**5. Conclusions**

In conclusion, this study has extended our understanding of secondary metabolism associated with the biosynthesis of phytoestrogens and proanthocyanidins in annual and perennial legumes. Metabolic profiling performed with a variety of hardseeded annual pasture legumes demonstrated that phytoestrogens quantified in this study were at concentrations insu fficient to pose negative impact on livestock production with the exception of gland clover, bladder clover, and lucerne. Results of the study also sugges<sup>t</sup> that annual hardseeded pasture legumes of Mediterranean origin o ffer viable and sustainable alternative pasture options for mixed farming systems of southeastern New South Wales. In the future, the use of an integrated experimental approach including multi-omics platforms could also potentially provide deeper insights into pathway dynamics and regulation of associated genes important in the production of secondary metabolites in pasture legumes. Our findings along with those of Butkut et al., 2018 sugges<sup>t</sup> strong potential to improve legume-based forage quality through recurrent selection or engineering for species- or cultivar-specific phytochemicals [53]. In addition, optimization of livestock managemen<sup>t</sup> to reduce health and reproductive issues by selective grazing through timing of animal movement, manipulation of plant growth stage at harvest, and appropriate selection of pasture species mixtures is also warranted.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/10/7/267/s1, Figure S1: Hierarchal clustering of molecular features in leaf tissue of annual pasture legumes acquired using UHPLC-QToF-MS in positive and negative mode. Hierarchical clustering algorithm and Euclidean distance metric were used on normalized abundance using MPP (ver. 14.5 Agilent Santa Clara, CA, USA), Figure S2: Hierarchal clustering of relative abundance of flavonoids, their glycosides, and coumestrol in inflorescence tissue in pasture legumes collected in 2016. Hierarchical clustering algorithm and Euclidean distance metric were used on normalized abundance using R package.

**Author Contributions:** Conceptualization, S.L., P.A.W. and L.A.W.; Methodology, S.L., J.W.P., P.A.W. and L.A.W.; Formal analysis, S.L., P.A.W. and L.A.W.; Writing, S.L., S.G., R.A.B., P.A.W. and L.A.W.; Original draft preparation, S.L.; Review and editing, S.G., R.A.B., P.A.W., J.W.P. and L.A.W. Funding acquisition, J.W.P. and L.A.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Meat and Livestock Australia (MLA) (Grant: B.WEE.0146 awarded to L.A. Weston and J. Piltz).

**Acknowledgments:** The authors acknowledge funding support from Meat and Livestock Australia (MLA) (Grant: B.WEE.0146 awarded to L.A. Weston and J. Piltz) for field trials and the Australian Center for International Agricultural Research (ACIAR) for sponsoring the PhD fellowship awarded to S. Latif for studies associated with this project. The authors also wish to acknowledge Ulrike Mathesius of The Australian National University (Acton, ACT, Australia) for providing analytical standards of flavonoids used in this study, and Willian Brown, Graeme Heath, and Simon Flinn for their support in data collection.

**Conflicts of Interest:** Authors declare no conflict of interest, there is no connection between Plus 3 Australia Pty Ltd. and the subject of this manuscript.
