What is NSIP?
NSIP -- the National Sheep Improvement Program -- is a computerized, performance-based program for genetic
selection. NSIP is designed to help purebred sheep producers identify the best genetic stock for their breeding
programs. NSIP also gives breeders reliable information that they can use to advertise and sell their breeding
stock. NSIP uses the most modern, scientifically-proven technology to measure genetic performance. This
technology -- called EPDs -- has been used extensively in the dairy, beef cattle, and swine industries for many
years, and is only now being implemented in the sheep industry.
NSIP works through the breed associations, and in certain situations groups of producers, to deliver across-flock
EPDs to purebred producers. Breeders use these EPDs to guide them in their selection and genetic improvement
programs. A producer who is a member of NSIP receives reports on the genetic values for every animal in a flock,
based on the performances of those animals and all the animals that are genetically related to them, over many
years and management systems. By using EPDs, a breeder can make genetic improvements efficiently and
reliably. EPDs allow a breeder to rank all the animals by genetic value, identify high-producing replacements, and
cull poor-producing animals.
What are EPDs?
"EPD" is short for "Expected Progeny Difference." An EPD is an estimate of the genetic merit of an animal for a
single trait. Specifically, the EPD of an animal is the expected difference between the performance of that animal's
progeny and the average progeny performance of all the animals in the breed, for that trait.
How are EPDs calculated?
First, purebred producers record the performance values for their animals (weights, numbers of lambs born, wool
characteristics, etc) and enter all this information into electronic data entry forms. They then send these forms to
the breed association offices, where the data is compiled and checked and then sent to the NSIP computer. For
each breed, NSIP collects these performance records from purebred flocks across the country, breed by breed.
This data comes from sheep reared under many different management systems, year after year after year.
The NSIP computer then identifies the genetic linkages between these flocks and across years -- like when rams
are sold or traded, or when progeny are distributed into many flocks -- and puts this data into one massive
calculation for each breed. The NSIP dataset for a breed also includes all the data from previous years, for all the
relatives, across generations. The EPD calculations even include data from related traits, because an animal's
performance in any trait gives information on how it will perform in a similar trait (for example, a good preweaning
weight for a fast-growing lamb suggests it will also have a good postweaning weight). These calculations produce
EPD values on every trait for every ram, ewe, and lamb in the system. And these EPDs are recalculated annually
(or more often for accelerated flocks), after the performance records from each new production cycle are entered
into the computer.
Definition of Prediction Error
The 2001 NSIP genetic evaluations introduce a new measure of the accuracy of EPDs. The measure is known as
"prediction error" and is designed to avoid some of the confusion and misunderstanding that has been associated
with the accuracy measures used in the past. The prediction error directly reflects the amount of future change in
EPD that can be anticipated as more data accumulates on an animal, its relatives, and, most importantly, its
progeny. Prediction error is expressed in the same units as the trait being evaluated (pounds for weight traits,
microns for fleece grade, etc.). Thus each animal has a prediction error associated with the EPD for each trait.
Recall that the EPD is an estimate of its genetic merit based on accumulated performance records. As more
information accumulates, the EPD can change. When little information is initially available, future changes can be
relatively large. But once a substantial amount of performance data accumulates, the EPD becomes increasingly
stable. The prediction error is a measure of the anticipated stability of an EPD. It differs from the accuracy values
used before in that it directly addresses the magnitude of possible future change in the EPD whereas the
accuracy gave only a relative measure of stability of the EPD. The prediction error was used for a time in the beef
industry, where it was known as the "possible change", but was eventually discarded in favor of accuracy. In NSIP,
where accuracy values are lower than in the beef industry, we believe that prediction error is a more useful
measure of the stability of the EPD.
An EPD for an animal can be though of as an estimate surrounded by error. The prediction error quantifies the
magnitude of that error. The properties of prediction error can be summarized relatively easily:
1. There is about one chance in three (a probability of about .33) that an animal’s EPD for a given trait will change
(either increase or decrease) by more than the amount of the prediction error. The probability that the EPD will go
down by an amount greater than the prediction error is thus about one chance in six. The corresponding
probability that an EPD will go up by an amount greater than the prediction error is likewise about one chance in
six.
2. There is only about one chance in 20 that an EPD will change by more than two times the prediction error.
3. An EPD is unlikely to change by more than three times the prediction error (about one chance in 385).
It is important to realize that the reported EPD is the best estimate we have of the true EPD, and the most likely
value of the true EPD. When possible change is large, future changes in EPD may be relatively large. However, it
is also important to recognize that the direction of these changes is not predictable. An animal with a large
positive EPD and high possible change value could show a significant future drop in its EPD. Or its EPD could go
up substantially. Either result is equally likely, which is why breeders should focus on the reported EPDs, and
largely ignore accuracy and prediction error, when attempting to determine relative genetic merit. Prediction error
should be used only as a risk management tool. If two rams being considered for use have similar EPDs but differ
substantially in prediction error, the ram with the smaller prediction error is the less risky choice, since his EPDs
are more stable. Prediction error alone is of no value; a ram with mediocre EPDs and low prediction errors is
simply an animal that you can be confident will be mediocre.
Using Prediction Error
The main use of the prediction error comes when you are forced to choose between a young ram with very high
EPDs but also with relatively high prediction errors and an older progeny-tested ram with good, but lower, EPDs
and prediction errors. Again, the issue is risk aversion. If your goal is the leap to an elite position in the breed and
if you are willing (and have the resources) to take some chances to get there, the younger ram will be the best
choice. Over time, it is always better to go with the higher EPD animals, but when prediction errors are large, you
may have to weather a few disappointments along the way. On the other hand, if consistency and reliability of
production are key to you, you may pay more attention to prediction error, preferring to use proven rams with less
risk of future changes in EPD. But overall genetic progress in the flock may be slower.
A rough guideline to assessing reliability of an EPD is that if the EPD exceeds the possible change value, an
animal with a positive EPD is unlikely (one chance in six) to drop below zero in the future, and an animal with a
negative EPD is not very likely to move above the average. Also, small differences in possible change are not
worth worrying about. The issues of importance come when making choices between ram lambs and progeny-
tested sires or between adult ewes and ewe lambs. Differences in prediction errors among ewes in the breeding
flock are almost never large enough to be important. Focus on EPDs in selecting and culling breeding ewes. Don’t
worry about small differences in possible change!
Finally, realize that without widespread AI, the sheep industry will not have the large numbers of proven sires
found in dairy cattle. In most cases, our objective is not to find a few exceptional rams, although when such
animals do emerge, they will, of course, be welcome. Our goal is to select groups of replacement ewes and rams
that will provide consistent genetic improvement. Thus flocks of reasonable size need to focus on the average
genetic merit of the rams purchased or the ewe lambs retained each year.
The concept of prediction error can be extended directly to groups of animals. If a breeder goes out to buy four
ram lambs, each with EPDs for weaning weight of about +1.5 pound, the prediction error for these rams will
typically be around 1.7 pounds. For the group of four, the average EPD will still be +1.5, but the prediction error
of the group average will now be only about .85 pounds. Thus, your new rams, as a group, can be expected to
reliably enhance weaning weights in your flock. Similarly, if you purchase a group of 10 ewe lambs, each with EPD
and prediction error of .5 ± 1.2 pounds for maternal milk, the mean EPD of the group is still .5 but the prediction
error of the group mean reduces to .4 pounds, giving you more confidence in the genetic merit of the set of ewe
lambs. These examples demonstrate why larger flocks can pay less attention to prediction errors while relying on
difference among individual animals to average out future changes in EPDs. In contrast, small, single-ram flocks
will have to decide for themselves how much risk is acceptable when they only buy a new ram every year or two.
Can Rams Be Compared Under Different Management Conditions?
Yes. across-flock EPDs are designed to allow this comparison. The calculation of EPDs uses data from many
different flocks, and this procedure is mathematically valid across flocks. This means that a range operation in dry
country can use rams from a Midwestern corn-soy crop farm, and that a Midwest farm can identify top-quality
range rams reared on sagebrush and rattlesnakes. Of course, on each farm, the groceries and health still have to
be good enough to permit good performance, and in particularly stressful environments (such as desert range)
there may be unique genetic adaptations that affect performance. But at least EPDs give a producer a clear and
reliable report about an animal's genetic potential.
What about Commercial Producers?
EPDs are only calculated on purebred animals. EPDs are not calculated for commercial flocks. Commercial
producers do not join NSIP directly. Commercial producers, however, can really benefit from NSIP because they
can purchase rams (and ewes) from NSIP purebred flocks that have precisely the improved traits that they need.
Because EPDs are provided on a trait-by-trait basis, commercial producers can decide what traits they need for
their operation and then use NSIP to find rams and ewes that excel in those specific traits. The Breed
Associations publish "Sire Summaries" -- in printed form and on their websites -- which are genetic catalogs that
list all the NSIP sires in that breed, trait by trait. These sire summaries often include lists of "trait leaders," which is
convenient for quickly identifying top genetics. Commercial producers can study these sire summaries and easily
find the best sires and dams which carry the improved traits for their own operations
What Traits does NSIP Evaluate?
Maternal Traits:
NSIP evaluates all individual animals within a flock for three very important maternal traits: (1) number of lambs
born per ewe lambing, and (2) maternal milk, and (3) Milk+Growth. To obtain an accurate evaluation of genetic
merit for each of these traits, producers record information on all ewes exposed for breeding and all lambs born in
each production cycle.
Growth Traits:
NSIP evaluates growth for three possible weights: weaning weight, postweaning weight, and yearling weight. Farm
flocks and range flocks are analyzed differently because their weighing schedules are so different. Farm flocks
receive 60-day weaning weights and 120-day postweaning weights. For farm flocks, the cutoff point between
weaning weight and postweaning weight is 90 days. Range flocks receive 120-day weaning weights and yearling
weights. Some range flocks also chose to take 60-day preweaning weights, and those weights are used in their
genetic analysis. NSIP accepts generous time windows around each age to weigh lambs, so that any flock can
arrange convenient weigh dates to fit its management schedule.
NSIP -- the National Sheep Improvement Program -- is a computerized, performance-based program for genetic
selection. NSIP is designed to help purebred sheep producers identify the best genetic stock for their breeding
programs. NSIP also gives breeders reliable information that they can use to advertise and sell their breeding
stock. NSIP uses the most modern, scientifically-proven technology to measure genetic performance. This
technology -- called EPDs -- has been used extensively in the dairy, beef cattle, and swine industries for many
years, and is only now being implemented in the sheep industry.
NSIP works through the breed associations, and in certain situations groups of producers, to deliver across-flock
EPDs to purebred producers. Breeders use these EPDs to guide them in their selection and genetic improvement
programs. A producer who is a member of NSIP receives reports on the genetic values for every animal in a flock,
based on the performances of those animals and all the animals that are genetically related to them, over many
years and management systems. By using EPDs, a breeder can make genetic improvements efficiently and
reliably. EPDs allow a breeder to rank all the animals by genetic value, identify high-producing replacements, and
cull poor-producing animals.
What are EPDs?
"EPD" is short for "Expected Progeny Difference." An EPD is an estimate of the genetic merit of an animal for a
single trait. Specifically, the EPD of an animal is the expected difference between the performance of that animal's
progeny and the average progeny performance of all the animals in the breed, for that trait.
How are EPDs calculated?
First, purebred producers record the performance values for their animals (weights, numbers of lambs born, wool
characteristics, etc) and enter all this information into electronic data entry forms. They then send these forms to
the breed association offices, where the data is compiled and checked and then sent to the NSIP computer. For
each breed, NSIP collects these performance records from purebred flocks across the country, breed by breed.
This data comes from sheep reared under many different management systems, year after year after year.
The NSIP computer then identifies the genetic linkages between these flocks and across years -- like when rams
are sold or traded, or when progeny are distributed into many flocks -- and puts this data into one massive
calculation for each breed. The NSIP dataset for a breed also includes all the data from previous years, for all the
relatives, across generations. The EPD calculations even include data from related traits, because an animal's
performance in any trait gives information on how it will perform in a similar trait (for example, a good preweaning
weight for a fast-growing lamb suggests it will also have a good postweaning weight). These calculations produce
EPD values on every trait for every ram, ewe, and lamb in the system. And these EPDs are recalculated annually
(or more often for accelerated flocks), after the performance records from each new production cycle are entered
into the computer.
Definition of Prediction Error
The 2001 NSIP genetic evaluations introduce a new measure of the accuracy of EPDs. The measure is known as
"prediction error" and is designed to avoid some of the confusion and misunderstanding that has been associated
with the accuracy measures used in the past. The prediction error directly reflects the amount of future change in
EPD that can be anticipated as more data accumulates on an animal, its relatives, and, most importantly, its
progeny. Prediction error is expressed in the same units as the trait being evaluated (pounds for weight traits,
microns for fleece grade, etc.). Thus each animal has a prediction error associated with the EPD for each trait.
Recall that the EPD is an estimate of its genetic merit based on accumulated performance records. As more
information accumulates, the EPD can change. When little information is initially available, future changes can be
relatively large. But once a substantial amount of performance data accumulates, the EPD becomes increasingly
stable. The prediction error is a measure of the anticipated stability of an EPD. It differs from the accuracy values
used before in that it directly addresses the magnitude of possible future change in the EPD whereas the
accuracy gave only a relative measure of stability of the EPD. The prediction error was used for a time in the beef
industry, where it was known as the "possible change", but was eventually discarded in favor of accuracy. In NSIP,
where accuracy values are lower than in the beef industry, we believe that prediction error is a more useful
measure of the stability of the EPD.
An EPD for an animal can be though of as an estimate surrounded by error. The prediction error quantifies the
magnitude of that error. The properties of prediction error can be summarized relatively easily:
1. There is about one chance in three (a probability of about .33) that an animal’s EPD for a given trait will change
(either increase or decrease) by more than the amount of the prediction error. The probability that the EPD will go
down by an amount greater than the prediction error is thus about one chance in six. The corresponding
probability that an EPD will go up by an amount greater than the prediction error is likewise about one chance in
six.
2. There is only about one chance in 20 that an EPD will change by more than two times the prediction error.
3. An EPD is unlikely to change by more than three times the prediction error (about one chance in 385).
It is important to realize that the reported EPD is the best estimate we have of the true EPD, and the most likely
value of the true EPD. When possible change is large, future changes in EPD may be relatively large. However, it
is also important to recognize that the direction of these changes is not predictable. An animal with a large
positive EPD and high possible change value could show a significant future drop in its EPD. Or its EPD could go
up substantially. Either result is equally likely, which is why breeders should focus on the reported EPDs, and
largely ignore accuracy and prediction error, when attempting to determine relative genetic merit. Prediction error
should be used only as a risk management tool. If two rams being considered for use have similar EPDs but differ
substantially in prediction error, the ram with the smaller prediction error is the less risky choice, since his EPDs
are more stable. Prediction error alone is of no value; a ram with mediocre EPDs and low prediction errors is
simply an animal that you can be confident will be mediocre.
Using Prediction Error
The main use of the prediction error comes when you are forced to choose between a young ram with very high
EPDs but also with relatively high prediction errors and an older progeny-tested ram with good, but lower, EPDs
and prediction errors. Again, the issue is risk aversion. If your goal is the leap to an elite position in the breed and
if you are willing (and have the resources) to take some chances to get there, the younger ram will be the best
choice. Over time, it is always better to go with the higher EPD animals, but when prediction errors are large, you
may have to weather a few disappointments along the way. On the other hand, if consistency and reliability of
production are key to you, you may pay more attention to prediction error, preferring to use proven rams with less
risk of future changes in EPD. But overall genetic progress in the flock may be slower.
A rough guideline to assessing reliability of an EPD is that if the EPD exceeds the possible change value, an
animal with a positive EPD is unlikely (one chance in six) to drop below zero in the future, and an animal with a
negative EPD is not very likely to move above the average. Also, small differences in possible change are not
worth worrying about. The issues of importance come when making choices between ram lambs and progeny-
tested sires or between adult ewes and ewe lambs. Differences in prediction errors among ewes in the breeding
flock are almost never large enough to be important. Focus on EPDs in selecting and culling breeding ewes. Don’t
worry about small differences in possible change!
Finally, realize that without widespread AI, the sheep industry will not have the large numbers of proven sires
found in dairy cattle. In most cases, our objective is not to find a few exceptional rams, although when such
animals do emerge, they will, of course, be welcome. Our goal is to select groups of replacement ewes and rams
that will provide consistent genetic improvement. Thus flocks of reasonable size need to focus on the average
genetic merit of the rams purchased or the ewe lambs retained each year.
The concept of prediction error can be extended directly to groups of animals. If a breeder goes out to buy four
ram lambs, each with EPDs for weaning weight of about +1.5 pound, the prediction error for these rams will
typically be around 1.7 pounds. For the group of four, the average EPD will still be +1.5, but the prediction error
of the group average will now be only about .85 pounds. Thus, your new rams, as a group, can be expected to
reliably enhance weaning weights in your flock. Similarly, if you purchase a group of 10 ewe lambs, each with EPD
and prediction error of .5 ± 1.2 pounds for maternal milk, the mean EPD of the group is still .5 but the prediction
error of the group mean reduces to .4 pounds, giving you more confidence in the genetic merit of the set of ewe
lambs. These examples demonstrate why larger flocks can pay less attention to prediction errors while relying on
difference among individual animals to average out future changes in EPDs. In contrast, small, single-ram flocks
will have to decide for themselves how much risk is acceptable when they only buy a new ram every year or two.
Can Rams Be Compared Under Different Management Conditions?
Yes. across-flock EPDs are designed to allow this comparison. The calculation of EPDs uses data from many
different flocks, and this procedure is mathematically valid across flocks. This means that a range operation in dry
country can use rams from a Midwestern corn-soy crop farm, and that a Midwest farm can identify top-quality
range rams reared on sagebrush and rattlesnakes. Of course, on each farm, the groceries and health still have to
be good enough to permit good performance, and in particularly stressful environments (such as desert range)
there may be unique genetic adaptations that affect performance. But at least EPDs give a producer a clear and
reliable report about an animal's genetic potential.
What about Commercial Producers?
EPDs are only calculated on purebred animals. EPDs are not calculated for commercial flocks. Commercial
producers do not join NSIP directly. Commercial producers, however, can really benefit from NSIP because they
can purchase rams (and ewes) from NSIP purebred flocks that have precisely the improved traits that they need.
Because EPDs are provided on a trait-by-trait basis, commercial producers can decide what traits they need for
their operation and then use NSIP to find rams and ewes that excel in those specific traits. The Breed
Associations publish "Sire Summaries" -- in printed form and on their websites -- which are genetic catalogs that
list all the NSIP sires in that breed, trait by trait. These sire summaries often include lists of "trait leaders," which is
convenient for quickly identifying top genetics. Commercial producers can study these sire summaries and easily
find the best sires and dams which carry the improved traits for their own operations
What Traits does NSIP Evaluate?
Maternal Traits:
NSIP evaluates all individual animals within a flock for three very important maternal traits: (1) number of lambs
born per ewe lambing, and (2) maternal milk, and (3) Milk+Growth. To obtain an accurate evaluation of genetic
merit for each of these traits, producers record information on all ewes exposed for breeding and all lambs born in
each production cycle.
Growth Traits:
NSIP evaluates growth for three possible weights: weaning weight, postweaning weight, and yearling weight. Farm
flocks and range flocks are analyzed differently because their weighing schedules are so different. Farm flocks
receive 60-day weaning weights and 120-day postweaning weights. For farm flocks, the cutoff point between
weaning weight and postweaning weight is 90 days. Range flocks receive 120-day weaning weights and yearling
weights. Some range flocks also chose to take 60-day preweaning weights, and those weights are used in their
genetic analysis. NSIP accepts generous time windows around each age to weigh lambs, so that any flock can
arrange convenient weigh dates to fit its management schedule.
| About NSIP The same information is found on the NSIP web site. Info on NSIP Uncompahgre Polypay Farm The home of production Polypays |
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