Diversity and path coefficient analysis of Southern African maize hybrids

Abstract

Detailed knowledge on genetic diversity among germplasm is important for hybrid maize (Zea mays L.) breeding. The objective of the study was to determine genetic diversity in widely grown hybrids in Southern Africa, and compare effectiveness of phenotypic analysis models for determining genetic distances between hybrids. Fifty hybrids were evaluated at one site with two replicates. The experiment was a randomized complete block design. Phenotypic and genotypic data were analyzed using SAS and Power Marker respectively. There was significant (p < 0.01) variation and diversity among hybrid brands but small within brand clusters. Polymorphic Information Content (PIC) ranged from 0.07 to 0.38 with an average of 0.34 and genetic distance ranged from 0.08 to 0.50 with an average of 0.43. SAH23 and SAH21 (0.48) and SAH33 and SAH3 (0.47) were the most distantly related hybrids. Both single nucleotide polymorphism (SNP) markers and phenotypic data models were effective for discriminating genotypes according to genetic distance. SNP markers revealed nine clusters of hybrids. The 12-trait phenotypic analysis model, revealed eight clusters at 85%, while the five-trait model revealed six clusters. Path analysis revealed significant direct and indirect effects of secondary traits on yield. Plant height and ear height were negatively correlated with grain yield meaning shorter hybrids gave high yield. Ear weight, days to anthesis, and number of ears had highest positive direct effects on yield. These traits can provide good selection index for high yielding maize hybrids. Results confirmed that diversity of hybrids is small within brands and also confirm that phenotypic trait models are effective for discriminating hybrids.

Linear associations among phenological, morphological, productive, and energetic-nutritional traits in corn

“…These results are similar to those reported by Ghimire & Timsina (2015). EW showed a positive correlation of r = 0.993 and r = 0.991 with GY for early maturing and super-early maturing genotypes, respectively, corroborating the results of Mhoswa et al (2016) and Ghimire & Timsina (2015).…”

“…In early maturing genotypes, the estimates of phenotypic correlation ranged from r = -0.847 between FF and EI and between FF and NE to r = 0.993 between EW and GY (Table 3). A positive correlation among EW and GY (r = 0.64) was also found by Mhoswa et al (2016). In super-early maturing genotypes, the estimates of Pesq.…”

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“…The negative direct effect of plant height on the grain yield of this finding at Bambasi is similar with the pervious finding Sreckov et al (2011) while partially contradicted with the finding of Kumar et al (2006) and Patil et al (2016) who reported that plant height and EBM showed positive direct effect on the grain yield. The positive direct effect of root biomass on the grain yield agreed with the findings of Mhoswa et al (2016) and Silva et al (2016) who reported that root dry matter and root fresh weight had positive direct effect on grain yield which directly related with the nitrogen absorption capacity of the crop that helps to afford the nutrient demand of the crop and increasing the yield. Therefore direct selection of these traits is advisable for improving the grain yield.…”

Dealing with multiple traits in maize: A new approach for selecting progenies

“…Understanding the direct and indirect effects on a main trait according to the variation of the others (explanatory) can help in the weighting of the traits for the selection indices and in the estimation of the correlated response in breeding programs (Mhoswa et al., 2016; Rios, Borém, Guimarães, & Paes, 2012). In path analysis, to declare that the effect of a primary trait is significant, and that the trait is a candidate for combined selection, the estimate of the direct effect on the main trait must be greater than the residual effect.…”