- Email: [email protected]
Sampling Bulls from Planned Matings in Artificial Insemination
Sampling Bulls f r o m Planned Matings in Artificial Insemination W. LEE KUCKER ~ Select Sires, Inc. Plain City, OH 43064 INTRODUCTION A bull's genetic worth for milk production can be measured only in terms of the progeny he leaves. An indirect measurement such as this may be subject to many different forms of bias. These biases are of varying degrees of complexity; some can be corrected for with relative ease, (i.e., record length); others are difficult (i.e., genetic trend). Fortunately, from a genetic standpoint the incidence of single-herd proofs being used extensively is becoming less. Even syndicate proofs, where dairymen join together to purchase and progeny test a bull and have vested interest in the bull, are recognized as less than ideal. Because of declining numbers in some breeds, some organizations are forced to use this type of sire proving. However, in the Holstein breed this is the exception. Most artificial insemination organizations have some form of young sire sampling program through which they hope to obtain at least part of their future proven sire battery. Naturally, the percentage which will come from sampling program is dependent upon such variables as the rate of turn over of proven bulls, current semen needs by the organization, and even philosophy of management personnel. The accurate measure of genetic merit is not an equation but a system for evaluation. There are many different types of sampling programs used in AI. No doubt each organization has some feature which makes its program unique. The optimum sampling program would provide maximum information with the fewest number of daughters. This would allow the largest number of bulls to be sampled on the available testing population.
Dairyman's Choice The first of the AI systems of sampling young bulls has been referred to as the "dairy-
Received October 22, 1974. ~Address: Curtiss Breeding Service, Cary, [L 60013.
man's choice". Most AI organizations have used such a system in the past, and because of its simplicity some continue to use it today. Under this system when the young bull becomes available, he is advertised, ~nd the dairyman chooses the young bull he wants to use. Problems with this type of program are: AI has no control over which bull the dairyman chooses, how he is used, when he is used, or even if the dairyman is an official test. Consequently, some bulls may be over-sampled while others may scarcely be sampled at all. The sampling period as well may vary widely from bull to bull.
Supervised Program A second type of AI sampling can be referred to as a "technician-implemented and supervised program". These programs have worked well where the organization has employed technicians located in areas with large concentrations of cows. Bulls can be sampled uniformly and quickly under such a system; however, an effort should be made to recognize the dams on which the young sires are being used. If the dairyman allows the technician to breed only cows below average to the young sires, bias in selection could jeopardize their eventual proofs. The dairyman needs to feel he is a part of the sampling program, be aware of its goals, and strive to uphold them.
Organized Sampling An organized sampling program is the third type of program. It represents the ideal and has many advantages over the two previously mentioned systems. Both parties know what is expected of them and agree to specific objectives from the start. The dairyman, for example, agrees to breed a certain percentage of his herd randomly to young sires and supply AI with any other information it may request. He also realizes the importance of rearing the daughters of the young sires and bringing them into the milking herd with neither favoritism nor discrimination. Daughters of young sires are
1083
1084
KUCKER
also subject to the same culling standards as their herdmates. The AI organization, on the other hand, agrees to provide the dairyman with various additional incentives which will be of immediate benefit to him (i.e., supplying semen from proven sires in short supply). Such herds are extremely important to the future of AI. In the symposium on "expanding the influence and participation in the national cooperative dairy herd improvement program", the point was made again that herds on official test were carrying an unfair part of the load for progeny testing of bulls. Because of this they should be given a "price break" on proven bulls available through AI. Because a herd is on official test and using AI, does not qualify it automatically for such advantages. A herd that adds the 1000th daughter to a sire's proof, for example, contributes little information on that sire; however, those that have one of the first 40 to 50 daughters contribute a great deal. Such herds merit and receive recognition and rewards from the A1 organization for their efforts. The major goal of any structured sampling program is to obtain an accurate progeny test on as man 3, selected young sires as possible. To accomplish this goal the underlying assumptions of the Predicted Difference must be met. Randomization of dams is the first assumption which comes to mind. Dams can be randomized a number of ways; every fourth c o w , second service cows, next cows in heat upon receiving the young sire semen, and the like. It is advisable to offer a number of options because of the individuality of dairymen. Human nature says a dairyman is more likely to comply with a program he selects rather than one that is inflexible where no choice is given. As long as the A1 organization obtains a cross section of the genetic value of the herd, the manner in which it is obtained should be inmaterial, providing it is random. Removing Correctional Factors
In organizing a sampling program the model for calculating the Predicted Difference should be studied in detail. The design of the sampling program should obtain an efficient and accurate proof on all young sires. It also should eliminate as many correctional factors as possible from the equation. Journal of Dairy Science Vol. 58, No. "7
One way to increase the accuracy of a sire proof is to raise repeatability by increasing the number of daughters summarized. There is a point of diminishing returns which AI organizations must determine for themselves. It is likely that it will take somewhere between 8 to 10 reported services for each daughter in the initial proof of a sire. Thus, to obtain 50 daughters of a young sire, for example, a minimum of 500 doses of semen must be distributed for each bull. By distributing the semen over many herds, the genetic merit of a sire is determined through a variety of environmental situations. This randomizes environmental effects that strongly influence production by a progeny group. By maximizing the daughter distribution, the correction for the distribution of daughters over herds, as well as the c 2 term, will be eliminated from calculations of repeatability. This in itself will raise the repeatability of the sire's proof and increase its accuracy. It generally is accepted in research that the design of an experiment should eliminate as many correctional factors as possible. This basic philosophy has a place in the progeny testing of young bulls as well. By studying the current model for calculating the Predicted Difference, it appears the most accurate proof should result for sampling all young bulls across a similar cross-section of herds whose mean is essentially breed average. By standardizing the herds and herdmates against which each bull will be compared, and randomizing the dams on which they are used, the raw daughter average should rank the young bulls similarly to the calculated Predicted Difference. An organized sampling program also has the advantage of using the young sire semen only in herds on official test. These should be wellmanaged herds where the owner agrees to identify accuarely and record parentage of e~tch female offspring, and report them to his Dairy Herd Improvement (DHI) supervisor at the time she enters the milking herd. In such herds little information on young bulls should be lost. A fast sampling of young sires is important to the AI organization. Not only is it expensive to house bulls of low genetic value, but identification of those which are genetically superior early in life may lengthen their productive life within AI. Also the sooner lower-proven older bulls can be replaced by these higher proven youngsters, the more genetic progress
SYMPOSIUM: CHOOSING AND SAMPLING YOUNG BULLS the AI organization can make. The need for a shorter generation interval is imperative when the a t t e m p t is to increase genetic gain. An organized sampling program is capable of supplying AI with a wealth of additional data. By feeling they are a part of the sampling program, these dairymen can collect valuable management data on daughters of young and proven bulls as well in their herd. The A1 organization also knows the location of the daughters of various bulls and can make efficient use of its time in collecting the necessary information for promotion of the bull once he comes through with a favorable proof. of Problems
Young Sire Sampling
There are a number of factors which limit young sire sampling in AI. The first problem AI has to contend with is a low fraction of dairy herds on official test. A possible alternative is to consider owner-sampler and a . m . - p . m , records in proving of young sires in AI. Granted, such records could be subject to many intentional biases in cases where the dairyman has a vested interest in the bull being proven; however, where the bull is owned b y an AI organization and the dairyman has but one daughter of any given young bull, the likelihood of an intentional bias is remote. Still another problem confronting AI is that of accurate sire identification. It does little good to involve poorly managed herds in a sampling program. Unless calves are identified accurately at birth, it is unlikely they will be identified properly when these same heifers enter the milking herd. Another obvious problem is that of apathetic DHI supervisors. It is important to encourage teamwork between the dairyman and his supervisor so that accurate information is recorded and transferred to DHI computing centers. Many DHI supervisors have been in the practice of reporting only the AI stud codes rather than the appropriate registration number of the sire. Consequently, these records have been lost from the files at United States Department of Agriculture (USDA) for sire proving. The uniform stud codes recently adopted by AI mainly
1085
through the efforts of National Association of Animal Breeders (NAAB), should allow computer centers to establish cross-indexing files to increase the percentage of usable records for sire proving. AI is in a frustrating position when it comes to evaluating the herds on sampling programs. Without the help of DHI computer centers, it is difficult to know which herds are doing the proper kind of job and which are not. Many centers print the portion of the herd without sire identification on the monthly summary sheet, but many do not. By identifying those herds which need additional attention, area representatives can work closely with the herd to correct its sire identification problems. Still another concern by AI is relative to the time lag between the reporting of significant findings in the field of animal breeding and their implementation into the industry. In the past, AI has been confronted with a monumental task of attempting to gather information on records in progress, in an attempt to project proofs on sires-in-waiting. This was extremely expensive and time consuming. With the advent of the preliminary sire summaries, more complete and more accurate information is now available than ever before. This has been one of the biggest strides taken in the area of sire proving since the adoption of the Predicted Difference itself. This is a good example of what can be done when segments of the dairy industry tackle a common problem together. Finally, the current model to calculate the Predicted Difference causes many of the young bulls being proven today to be in jeopardy. This is especially true in organizations where a high percentage of plus-proven AI sires have been used in the past. In most organized sampling programs the dairymen are given priority on the higher-proven sires, whose semen is usually in short supply. This causes the genetic trend in these herds to be even higher than the rest of the population. It is the hope of all AI organizations that when the new model is implemented, this is one bias that will be standardized, and more accurate sire proofs will result.
Journal of Dairy Science Vol. 58, No. 7
Dairyman's Choice The first of the AI systems of sampling young bulls has been referred to as the "dairy-
Received October 22, 1974. ~Address: Curtiss Breeding Service, Cary, [L 60013.
man's choice". Most AI organizations have used such a system in the past, and because of its simplicity some continue to use it today. Under this system when the young bull becomes available, he is advertised, ~nd the dairyman chooses the young bull he wants to use. Problems with this type of program are: AI has no control over which bull the dairyman chooses, how he is used, when he is used, or even if the dairyman is an official test. Consequently, some bulls may be over-sampled while others may scarcely be sampled at all. The sampling period as well may vary widely from bull to bull.
Supervised Program A second type of AI sampling can be referred to as a "technician-implemented and supervised program". These programs have worked well where the organization has employed technicians located in areas with large concentrations of cows. Bulls can be sampled uniformly and quickly under such a system; however, an effort should be made to recognize the dams on which the young sires are being used. If the dairyman allows the technician to breed only cows below average to the young sires, bias in selection could jeopardize their eventual proofs. The dairyman needs to feel he is a part of the sampling program, be aware of its goals, and strive to uphold them.
Organized Sampling An organized sampling program is the third type of program. It represents the ideal and has many advantages over the two previously mentioned systems. Both parties know what is expected of them and agree to specific objectives from the start. The dairyman, for example, agrees to breed a certain percentage of his herd randomly to young sires and supply AI with any other information it may request. He also realizes the importance of rearing the daughters of the young sires and bringing them into the milking herd with neither favoritism nor discrimination. Daughters of young sires are
1083
1084
KUCKER
also subject to the same culling standards as their herdmates. The AI organization, on the other hand, agrees to provide the dairyman with various additional incentives which will be of immediate benefit to him (i.e., supplying semen from proven sires in short supply). Such herds are extremely important to the future of AI. In the symposium on "expanding the influence and participation in the national cooperative dairy herd improvement program", the point was made again that herds on official test were carrying an unfair part of the load for progeny testing of bulls. Because of this they should be given a "price break" on proven bulls available through AI. Because a herd is on official test and using AI, does not qualify it automatically for such advantages. A herd that adds the 1000th daughter to a sire's proof, for example, contributes little information on that sire; however, those that have one of the first 40 to 50 daughters contribute a great deal. Such herds merit and receive recognition and rewards from the A1 organization for their efforts. The major goal of any structured sampling program is to obtain an accurate progeny test on as man 3, selected young sires as possible. To accomplish this goal the underlying assumptions of the Predicted Difference must be met. Randomization of dams is the first assumption which comes to mind. Dams can be randomized a number of ways; every fourth c o w , second service cows, next cows in heat upon receiving the young sire semen, and the like. It is advisable to offer a number of options because of the individuality of dairymen. Human nature says a dairyman is more likely to comply with a program he selects rather than one that is inflexible where no choice is given. As long as the A1 organization obtains a cross section of the genetic value of the herd, the manner in which it is obtained should be inmaterial, providing it is random. Removing Correctional Factors
In organizing a sampling program the model for calculating the Predicted Difference should be studied in detail. The design of the sampling program should obtain an efficient and accurate proof on all young sires. It also should eliminate as many correctional factors as possible from the equation. Journal of Dairy Science Vol. 58, No. "7
One way to increase the accuracy of a sire proof is to raise repeatability by increasing the number of daughters summarized. There is a point of diminishing returns which AI organizations must determine for themselves. It is likely that it will take somewhere between 8 to 10 reported services for each daughter in the initial proof of a sire. Thus, to obtain 50 daughters of a young sire, for example, a minimum of 500 doses of semen must be distributed for each bull. By distributing the semen over many herds, the genetic merit of a sire is determined through a variety of environmental situations. This randomizes environmental effects that strongly influence production by a progeny group. By maximizing the daughter distribution, the correction for the distribution of daughters over herds, as well as the c 2 term, will be eliminated from calculations of repeatability. This in itself will raise the repeatability of the sire's proof and increase its accuracy. It generally is accepted in research that the design of an experiment should eliminate as many correctional factors as possible. This basic philosophy has a place in the progeny testing of young bulls as well. By studying the current model for calculating the Predicted Difference, it appears the most accurate proof should result for sampling all young bulls across a similar cross-section of herds whose mean is essentially breed average. By standardizing the herds and herdmates against which each bull will be compared, and randomizing the dams on which they are used, the raw daughter average should rank the young bulls similarly to the calculated Predicted Difference. An organized sampling program also has the advantage of using the young sire semen only in herds on official test. These should be wellmanaged herds where the owner agrees to identify accuarely and record parentage of e~tch female offspring, and report them to his Dairy Herd Improvement (DHI) supervisor at the time she enters the milking herd. In such herds little information on young bulls should be lost. A fast sampling of young sires is important to the AI organization. Not only is it expensive to house bulls of low genetic value, but identification of those which are genetically superior early in life may lengthen their productive life within AI. Also the sooner lower-proven older bulls can be replaced by these higher proven youngsters, the more genetic progress
SYMPOSIUM: CHOOSING AND SAMPLING YOUNG BULLS the AI organization can make. The need for a shorter generation interval is imperative when the a t t e m p t is to increase genetic gain. An organized sampling program is capable of supplying AI with a wealth of additional data. By feeling they are a part of the sampling program, these dairymen can collect valuable management data on daughters of young and proven bulls as well in their herd. The A1 organization also knows the location of the daughters of various bulls and can make efficient use of its time in collecting the necessary information for promotion of the bull once he comes through with a favorable proof. of Problems
Young Sire Sampling
There are a number of factors which limit young sire sampling in AI. The first problem AI has to contend with is a low fraction of dairy herds on official test. A possible alternative is to consider owner-sampler and a . m . - p . m , records in proving of young sires in AI. Granted, such records could be subject to many intentional biases in cases where the dairyman has a vested interest in the bull being proven; however, where the bull is owned b y an AI organization and the dairyman has but one daughter of any given young bull, the likelihood of an intentional bias is remote. Still another problem confronting AI is that of accurate sire identification. It does little good to involve poorly managed herds in a sampling program. Unless calves are identified accurately at birth, it is unlikely they will be identified properly when these same heifers enter the milking herd. Another obvious problem is that of apathetic DHI supervisors. It is important to encourage teamwork between the dairyman and his supervisor so that accurate information is recorded and transferred to DHI computing centers. Many DHI supervisors have been in the practice of reporting only the AI stud codes rather than the appropriate registration number of the sire. Consequently, these records have been lost from the files at United States Department of Agriculture (USDA) for sire proving. The uniform stud codes recently adopted by AI mainly
1085
through the efforts of National Association of Animal Breeders (NAAB), should allow computer centers to establish cross-indexing files to increase the percentage of usable records for sire proving. AI is in a frustrating position when it comes to evaluating the herds on sampling programs. Without the help of DHI computer centers, it is difficult to know which herds are doing the proper kind of job and which are not. Many centers print the portion of the herd without sire identification on the monthly summary sheet, but many do not. By identifying those herds which need additional attention, area representatives can work closely with the herd to correct its sire identification problems. Still another concern by AI is relative to the time lag between the reporting of significant findings in the field of animal breeding and their implementation into the industry. In the past, AI has been confronted with a monumental task of attempting to gather information on records in progress, in an attempt to project proofs on sires-in-waiting. This was extremely expensive and time consuming. With the advent of the preliminary sire summaries, more complete and more accurate information is now available than ever before. This has been one of the biggest strides taken in the area of sire proving since the adoption of the Predicted Difference itself. This is a good example of what can be done when segments of the dairy industry tackle a common problem together. Finally, the current model to calculate the Predicted Difference causes many of the young bulls being proven today to be in jeopardy. This is especially true in organizations where a high percentage of plus-proven AI sires have been used in the past. In most organized sampling programs the dairymen are given priority on the higher-proven sires, whose semen is usually in short supply. This causes the genetic trend in these herds to be even higher than the rest of the population. It is the hope of all AI organizations that when the new model is implemented, this is one bias that will be standardized, and more accurate sire proofs will result.
Journal of Dairy Science Vol. 58, No. 7