Developing new tools for pasture plant breeding

Abstract

The rate of genetic gain represented in the Forage Value Index of perennial ryegrass (Lolium perenne) is a major factor underpinning sustained profitability in pastoral farming. Effective new technologies for trait data acquisition and parent plant selection are used in many animal and crop improvement programmes to lift the rate of gain, but have yet to be developed and integrated in forage breeding. For forage improvement, hypotheses tested were: a) genomic selection (GS) offers a viable breeding strategy, and b) key enabling technologies for non-destructive, high-throughput phenotyping (HTP) in the field will improve trait data acquisition. To evaluate GS, extensive molecular marker and phenotypic datasets in structured populations of perennial ryegrass were developed. Phenotypic data for seasonal dry matter yield (DMY), the core trait in the Dairy NZ Forage Value Index, were obtained replicated field trials. Data on heading date (HD) as a useful trait to assess the efficacy of GS for simply inherited traits, were also collected. Genomic prediction models were developed for seasonal DMY and HD. Application of GS for HD was effective in selecting for both early and late heading, with movement of up to 7 days in a single generation of selection. The HTP research used iterative development of computational methods supporting a repeatable, non-invasive means of accurately and rapidly measuring DMY of perennial ryegrass in single row plots. These findings demonstrate effective genetic prediction and phenotyping approaches which may enable breeders to lift the rate of genetic gain in perennial ryegrass.