Nuffield scholar uses EID to boost bottom-line

Hannah Marriott is convinced the electronic identification of sheep increases farm productivity and ultimately boosts producers’ bottom line.

She manages “Yarallah”, a family-owned self-replacing sheep and cattle property at Greta.

Since 2015, Ms Marriott has been recording the lifetime data of the Merino flock’s six genetic lines for weaning weights, weight gains, lamb survival, and slaughter weight.

Her data collection method was revealed at the recent Best Wool Best Lamb conference in Bendigo, where Ms Marriott explained she connected the “tag bucket file”, or individualised electronic identification codes, with visual tag numbers on a computer spreadsheet.

At lamb marking, Ms Marriott scanned individual sheep and recorded the results from mobs of maiden and older ewes, as well as correlated ewes’ condition scores with kilograms of lamb produced.

“There was a range in condition but it didn’t correlate to a scanning percentage in lamb,” she said.

“It might indicate feeding ewes more to get a higher condition doesn’t necessarily see higher scanning percentages.”

As part of her 2014 Nuffield Australia Farming Scholarship, where she investigated individual animal management in sheep production in New Zealand, South Africa, United Kingdom, Ireland and Denmark, Ms Marriott said the true value of data collection was revealed.

SCOTTISH SUCCESS: Ms Marriott observed this flock of easy care sheep, derived from a maternal Scottish blackface with exceptional carcase attributes in Scotland.

SCOTTISH SUCCESS: Ms Marriott observed this flock of easy care sheep, derived from a maternal Scottish blackface with exceptional carcase attributes in Scotland.

“(While) on a farm in NZ, they compared maiden ewes to older ewe because they knew the older ewes would milk better so the lambs would likely be heavier at weaning,” she said.

“But lambs that were born to maiden ewes only really had to be 37 kilograms to achieve 80 per cent in lamb, whereas lambs born from a mature ewe needed to be around 40kg.

“Genetically, the lambs from maidens were probably better but didn’t get enough milk – the life data which you can’t see can help make some pretty important decisions.”

She said this information supported the genetic selection of favourable traits, such as fertility, number of lambs born, lamb survival, lactation and lamb growth rate.

In the past two years of measurement, Ms Marriott found the top 25 per cent of ewes were more than twice as efficient as the bottom 25pc of ewes, under identical management.

She said linking processed carcase data back to on-farm production was critical in meeting market specifications, controlling penalties, while achieving productivity gains.

“We had data on the percentage of wethers’ turned-off, ranging from 60-70kg, (as well as) the different values of those wether lambs, dressing percentages, carcase liveweight, lean meat yield, fat score and (carcase) compliancy,” Ms Marriott said.

“I made use of that (data) by converting it to a dressing percentage, which had a 20pc variability and at 600 cents a kilogram, that is quite a lot of money.

“Using Livestock Data Link gives direct feedback of what non-compliant animals cost and how many fell in the specifications, allowing informed changes for next year.”

Individual data is also connected to wool values per mob.

“With the lamb value, wool value and growth rate, we have an economic value per animal so we can make informed decisions,” she said.

Ms Marriott said having the ability to identify the animals that fall below the average on an economic measure allowed for greater gains through more precise selection pressure.

She said the data collected captured the operation’s range of production and average production of the flock, leading to accurate selection on genetically superior stock over inferior stock.

“It also allowed identification of production that you can’t see, such as weight gain per day or what ewe the lamb was from, or what the ewe scanned last year,” she said.

“These are all things you can’t see that are quite critical.

“It has helped us get where we would like to go.”

However she said learning to collect, and importantly use the data, has been a learning curve.

“What I have learnt, and am still learning, is that data analysis, using it and making sense of it, takes a lot of time,” she said.

“There will be things that you measured and don’t need and others you find that you haven’t measured the next year - it is an evolving thing.”