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This project focuses on improving eggshell quality and laying performance in Australian layer hens by fine-tuning calcium and phytase nutrition during the pullet and late-lay phases. It will combine controlled research trials with large-scale commercial validation to generate practical, industry-relevant data.

The outcomes are expected to support bird welfare, reduce feed costs, and extend the productive lifespan of hens. Results will be translated into clear nutritional guidelines for commercial use, enabling layer nutritionists to implement more sustainable and cost-effective feeding strategies without altering routine management practices.

The project is industry-driven and designed for seamless adoption across diverse production systems.

We use advanced proteomics, novel cell culture, immunology assays and in vivo imaging techniques to investigate how ram spermatozoa and seminal plasma interact with the ewe’s reproductive tract. These studies aim to uncover immune mechanisms that influence sperm transport, survival, and fertilisation success, ultimately improving outcomes with frozen-thawed semen in artificial insemination programs.

We are developing and validating in vitro tools to predict fertility before breeding. These include advanced semen analyses (including morphology, motility, and functional assays), assessments of female reproductive status, and monitoring of environmental conditions. We are also integrating omics technologies such as transcriptomics and proteomics to identify molecular markers of fertility and build more accurate, multi-factorial predictive models.

Our group is actively developing and refining a broad suite of assisted reproductive technologies (ARTs) to support both livestock production and wildlife conservation. These include sperm sexing, artificial insemination (AI), in vitro fertilisation (IVF), embryo transfer (ET), multiple ovulation and embryo transfer (MOET), and ovum pick-up (OPU). We aim to enhance genetic gain, reproductive efficiency, and access to high-value genetics across a range of animal species.

We apply machine learning and artificial intelligence to develop next-generation reproductive diagnostics. This includes automated assessment of sperm morphology and motility, predictive models for pregnancy detection in sheep, and sensor-based monitoring of reproductive events in real time. These tools aim to improve decision-making, precision management, and reproductive efficiency across species.

We are exploring sperm metabolism to identify the cellular and molecular pathways that support sperm function during storage. This work informs the development of improved extenders, cryoprotectants, and storage protocols—both chilled and frozen—that preserve sperm viability and enhance fertility after thawing.