Analyzing the Impact of Weather Variables on Agricultural Yield Variability

Thesis Abstract

The variability in agricultural yields due to fluctuating weather conditions poses significant challenges to food security and sustainable farming practices in many regions worldwide. This study aims to analyze the impact of key weather variables—namely temperature, rainfall, humidity, and solar radiation—on agricultural yield variability, with a focus on maize production within the Midwest agricultural zone. The specific objectives include quantifying the relationship between weather variables and crop yields using time-series data, identifying the most influential weather factors, and developing predictive models to assist farmers and policy makers in adaptive agricultural planning. The research adopts a quantitative, empirical design rooted in a correlational framework. The population comprises maize farms within the Midwest region, with a stratified random sampling technique employed to select a representative sample of 150 farms. Data collection instruments include automated weather station data streams, archived meteorological records spanning the past decade, and farm yield records obtained through farmer surveys and agricultural extension agencies. Data validity is ensured through calibration of weather instruments and cross-validation with national meteorological databases, while reliability is assessed via Cronbach’s alpha for survey instruments, targeting a threshold of 0.75. Data analysis involves descriptive statistics to summarize variability patterns, followed by multiple linear regression models to examine relationships between weather variables and maize yield variability. The analytical framework integrates time-series regression analysis to account for seasonal and trend effects, with model selection guided by the Akaike Information Criterion (AIC). Further, structural equation modeling (SEM) is employed to explore causal pathways, grounded in the Weather-Yield Relationship Theory and Climate Variability Adaptation Framework. These frameworks provide conceptual underpinnings for systematically evaluating how climatic factors influence crop productivity and adaptive capacity. Expected findings indicate significant correlations between temperature and humidity with maize yield fluctuations, with rainfall variability contributing substantially to yield unpredictability. The models are anticipated to identify solar radiation as a key determinant in photosynthesis rates affecting crop growth. These results aim to establish a statistically robust basis for predicting yield outcomes based on weather forecasts, thereby enhancing decision-making processes for farmers regarding planting schedules, irrigation, and pest management. This study contributes to existing knowledge by integrating localized meteorological data with farm-level yield records, offering nuanced insights into weather-induced yield variability within the context of climate change adaptation. It extends prior research by employing advanced analytical techniques such as SEM and time-series regression, addressing gaps related to the influence of combined climatic factors rather than isolated variables. Moreover, the development of predictive models tailored to maize cultivation provides practical tools for stakeholders in agricultural planning and climate resilience strategies. In conclusion, the findings underscore the importance of precise weather monitoring and data-driven decision support systems in mitigating adverse effects of climate variability on agriculture. The recommendations include adopting weather-based predictive models in extension services, encouraging the use of crop simulation tools, and promoting climate-resilient farming practices. Further research is suggested to include climate change projections and to explore socio-economic factors moderating weather-yield relationships, thereby fostering comprehensive adaptation frameworks. This study emphasizes the critical role of meteorological analysis in securing agricultural productivity amidst uncertain climatic futures.