Cows that produce 40,000 lbs. milk in a 365 day period have
been around for awhile. The
(additive) gene effects that enable such production are segregating throughout
the dairy population, and are concentrated in the highest ranking AI bulls on
the market. This suggests that
sufficient genetic improvement for 40,000 lb. milk per year can be achieved by
use of the highest ranking bulls on the USDA Net Merit sire lists. However, the process cannot guarantee 40,000 lbs. of
production, nor can any other genetic process, including cloning of 50,000 lb.
cows. While genetic control of
production will never be absolute, genetic improvement will be an important part
of achieving higher yields. A
critical question is whether current selection programs discard genes that are
necessary to increase herd averages to 40,000 lbs.
What breeders have accomplished
in individual cows, perhaps through specific gene combinations, may be more
difficult to achieve for an entire herd relying on additive genetic improvement
to build the functional genome.
An excellent strategy to breed for high producing cows
would be to breed all females in a herd to reliably proven bulls that rank above
the 80th percentile for Net Merit based on current proofs at the time
of insemination. Major factors that
reduce genetic progress include herd infertility leading to use of natural
service bulls, selection for traits of minor economic importance, delay between
purchase and use of semen, and inconsistent use of genetically superior sires.
This recommendation is not new and applies equally to herds
aspiring to 25,000, 30,000, or 40,000 lbs. of production. Improved production will remain important, but more
comprehensive selection goals are being formulated. Lifetime
economic merit depends on fitness and reproductive traits in addition to yield.
USDA will change the Net Merit index in August 2000 to include additional
traits related to maintenance and milk harvest costs.
The new Net Merit will not include reproduction directly, as national
genetic evaluations are not available. Health
costs will be considered indirectly through somatic cell score and udder
evaluations. Both health and fertility will influence Net Merit indirectly
by affecting length of productive life.
Genetic improvement of dairy cattle in the United States
depends on analysis of data originally collected for other purposes.
Producers pay for yield and somatic cell score data for within herd
management decisions. Productive life evaluations are a by-product of milk records.
Many producers have classified their cows for marketing purposes rather
than to provide data for genetic evaluations. Current selection policies rely on
correlated response to change economically important traits not measured
directly. Success depends on genetic correlations between the traits measured,
the traits to be changed, and the genetic control of both.
Future breeding programs will likely rely more heavily on direct
measurement of fitness parameters that are presently ignored due to the expense
of data collection and other problems.
Higher milk yields have been achieved by combinations of
additional intake and by greater mobilization of body tissue stores to meet
metabolic demands of additional production.
The selection strategies followed to produce the dairy cow on today’s
high producing farms have not attempted to control the way cows meet body energy
needs. Intake has increased, more
in some cows than in others, but body tissue mobilization has provided energy
for higher yields as well. We may
have even selected for the cow that loses weight rapidly in early lactation by
selecting for “improved dairy character” in addition to high milk
production. Cows that meet energy
requirements for higher production through body tissue mobilization reduce
energy available for growth, fertility, and immune function.
Future selection goals will need to provide energy for all essential body
functions while increasing genetic merit for yield.
Measurement of feed intake and body weight change, in addition to milk
production will be required to identify those animals that meet energy
requirements for high production through increased intake and/or improved
utilization of feedstuffs rather than through increased body tissue
mobilization. Future breeding goals
should be broadened to include health and fertility traits or precursors to them
such as traits related to negative energy balance and metabolic stress.
Specialized data collection and evaluation schemes and increased cost of
genetic improvement programs will be part of such changes.
Technical advances in manipulation of the bovine genome may
dramatically alter traditional methods of identifying genetically superior
animals. Direct measurements of
gene effects, if possible on a large enough group of animals to allow selection
intensity to operate, could replace time consuming, expensive, inefficient
progeny testing systems. Alternately,
marker assisted collection could expand the impact of specialized trait
measurement by reducing the need to evaluate phenotypes on many offspring to
estimate breeding values on ancestors. Further, cloning could reduce reliance on
Mendelian segregation to produce genetically superior animals, particularly for
those specialized traits not now widely measured.
The role of new technology on future breeding plans remains tenuous,
however. Traditional progeny
testing programs work reliably, despite delay and expense. Undesirable (and perhaps, unpredicted) changes due to
antagonistic genetic correlations occur slowly enough through traditional
progeny testing programs to allow for mid-course adjustments in selection
strategies. Direct manipulation of
the genome or widespread use of marker assisted selection increase the risk of
unwanted correlated responses. Economic
issues will ultimately determine the role of new technology on genetic
improvement. Bulls studs, for
instance, will make major efforts to market sexed semen from outstanding bulls
only when lifetime income from semen sales for individual bulls is not reduced
in the process. Producers will use
sexed semen only when returns from its use are roughly comparable to use of
unsexed semen. Outside of
subsidized applications, adoption of new technology in genome manipulation,
sexing, and cloning may be very slow. That
said, breakthroughs in technology could invalidate all predictions.
Current high investments in human genetic research should ultimately
benefit genetic improvement programs throughout agriculture.
We have learned much about the relationships between
conformation traits and lifetime economic traits in recent years.
While certain phenotypes, particularly very deep udders, wide front
teats, and shallow foot angles hamper lifetime performance, the consensus of
research argues for less emphasis on type in future breeding programs.
Dairy industry perceptions and tradition, and some strongly entrenched
type collection and evaluation systems make such change unlikely.
The evidence remains, however, that dairy cows of many different shapes
attain high lifetime economic performance. We
will not make major improvements in lifetime economic merit of cows of the
future by changing the shape of the cow we milk today.
Future genetic improvement programs will rely on
multi-trait evaluation procedures, and combinations of genetic information from
different evaluation systems, both within and across international borders.
Economic pressure exists to eliminate certain international borders in
terms of data collection. Fitness and functional traits are often lowly
heritable (reproduction in the female is about 2% heritable), which makes shared
data more valuable. Other traits of growing importance such as rate of
mobilization of body tissue in early lactation require expensive and difficult
to obtain measurements of energy intake and expenditure.
Technical changes in computers and monitoring equipment, on the other
hand, have provided access to daily milk weights, electrical conductivity of
milk, and cow activity. The proper role of much of such automatically collected
information in selection decisions is not yet well defined.
Future technical innovations will add to the data already collectable on
a routine basis in modern dairy facilities.
Such information promises to improve our ability to identify those
animals with genotypes conducive to long, highly productive lifetimes and/or
with reduced maintenance, health and reproductive expense.
Correlated response may well be insufficient to change such
traits as fertility, energy balance, and immune response to the degree desired
as yields increase. Progeny testing
programs, already highly dependent on large herds, will likely evolve to rely
even more heavily on such herds where specialized data collection could be
implemented. It could well
become necessary to house and manage potential bull mothers in highly
specialized data-collection herds – nuclear breeding herds - where feed
intake, hormonal profiles, immune response data, and other useful phenotypic
information can be monitored as part of the process of identifying the
outstanding individuals. Direct
manipulation or measurement of the genome of the dairy cow would most likely
occur in nuclear breeding herds where control of gene flow to the general
population could be managed to optimize return to investment.
Genetic relationships between highly selected AI proven
bulls and cows in dairy herds have been increasing for two decades, accelerated
by international exchange of dairy germplasm.
Increased relationships are a natural and not entirely undesirable
consequence of selection to improve the dairy cow. Long-term effects, however, are an inevitable increase in
inbreeding in dairy herds that rely on traditional sources of germplasm.
Recent studies of lifetime economic performance of dairy cows documented
a $24 loss in lifetime net income for each 1% increase in inbreeding among those
cows surviving to initiate at least one lactation.
Additional costs of inbreeding result from early embryonic death,
calfhood or heifer mortality, reduced growth rates, and infertility in virgin
heifers. Use of bulls unrelated to females produced by standard genetic
improvement programs would, in almost all cases, severely retard or negate
genetic improvement. The goal,
then, of the rational dairy herd manager seeking to achieve a 40,000 lb. herd
average, should be to manage inbreeding and selection jointly, rather than to
avoid inbreeding altogether.
Options to manage inbreeding include mating programs that
avoid inbreeding above specified maximums and procedures that choose the best
mating after adjustment for inbreeding depression in specific sire-dam
combinations. The first method
avoids major inbreeding depression, but may not properly consider the joint
effects of additive genetic improvement and inbreeding depression.
The second method, by reducing PTAs by inbreeding depression caused in a
specific matings, does consider both factors jointly.
Both methods require good pedigree data and computer support but
adjustment of PTAs is the more demanding procedure computationally.
Optimal breeding programs for the 40,000 lb. cow will consider inbreeding
in mate assignment, but only if producers maintain relatively complete pedigree
information, which is a particular challenge in large herds.
Major inbreeding depression results from close relationships such as
half-sib matings and aunt-nephew matings.
However, damaging inbreeding can result from multiple common
ancestors 3 to 5 generations’ back for potential mating pairs.
Recent research shows that both average inbreeding and variation in
inbreeding from animal to animal are suppressed by incomplete pedigree
information. Thus, pedigree data
for multiple generations will assume a higher economic value as casual
inbreeding rises in tomorrow's increasingly related dairy population.
The dairy breeding industry will ultimately benefit from the large investment in human genomic research currently underway. Dairy producers accepted the importance of genetic change early, and will offer venture capitalists a potential market for at least some of what is learned in human genome research. One challenge, however, will be to retain a core group of trained individuals knowledgeable in both the details of genomics or bioinformatics and the dairy industry itself. Current investments in dairy breeding research by public and private sources may be inadequate to nurture, even minimally, that core group. Particularly threatened is the group of mid-level industry personnel with Masters degrees in dairy cattle breeding.