Assessing the impact of climate variability on wheat yield in Bloemfontein wheat farms through time series analysis

Climate change, characterized by long-term shifts in global or regional weather patterns, is a consequence of natural processes and human activities. These shifts encompass al-terations in temperature, precipitation, wind patterns, and other climatic variables, all of which exert direct influence on crop growth, development, and overall agricultural productivity. Comprehending the intricate relationship between climate change and crop production is par-amount for formulating strategies to counteract adverse effects and adapt to evolving conditions. This study focuses on assessing the impact of climate variability on wheat yield in Bloemfontein wheat farms through rigorous time series analysis. The research involved the application of various time series models, including SARIMA, ARIMA, Facebook Prophet, LSTM, VAR, and Multiple Linear Regression. The investigation began with forecasting temperature patterns using SARIMA and Facebook Prophet models. SARIMA outperformed Facebook Prophet in this context, as evidenced by lower RMSE and MSE metrics. Subsequently, the study delved into predicting rainfall and precipitation, employing ARIMA and LSTM models. In this case, LSTM demonstrated superior predictive capabilities. Finally, wheat production yield was analyzed using VAR and Multiple Linear Regression, with VAR yielding more accurate predictions. The findings of this study hold profound implications for policymakers, farmers, and stakeholders deeply invested in agriculture and food security. By shedding light on the repercussions of climate change on crop production through the application of time series analysis, this project aspires to contribute to developing sustainable agricultural practices, robust farming systems and proactive policies de-signed to mitigate the adverse effects of climate change on global food production.