association with factors that promote tea production. Maximum temperature consistently shows a positive correlation with Principal Component One (PC1) but a negative correlation with Principal Component Two (PC2). Conversely, Rainfall positively correlates with PC2, benefiting certain tea production factors, but negatively correlates with PC1, suggesting that managing both rainfall and temperature is crucial for optimizing tea yields in the Western Province.
