<p>Understanding the scientific relationship between crop yield and weather variables is crucial for all stakeholders aiming to enhance agricultural production effectively. This study seeks to explore such relationships in rice production using various multivariate techniques. The analysis utilizes 72 years of data on rice yield and the associated weather variables in India, providing valuable insights for improving agricultural strategies and decision-making. Rice yield was categorized into two and three groups using three distinct methods. Linear discriminant analysis was conducted to assess the impact of weather variables on the classified rice yield. The findings revealed that variables such as winter, pre-monsoon, monsoon, and post-monsoon minimum temperatures positively influence the yield classification, whereas annual minimum temperature, winter rainfall, and pre-monsoon rainfall had a negative effect. The Pearson correlation confirms the presence of strong correlation between most weather variables and rice yield. The results are further supported by the canonical correlations which emphasize the significant association between weather variables and rice yield for both two-group and three-group comparisons across all methods. The three-category classification outperformed the two-category approach, achieving an accuracy of 93.4%. These findings highlight the effectiveness of discriminant analysis in classifying rice yields based on weather conditions, facilitating improved yield assessment and management.</p>