Genetic Parameters
Calving Difficulty
Dairy farmers around the world are faced with an increasing amount of information and number of choices regarding their breeding programs. Genetic is now available as an easy formed to build up by developed re-evaluation of dairy cattle ability reproductive. In imagine know we could be choose the animal that good conditions. And many technologies to enlarge ability of bulls from a variety of countries with breeding values presented on a variety of traits. However, many non-production traits are not evaluated and presented by all countries. The rate of stillbirths and dystocia (calving difficulty) are examples of traits that are not always evaluated.
These differences have been summarized by Smith (1978) and can affect decision-making in all three-core areas (breeding objectives, evaluation and selection, and mating programs). An additional difference is that national improvement programs have invariably enjoyed some form and amount of governmental support in response to various aspects of market failure, based on the premise that was quite valid until the introduction of BLUP, that genetic improvement programs are both very slow and quite risky, and on the fact that few individual firms could fund the necessary scientific/technical expertise and R&D programs. Responses to these are changing: firstly because attitudes to role of government are changing, and secondly because well-organized improvement programs using BLUP invariably generate proven high rates of genetic change and these in turn stimulate more attention to optimizing the entire program.
Allowing that management and funding of "review as one method to making of improvement" is changing and will likely change further, a simple definition of such programs is that they involve some collective reproduction and production data from the whole industry and possibly producers to reviewing back of result statistical analysis, and there may be some attempt to redistribute profits with the true of the result, either directly through the result of the statistical analysis will corrected be based by theoretical. And we are importing many literate to discussing of cases on genetic parameters. Some form literate will help to reply statistical analysis.
Genetic, environmental, and management factors influence the scores for calving difficulty assigned by dairy personnel at the birth of a calf. These scores are analyzed by use of mixed model methods, which are applicable to categorical traits, to estimate the effect of factors (e.g., herd-year, sex of calf, parity, season, and sire of calf) that affect calving difficulty. The nonlinear methods used in the analysis are based on the standardized threshold model concept (7), which assumes the existence of an underlying unobservable normal variable that is categorized through fixed thresholds. The relationship between scores (1 = no problem to 5 = extreme difficulty) and the underlying continuous scale of risk associated with the unobserved variable. Sires are evaluated for calving ease based on a standardized threshold model (5) at Iowa State University; the evaluation is supported by NAAB. The model includes effects of herd-year, sex of calf, parity of dam, season of birth, birth-year group of sire, and sire of calf (5).
The evaluation of each sire is reported to dairy producers as the expected percentage of difficult births (EDB) in primiparous cows. Other information available about the evaluation of each sire includes the effective progeny number, the expected progeny difference (EPD), a 67% confidence interval, and the probability that the EDB is greater than breed average (P. J. Berger, 1990, unpublished data). Scores for calving difficulty are each 1 unit apart on the observed scale; however, differences between consecutive scores on the underlying scale of risk may be <1>1 unit apart. Therefore, thresholds or points on the continuous scale of risk marking the transition between outcomes for calving ease are estimated as part of the analysis. Herd-years are included in the threshold model analysis to account for 1) different uses of the scale for scoring of calving difficulty and 2) natural variability in the frequency of dystocia among herds and years within herds. Estimates of risk factors can be severely biased by ignoring the effect of herd-year, particularly if herd effects are large. Consideration of herds as fixed or random effects remains debatable. A threshold model with herds as fixed effects can give arbitrarily small or large estimates of herd effects when all scores for a subclass fall in the same category (7). The problem can be avoided by deletion of subclasses with all scores in the same category Q, treatment of herds as random variables (7), or fixing of the numerical limit for estimates (9). Random herd-years seem to be more appropriate for the national calving ease evaluation because more effective use is made of all information rather than a censored subset that is not representative of the frequencies of scores for dystocia in the population.
Other adjustments are also necessary: season, because dystocia is more frequent during winter than during summer; sex of calf, because males have a higher frequency of dystocia than females; parity, because primiparous cows have the highest frequency of dystocia, followed by second parity cows, and third and later parity cows in decreasing proportions; and birth-year group of sire, because the average genetic merit of sires can change over time. A fixed genetic base was implemented in 1990. Currently, progeny of all bulls born before 1977 determines the genetic base. The threshold model makes possible comparison of sires with different frequencies of progeny in one or more of the discrete categories for calving difficulty. Estimates of sire effects or EPD are the primary indication of the genetic merit of each sire for calving ease. The EPD are relative to the underlying mean of all sires in the population. A standardized threshold model (5, 7) is used; therefore, the EPD are distributed on a scale with a mean of 0 and a variance of 1, which is similar to the normal distribution that forms the basis for theoretical statistics. The EPD are estimated by use of progeny information from herds throughout the US. All progeny from all sires contribute to the estimation procedure, which is based on differences among sire progeny means for dystocia within herd-years, and these differences are pooled across all herd-years. Therefore, comparisons among sires in the population are direct and indirect.
Sires with progeny in the same herd-years are compared directly. Two sires are indirectly compared when they each have progeny in the same herd-year as a third sire. For example, the difference in sire progeny means between sire A and sire B in one herd and sires A and C in a second herd, (B-A) - (C - A) = B - C, gives the desired result. Sire A is a reference sire in this example and because data are from a large AI population, many reference sires provide comparisons among sires across herds and years. Calving difficulty scores for progeny of all sires and pedigree information are vital parts of the evaluation procedure for calving ease.
Pedigree information is a supplemental source of genetic information to actual progeny records but does not replace actual progeny records. Sires, generally those with larger progeny groups, contribute to the evaluation of sons and grandsons that are usually young AI bulls with fewer progeny. This pedigree information is incorporated into the analysis by use of the inverse of the matrix of relationships among all bulls with progeny, their sires, and maternal grandsires (8).
Calving Ease
Dystocia context are the several of the partus processing on female. Dystocia is not an all-or-none trait. Variation exists within sire progeny groups. Easy calving sires may have some progeny that are born with difficulty. Difficult calving sires can have some progeny that are born without assistance. Unlike other traits with genetic predictions expressed on a continuous scale of measurement (e.g., PTA for milk yield), EPD for calving ease are in standard deviation units and are difficult to interpret, particularly when the original data were scores of 1 to 5. The method of reporting each sire’s evaluation helps dairy producers understand the expected risk of using a sire chosen for easy or difficult calving and addresses their needs in planned mating to minimize calving difficulty of future progeny. The evaluation of each sire is reported as the EDB (i.e., the percentage of all births in the future scored 4 or 5 when the sire is mated to heifers). The EDB is calculated by transforming a linear function of the solutions from the threshold model (e.g. EPD) to a scale of probabilities. Figure 3 gives the EDB range and the frequency of sires with these values for the 20,195 sires evaluated during 1992. Some sires may have limited progeny information, particularly young sires, and their evaluation for calving ease is subject to change as more progeny information becomes available for later evaluations. The reliability of each sire’s evaluation for calving ease indicates the amount of information (i.e., from progeny records and the pedigree) available to determine the predicted merit for calving ease. Reliability is calculated by using the procedures reported by Berger (1). Reliability is properly used to choose among bulls with similar predicted genetic values for calving ease.
Dairy producers are encouraged to choose a bull with a higher reliability if the choice is between two bulls with the same EDB for calving ease. Of course, considerations other than calving ease affect the choice of bulls for breeding (e.g., PTA for milk yield). Therefore, a dairy producer may choose a bull with lower reliability for calving ease to achieve higher performance for other traits. In this case, bulls with lower reliability for calving ease should be used less frequently than other bulls of equal genetic merit for calving ease and higher reliability.
The analysis is based on direct the correct of main point from statistical result. But statistical result get is in form skore that is heritability value (h2), by heritability value we can prediction main position character’s for an animal with compared comparisons among with progeny in the same herd-years. Sires in herd-years without progeny of other AI sires in the same herd-years contribute little information to the analysis. Therefore, it is important to have as much information as possible from all sires in the same herd-years. Bulls achieve higher reliability of their genetic prediction for calving ease because their progeny are distributed over many herd-years with the progeny of other sires. The relationship between reliability and the effective progeny number, the number of progeny for a sire with directs comparisons with progeny of other sires in the evaluation.
Dystocia evaluations are intended to increase the use of AI for heifers. Bulls can be selected so that the likelihood of a difficult calving is reduced, When bulls chosen for easy calving are selected for mating to heifers, this selection should follow or be within groups of bulls of comparable genetic merit for traits with higher economic value than that for dystocia (e.g., milk yield). The sire evaluations for calving ease can be used to improve the economic efficiency of herd breeding programs without giving up genetic improvement for traits that are part of the selection objective. Yearly mean EDB of bulls that are available for breeding (unweighted) and the mean EDB of bulls used in breeding programs (i.e., weighted by the number of progeny). Breeding organizations do not select bulls for calving ease, which is confirmed by the relatively constant unweighted mean EDB. Because the weighted mean EDB is lower than the unweighted mean, bulls chosen for easy calving are used more frequently than their contemporaries with higher EDB. Therefore, the opportunity to reduce calving difficulty has been an effective incentive for producers to mate more heifers to AI bulls. National in scope and international in implication, the NAAB Calving Ease Sire Evaluation program benefits AI organizations, dairy producers in the US, and dairy producers in other countries who routinely import semen, embryos, and animals.
Birth Weight
Calving Difficulty
Dairy farmers around the world are faced with an increasing amount of information and number of choices regarding their breeding programs. Genetic is now available as an easy formed to build up by developed re-evaluation of dairy cattle ability reproductive. In imagine know we could be choose the animal that good conditions. And many technologies to enlarge ability of bulls from a variety of countries with breeding values presented on a variety of traits. However, many non-production traits are not evaluated and presented by all countries. The rate of stillbirths and dystocia (calving difficulty) are examples of traits that are not always evaluated.
These differences have been summarized by Smith (1978) and can affect decision-making in all three-core areas (breeding objectives, evaluation and selection, and mating programs). An additional difference is that national improvement programs have invariably enjoyed some form and amount of governmental support in response to various aspects of market failure, based on the premise that was quite valid until the introduction of BLUP, that genetic improvement programs are both very slow and quite risky, and on the fact that few individual firms could fund the necessary scientific/technical expertise and R&D programs. Responses to these are changing: firstly because attitudes to role of government are changing, and secondly because well-organized improvement programs using BLUP invariably generate proven high rates of genetic change and these in turn stimulate more attention to optimizing the entire program.
Allowing that management and funding of "review as one method to making of improvement" is changing and will likely change further, a simple definition of such programs is that they involve some collective reproduction and production data from the whole industry and possibly producers to reviewing back of result statistical analysis, and there may be some attempt to redistribute profits with the true of the result, either directly through the result of the statistical analysis will corrected be based by theoretical. And we are importing many literate to discussing of cases on genetic parameters. Some form literate will help to reply statistical analysis.
Genetic, environmental, and management factors influence the scores for calving difficulty assigned by dairy personnel at the birth of a calf. These scores are analyzed by use of mixed model methods, which are applicable to categorical traits, to estimate the effect of factors (e.g., herd-year, sex of calf, parity, season, and sire of calf) that affect calving difficulty. The nonlinear methods used in the analysis are based on the standardized threshold model concept (7), which assumes the existence of an underlying unobservable normal variable that is categorized through fixed thresholds. The relationship between scores (1 = no problem to 5 = extreme difficulty) and the underlying continuous scale of risk associated with the unobserved variable. Sires are evaluated for calving ease based on a standardized threshold model (5) at Iowa State University; the evaluation is supported by NAAB. The model includes effects of herd-year, sex of calf, parity of dam, season of birth, birth-year group of sire, and sire of calf (5).
The evaluation of each sire is reported to dairy producers as the expected percentage of difficult births (EDB) in primiparous cows. Other information available about the evaluation of each sire includes the effective progeny number, the expected progeny difference (EPD), a 67% confidence interval, and the probability that the EDB is greater than breed average (P. J. Berger, 1990, unpublished data). Scores for calving difficulty are each 1 unit apart on the observed scale; however, differences between consecutive scores on the underlying scale of risk may be <1>1 unit apart. Therefore, thresholds or points on the continuous scale of risk marking the transition between outcomes for calving ease are estimated as part of the analysis. Herd-years are included in the threshold model analysis to account for 1) different uses of the scale for scoring of calving difficulty and 2) natural variability in the frequency of dystocia among herds and years within herds. Estimates of risk factors can be severely biased by ignoring the effect of herd-year, particularly if herd effects are large. Consideration of herds as fixed or random effects remains debatable. A threshold model with herds as fixed effects can give arbitrarily small or large estimates of herd effects when all scores for a subclass fall in the same category (7). The problem can be avoided by deletion of subclasses with all scores in the same category Q, treatment of herds as random variables (7), or fixing of the numerical limit for estimates (9). Random herd-years seem to be more appropriate for the national calving ease evaluation because more effective use is made of all information rather than a censored subset that is not representative of the frequencies of scores for dystocia in the population.
Other adjustments are also necessary: season, because dystocia is more frequent during winter than during summer; sex of calf, because males have a higher frequency of dystocia than females; parity, because primiparous cows have the highest frequency of dystocia, followed by second parity cows, and third and later parity cows in decreasing proportions; and birth-year group of sire, because the average genetic merit of sires can change over time. A fixed genetic base was implemented in 1990. Currently, progeny of all bulls born before 1977 determines the genetic base. The threshold model makes possible comparison of sires with different frequencies of progeny in one or more of the discrete categories for calving difficulty. Estimates of sire effects or EPD are the primary indication of the genetic merit of each sire for calving ease. The EPD are relative to the underlying mean of all sires in the population. A standardized threshold model (5, 7) is used; therefore, the EPD are distributed on a scale with a mean of 0 and a variance of 1, which is similar to the normal distribution that forms the basis for theoretical statistics. The EPD are estimated by use of progeny information from herds throughout the US. All progeny from all sires contribute to the estimation procedure, which is based on differences among sire progeny means for dystocia within herd-years, and these differences are pooled across all herd-years. Therefore, comparisons among sires in the population are direct and indirect.
Sires with progeny in the same herd-years are compared directly. Two sires are indirectly compared when they each have progeny in the same herd-year as a third sire. For example, the difference in sire progeny means between sire A and sire B in one herd and sires A and C in a second herd, (B-A) - (C - A) = B - C, gives the desired result. Sire A is a reference sire in this example and because data are from a large AI population, many reference sires provide comparisons among sires across herds and years. Calving difficulty scores for progeny of all sires and pedigree information are vital parts of the evaluation procedure for calving ease.
Pedigree information is a supplemental source of genetic information to actual progeny records but does not replace actual progeny records. Sires, generally those with larger progeny groups, contribute to the evaluation of sons and grandsons that are usually young AI bulls with fewer progeny. This pedigree information is incorporated into the analysis by use of the inverse of the matrix of relationships among all bulls with progeny, their sires, and maternal grandsires (8).
Calving Ease
Dystocia context are the several of the partus processing on female. Dystocia is not an all-or-none trait. Variation exists within sire progeny groups. Easy calving sires may have some progeny that are born with difficulty. Difficult calving sires can have some progeny that are born without assistance. Unlike other traits with genetic predictions expressed on a continuous scale of measurement (e.g., PTA for milk yield), EPD for calving ease are in standard deviation units and are difficult to interpret, particularly when the original data were scores of 1 to 5. The method of reporting each sire’s evaluation helps dairy producers understand the expected risk of using a sire chosen for easy or difficult calving and addresses their needs in planned mating to minimize calving difficulty of future progeny. The evaluation of each sire is reported as the EDB (i.e., the percentage of all births in the future scored 4 or 5 when the sire is mated to heifers). The EDB is calculated by transforming a linear function of the solutions from the threshold model (e.g. EPD) to a scale of probabilities. Figure 3 gives the EDB range and the frequency of sires with these values for the 20,195 sires evaluated during 1992. Some sires may have limited progeny information, particularly young sires, and their evaluation for calving ease is subject to change as more progeny information becomes available for later evaluations. The reliability of each sire’s evaluation for calving ease indicates the amount of information (i.e., from progeny records and the pedigree) available to determine the predicted merit for calving ease. Reliability is calculated by using the procedures reported by Berger (1). Reliability is properly used to choose among bulls with similar predicted genetic values for calving ease.
Dairy producers are encouraged to choose a bull with a higher reliability if the choice is between two bulls with the same EDB for calving ease. Of course, considerations other than calving ease affect the choice of bulls for breeding (e.g., PTA for milk yield). Therefore, a dairy producer may choose a bull with lower reliability for calving ease to achieve higher performance for other traits. In this case, bulls with lower reliability for calving ease should be used less frequently than other bulls of equal genetic merit for calving ease and higher reliability.
The analysis is based on direct the correct of main point from statistical result. But statistical result get is in form skore that is heritability value (h2), by heritability value we can prediction main position character’s for an animal with compared comparisons among with progeny in the same herd-years. Sires in herd-years without progeny of other AI sires in the same herd-years contribute little information to the analysis. Therefore, it is important to have as much information as possible from all sires in the same herd-years. Bulls achieve higher reliability of their genetic prediction for calving ease because their progeny are distributed over many herd-years with the progeny of other sires. The relationship between reliability and the effective progeny number, the number of progeny for a sire with directs comparisons with progeny of other sires in the evaluation.
Dystocia evaluations are intended to increase the use of AI for heifers. Bulls can be selected so that the likelihood of a difficult calving is reduced, When bulls chosen for easy calving are selected for mating to heifers, this selection should follow or be within groups of bulls of comparable genetic merit for traits with higher economic value than that for dystocia (e.g., milk yield). The sire evaluations for calving ease can be used to improve the economic efficiency of herd breeding programs without giving up genetic improvement for traits that are part of the selection objective. Yearly mean EDB of bulls that are available for breeding (unweighted) and the mean EDB of bulls used in breeding programs (i.e., weighted by the number of progeny). Breeding organizations do not select bulls for calving ease, which is confirmed by the relatively constant unweighted mean EDB. Because the weighted mean EDB is lower than the unweighted mean, bulls chosen for easy calving are used more frequently than their contemporaries with higher EDB. Therefore, the opportunity to reduce calving difficulty has been an effective incentive for producers to mate more heifers to AI bulls. National in scope and international in implication, the NAAB Calving Ease Sire Evaluation program benefits AI organizations, dairy producers in the US, and dairy producers in other countries who routinely import semen, embryos, and animals.
Birth Weight
Birth weight will changes for every partus and every time. Birth weight can helped to the explaining capability of the genetic parameters concept. Some researchers were back up birth weight to data and their do it for every time. We know that we will found the difference an animal of birth weight for every time. Calves will has birth weight ….
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