Variation in floral and growth traits in a macadamia breeding populationExport / Share PlumX View Altmetrics View AltmetricsO'Connor, K. M., Hardner, C. M., Alam, M. M., Hayes, B. J. and Topp, B. (2018) Variation in floral and growth traits in a macadamia breeding population. Acta Horticulturae, 1205 . pp. 623-630. ISSN 2406-6168 Full text not currently attached. Access may be available via the Publisher's website or OpenAccess link. Article Link: https://doi.org/10.17660/ActaHortic.2018.1205.77 AbstractMacadamias are grown commercially around the world for their edible nuts. Identifying elite macadamia breeding selections with high yield potential is difficult due to the polygenic control of the trait, and more importantly the low heritability of yield. Indirect selection for yield may be possible through identification of correlated traits that have higher heritability, and are more efficiently measured. This study aimed to investigate component traits for yield by dissecting floral and growth traits of macadamia, including: raceme length, rachis width, floret density, number of florets raceme-1, raceme density, and trunk circumference. This is a preliminary study of 143 seedling progeny from 33 families across two sites in Bundaberg, Queensland, Australia, planted between 2001-2003. Average raceme lengths were variable between families, ranging from 10.2 cm in the '344' × '804' family to 21.1 cm in 'A38' × '816' progeny. Raceme length was significantly correlated with number of florets per raceme (rp=0.85 and rg=0.91, p<0.001), whilst raceme length was negatively correlated with floret density (rp=-0.30, p<0.001). Raceme length, raceme density and number of florets raceme-1 were statistically different between families (p<0.01). This paper shows that it is useful to identify component traits that may be more efficient to measure in order to indirectly select for complex traits like yield. These are preliminary findings of a larger study in which flowering characteristics will be compared with nut and shell characteristics and yield. Additionally, these findings will inform genomic selection models for yield prediction, as well as genome wide association studies for component traits.
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