Advanced sensor and data analytics in pasture-based dairy systems

Summary

The focus of this PhD will be to develop and evaluate innovative methods to capture, analyse and optimise data derived from a range of technologies and sensors to optimise individual cow feeding and milk efficiency in advanced pasture based dairy systems. Data sources may include combination of soil, pasture, animal, milking machine (robots), climate; and others. This multidisciplinary project interrelates diverse areas of research including dairy science, pasture production and utilisation, complex data acquisition and analysis, machine learning and optimisation using sophisticated modelling tools. The broad scope of the project, and its interdisciplinary nature, allows emphasis in specific area/s to be placed according to candidate's background and expertise.  

The supervisory team for this project also includes Dr Reza Arablouei (CSIRO),Dr Brano  Kusy (CSIRO) and Dr Dave Henry (CSIRO).

A complimentary scholarship for this project is available. To find out more, refer to the Postgraduate Research Stipend and Supplementary Scholarship in Digital Agriculture Data61.

Supervisor(s)

Professor Sergio (Yani) Garcia, Associate Professor Cameron Edward Fisher Clark

Research Location

School of Life and Environmental Sciences

Program Type

PHD

Synopsis

There is a clear need for advanced integration and utilisation of sensor-derived data to optimise feeding management and milking efficiency of individual dairy cattle. Technologies exist that allow pasture biomass and quality to be measured, but these have not been integrated with other sources of information from the soil, the pasture, the animal, the weather and the whole system. 

This PhD will be to develop and evaluate innovative methods to capture, analyse and optimise data derived from a range of technologies and sensors to optimise individual cow feeding and milk efficiency in advanced pasture based dairy systems. Data sources may include combination of soil, pasture, animal, milking machine (robots), climate; and others.   
Different methodologies including controlled and field studies, machine learning and advanced modelling are envisaged. Due to its interdisciplinary nature, this PhD opportunity may suit candidates from a range of background areas and expertise.

Additional Information

The successful applicant will be based at the Camden campus or a combination of sites including CSIRO facilities in Brisbane and The University of Sydney in Camden or Sydney campuses as part of the School of Life and Environmental Sciences and the Sydney Institute of Agriculture

The candidate must have a valid driver license and be willing to travel to University and commercial farms and CSIRO facilities. All students and staff working at the University's dairy farm are required to be vaccinated against Q fever.

The supervisory team for this project also includes Dr Reza Arablouei (CSIRO) and Dr Brano Kusy (CSIRO).

A complimentary scholarship for this project is available. To find out more, refer to the Postgraduate Research Stipend and Supplementary Scholarship in Digital Agriculture Data61.


In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include: 

  • Confidential disclosure and registration of a disability that may hinder your performance in your degree; 
  • Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.); 
  • Ability to perform independently and/or with minimal supervision; 
  • Ability to undertake certain physical tasks (e.g. heavy lifting); 
  • Ability to undertake observatory, sensory and communication tasks; 
  • Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden); 
  • Ability to work in confined spaces or at heights; Ability to operate heavy machinery (e.g. farming equipment); 
  • Hold or acquire an Australian driver’s licence; 
  • Hold a current scuba diving license; 
  • Hold a current Working with Children Check; 
  • Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.) 
You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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Keywords

digital, Animal Agriculture, Data61, CSIRO, School of Life and Environmental Sciences, Sydney Institute of Agriculture

Opportunity ID

The opportunity ID for this research opportunity is: 2365

Other opportunities with Professor Sergio (Yani) Garcia

Other opportunities with Associate Professor Cameron Edward Fisher Clark