- This event has passed.
From data to research and decisions – the experience of New Zealand pasture-based dairy farms
Mark Neal
Head of Data Science
Cnr Ruakura & Morrinsville Roads | Newstead | Private Bag 3221| Hamilton 3240 | NEW ZEALAND
Mob +64 27 807 0165
Bio – Mark Neal
Mark Neal works at DairyNZ, where the purpose is to progress a positive future for New Zealand dairy farming, where grazing pasture forms the basis of the sector. The organisation delivers research, development, extension and advocacy. Mark’s role is Head of the Data Science and Modelling team. He has been a key part of DairyNZ’s Modern Science Workflows project, which has transformed DairyNZ’s ability to manage big data and enabled the use of new techniques, like machine learning. This team primarily supports the Research and Science team, but also has a wider role in the organisation to provide advanced analytical capability where it is needed. He comes from a strong grounding in dairy farming, having worked in management roles on his family’s farms in Australia, and on pasture-based farms in Chile and Missouri. Prior to DairyNZ, Mark has had research roles at the University of Queensland and University of Melbourne. Researchgate and Linkedin.
From data to research and decisions – the experience of New Zealand pasture-based dairy farms
In this presentation, Mark will describe some of the data-centric research and development in pasture-based dairy farm systems of New Zealand, where a lot of workflows have been improved, with models and tools developed to help both researchers, rural advisors and dairy farmers get the most out of their system. Some of these are research tools, and others are farmer-facing. Of the farmer-facing tools, we have a large back catalogue (see here), with modern web-first coding such as our Breeding worth tool, Econ tracker tool , and Pasture Potential tool, with many of our major reports now online and interactive with data download options (e.g. Dairy Statistics). We also have a view on what are the tools that farmers, rural professionals and researchers use that would be of high value, which are enabled by connected data, machine learning or generative AI tools. Over the next year or so we will be developing our first LLM RAG-based solutions for farmer and research audiences, and further improving our research data capture (100% paperless/digital first to warehouse, with 48-hour validation and correction). Come and share some ideas!