SUMMARYThis work was undertaken to look into the interrelationships among morphological traits and rice grain shape. For this purpose, a set of 25 rice genotypes was sown and subjected to a farm survey, based on the standard evaluation system for rice. Correlation coefficient analysis showed that the grain shape was positively correlated with grain length, panicle length, plant height and the number of tillers while, there were statistically significant and negative correlations between grain shape with maturity date, number of grains per panicle, grain breadth, 100-grain weight and flag leaf width. Sequential path analysis revealed that grain breadth, grain length and number of grains per panicle, as first-order variables, was responsible for about 98% of the variation in grain shape. Also, 100-grain weight, maturity date, number of tillers and flag leaf width were determined as second-order predictors.Amongst second-order predictors, 100-grain weight was a noteworthy trait regarding its high direct and indirect effects on grain breadth and grain length. Study of multicollinearity measures revealed that sequentializing of predictor variables reduced problems due to multicollinearity leading to a better understanding of the interrelationships among the various traits and their relative contribution. Also, the bootstrap analysis indicated that all direct effects were significant.The results suggested that grain breadth, grain length and number of grains per panicle, as first-order predictor variables had the highest direct effect on grain shape and could be used as a selection criterion to improve rice grain shape. Also, 100-grain weight, maturity date, number of tillers and flag leaf width, as second-order predictor variables affect the rice grain shape indirectly through their effects on first-order predictors. The authors recommend for the use of sequential equation modeling to conduct a proper sequential path analysis. Keywords INTRODUCTIONRice appearance is a character considered as one of the main quality attributes by consumers therefore, measuring and understanding factors influencing rice grain appearance is a great challenge for industries and breeders in meeting consumer preferences (Haider et al., 2014). In genetics, grain shape has been widely accepted as a complex trait controlled by multiple genes with small effects (Yin et al., 2015). Thus, understanding the complexities governing the relationships among traits leads to increased selection gain in breeding programs. In this regard, correlation coefficients, multiple linear regression and path analysis are some of the common statistical methods. Determination of correlation coefficients is an important statistical procedure to examine the relationship between traits. However, increase of the number of independent variables controlling a particular dependent variable can lead to increased interdependence. In such situation, correlations may be insufficient to explain the associations in a way that will enable breeders to decide on a direct or indir...
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