Visual vs Data Based Sheep Classing: How Using Data Produces Better Results

Before EID tags and sheep management technology were developed, farmers depended solely on visual assessments to classify sheep. Even now, many businesses continue to use this method to evaluate their animals.

While there’s still a place for visual classing, the practice is highly subjective which leaves plenty of room for error. Ram buyers and commercial producers are also becoming savvier and realise the importance of ASBV’s and measured data in ram selections. Studs that aren’t able to provide this information are being left behind.

Studies have shown that objective classification, using data collected with the help of EID tags and Pedigree and Performance recording Software, consistently produces better results.

In this article, we’ll look at one such study that highlights the advantages of using objective data to class your sheep.

About The Study

The study was published by CSIRO and aimed to compare how visual classers performed against measured data. Here’s how the study was constructed:

• It included 3,725 merino sheep from 370 sires and ten bloodlines.
• All sheep were classed at 18 months old. 
• Classers were provided with greasy fleece weight, fibre diameter, and body weight for each sheep if they wanted to use them.
• Sheep were classed into tops, flock, and culls.

Performance Of Visually Classed Sheep

The study found that the heritability of classing grades given to sheep by the visual classers (tops, flock, or cull) was only 0.12, which is extremely low. With results like this, it would take you upwards of 20 years before you saw a noticeable improvement in the performance of your animals. There was also a phenotype variance of 0.69, which means the visual appearance of the sheeps progeny varied wildly.

The classer grades were mostly correlated with wool style, wool colour, and body weight. This isn’t surprising as these are the traits that are most easily judged with a visual assessment. On the other hand, the classing grade had no association with the measured fleece traits. This includes metrics like greasy fleece weight and fibre diameter, which are the traits that drive profitability for a wool flock.

Visual Classing Compared To Data Based Sheep Classing

As part of the study, all the sheep were ranked using a 12% Micron Premium Index derived from measured data and Sheep Genetics. Below is a chart that compares the average fleece value of the classer’s tops compared to the average fleece value of the index’s tops.

The y-axis lists the average value per fleece while the x-axis lists the various bloodlines that were classed.

As you can see, the index consistently outperformed the visual classers across all bloodlines. In each case the index selected sheep with a higher average fleece value, in some cases by a significant margin.

You’ll see similar results when you compare the classers’ culls to the index’s culls.

The classers’ culls had a higher average fleece value across the board, showing that the index was more effective at identifying poor-performing sheep.

Conclusion

As we mentioned before, visual assessments should still factor into the classification process. However, as the data shows, if you’re not using some kind of measured data and Sheep Genetics indexes then you’re likely leaving a lot of money on the table. 

Our recommendation is this: when you can measure something measure it. When you can’t, use a scoring system combined with a visual assessment. The combination of both these approaches is the winning formula.