Traits association, path analyses and multiple linear regression estimates in rice (Oryza sativa L.)

Rice yield is the ultimate target of any plant breeder that needs to be increased for fulfilling the growing population demand. Yield is a complex character in nature which is directly and/or indirectly influenced by other traits. As a result, screening of segregating breeding lines based on only yield may mislead selection in the breeding programs. So the research objectives were to measure the association existed among yield and yield related traits, measuring the direct and indirect effects of yield associated traits on grain yield and estimate the influence of secondary traits in determining yield of rice. Forty seven rice genotypes including BINA dhan10, BINA dhan17, NERICA mutant and rest developed genetic materials were planted following randomized complete block design using three replications at BINA complex from July 2015 to December 2016. The statistical program MSTAT-C, BASICA and multiple linear regression analyses model implemented in R were used to analyze the data. Traits association analyses suggested that yield plant-1 had highly significant and positive association existed with plant height (rp: 0.577, rg: 0.591) followed by active tillers number plant-1 (rp: 0.372, rg: 0.364) and tillers number plant-1 (rp: 0.337, rg: 0.342) and negative significant association with days to first flowering (rp: -0.095, rg: -0.094). Plant height had the highest positive direct effect (0.685) and tillers number plant-1 (-0.364) showed maximum negative direct effect towards yield plant-1 as revealed from path analyses. The multiple linear regression analyses showed that the change of 1cm of plant height could influence about 0.19 gm rice yield plant-1 as per Model 1 and 0.16 gm rice yield plant-1 as per Model 2. Measured plant height, active tillers number plant-1 and days to first flowering could be more influencing traits for selecting breeding lines in segregating generations to develop high yielding of rice genotypes. [Fundam Appl Agric 2019; 4(4.000): 1019-1024]