Path coefficient analysis of quality of two-row spring barley

Abstract

Malting quality is composed of numerous interacting traits with a high complexity concerning their biochemical and genetic basis. Malt extract is key indicator of barley malting quality and it is a mega-trait since it is influenced by a number of independent component traits. Understanding genetic and non-genetic factors that effects grain quality and grain yield is crucial in developing new cultivars, seed and mercantile production. Path analysis is one of the reliable statistical techniques which allow separation of the direct effect of each component trait on malt quality from the indirect effects caused by the interdependence component trait. The aim of this study was to investigate spring two-row barley quality as mega-trait depending on the component traits in the conditions of the Pannonian environments. Regression analysis with extract (EXT) as dependant and other traits (yield-YIL, test weight-TW, grain weight-GW, grading-GRA, grain protein concentration-GPC, viscosity-VIS, Kolbach index-KOL, Hartong number-HAR) as independent traits was performed out. Simple coefficient of correlations were calculated between independent traits and EXT in all pair combination and then used as inputs for path coefficient analysis. The quadratic curve fitted the best relationship between EXT and the independent traits. EXT was in positive (P<0.01) relationship with GW, GRA, KOL, and HAR with simple correlation coefficient of 0.47, 0.42, 0.39 and 0.50, respectively and in negative (P<0.01) relationship with GPC and VIS with simple correlation coefficient of -0.72 and -0.51, respectively. Path analysis explained more than 70% of the variation in EXT of which 34.3% was determined by direct negative path coefficient (P<0.01) of GPC without significant any indirect path effect. VIS negatively directly, (P<0.01) and negatively indirectly via GPC effected EXT. KOL did not have significant direct effect on EXT, but had rather prominent indirect effect via GPC, VIS and HAR. HAR positively directly (P<0.01) and positively indirectly via GPC effected EXT. The direct effect of VIS and HAR determined 13.0% and 14.1% of the variation, respectively.