Professor Geoff Hinch
University of New England, Armidale
Tel: 02 6773 2202
Email – firstname.lastname@example.org
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The average annual mortality rate of adult sheep run on commercial properties is around 4%. Many factors are known to contribute to the risk of death and compromised wellbeing, such as poor nutrition, parasite infection, pregnancy and lambing. It is difficult to quantify these risks and it is also difficult to manage the impact of the risks due to the between-animal-variability that exists in large flocks and difficulty of monitoring the condition of individual animals. Welfare indicators and risk for each sheep in the flock can be assessed at key times in the year such as at shearing, pregnancy scanning, joining and weaning but there is currently little use of objective information for management decisions at these times.
The development of big data technologies facilitates modelling and prediction of the major risks to wellbeing that are largely determined by climatic condition both historic and future. Regularly updated models predicting pasture production, parasite development cycles and risk of flystrike can produce accurate information for better nutritional management and parasite prevention.
The hypothesis behind the research program is that better use of information on body condition, weight change, genetic background and previous production history can be used in conjunction with bio-physical models to better manage sheep and improve both wellbeing and productivity.
There are two areas of research that contribute to a new approach to better management of sheep for improved wellbeing outcomes.
Activities under R1.1 focus on development of a web-based app that utilises climate data, supplied by the Bureau of Meteorology, to drive predictive models for a range of ‘environmental’ risks that include: worms; flies; heat stress; extreme cold; and pasture production (feed sufficiency). The app will also provide predictions regarding susceptibility of different segments of the flock to different risks as well as identifying well adapted and productive animals.
Activities under R1.2 include new technologies for monitoring animal wellbeing such as: video image analysis (behavioural change, dags and flystrike); body temperature change; and automated body condition measurement.
It is anticipated that the outputs from the two Projects will be integrated under a single set of utilisation activities (U1.1), to allow proactive management of wellbeing through understanding the risk faced by individual animals at different times during the year.
Through web-based data management and the collaboration with companies specialising in on-farm data collection, new algorithms for data use will be incorporated into a data platform and proprietary software to allow processing of information and subsequent delivery of decision support information to producers. This will provide a major advance in the ability to use livestock data and information to manage sheep according to their needs and production potential.
The concept of using physiological measures such as cortisol levels in the wool fibre to better understand stress in relation to changes in productivity will also be investigated.
The development of monitoring systems that can be used in conjunction with risk analysis and production data will also facilitate better management and culling decisions. Monitoring systems development will include support of Participants involved in the development and evaluation of walk-over weighing and investigation of prediction of condition score using 3-D image analysis of sheep post-shearing.
The program will also review the welfare standards for grazing animal management in terms of the expectations of our major trading partners.
The program is designed to reduce on-farm mortalities from 4% to 3% in adult animals in the same year that the management system is introduced and it is anticipated that there will be flow-on benefits in terms of better lamb survival and increased productivity. Gains in productivity through within-flock selection are expected to increase average gross margin by around $3 per ewe within three years of commencement. It is anticipated that there will be increased expenditure on measurement and data analysis ($0.90/ewe), as well as additional costs of supplementary feeding ($6/ewe for 20% of the flock). Benefits from better management decisions leading to reduced mortality are anticipated to be realised in the same year that practice change occurs with an estimated value of $1.0/ewe under management. Benefits resulting from improved productivity are predicted to increase over a 10 year period from first application of the CRC outputs. A whole of industry benefit is also expected through active communication about our innovative approach to enhance sheep wellbeing.