Vol. 8, Issue 11, Part K (2024)
Multivariate statistical approaches to enhance biochemical traits in mungbean (Vigna radiata L. Wilczek)
Atul Kumar Pachauri, Govind Gupta, Ashish Kumar and Deepak Kher
Biochemical research on mungbean focuses on its nutritional value, antioxidant properties, and potential health benefits. Studies often explore the composition of proteins, vitamins, and minerals, as well as phytochemicals like flavonoids and phenolic compounds that contribute to its health effects. Research also examines Mungbean’s role in sustainable agriculture and its ability to improve soil health through nitrogen fixation. Overall, mungbean is valued for its nutritional benefits and potential in promoting health and environmental sustainability.
In the present study 10 genotypes used for the study for the assessment of correlation coefficient and simple linear regression analysis. According to analysis for correlation Plant stand, Days to 50% flowering, Branch/Plant, Pod length, Pod/plant, Seed/plant, Plant height, Days to maturity and 100 Seed wt. was highly significant correlated with yield. The implication is correlation suggests that the relationship is not due to random chance. It’s statistically robust. In practical terms, this means that by selecting for or improving the maximum yield attributing character, you are likely to enhance the overall yield per plant. This could lead to more efficient farming practices and better resource utilization. In practical footings suggests that enhancing or selecting for this character could lead to improved yields in the crop. The situation valuable finding for breeding programs, as it identifies a key trait that could be targeted to optimize production. The simple linear regression result shows that he results can help in this study understand the extent and nature of the relationship between the two variables, which can be useful for prediction further analysis. Linear regression algorithms are used to make precise predictions, and having a large dataset can enhance the effectiveness of the decision-making model.
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