Assessing the Impact of Climate Variables on Crop Yield Variability in Agricultural Regions

Thesis Abstract

Climate variability has emerged as a critical determinant of agricultural productivity, with fluctuations in temperature, rainfall, humidity, and solar radiation significantly influencing crop yields across diverse regions. Despite the increasing recognition of climate's impact on agriculture, systematic assessments that quantify these effects and elucidate the relationships between specific climate variables and crop yield variability remain limited, particularly in the context of smallholder farming systems. This study aims to evaluate the impact of climate variables on crop yield variability in the Midwestern agricultural region, focusing on maize and soybean production over a ten-year period (2012–2021). The specific objectives are to analyze trends in climate variables, assess the correlation between climate factors and crop yields, identify the most influential climate variables affecting yield fluctuations, and develop predictive models to inform adaptive strategies. The research adopts a mixed-methods approach, combining quantitative and qualitative methodologies to comprehensively address the study objectives. The population comprises smallholder farmers, crop scientists, and meteorological stations within the region. A purposive sampling technique selects 200 smallholder farmers for survey administration, ensuring demographic and geographic diversity. Primary data on crop yields are collected through structured questionnaires, yield records, and remote sensing data. Climate data—including temperature, rainfall, humidity, and solar radiation—are sourced from the National Meteorological Agency’s publicly available databases, supplemented with in-situ measurements where necessary. To verify the reliability and validity of collected data, the study employs Cronbach’s alpha for internal consistency of survey instruments and cross-referencing with official meteorological data. Data analysis involves descriptive statistics to summarize climate trends and crop yields, followed by inferential techniques such as multiple regression analysis to examine the relationships between climate variables and crop yield variability. Specifically, stepwise regression models identify the most significant climate predictors, while time-series analysis evaluates trends and seasonal patterns. Variance Inflation Factor (VIF) tests address multicollinearity among predictors. The study applies the Climate Variability Index (CVI) and the Drought Severity Index (DSI) to quantify climate extremes. The analytical framework is grounded in the Climate Change Vulnerability Theory, which conceptualizes climate-induced agricultural risks, and the Resilience Theory, emphasizing adaptive capacity. Ethical considerations include obtaining informed consent from participants and ensuring data confidentiality. Expected findings suggest that temperature increases and irregular rainfall patterns significantly contribute to yield fluctuations, with drought conditions exacerbating crop stress during critical growth phases. The models are anticipated to demonstrate strong predictive capacity (R-squared > 0.75), providing valuable insights into the climate drivers of yield variability. The results will elucidate localized climate-crop relationships, thereby refining existing agronomic models and informing policy interventions targeting climate resilience in agriculture. This research contributes to the existing body of knowledge by quantitatively delineating the impacts of individual climate variables on crop yields at a regional scale, addressing identified gaps related to smallholder farming contexts and the integration of climate indices in yield prediction models. It advances understanding of climate-agriculture interactions and offers practical tools for farmers, extension agencies, and policymakers to optimize adaptive strategies and mitigate climate risks. In conclusion, the study underscores the urgent need for integrating climate data into agricultural planning and promotes evidence-based adaptive measures to improve crop resilience amid increasing climate variability. Recommendations include the development of climate-smart agricultural practices, improved early warning systems based on climate forecasts, and targeted policy support for climate resilience initiatives. Future research should explore the socio-economic dimensions of climate adaptation and extend the analysis to other crops and regions to enhance generalizability.