In my last post, I talked about how I used NSIP data to analyze which performers were the highest and lowest in my herd and compare individuals against each other. But the accuracy of those numbers is still somewhat low, so I also rely on other judgment to assist in decision making. And, there are several “soft” decision factors, or subjective things which can’t easily be quantified in a metric.

I chose to prioritize BWT over other metrics, since I really want to increase my birth weights. Everyone would have their own priorities in this regard, what things they want to work on, or whether they just want to focus equally on all the metrics.

I also (mostly) prioritized keeping mature ewes over ewelambs born this year. Mature ewes are more likely to twin or triplet, have easier births, and do a better job mothering. So they are more productive, with less risk, in the immediate term. A ewelamb, therefor, needs to have strong scores to displace a mature ewe in my flock.  This is a balance: new ewes with high scores are likely to move the genetic ball across the field faster than sticking with the same old batch of aged ewes. But a big group of new ewes can lower near-term profitability if many of them single, have small twins, or make mothering mistakes in their first year.

I took into consideration a few traits which can’t easily be measured. Foot health, for one. Temperament is important to me, too, though I don’t currently have any animals I’m unhappy with there (save the Jacob ewe, who was culled because of that, a ruined udder, and unthrifty condition). Hair coat quality is something to watch, as are scurs (partial stubs of horns), and teeth problems. And I do consider a few conformational traits, if the numbers are otherwise fairly close: long bodies, big “hams” and moderate frames. I culled an otherwise nice ewe because I just couldn’t keep weight on her <sigh>.

Retaining genetic diversity is also a consideration for me. Solely using metrics for selection can lead you down a path of concentrating-up on a whole bunch of sisters and closely related animals. This may lead to the quickest genetic advances in the places where we can measure; but it introduces vulnerability for things we can’t measure. They could all be more susceptible than average to some disease or condition of which I’m not yet aware; causing a catastrophic loss in a future year. So, in cases where two ewes were close, I’d sometimes favor the one that has fewer relatives already in the herd.

And there were a couple of ewes I forgave for losses. One two-year-old had triplets unattended and something went wrong. I only managed to salvage one, and he was an orphan rear because I couldn’t return him to his mother. The NSIP model penalized her severely for this, as well as her yearling daughter, who had twins, and lost one to a likely case of enterotoxemia (which could have even been caused by vigor and heavy milk supply-good genes gone bad). I suspect that sometimes, this is just bad luck, and these ewes may salvage their scores if subsequent years go fine. NSIP does assume that a trend of losses is a reflection of poor genetic capability, however, so more losses for these ewes would put their scores in the toilet.

So this is how I pick replacement stock for me, based on my goals and business model. Next I’ll talk about what I think it looks like from the other end: what I think buyers should consider when choosing breeding stock to purchase.