Assessment of Reliability of Secondary Traits in Selecting for Improved Grain Yield in Drought and Low‐Nitrogen Environments

Abstract

Grain yield of maize {Zea mays L.) has low heritability under low soil nitrogen (low N) and drought, necessitating the use of secondary traits with strong associations with yield for selection. A base index involving anthesissilking interval, plant and ear aspects, ears per plant, and stay green characteristic is used for selection for yieid under drought and low-N stresses. Reports are contradictory on the reliability of stay green characteristic for selecting for yield under drought stress and of ears per plant and anthesis-silking interval in selecting for low-N tolerance. Ninety extraearly inbreds were evaluated for 2 yr at three locations in Nigeria under low N and drought to confirm reliability of stay green characteristic for selecting for drought tolerance and of ears per plant and anthesis-silking interval for low N. Plant aspect and plant and ear heights were identified as the most reliable traits for simultaneous selection for yield under low N and drought in the extra-early inbreds. Stay green characteristic was unreliable for selecting drought tolerant genotypes while ears per plant and anthesis-silking interval were not among the reliable traits for selecting low-N tolerant genotypes. Ear height, plant and ear aspects, and stay green characteristic were identified by both path-coefficient and genotype main effect plus genotype x environment interaction (GGE) biplot analyses as reliable for selecting for low N and ear aspect, plant height, and anthesissilking interval for drought tolerance.

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

“…This result suggests that a hybrid with poor score for husk cover and larger ear number per plant is likely to have low grain yield. An earlier report by Badu-Apraku et al (2012), showed similar findings except for the negative correlation between grain yield and ear number per plant. The differences in results could be due to the environmental conditions (drought and low soil nitrogen environments) under which their experiments were conducted as against the environment (favourable condition) that was used in this study.…”

“…Filipovic et al (2014) reported highly significant genetic and phenotypic direct effects of plant height on grain yield. Also, plant height was reported by Badu-Apraku et al (2012) to have a high significant influence on grain yield under drought. Days to silking (0.52), days to anthesis (0.58) and anthesis-silking interval (-0.05) were identified as traits with indirect effects on grain yield via plant height (Fig 1), suggesting that improvement in yield could be achieve indirectly by simultaneously selecting hybrids with increased plant height, longer days to anthesis and silking, and reduced anthesissilking interval.…”

“…This suggests that prevailing environmental conditions at the evaluation sites during the period of this experiment may have been the greatest cause of variability observed among the hybrids for grain yield. Several field evaluations of maize varieties have revealed existence of environmental influences on yield and many important yield components (Filipovic et al, 2013;Fakorede et al, 2011;Badu-Apraku et al, 2012) and as such the need to identify traits associated with improved grain yield performance under target growing conditions.…”

“…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).…”