Friday, January 30, 2015

What the “genetic base change” really means

From the January-February 2015 Dairy Route letter.

What the “genetic base change” really means

Every five years, AIPL recalculates the “genetic base” by the simple expedient of calculating testing averages off the most recent five years of cow birth dates and completed lactations.    Thus for this current cycle, the base period is 2010 (cows born 2010 or after with completed lactations) which is five years since the last base period (2005 through 2009 birth dates with completed lactations).

The concept of a “rolling” (annual, as Canada uses) or “stepwise” (five years, as USA uses) genetic base is that we need to compare “current” performance against “contemporaneous” standards.    Thus as long as herd averages in DHIA tested herds generally increase, the genetic “base” (average of recent yields) increases to keep pace with it.     If this were not done the argument is we would be printing evaluations with sires as high as +14,000 PTA Milk (ie, the first base period from the early 1960s found the average cow producing 12,000 pounds ME; today’s base average is more like 26,000 pounds ME).

The amount of change to “average” between 2005 and 2010

This latest base change produced an average gain in milk yield of:
Holstein = 382 pounds        Jersey = 327 pounds        Brown Swiss = 157 pounds
Equivalent gains in butterfat and protein yields of:
Holstein = 17 + 12              Jersey = 19 + 12               Brown Swiss = 6 + 6 pounds
Productive Life gains in months of lactation
Holstein = 1.0 months        Jersey = 0.8 months          Brown Swiss = 0.3 months.
Somatic Cell Score (logarithmic base)
Holstein = - 0.07                Jersey = 0.04                     Brown Swiss = - 0.02
Daughter Pregnancy rate
Holstein =  0.2                   Jersey =  0.0                      Brown Swiss =  0.0
Basically, if a given Holstein sire was + 382m +17bf +12pr and +1.0 PL 2.93 SCS +0.2 DPR “before” the base change, and he had no new daughters (born in the 2010 base period) his PTA values on the new base would now be +0 m +0 bf +0 pr  +0.0 PL 3.00 SCS +0.0 DPR.     If he had new daughters in the latest base period, they are part of the “current” base and his evaluation will basically be daughters vs contemporaries adjusted somewhat for whether these animals are in herds above or below the “base” average (ie, are the contemporaries providing the same level of competition as the base average?).

On the type side…

The Holstein Association is postulating the following base changes for type data:
0.99 Final Score    0.92 Udder Composite    0.78 Feet and Legs Composite
The Jersey and Brown Swiss Associations postulated 0.53 and 0.28 gains in final score.

Did all the classifiers in these breeds actually see type scores going up by a third (BS) to half (JE) to a full point?    I am guessing that in actuality there is an imputation based upon changes in linear scores for the “objective” measured traits, as the actual scoring procedures followed by type classifiers bases final scores in part on a standardized ratio of scores so that the population results conform to the “bell shape” distribution curve.     Ultimately, this just means “new” bulls have higher type ratings than “old” bulls (no matter how good in their era, Ivanhoe, Citation R, Chief, Bootmaker, Elevation, Astronaut, Valiant, Starbuck, Blackstar are all listed now as “minus” for type).

Are  we  really  making  genetic progress  ?

Of course we are—that is what the data says, and figures never lie.

We have to remind ourselves constantly in the business of breeding that “genetic” means “population” NOT  biological function or relative transmitting ability or even realized performance.    Genetics only measures the “current” measured generation, it does not compare the “new” against the “old” on either an individual animal basis or a philosophy of selection basis.

What do I mean?     Geneticists cannot tell you if “Mogul” today is superior to “Elevation” in his day.     You actually need a supply of Elevation semen to use at the same time on the same mates as Mogul to answer that question.     They prefer to assume that, because today’s herd averages are higher than in the 1970s, at least for yield traits, Mogul must be superior to Elevation.    

And as much as “breeders” will generally object to the idea that these newer, genomically identified “supersires” are “genetically” better (Genomic definition: possess a higher percentage of the desired “marker”genes associated with measured traits) –  geneticists, not breeders, control the agenda of selection for future AI sires, and are not concerned with prior industry definitions of “greatness”.

Thus, a sire like Rosafe Citation R *RC whose entire list of classified daughters averages an actual 84.0 points, but virtually all scored between 1962 and 1990, can be imputed as “minus” for type against the current type “headliners” whose actual score average may be less than 80.0 points.     The physical type considered superior in that era, while having transmitting influence behind today’s cattle, is different in many traits and can produce a different resulting score under current linear assumptions.

Likewise, while a higher percentage of tested “Citation R” daughters exceeded 100,000# and 200,000# lifetime production (before rBST or TMR) than any leading sire of the modern era, their annual lactation averages followed the classical pattern to maturity, doing their best efforts at ages cows no longer reach.
The relevant question is not being asked       

What is the goal of your breeding program?     If it is simply to have the highest indexing herd (without necessarily producing competitive animals in the high pressure Genomic market) just use the newest of the new, and buy them off the top of the list—the same way most AI salesmen tell you.     If you have a constantly updated physical and nutritional environment for your dairy, you will gain production.

My only question for you then, is “can you afford to constantly update your facilities, equipment and feeding to harvest all this accelerated genetic potential” if the only payback is a 382 pound gain over a five year period??      [How much equipment can you buy on the marginal profit from 76 pounds of milk per cow per year?]    

This is the ultimate fact of the numbers—55% of all semen sold is selected on the above premise, with data promising up to 2500 pound gains on individual sires—yet we are not harvesting a fraction of that.
Even if you argue but we were focusing Net Merit on health traits and longevity, not milk yield, why did Holsteins only gain one month (on an already short 29 month herdlife) over the same five years?

Genetic potential is only half of what it takes.   The other half is matings to produce sound physiques.

Most dairymen are going to use facilities already built for the remainder of their dairy career.    Each facility design was in part based on a given target production and requires a certain margin over feed and all other costs to generate the profitability needed to maintain desired household incomes.   If we try to push production above the level expected from the design, cows will die at earlier ages.    Margins fall on the incremental milk produced.   Profitability declines over time no matter how high the production.

Cows differ in their cost of production.    One of the long held assumptions of geneticists is that the cow who milks more has lower feed costs—as if every cow eats the same feed volume.    However, this idea never made sense even when we did stick cows in stanchions and give them all one coffee can of grain.  
As soon as we started “challenge feeding” cows, some of them went up in yield.    Because we did not measure anything else, we based evaluation on the assumption this was the core of “genetic value”.

Dairy profitability is based on controlling costs of production, not total yield.    This is because profit per unit in commodity production goes to the “least cost” producer, not the “biggest volume” producer.   
There are few economies in scale in animal housing, care and milking—only on the equipment side.   As a result, the best breeding program combines multi trait gene selection with physique-based mating to produce cows capable of competitive, high value per unit production at a minimal involuntary cost to sustain production.      This is the goal of our sire selection and mating guide.

Friday, January 23, 2015

How do we select for “healthy” animals?

Article by Greg Palen

“Longevity” is again a topic of genetic conversations

We have found it interesting to see how the desire to reverse the genetic trend toward fast turnover cows has led to new genetic trait measures relating to fertility (DPR), immunity (SCS) and length of functional  life (PL).    These are of special interest to the Holstein industry, now lagging 150+ days of “Productive Life” behind the Jersey breed, which has significant trouble with first calving difficulty, and which has seen its rates of cow fertility drop steadily in the last two decades-- based upon DHIA data reported to USDA.     Infertility remains a leading reason for culling of cows prior to reaching a mature age.

Because Jerseys do lead in the Productive Life comparisons (breed #1 of the six measured by USDA) as well as in general measurements of cow fertility, there is a general level of complacency over these traits among breed leadership.    The general belief is that commercial dairy interest in Jerseys is primarily the result of Multiple Component Pricings’ impact on the marketing value of Jersey milk.    But when you read and analyze comparison data published by Holstein USA (directly comparing Holsteins vs Jerseys at various herd sizes and lactation averages for dollars generated), you suspect basic cow management issues also contribute to the interest.   Thus any complacency over established breed genetic differences, plus added behavioral differences, not related to lactation yields, may be a dangerous trend.

Never take any genetic advantages for granted

As soon as you have established that there are genetic links to fertility, calving ease, calf survival rates, immune function, disease resistance, in addition to milk volume and composition, resulting in lifetime differences in profitability, you have to realize that any advantage currently held can be lost by neglect in genetic selection and mating preferences.     What genetics earlier gave, it can later take away.    The Holstein breed in fact used to be superior for many traits in which it now finds itself inferior.   This is a direct result of taking desirable health, fitness and behavioral traits for granted in sire selection.

Can lost traits be regained?

Statistically, all measured traits are “improving” when the breed base average (recalculated every five years) improves.   But for selection purposes, half of all sires evaluated are in fact minus for one if not several of the desired genetic traits—this is what enables the other half to be plus.    Thus it is better to be aware of all breeds’ positions, to judge whether these gains are resulting from genetic selection or from more intense management focus.     A plus sire may just be above a mediocre breed average.   

For example, recall that prior to 1980, there was no meaningful Ov Synch reproduction, as is rapidly being considered “normal” practice today.    So comparing fertility rates after 1980 vs the fertility rates prior to 1980 has to factor in that added technological “band aid” to measured cow fertility, to analyze the genetic impact of ignoring or selecting for cow fertility (ie, prior to OvSynch, cows that would not become pregnant from visual heat detection and breeding would be considered culls).

The key problem with statistical evaluation of genetic traits is the inability to determine cause from the data.     Without identifying causes of a desirable or undesirable condition, we cannot either select for it or away from it with any degree of dependability.
Thus, in the case of PL within Holsteins, in the first published generation of sires ranked on Productive Life, the bull Pen Col Duster rose to the top of the PL rankings.    Many sons were sampled (but not on the basis of PL, just for the traditional reasons of TPI and NM$ index)—and none proved to be as good as their sire for PL in their generation, even without any contemporary sires being selected on that basis.   The inference is that until selection for any trait is “mainstreamed”, the pattern of inheritance for that trait will be random.    It does not automatically improve just because some sires in wide use appear to be superior to what proves to be a mediocre breed average.    The focus of mainstream selection will dictate the range of result allowed other traits within the “high genetic value” cattle population.   Thus heritability of the trait as measured will appear lower than the potential of the trait following more focus to selection in its favor.     (New Zealand data assigns twice the heritability to DPR as does USA data.)

“Maternal traits” more at risk of loss than performance traits

The concept of “maternal” vs “performance” traits is more of a beef or swine than dairy terminology, but it has always seemed to me that issues such as calving ease, calf survival, fertility, disposition, and feed efficiency are more rightly considered “maternal” traits, and require selection on an equal basis to the industry’s focus on performance traits (which until recently defined concepts of “genetic value”).

On a biological level, the male is designed to cover, while the female is designed to be a receptor.   We see “breeding bulls” who have produced well over one million units of semen, while “breeding cows” are born with their lifetime supply of eggs and may only release 300 eggs in a full lifetime (ie, one per month after puberty except when pregnant, plus multiples from induced superovulation).     Semen once processed and frozen can be stored and remain viable indefinitely—but eggs only freeze viably after fertilization and initial cell division, and produce lower pregnancy rates after longer storage.    Once you factor in cost differences ($3 average to produce a straw of semen, $75 to produce a frozen embryo) it is easy to see that maternal gene influences can be easier to lose and harder to retrieve.

On a practical level, this means that top sires can live on for decades—thus preserving their genetic traits into later generations—but for 99% of all cows, their genetic traits die with them, after producing an average of under two live female offspring.   The need for “maternal trait” identification within our sire selection procedures— crediting cow lines as parallel to any sire line performance ranking--  is critical to maintaining any breed’s adaptive ability.

Can we select for “longevity” on genetic traits alone?

It is our considered opinion that “longevity” is not a genetic trait so much as a composite result from a multi trait selection process.     In addition, its expression is limited by environmental design and many management choices, such that adaptive ability is a prerequisite to successful expression.

The traditional definition of “longevity” was that a cow remained fully functional and competitive in yields at a matured age.    This was based upon known factors:
(1)    Mature Equivalent tables:  cows reached their peak lactation ability at five to eight years of age (lactation #s 4, 5, 6).
(2)    First lactation 2 yr old heifers on average were 30+% below mature cow daily yields.
(3)    Cows who calved annually represented optimal fertility genetics and defined normal lactation length at 305 days.

Why did we develop ME tables?
Sire evaluations were originally daughter vs dam, thus you needed a method to equalize comparison of heifer lactations against maturing and matured lactations, to determine sire merit.
Why did we value mature cows over young heifers?

Mature cows milked more, thus we sold more milk than if we only milked heifers.    Heifers were bred quickly, to advance them to the higher lactation yields of later lactations.
 Why did we value annual calving?
Because cows used to pasture, and seasonal calving could follow peak grass growth—plus the earlier milk payment plans were based upon a seasonal “base” production matching the school milk demand.

Have cow’s maturity patterns changed?

Yes— a selection focus upon PD Milk resulted in earlier maturing heifer production, higher daily peak production, and demanded higher ration energy density to sustain production (conveniently provided in concert with the focus of feed research in favor of heavier grain usage).    Thus we started to see mature production levels within immature age (first and second) lactations—and herd averages began to rise accordingly, leading us to devalue “longevity” in favor of “fast maturity” genetics.

The accepted tradeoff in that generation was that higher milk lines were usually (not always) lower bf% expression lines.    So more grain to sustain higher peaks meant lower bf% from less fiber in the ration.
The unforeseen problem was that (a) faster maturity roughly equates to faster physical aging, ie, what we now hypothesize as the first step in trading away health and fertility for accelerated productivity.

Genetic evaluations changed from daughter-dam to daughter-herdmate (now “contemporary”) basis of comparison—but continued to use ME tables, even though today, less than half of all cows who calve ever reach a mature age in a functionally productive state.     To acknowledge this, lactations are now factored to consider the second lactation the “Mature Equivalent” level of average cow production – a clear recognition that modern cow life is much shorter than the species capability in nature.

Because maturable bloodlines continue to follow “classical” [earlier ME table] patterns to maturity, on a lactation yield comparison, increasingly will show up as “minus” for PTA milk, relative to fast maturing bloodlines mainstreamed in the commercial cow population.    Thus the only longevity lines which will be preserved through commercial AI would be those lines that possess above average cow fertility rates combined with appeal to the type-oriented (or the milk component % oriented).    Beyond that, our focus on PD Milk yield (more recently, PTA Fat and Protein yield) in the absence of any ratings for daughter fertility or calf survivability or PL, has narrowed our pedigrees to the “performance” bloodlines, rather than the “maternal instinct” lines.

What links exist between milk yield, cow fertility, and functional longevity?

A systematic survey of the entire sire summary (rather than just looking at bulls activated into AI) will show that in general, the higher the PTA Milk value, the lower the “daughter pregnancy rate” (DPR).   In spite of higher energy ration density (and available hormone injections), high peak producing cows tend to fall into a negative energy balance quicker, and stay in it longer than flat lactation curve cows.   Their bodies ration the distribution of nutrient energy differently, between body maintenance, milk production, and reproduction (all of which demand a piece of the total daily energy intake).   As a result of selection focus on production, many such lines lost ability for reproduction.

Earlier Cornell and later NCSU data indicated the bias amounts to 400 lbs PTAM in first lactation and over 1000 lbs PTAM in second and later lactations, in favor of the minus DPR sires.

Exacerbating this trend was the concurrent decision that genetic selection should prefer an “angular” cow physique.   Thus cows who had better fresh cow appetites and never milked off all their condition,
would classify lower than cows who looked like they were “working hard” – and the bias in favor of the delayed fertility over the timely fertility cow increased within the type selected genetic population.

In fact, visual judgment of cow productivity was never as efficacious as type oriented people want it to be and we suffer many negative consequences of that misbelief.    It is ironic that the least type oriented sector (University dairy specialists), once assigned the job of designing the linear trait method for type evaluation, repeated the classic error— by designing type preferences on the basis of “this is what the highest producing heifers look like” in the later 1960s stall barn, challenge feeding environment, state of the art at the time (but woefully obsolete in today’s group fed TMR free-stall environment).   So using a purely statistical view of type preferences at an immature evaluation age, the scientists came up with a preference for early matured, highly angular physiques—again, reinforcing selection trends that proved to be antagonistic to maintaining herd longevity.    

A more biogenetic view of functional structure and performance

Scientific breeders of the 1940s, prior to the ascendancy of the “numbers crunchers”, asserted that the longer the time frame of measurement, and the broader the field of measurement, the more accurate the estimation of genetic value.   Thus they decried any tendency to presume genetic superiority on single lactation measurement or early-age type scores, in favor of a bloodline approach of accumulated value and a higher level of trust in the depth of maternal line lifetime (pedigree) performance.

For those of us who have the opportunity to observe famous breeding cattle on their home turf, the wide disparity between the “model environment” and the individual real-world environments in which cattle must function, raises the desirability of selection upon “adaptive” qualities, rather than a strictly ranking based genetic selection.    In this sense, the biological concept of “adaptation” means the ability of any living being to adapt to wide variation in environments (success in contending with variable limitations to performance and function).   It would look at production, type and health as a symbiosis of result.

Each environment contains both opportunities and limitations for the cattle within it to express their productive ability.    The limiting factors include soil quality (as it impacts on the forage base of rations), water quality and availability, cow comfort, restrictions on mobility, design of milking systems, slug or TMR feeding, feedstuffs employed, climatic factors (heat, humidity, seasonal fluctuation, breaks if any to parasite cycles), level of human skill, speed of human intervention after impact events to individual animals, seasonal vs 365 day fertility expectations, level of enhanced technologies employed.

We have made a dangerous assumption in believing that the highest production level environment gives us the most accurate reading of genetic value— a view based upon the limiting view of genetics as only applying to production yields—rather than recognizing how much technological adaptation within these higher performance environment is able to mask genetic weaknesses in maternal and health traits.   To a biologist, the more accurate environment is the least forgiving one.     Thus as we move toward better understanding the genetics of health and fitness, there is a need to value more “average” environments where cows must fend for themselves, rather than the highly evolved technocrat environments.

Challenges to using PL ranking by itself to select for “longevity”

Unlike the individual genetic traits for calving ease, stillbirth rates, daughter pregnancy rates, somatic cell scores, production yields, priority linear type traits, and type scores, the “PTA- PL” feels like a summary trait that wraps it all up as a “composite” of overall cow desirability.    “The cow who lasts longer than average must be the better cow” is a pretty easy assumption to make—and on a commercial level, where “average” is measured as superior to “mediocre”, this is statistically true.

Our problem is, we need to identify the causes of longer Productive Life, before we can be assured we have a formula for replicating longer productive life against our own herd average, where we have made the decision that “longevity” is an important quality-- rather than the broad population average, which includes within it the large numbers of cows managed by people who really don’t care to milk “old” cows, and skew the data by culling on daily production only and constantly buying in cows.

Our current PL data has a flaw no one has felt obligated to discuss—and that is rBST use in the more technology-adaptive herds since release of “Posiliac” in 1994 (four cow generations ago).    

PL is calculated as “months in lactation since first calving”.   In the PL calculation, a full months’ credit is given to the first ten months of lactation, 70% credit is given to additional months beyond ten within any extended lactation.     Thus the following is possible:

Bull A – dtrs first calve at 24 months age, breed back at 90 days fresh:
               First lactation, ten months’ production equals 10 months’ PL credit.
               Second lactation, ten months’ production equals 10 months’ PL credit.
               Third lactation, ten months’ production equals 10 months’ PL credit.        Total PL: 30 months.

If bull A were a Holstein bull, he would at this point be +1.0 PL, because the Holstein base average is 29 months currently.     (In actuality, he is also compared to contemporaneous sires used).

Bull B – dtrs first calve at 25 months of age, delay breeding back to 150 days fresh:
              First lactation, twelve months’ production equals 11.4 months’ PL credit.
              Second lactation, twelve months’ production equals 11.4 months’ PL credit.
              Third lactation, twelve months’ production equals 11.4 months’ PL credit.      Total PL: 34.2

If bull B were also Holstein, he would at this point be +5.2 PL against a base average of 29, or +4.2 PL if his contemporary sires were all like bull A (who then falls to a +0.0 PL to represent “average”).   But his daughters would average 68 months of age at this point, thus may have less life left than bull A’s – who had enough better fertility to breed earlier as heifers and calve back quicker as cows.

Bull A—due to higher fertility, gets a higher percentage of daughters bred back each lactation.   Thus on this example, the majority of daughters are poised for a fourth calving, at a true matured age and yield.   So his future PL data may increase relative to bull B.    But his PTA Milk will be lower than bull B.

Bull B—due to lower fertility, and resulting higher peak production, has fewer daughters remaining in later lactations who will calve again.    So his future data for PL may decline relative to bull A.   But his longer days open at peak production means he will always be higher than bull A for PTA Milk.    Higher PTA Milk based on 305 days gives unfair advantage when lack of fertility produces 450 day lactations.

Thus to fully compare the two, you need to break production down to monthly test days, and calculate a “pounds milk per day of life” (rather than a lactation based PTA Milk) to determine who is “better”.

The way in which rBST skews the data is as follows:

Bull C—dtrs first calve at 26 months of age, delay breeding until 150 days fresh:
               First lactation— fully formed udder, milk 12 months = 11.4 months PL credit.
               Second lactation- capacious udder, milk 15 months (last 9 on rBST) = 13.5 mos PL credit.
               Third lactation—deep sloppy udder, coded “Do Not Breed”, milk 18 months on rBST = 15.6 m

Total result for PL comparison:  40.5 months of “productive life” thus a +11.5 over Holstein base ave.

How “productive” is delayed fertility and a progressively failing udder that defines the cow as a “cull”, but still allows for incremental credit as a genetic source of “longevity”??

PL is most useful in a negative selection sense—ie, it may assist us in avoiding those sires that, for whatever reason, produce shorter than average herdlife daughters.    Note also that the published data gives a result without reference to “cause” – ie, why are they leaving herds early?

(1)  Are they infertile??      Check the DPR rating and see if it is a significant negative (-2.0 or more).
(2)   Are they unhealthy??   Compare udder trait scores against the SCS rating, it may be mastitis.
(3)  Are they poor type??   (But the udders may be OK— so check the foot and leg scores.)
                                           (But the legs may appear OK—so ask about for metabolic issues, again,
                                             the DPR ratings should reflect this as a negative to timely breedback.)
(4)  Are they bad tempered??     No genetic data—have to ask around for anecdotal reports.
(5)  Do they lack maternal instinct??     Check the stillbirth rates reported against calving ease data.
(6)  Are they frail??          Check if angularity ratings are high vs aAa ratings with 4 toward the back.
                                   [or] Type looks quite good but milk yield is minus without high DPR to justify it.

When it comes to longevity selection, you cannot take data at face value—you have to analyze what it is or is not telling you that you want to know.

“Healthy” is not the same as “absence of symptoms”

My chiropractor has a chart on his wall that says the following:
          100% function =  fully functioning organically and within the external environment.
           90% function  =   new cell production replaces old cells, sense of contentment.
          80% function   =   cell rejuvenation is occurring, more aware of surroundings.
          70% function   =   immune system is functioning.
          60% function   =   high level of physical energy.
          50% function   =   absence of negative symptoms.
          40% function   =   easily fatigued, low level of physical energy.
          30% function   =   pain, sickness, feel run down.
          20% function   =   diagnosable condition(s).
          10% function   =   life threatening condition requiring treatment.
           0% function    =   terminal prognosis.
The more I read that, the more I felt it added some insight to the questions around longevity selection.

The ultimate difficulty with using the current genetic traits is they are targeted to get us to the 50% level of “absence of symptoms”.   Cows can have +PL who are already determined to be “culls” by their herd managers.     Selecting to be above average in the case of “productive life” when its base benchmark is an age below the species age of physiological maturity, is perhaps half the way to “longevity”.

Health  is best measured by  comparative lifetime performance  (in an absence of specific traits)

In reality, we have yet to identify “healthy soundness” in a linearizable/measureable way for dairy cows. 
This remains a visualized quality, as opposed to discrete defined traits with a known scale on which to base the measurement.    

The breeder who is well connected and in communication with other breeders can identify cow line as well as sire line sources of “health” – but beyond that, most of us are limited to what we can observe in our own herd.     But that does not preclude developing our own internal method of recognition for the quality of “healthy” adaptation to our environment.    You have to get comfortable with anecdotal data.

The closest you will come is to analyze pedigree information for both the direct ancestors and siblings to the maternal line of any sire you consider.    The most important pieces of pedigree data will be:

(a)  Progression of type scores into maturity.     When you find a cow who starts at 80 as a heifer and she climbs to 90+ as a matured cow, you have a basically sound physique that is maturing gracefully—not wearing out prematurely.   This is suggestive of a highly adaptive cow.    (A young cow score is not.)

(b)  Regularity of calving interval.     When you find a cow who calves regularly (12 to 14 months) and has calved multiple times, and the production post-calving appears consistent to increasing, you again have evidence of a cow that is maturing gracefully—but also a cow with good fertility characteristics.

(c)  Number of calves registered.       This is supportive information to calving interval fertility—if it appears that all female calves were registered, the inference is that she births live calves—not stillborns.   Even for ET cows, a high number of calves registered compared to ET opportunities (which will be no less than 40 days apart during any lactation) may support the same inference.

(d)  Significant lifetime production.    If breed average PL is 29 months for Holstein (34 for Jerseys) and your target production is 70+ pounds daily (50+ for Jerseys) than “average” lifetime is perhaps 60,900m for Holsteins (51,000m for Jerseys).    Look for those cows who are at least double the “average” level.

(e)  Multiple generation consistency.    When you can find positive lifetime evidence within a multiple of maternal generations, you have a higher degree of confidence that the line has “genetic” longevity.  
Given the frequency of ET in favorite cow lines among breeders, a multiple generation approach helps to answer the questions that frequent ET of a favorite cow may hinder answering.

Direct selection is more reliable than indirect inference and estimation

Why do we profess to want “longevity” and then select sires without regard to evidence of longevity – ie, by selecting primarily on Net Merit $ or TPI/JPI or Type, even if we include PL ratings (which on most living sires contain significant levels of estimation and factoring from statistical correlations) ??
None of these measure “longevity” directly—they measure other traits assumed related to longevity.

In all other species that have been studied for the genetics of longevity, the most consistent advice is to select mates from ancestry that lives longer than average in similar or challenging environments.

Thus even if a sire is +1.0 PL, when his dam and grandam only ever calved once or twice, there is no proof that “longevity” can result from estimating current offspring to be “above commercial average”.

The current high PL Holstein sire globally (Ramos, from France) at +8.0 PL, comes from a living cow who has recently exceeded 310,000 pounds lifetime and is bred back again.      Think about it.

Fertility is the key enabling trait to longevity

If we were to really ponder what is absolutely necessary for a cow to reach mature or old age, it must be clear that “fertility” is the primary trait.

In most herds, under today’s shorter herd lives, the cow who breeds back gets to stay—in the absence of rBST, the infertile cow gets asked to leave.     This is simple, therefore we miss its profundity.

Fertility provides three income streams:  (1) renewed production, (2) reproduction, (3) eventual surplus of reproduction leading to a second income stream.    (Culling income, while delayed, loses importance.)

Infertility only provides two serial income streams: (1) immature production, (2) early culling.   Thus the lack of fecundity limits us both for income opportunities and for in-herd genetic selection opportunity.

The scientific definition of fertility is greater than just conception or pregnancy rates.   Those studying the subject include calving ease and stillbirth rate within the “fertility” arena, for the obvious reason that “fertility” without a living and healthy result will not sustain future generations.

Thus, when Eli Hilty stated years ago in a NorthCoast Group meeting, “Fertility begins at birth”, he was cognizant of the true distinction between “health” and the earlier statistical measures of survival as a result of above average immature lactation yields.     (Wisdom does not require a PhD to see truth.)

Milk components do not just measure milk prices

Butterfat % and Protein % is increasingly seen by ruminant nutritionists as a window into the health and level of function of the rumen—the key internal organ that can affect feed efficiency in a genetic way.

Cows with persistent negative energy balance not only lose weight and fail to conceive, they also fail to provide all the protein yield of which their genetics are capable.    When a cow fails to receive adequate nutrient energy for all body processes, her rumen will begin to convert protein intake to energy.   Thus less protein will secrete in the milk, due to the body’s drive to maintain all its organic functions.

You will say “that just means the feed ration is deficient in energy”—but keep in mind, whenever most of your cows are gaining condition and breeding back (and testing average or above) the ration was good enough for them—therefore, the cow who neither breeds back (often found cystic on vet check, again, a result of persistent negative energy balance) nor excretes protein, is not an “efficient” feed converter.     No matter how much milk she makes.      Keep in mind the rule “if the entire herd is going south, that is a management problem; if a couple cows are going south, that is a genetic problem.
Too often the genetics industry has passed the buck for lost fertility and longevity to “management”.

Grazing dairy cultures like New Zealand do not measure feed efficiency on lactation yield of milk – they measure it on per acre yield of milk.     Ruminant function within the biological food chain is as a forage (grass, roughage) converter—feeding grain to get more milk actually requires more acres per cow than might be achieved by selection for feed efficiency via pounds of solids produced from forages.   Thus a cow possesses “genetic value” if she milks until the grass dies, and is carrying calf within the window.

Given the (low bf% testing) acidotic rumen and (low pr% testing) negative energy rumen will both lead to shortened herdlife from metabolic diseases in later calvings, there is a growing body of breeders who suspect that selection on positive bf% and pr% levels will contribute to greater potential longevity.

Rations higher in forage, higher in digestible “energy dense” fiber, will reduce our future dependency on higher price grains for balancing ration nutrients.   Genetic selection in parallel can optimize the result.
The point for longevity selection is, genetics must adapt to the changing environment to be a success.

Mating effects beyond genetic selection

The ultimate problem with using genetic traits as our sole guide to achieving longevity, is their levels of heritability as measured.    Current estimates assign heritabilities ranging from 4% to 15% for the health and fitness traits identified to date.

What does this mean in practical terms?    Consider the following approximation of gene action:

Yield [result] = genetic selection effect  +  gene combination effect  +  gene-environment interaction

“genetic selection effect” is what results from our ranking of sires by traits dictating a mating choice

“gene combination effect” [mating effect] is what results from the sire and dam being more genetically than just the handful of traits we bother to measure genetically.     

“gene-environment interaction” is what results as the variable environmental factors trigger genes to either stay dormant or act in either negative or positive ways, ie, the level of adaptation of the animal.

In practical terms, the higher the heritability, the less mating effects or environment will impact on the result.    But as the only genetic traits measured over 30% heritable are butterfat% (.55) and  protein% (.60), the simple truth is that genetic selection at best only covers one third the range of results.

For “longevity” selection, sorting c/e, s/b, SCS, DPR and PL might get you 25% of the potential change in longevity that is possible.   If you cannot wait that long, you need to also impact upon mating effect.

“The whole is greater than the sum of its parts”

A simple reason that the mating effect needs to be considered as well as the genetic effect, is that no single genetic trait will either guarantee longer life nor prevent its achievement.     A cow can function just fine with sickled legs, or low foot angle, or a capacious udder, as long as the overall physique is in balance within its environmental limitations.    She cannot function well from an “extreme” physique that demands a more narrow range of environmental variation to avoid events that cause her to become dysfunctional—ie, putting a low efficiency rumen into a low grain, high forage grazing environment.

How do you manage the “mating” effect?    You consider using the “aAa” Breeding Guide.   It does not break cows or bulls into discrete parts—it looks at them as an interconnected whole, and as functioning within the real, not the model, environment.    It seeks a mating combination between sire and dam that provide a balancing of qualities essential to a full lifetime of function.   

But more than that, “aAa” has fifty plus years of experience in identifying the cause of defective animal traits.     It can offer a specific guide to improving the aspect of each cow’s physique you do not wish to see replicated in her offspring.     “aAa” believes the answer to “why” is more important than measuring “how much” a trait deviates from a positive level of functional balance.

Nature culls extreme physiques in favor of specie traits adaptive to their natural environment, as a whole (that allows for variation to reflect genetic variety and vigor).    Domesticated environments impose an added layer of limitations dictated by economic and facility considerations—thus a higher level of physical uniformity is dictated for optimal adaptation to group handling.    “aAa” seeks this result, by suggesting the order in which phenotypic qualities should be addressed within each mating.

An instructive example of survival (“maternal”) instincts in male selection

Tom and Cheri Harsh of Tipton MI selected “Tisol of Reber P” for their annual “cleanup” sire on the basis of a 75% chance he was homozygous, and on “aAa” analyzation indicating he had a desired level of the “2” [tall] quality.    

He was exposed first to yearling virgin heifers, then (after six weeks) to cows that had not yet conceived from three AI services.     He proved to have a high conception rate (male fertility measure) given both the heifer and the late cow group proved to be pregnant to his exposure.

As his calves were being born, it was noted that every calf was born alive (optimal 0% stillbirth rate!) and tended to possess health, ie, a willingness to eat and a desire to live (no calves who refused bottles, who fought transition to pails, who refused weaning to dry feed).     

Tom is quite observant, and he noted that when a cow had a calf in pasture, this bull (sharing the dry cow pasture after his “cleanup” duties) would stand guard whenever the cow left the calf—in effect, his instinct was to protect his “genes” from predation.

This bull was in strong contrast to the bull they used the previous year, who had a problematic temper, whose calves were less thrifty after birth, and who produced fewer live heifers as a result.   Yet that was the bull with higher “genetic value” on the statistical basis of ranking only measured traits (under belief that certain traits are always more important than others).

On this basis, we chose to bring this bull to Netherhall and expose him (1) to all heifers ready to breed, (2) to any delinquent AI cows.     As he remained tractable (evidence of good disposition), he is now in hand service at David Nisley’s grazing herd in Bloomingdale MI.

All of us are rotational graziers, and prefer to calve in seasons.    Traits related to cow fertility, tractable disposition, and basic health and appetite characteristics, are of high priority for us.   This bull provided observable evidence that he may possess desirable genes for these characteristics.    Little of what he has demonstrated to us is, however, directly covered by any genetic trait measurement.


You are motivated to breed for “longevity” in your dairy herd.

The following steps will optimize the result:

(a)     Choose sires from bloodlines with known “longevity” on both the sire and dam pedigrees.   Avoid sires from serial “single lactation” cow lines selected on yield index rank rather than longevity.

(b)    Prefer positive over negative bf% and pr% expression.

(c)     Avoid sires that perpetuate the “high milk—low fertility” combination of mediocre genetic lines resulting from the eras of single trait yield/index selection emphasis.

(d)    Prefer desirable type while avoiding extreme expression of angularity traits.

(e)     Look at all the fitness traits specifically for what they are telling you, and do not assume what they cannot tell you accurately.

(f)     Have your cows “aAa” analyzed, try to fit the sires identified from the above screening into matings that result in at least 80% aAa match [based upon a “percent of aAa use” form they provide].

(g)     Avoid confusing linear trait concepts with aAa concepts, so you do not negate the power to manage the mating effect as needed to insure receipt of desirable trait selection.

Ultimately, it may be more important to define which sires to avoid than to insist that certain bulls are the best to use.     Genetic selection is more a process of exclusion than inclusion.   When you enter the “multiple trait selection” arena, you can expect you will weed out more sires than you expect, including many sires that rank highly on currently in vogue composite trait indexes.

What about those composite indexes??

Again, these use a “one size fits all” formula to rank bulls on selected traits according to expectations of performance in a “model” environment that includes the entire cow population.

Your problem is, you do not have the entire cow population—you have the cows you have, maybe 25, maybe 50, maybe 250.    You can expect maybe 10-12, or 20-25, or 100-120 heifers born per year.   You need every one of those heifers to count, either as a replacement in your herd, or for your neighbor.

Experience tells us that, beyond well-grown and healthy, buyers mostly look at type.    They have some mental concept of what a “good” cow looks like, based upon prior personal experience.    They will not bid on a heifer that is “extreme” in physique, as she will be outside the range of what experience tells him will adapt successfully to his farm.

Thus, outside the purebred breed sales, where sire line and pedigree add potential speculative value to a heifer, most buyers are not particularly concerned whether you used a $10 bull that “fit” her mother, or a $50 bull that gave you bragging rights.    That becomes money spent the market does not recover. 
(page twelve)

The unsure/inexperienced buyer may feel comfort if the sire comes from a big, establishment AI system— but if the heifer is a “pig”, she will not produce repeat sales nor a premium price.

Given you are focused on “longevity” you need to devise your own “longevity index”, which is what I described in my “summation” – the selection matrix we use in our herd.     In this you will be ahead of
the curve of the mainstream, which still is confused about the economic value of longevity over making big single lactations and having a high herd average.    “Longevity” is about net profit—not more gross income with unmanageable inflationary costs.

Each added lactation a cow gives you, when competitive in yield, also frees a replacement heifer to sell as a second income stream.    This lowers your cost of producing milk per cwt, making your dairy more sustainable in times of lower or fluctuating milk prices and feed costs.

Just on that basis, selection in favor of “longevity” has more net profit potential than any other genetic selection strategy you might implement.
How about crossbreeding??

Genetic effects and mating effects do not change just because you use parents from more than one breed.  

The only thing that changes is the short term benefit from “heterosis response”:  what we usually call “hybrid vigor”.      New Zealand data says, in a two breed rotation, you get an extra 7% from “hybrid vigor” in the initial cross, and half (3.5%) in the following rotational crosses.

Dr Hanson suggests in a three breed rotation, you get closer to the 7% level in each cross, up to the third cross (when most programs rotate back to the base breed).

There are two problems however that crossbreeding advocates tend to forget to mention:

(1)    You cannot define any individual bull as possessing the average of his breed’s trait advantages.   He is what he is individually, a composite mix of desirable and undesirable traits, subject to the mating  effect (that has that potential to cancel out the heterozygosity of the selection effect).

(2)    By mixing breeds, you increase the randomness of your gene pool.    This dilutes any prior selection effort for desired genetic traits—ie, those desired traits breed more ‘true” when the underlying gene pattern is homozygous, rather than heterozygous.

Beyond this, most bad experiences in crossbreeding come from breed promoters overselling their breed advantages and conveniently forgetting to mention points of breed weaknesses that may be outside our current desire to improve a couple specific traits.    So we trade weaknesses A, B and C in our original breed for new weaknesses D, E and F in the added breed we knew less about (and thus perceived as a more glamorous, rather than a fully realistic, assessment)—retaining a level of A, B and C as well from the cow side of the replacement heifer pedigrees.

Crossbreeding thus may act as a temporary substitute for genetic selection, but at some point, you end up with a greater need to “select” and “mate” to stay within a desired range of phenotypic expression for sustainable adaptation to your environment.