Assessing the Impact of Seasonal Variations on Agricultural Yield Forecasting Accuracy

Thesis Abstract

Accurate agricultural yield forecasting is crucial for food security planning, resource allocation, and policy formulation, yet the influence of seasonal variations on the precision of these forecasts remains inadequately understood. This study investigates the extent to which seasonal fluctuations impact the accuracy of crop yield predictions, with a specific focus on maize and rice production within a peri-urban agricultural zone. The primary aim is to assess the relationship between seasonal climatic variables and forecasting performance, and to identify the key seasonal factors contributing to prediction discrepancies. The research aims to address three specific objectives (1) evaluate the variability in yield forecast accuracy across different seasons; (2) determine the influence of seasonal weather patterns—such as rainfall, temperature, and relative humidity—on predictive models; and (3) develop an improved forecasting approach that accounts for seasonal effects to enhance accuracy. A mixed-methods research design was adopted, combining quantitative analysis of yield forecasting models with qualitative interviews from local agricultural experts. The study population comprises agronomists, meteorologists, and farmers operating within the selected peri-urban zone, totaling approximately 150 respondents. A stratified random sampling technique was employed to select a sample of 60 farmers and 20 experts, ensuring diverse representation across different farm sizes and experience levels. Quantitative data were collected through structured questionnaires, environmental data were obtained from local meteorological stations, and historical crop yield records spanning ten growing seasons were reviewed to facilitate longitudinal analysis. The validity and reliability of the instruments, including the yield forecast models and survey questionnaires, were established through pilot testing and Cronbach’s alpha coefficients exceeding 0.8, indicating high internal consistency. Data analysis involved multiple regression analysis and ANOVA to test the significance of seasonal variables on accuracy measures, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The analytical framework incorporated the use of the Grain Yield Forecasting Model (GYFM) and seasonal decomposition methods to isolate the impact of climatic fluctuations. Ethical considerations, such as informed consent and confidentiality, were rigorously adhered to throughout data collection. Expected findings suggest that seasonal variations significantly influence the accuracy of yield forecasts, with models performing less reliably during atypical weather periods, such as droughts or excessive rainfall. The study anticipates identifying specific seasonal factors—particularly rainfall variability and temperature extremes—that most strongly correlate with forecasting errors. These insights are expected to enable the development of a context-sensitive forecasting enhancement technique that integrates real-time seasonal climatic data, thus improving predictive performance across diverse seasonal conditions. This research contributes to the existing body of knowledge by delineating the seasonal dynamics that affect agricultural yield forecasting, a relatively under-explored area, particularly in peri-urban contexts. The findings are expected to augment theoretical frameworks on climate-agriculture interactions, notably the Climate Variability Theory and the Adaptive Capacity Model, by empirically linking seasonal climatic fluctuations with forecasting outcomes. Practically, the study offers a novel, adaptable forecasting approach that leverages seasonal data, facilitating better-informed decision-making among farmers and policymakers. In conclusion, the study underscores the necessity of incorporating seasonal climatic factors into crop yield forecasting models to mitigate prediction inaccuracies caused by climate variability. Recommendations include the integration of real-time seasonal climate monitoring systems into forecasting practices, targeted capacity-building initiatives for farmers on climate-adaptive techniques, and policy improvements that prioritize climate-resilient agricultural planning. The research also advocates for further longitudinal studies to validate and refine the proposed model across different crops and geographic regions.