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

Thesis Abstract

Climate variability significantly influences agricultural productivity, with fluctuations in temperature, rainfall, humidity, and solar radiation directly affecting crop yields. Amid rising concerns about climate change, understanding the specific impacts of climate variables on crop yield variability in agricultural regions is critical for developing adaptive strategies to enhance food security and sustainable farming practices. This study aims to empirically assess the extent to which selected climate variables influence crop yield variability in agricultural regions, with specific objectives to quantify the relationship between temperature, rainfall, humidity, and solar radiation with crop yields; to identify the most influential climate variables affecting different crops; and to develop predictive models for crop yield based on climate data. Employing a mixed-methods research design, the study integrates quantitative analysis with qualitative insights. The quantitative component utilizes a correlational research approach, analyzing secondary data collected over a ten-year period (2012–2021) from 50 agricultural districts within a specific region characterized by diverse cropping systems. The study population comprises farms with documented crop yields and corresponding climate data. A stratified random sampling technique was adopted to select 500 fields, ensuring representation across different crop types such as maize, rice, and millet. Climate data were obtained from regional meteorological stations, while crop yield data were sourced from official agricultural records. Primary data collection involved structured questionnaires administered to farm managers to capture contextual factors influencing yield variability. The validity and reliability of data collection instruments were established through pretesting, Cronbach’s alpha (? = 0.87), and expert review. Quantitative data were analyzed using multiple linear regression models to determine the strength and significance of relationships between climate variables and crop yields, with model diagnostics performed to check for multicollinearity, heteroscedasticity, and normality. The study further employed advanced techniques including time-series analysis and ARIMA modeling to forecast future crop yields based on climate trends, complemented by thematic analysis of qualitative interviews to explore farmers’ adaptive strategies and perceptions. The anticipated findings suggest significant correlations between temperature fluctuations, irregular rainfall patterns, and crop yield variability, with humidity and solar radiation also contributing to yield outcomes. The regression analysis is expected to identify temperature and rainfall as the strongest predictors within the models, with the predictive models demonstrating potential for early warning systems. These results will contribute to the theoretical framework by extending ecological and climate adaptation theories—specifically, the Climate Resilience Model and the Crop Growth Model—by integrating empirical data on climate-yield relationships and adaptive responses among farmers. This research provides novel insights into the regional impacts of climate variability on crop productivity, filling existing gaps by combining extensive empirical climate data with farm-level yield records and farmers’ experiences. The findings will inform policymakers, extension services, and agricultural stakeholders on the critical climate variables requiring targeted intervention to mitigate yield losses. The study underscores the importance of integrating climate data into agricultural planning and proposes a suite of adaptive strategies, including crop diversification and water management practices, to enhance resilience. In conclusion, the study affirms that climate variables exert substantial influence on crop yield fluctuations, and predictive models developed can serve as valuable tools for strategic planning. Recommendations include the development of localized climate-based advisories, investment in climate-resilient crop varieties, and strengthening data collection systems for continuous monitoring. Future research should explore the socio-economic dimensions of climate adaptation and extend analyses to incorporate more diverse climatic zones and cropping systems, thereby broadening the scope of understanding climate-agriculture dynamics within the broader context of climate change adaptation.