I spotted an interesting bit of data on two sibling rams that made me pause for a moment. Usually, when rams are born, they simply inherit an average of the NSIP maternal traits coming from their parents. So their scores will be identical here, and won’t typically change until those rams have female progeny, which are subsequently bred, feeding data back up the pedigree to their sires. Where the rams will differ distinctly is on weight data, once it’s collected on them, and averaged with the scores coming out of their pedigree history. But a quick glance at these twins highlights something notable in their data at four months of age:

  BWT WWT PWWT PSC NLB NLW MBWT MWWT Maternal$ Hair
5007

0.354

2.033

3.247

0.552

23%

20%

-0.01

0.447

117.72

108.61

5008

0.361

1.669

2.263

0.372

22%

19%

-0.01

0.433

116.16

103.39

 

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We still have good green grass, but not for much longer with this string of no rain we’ve had. It’s unusual for it to get hot and sunny consistently before 4th of July here; but the entire month of June has felt like August! There is a possible thundershower in the forecast for next week, so crossing my fingers the pasture gets some watering. It sounds like our hay will be delivered next week, which will be a welcome backup: I can feed out of that store if I have to rest the pastures for a while. The pasture pictured above is mostly reed canarygrass. Though it is often an unmanageable pain in the butt, it is a great grower during dry times, since it has such deep roots. It will likely tap the water table no matter how long we go without rain, so it can continue to grow back after being grazed. It produces a huge volume of very nutritious grass, as well.

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In the olden days, people navigated the earth using a combination of maps, agreed-upon street numbering and naming conventions, and indicator signage. When folks wanted to go somewhere, first, they would consult a map. Then, as they drove, they would follow the map readings to identify turns along the way, helped by signs which indicated the location of the turns.

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In Katahdins, there is generally a preference for breeding ewes which have twinning genetics. We know that ewes ovulating more than one egg per cycle is heritable; and that when we select for it, we shift the bell curve toward the right: toward multiple births. So, some consider this a by-product of twinning selection, that we often get triplets or quads. This phenomenon, in itself, is a bell curve; with most ewes in a given season offering twins, and a smaller percentage having singles or triplets (and rarely, four or more). A 200% crop is fairly standard in our breed.

Because of this focus, it is considered a best practice when selecting breeding stock, to take into consideration whether the animal has twinning genetics, or not. For most people, the tendency is to ask about a particular animal “is she a twin?” This is a good start, but I’d like to illustrate why this “is a” descriptor is not as good as knowing the entire history of the dam’s twinning record. Or, better yet, her family’s entire history of twinning.

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image_thumb[2]In honor of Engineers Week, I thought I’d highlight a neat new company I’ve been watching with interest. GoldieBlox – a start-up founded by Debbie Sterling. Debbie is a mechanical engineer. She shares the same concern that most in our industry have: why aren’t more girls going into engineering?

Unlike some career paths which are bastions of manhood and very hostile and unwelcoming to women, I think engineering is hardly so. Engineers are generally very focused on smarts and output, and barely care about (or even notice!) a person’s gender or other physical traits. Given that most “boy nerds” grew up with some level of social ostracism as punishment for their smarts; adult boy nerds are rarely prone to exhibiting discrimination based on outward appearance. Rather, geeks of all types tend to band together based on their common love of math and science. Our field, as a whole, is very concerned about the shortage of engineers. Especially female engineers. So, a lot of effort is being invested in studying and trying to remedy the problem.

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imageContinuing along with my occasional discussion of Lean Six Sigma topics which apply well to farming, here is the next: the Pugh Matrix (or decision matrix method). We are often faced with making a decision between multiple choices which have complex variables. In engineering and manufacturing firms, obvious examples are deciding between two major design path choices, or selecting a vendor who will supply components long-term or perform some sub-contracted duty.

These are decisions where there are many pros and cons between all the choices, and it can be overwhelming trying to choose which is the best solution. The worry is that if we just default to our “common sense,” we may end up being biased and unable to make a truly objective choice. We may unconsciously place more importance on a certain consideration than other critical factors; and in the end, not select the best solution. With vendor selection, it can be easy to be swayed by one you know well and like; or by a good salesman. With design choices, the most assertive person in the room can sometimes sway the group in one direction. With farming, especially animal selection, the potential for bias towards our favorite animal, or best-looking animal, is huge.

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imageHere is another Lean Six Sigma (LSS) tool that I love: the Pareto chart. In Lean, there is constant focus on eliminating the “seven wastes.” Six Sigma is used to apply a mathematical approach to measuring the wastes and identifying ways to eliminate them. In general, problems often have multiple contributors, or root causes. Often it’s not feasible, affordable, or even worthwhile, to address all of them. Sometimes getting rid of 80% or 95% of the contributors is good enough. Often, we can’t achieve perfection, or complete elimination of a problem. So, how do we decide which root causes to tackle first?

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