Thursday, December 24, 2015

Inbreeding -- challenging the popular view with scientific analysis

From the June-July 2013 Dairy Route Newsletter

When it comes to “inbreeding”, everyone is sure (a) they should avoid it, and (b) they know how to avoid it.     Then they proceed to do it anyway.

Defining “inbreeding” scientifically

“Inbreeding” is the process of limiting sources of gene variation in matings, such that the proportion of homozygous gene pairs in the genotype increases.      ( Under current Genomic imputations, the level of inbreeding coefficient in each animal, expressed as “ibc%”, expresses exactly the above.    This is no longer calculated from a weighted charting of ancestors, as was done before Genomics.)

The goal of “inbreeding” was to produce animals that would “breed true” to some desired pattern.

“Inbreeding” is the opposite of “outcrossing” in which genes different from the genotype in the herd are introduced via mating, to promote a controlled amount of heterozygous gene pairings to renew “vigor” in the herd bloodline.

Why it got wrapped up in pedigree analysis

Prior to gene (DNA) mapping, ancestral [pedigree] charts were used as an approximation of the level of potential inbreeding.    However, in the pedigree selection era close-blood breeding was desirable, as the breeding industry increasingly recognized that the more productive animals resulted from the crossing of linebred, relatively unrelated paternal and maternal lines.

Fears of inbreeding accelerated with increased use of ranking indexes

What is the relationship between “index rank” and fears of inbreeding?
    
(1)    The first ranking indexes were based on single-trait selection.     Population geneticists started out in the 1950s ranking sires on lactation average yields only.     Then in the 1960s they switched to herdmate deviation of production yields.      It took failed udders and legs in the 1970s to bring “type” into the rankings; it took multiple component pricing to add protein yield in the 1980s; it took slower conception, higher stillbirths and inflating SCS by the 1990s to bring “health” into the ranking.   The constant factor throughout was the insistence that the entire gene package could be summarized into one single composite index ranking.     Thus “single trait selection” remains the mainstream dairy industry standard after sixty years of organized AI sire marketing.  
      More sophisticated genetic research blames “single trait selection” for inbreeding depression.
                                   
(2)    The pedigree was summarized into the “sire stack” and included in the ranking.     Pedigree had no impact on sire summaries in the 1950s-60s, as there was market desire for a wide bloodline variety.    However, in a desire to make summaries more “reliable” at earlier bull ages, the “pedigree index” concept was born, as [50% of the sire’s PD + 25% of the maternal grandsire’s PD.]    Thus before the first daughter was milked, sires were now pre-ranked by their “sire stack”.     Sons of the most recent #1 sire, born from daughters of the prior #1 sire, had the highest “pedigree index” and thus received disproportionate access to sampling, producing the next generation of “ranking” sires and over time, this eliminated many bloodlines that offered traits other than maximum early milk yield.
      Genomics has proven that gene transmission is not weighted equally within similar pedigrees.
(3)    Dairymen were losing fertility and health and were blaming narrowing sire choices.    Without the evidence of knowing the actual genotype (DNA) to trace the pattern of gene losses, or to even know which genes are involved in what physical/performance expressions, the increasing frequency of a limited number of ancestors in AI sire pedigrees was the most visible explanation.    Geneticists and AI marketing managers confirmed this as the likely cause, and invented “computer mating to avoid inbreeding” by calculating the lowest “expected future inbreeding” [efi%]    
    Gene mapping shows that different breeds can share gene patterns in common, thus it is possible to do
   “crossbreeding” and end up with a higher ibc% than a range of “within breed” matings would produce.

The “ranking index” is the ultimate likely cause of inbreeding depression

It is really quite simple, and it comes down to this:   breeding “like to like” for a multiple of generations eliminates heterosis  [hybrid vigor] and replaces it with a limited range of performance and behavior.

Reading the literature on inbreeding research from the AI era, which from the beginning discouraged the “sire to daughter” and “dam to son” sort of [truly inbred] matings practiced earlier, a common thread in the herds studied—herds showing “inbreeding depression”—was that a rigid sire selection process was in place, based on a literal reliance on a single “ranking index”.    Generally, after three generations with the sire selection dictated by the index ranking, production would plateau, while fertility and health trait expression would decline (slower growth rates—lower pregnancy rates—higher stillbirth rates—shorter productive herdlife after a higher level of veterinary expenses to prolong productivity).

What is significant about three generations?    There has been an 87.5% gene exchange, in which the genotype realigns into an “ideal genotype” that matches the gene pattern underlying the selection index.     At this point, you have reduced all “outcross” (heterosis potential) gene possession to 12.5%, thus only a 6.25% contribution in any future mating (not enough to stimulate continued heterosis response).

All your future matings under this index are truly “like to like” – similar in pedigree sire stack, but more importantly, similar in what gene traits were selected, what gene traits were excluded, and over all a limiting similarity in the physical proportions allowed to the physique of the cows produced.

What traits does your ranking index assign negative or zero weights?     This ultimately gives you the clue as to the form your “inbreeding depression” experience will take.

If it assigns a negative to size, your cattle will get smaller.    Smaller frame size ultimately limits both the rumen forage capacity and the circulatory system capacity of your cows, either loss in production yields or loss in udder health will be the “inbreeding effect” you experience.    If at the same time it assigns a negative to milk yield, even if in favor of higher milk component %s, the production loss will accelerate with each generation past the third, making the weaker udders a moot point.

If it assigns preference to fertility and health over milk volume,  you will ultimately lose profitability of feed consumption, and your cows will revert to “beef type” preference for weight gain over persistency of lactation.     The age of production maturity will regress.


If it assigns preference to type traits related to angularity, you will lose stamina, experience delayed reproduction, have a greater incidence of metabolic disease, and more leg and foot injuries.   Ultimately you will see a loss of general vigor reflected in TMR sorting, lack of appetite, more calf pneumonia, and loss of feed efficiency as rations demand increased energy density to maintain healthy body condition.

Avoiding “inbreeding” is a simple three part process.

Step one:

If following a selection index, throw it out after each three generations and seek true outcross potential (emphasize traits ignored in your preferred selection index)

Step two:

Mate cows on a physical quality basis, avoiding “like to like” physical combinations known to produce more extreme physiques with limited dimensions.

Step three:

Avoid random mating practices (such as never using the same bull twice) in favor of identifying sires who have the total package of traits that will produce more adaptable offspring.     Stick with sires that succeed—manage inbreeding generationally.

Monday, December 21, 2015

How many tons per acre of forage will grass produce?

U-W (Arlington, WI Ag Research Station) results from 2010 fall seedings, harvested as hay
                                                                                                         Two year             ** % of
Grass specie                                                   Variety                     Tons/acre             ave yield     
Festulolium                                                    Perun                             11.88                   118
Festulolium                                                    Lofa                               10.31                   103
Festulolium                                                    Spring green                    9.01                     90
Festulolium                                                    Fojtan                              8.97                     89
Meadow fescue                                              Li Herold                        8.33                    102
Meadow fescue                                              Pradel                              8.33                    102
Meadow fescue                                              Li Panther                       7.83                      96
Orchardgrass                                                  Intensiv                         10.12                    107
Orchardgrass                                                  Athos                              9.11                      97
Orchardgrass                                                  Potomac                         9.03                      96
Perennial ryegrass                                          Calibra                           8.09                     110
Perennial ryegrass                                          Remington                     7.99                     109
Perennial ryegrass                                          Piccadilly                       6.75                       92
Perennial ryegrass                                          Tivoly                            6.56                       89
Tall fescue                                                      Bar Elite                        9.55                     104
Tall fescue                                                      Premium Blend             9.51                     104
Tall fescue                                                      Kora                              9.12                       99
Tall fescue                                                      KY 31                           8.94                       97
Tall fescue                                                      STF-43                          8.73                       95


** 100% would be the average expected yield for that grass specie.

Sunday, December 20, 2015

First Hand Customer Account: observations on the effect of CONKLIN “Fastrack”

From the June-July 2013 Dairy Route Newsletter

A phone conversation that occurred in March between Sue and a favorite customer:

“My baleage in 2012 was ‘junk’ compared to our normal feed quality.     It only had 10%-12% protein although its energy values [from fiber digestibility] were closer to normal.     Our milk protein dropped to 3.0% and we were afraid the milk or butterfat could also fall as the season progressed.”


“Now with FASTRACK added (1 oz per cow per day) we are up to 3.2% protein and our butterfat is at 4.5%.     While we have not seen a production increase (it will take spring grass to raise that) we did not lose production either.     Those kinds of components raise the milk check values, and it is clear from the cow health this winter that FASTRACK keeps good rumen action.”