Browsing by Author "Ruhullah, A.B."

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    Influence of Seasonal Climate Variabilities and Phenology of Mangifera Indica on the Population Dynamics of Diptera Tephritidae in the Nadowli-Kaleo District
    (UENR, 2025-12) Ruhullah, A.B.
    Destruction of Diptera tephritidae has imposed quarantine restrictions and rapid economic losses in the horticulture industry. The nexus between agricultural productivity and climate change has garnered growing attention that requires scientific investigation. The study aims to examine the interactions between seasonal climate variability and the phenology of Mangifera indica on the population dynamics of Diptera tephritidae, anchored in climate ecology theory, phenological theory, population dynamics theory, and a systems ecological framework. Randomized Complete Block Design was adopted for the field layout, and Longitudinal studies were employed in gathering past and present data of fruit fly visitation and phenology of Mangifera indica, over a period of four production seasons. Climatic data were sourced from Ghana Meteorological Agency data base. Data analysis involved a generalized linear model and a Seasonal Harmonic Analysis. ANOVA test and multiple regression were performed, and a Random Forest regressor model was used for prediction. The model was trained on data from 2022 to 2024 and then tested to predict the fly count for the entire 2025. The study found a strong seasonal pattern in the fruit fly population. Total monthly rainfall and minimum temperature proved to be the most influential factors. In particular, rainfall showed a moderately positive correlation (r = 0.41) while minimum temperature showed a moderately negative correlation (r = -0.57). These results suggest that climatic conditions, especially during the rainy season and cooler months, play a crucial role in influencing the increase in fruit fly population. Phenology alone is insufficient as a predictor, but when considered in interaction with climatic variables especially temperature, it becomes a strong determinant. The study strongly recommends a longer-term data set and the inclusion of the fruit fly reproduction process and additional environmental variables to improve prediction accuracy.