Sequential Path Model for Determining Interrelationships among Grain Yield and Related Characters in Maize

Abstract

independent variables) (Li, 1975). The proportion of variance in the response variable explained by variance Knowledge of interrelationships between grain yield and its conin the predictor variable (partial coefficient of determitributing components will improve the efficiency of breeding programs through the use of appropriate selection indices. Previous path analy-nation) is the square of the path coefficient. ses studies in maize (Zea mays L.) treated yield components as first-In most studies involving path analysis, researchers order variables. The present study, based on evaluation of 90 expericonsidered the predictor characters as first-order varimental maize hybrids (comprising one diallel and one line ϫ tester ables to analyze their effects over a dependent or reset) at two locations in India, utilizes a sequential path model for sponse variable such as yield (Xu, 1986;Han et al., 1991; analysis of genetic associations among grain yield and its related traits Simon, 1993;Agrama, 1996;Board et al., 1997; Kumar by ordering the various variables in first-, second-, and third-order et al., 1999). This approach might result in multicollinpaths on the basis of their maximum direct effects and minimal collinearity for variables, particularly when correlations earity. The sequential path model showed distinct advantages over the among some of the characters are high (Hair et al., 1995; conventional path model in discerning the actual effects of different Samonte et al., 1998). There may also be difficulties predictor variables. Two first-order variables, namely 100-grain weight and total number of kernels per ear, revealed highest direct effects in (i) interpretation of the actual contribution of each on total grain weight (p ϭ 0.74 and p ϭ 0.78, respectively), while ear variable, since the effects are mixed or confounded belength, ear diameter, number of kernel rows, and number of kernels cause of collinearity (Hair et al., 1995), and (ii) suppleper row were found to fit as second-order variables. All direct effects mentation of unique explanatory predictions from were found to be significant, as indicated by bootstrap analysis. Test additional variables. Samonte et al. (1998) adopted a for the goodness-of-fit revealed that the sequential path model prosequential path analysis for determining the relationvided better fit to various datasets analyzed in the study. Correlations ships among yield and related characters in rice (Oryza between the predicted values of various response variables in the sativa L.) by organizing and analyzing various predictor second season dataset based on the path coefficients of the first season variables in first-, second-, and third-order paths. Howwere high, except for ear length and number of kernels per row. The ever, the collinearity of predictor variables was not tested applicability of the model has been confirmed through analysis of two additional datasets during 2000. The results indicated the utility for their agronomic performance during the monsoon season

Trait association for improved grain yield of extra-early maturing maize hybrids evaluated in the forest and transitional zones of Ghana

“…Also, the use of secondary trait with grain yield, rather than selection for grain yield alone, has been shown to increase selection efficiency in maize grown under stress by 20 to 50 % (Bänziger and Lafitte, 1997;Edmeades et al, 1997;Chapman and Edmeades, 1999). Traits such as number of grains per row; 100-grain weight, cob diameter, plant height, ear weight and ear length, are commonly used to predict grain yield (Ross, 2002;Mohammadi et al, 2003;Maman et al, 2004;Samonte et al, 2006). Yield related traits such as biomass, harvest index, plant architecture, and resistance to biotic and abiotic constraints may also indirectly affect yield by affecting the yield component or by other unknown mechanisms (Quarrie et al, 2006).…”

“…As the number of traits under consideration in a breeding programme increases, it becomes challenging to determine which traits are most important to yield improvement. In this case, a breeder's in-depth knowledge of interrelationships between grain yield and its contributing components can significantly improve the efficiency of the breeding programme through the use of appropriate selection indices (Mohammadi et al, 2003).…”

“…A review of other research indicates a special importance to determining relationships between yield and its components and also emphasizes on, the type of germplasm used, environmental variations and applied statistical analyses used in this kind of study ultimately influence the results, interpretations and conclusions drawn (Kumar and Kumar, 2000;Mohammadi et al, 2003;Fakorede et al, 2011;BaduApraku et al, 2012;Filipovic et al, 2014). Statistical analyses commonly used to study the existing relationships among traits in crop science include simple correlation analysis, linear regression, factor analysis and path analysis (Mohammadi et al, 2003;Badu-Apraku et al, 2012;Filipovic et al, 2014).…”

“…Statistical analyses commonly used to study the existing relationships among traits in crop science include simple correlation analysis, linear regression, factor analysis and path analysis (Mohammadi et al, 2003;Badu-Apraku et al, 2012;Filipovic et al, 2014). Simple correlation studies measures the degree of association between two random variables independent of other variables to be considered.…”

“…Multicollinearity can cause strange results when attempting to study how well individual predictor variables contribute to an understanding of the dependent variable as the effects of the predictor variables are usually confounded and this may lead to incorrect conclusion of the relationship between independent and dependent variables (Sharma, 1996;Hoe and Kim, 2004). To address this issue, sequential path analysis has been widely used by researchers for determining the relationship between yield and related traits in maize (Agrama, 1996;Mohammadi et al, 2003;Badu-Apraku et al, 2012) and in other crops such as rice (Samonte et al, 1998;2006) and sorghum (Maman et al, 2004;Ezeaku and Mohammed, 2006).…”