Multicollinearity among predictor traits is a major challenge in path coefficient analysis, distorting the estimation of direct and indirect effects on grain yield in rice. This study addresses this issue by applying ridge regression to an M2 population of rice (Oryza sativa L.), evaluating its effectiveness in stabilizing coefficients and improving model interpretability. Severe multicollinearity was identified in the conventional path analysis model, with traits like plant height(PH), Culm height(CH), days to maturity (DM), panicle weight (PW), and grain number per panicle (GNP) exhibiting inflated Variance Inflation Factor (VIF) values (>10). Ridge regression significantly reduced multicollinearity, stabilizing path coefficients while retaining key traits. In the modified Model 6, ridge regression combined with the exclusion of highly collinear traits (e.g., plant height (PH) and days to flowering (DF)) achieved superior model fit (R2=0.870R2 = 0.870) and reduced residual effects. Total tillers (TT) and biomass yield (BY) consistently emerged as primary yield determinants, with realistic direct effects (0.340 and 0.428, respectively). Additionally, ridge regression preserved essential interdependencies among traits like PW and GNP, which were distorted in the conventional model. This study provides a robust framework for handling multicollinearity in path coefficient analysis, emphasizing the practical utility of ridge regression in breeding programs

Multicollinearity, Path coefficient analysis, Ridge regression, Correlation analysis, Predictor variables, Direct effect, Variance Inflation Factor (VIF), Rice

This study demonstrates that ridge regression is an effective method for addressing multicollinearity in path coefficient analysis. By stabilizing coefficients and retaining critical predictors, ridge regression provides robust insights into the relationships among yield-related traits. Traits such as TT and BY consistently emerged as key contributors to grain yield, highlighting their potential as selection criteria in rice breeding. The proposed approach offers a valuable framework for addressing multicollinearity in other crop improvement studies. The stability of coefficients in Models 5 and 6 underscores the importance of integrating ridge regression into path coefficient analysis. This approach allows for the inclusion of interrelated traits like GNP and PW, which are vital for understanding complex yield dynamics in rice (Hoerl & Kennard 1970; Xu et al., 2014)

Kotadiya Trunal, Singh, S.K., S. Jayasudha, Kumar Ankit and Futane Aachal (2025). Addressing Multicollinearity in Path Analysis: Insights from Ridge Regression in Rice Yield Components. Biological Forum – An International Journal, 17(1): 08-13