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Changing Definition of Productive Life in US Holsteins: Effect on Genetic Correlations

¹ Animal and Dairy Science Department, University of Georgia, Athens 30602
² Holstein Association USA Inc., Brattleboro, VT 05301

Corresponding author: Shogo Tsuruta; e-mail: shogo{at}uga.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
Data included 392,800 records for cows born between 1995 and 1997. Traits analyzed were milk, fat, and protein yields, somatic cell score, days open (DO), 18 linear type traits, final score, and several measures of longevity. Productive life (PL) was defined as the total number of days in milk up to 84 mo of age with a restriction of 305, 500, or 999 d per lactation (PL₃₀₅, PL₅₀₀, or PL₉₉₉, respectively). Herd life was defined as the total number of days from the first calving date to the last (culling) date. A multiple-trait sire model including the effects of registration status, herd-year, age group, month of calving and stage of lactation, sire, and residual was used for parameter estimation. The average duration of the first lactation was 365 d for survivors and 386 d for culled cows. Lactation lengths for the survivors in the next 3 parities all exceeded 330 d. Heritability estimates of between 0.08 and 0.10 were obtained for all definitions of longevity. As maximum recordable PL was increased from 305 to 999 d per lactation, the genetic correlations with milk production increased (from –0.11 to +0.14) and with DO decreased (–0.62 to –0.27). Formulas for an indirect prediction of PL from correlated traits were developed. As maximum PL per lactation was increased, little change in the weights used to predict the various measures of PL, with the exception of DO was found. As the currently used value of PL₃₀₅ does not properly account for the longer lactation lengths that are routinely occurring with today’s cows, PL with longer lactations may be preferable in routine evaluation.

Key Words: genetic correlation • lactation length • longevity • productive life

Abbreviation key: DO = days open, HL = herd life, PL = productive life.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
A long productive life (PL) is a desirable trait from several different perspectives. Longevity combines all of the characteristics that are directly associated with a cow’s ability to successfully stay in the herd. No assumptions are required regarding the incidence of different health traits or the economic importance of any trait. No distinction is made between traits that are associated with involuntary or voluntary culling. They are all cumulatively rolled up into one simple measurement, when the cow is culled.

However, a complication with measuring the longevity of dairy cows is that the dairy production system is in a constant state of flux. Traits values that were perfectly acceptable at one time may later become a limiting factor. For example, the improvement in the genetic merit of dairy cows for production is being accompanied by deterioration in fertility. New technology, (e.g., bST) can change the lactation curve. Health issues, such as metritis, ketosis, or lameness, can change in incidence from being a minor to a major problem. Variable costs, such as feed and labor, can lead to important changes in how cows are managed. Therefore, PL should be considered more like a snapshot of the risk factors associated with culling rather than a definitive trait with a fixed definition that will provide steady, long-lasting associations with other traits, inasmuch as it is difficult to predict an optimal definition of PL in the future when genetic correlations of PL with other traits change over time.

When genetic evaluations for PL were first introduced in 1994, production records beyond 305 DIM were not available to the Animal Improvement Programs Laboratory of USDA; nor did the industry have access to any genetic information on reproduction. The current definition of PL is the total number of DIM with a limit of 305 DIM per lactation at 84 mo of age for US Holsteins (VanRaden and Klaaskate, 1993). Since the introduction of the PL evaluations, several factors, such as duration of the lactations, culling rates across lactations and the age distribution of the herds, may have changed. This study investigated whether previous assumptions are still applicable under today’s conditions.

Many countries include some measure of longevity in their national breeding objectives (VanRaden, 2002). Because culling decisions are not made until several months or years into a cow’s life, early predictions of PL are necessary to obtain an evaluation on young bulls and cows. Genetic evaluation of PL in Holsteins is obtained by combining direct PTA for PL along with an indirect prediction of PTA for PL from correlated traits (Weigel et al., 1998; VanRaden, 2001). Weigel et al. (1998) calculated genetic correlations of PL with type and production traits for bulls born up to 1983. Lawlor et al. (2002) and Tsuruta et al. (2004) calculated genetic correlations using data on younger animals and a model where genetic correlations were assumed as functions of year of birth. The correlations they obtained were mostly lower in magnitude than those by Weigel et al. (1998) and changed with time. The largest change in correlations was for fat yield from 0.21 (1983) to –0.08 (1993). The changes were larger than what would be expected by changes in selection intensity or breeding goals (Lawlor et al., 2002). One possibility is that many of these changes were directly related to restricting the measurement of the productive period to only 305 DIM per lactation. When almost all the cows have lactations >305 d, PL becomes a parity count.

The goals of this study were to estimate genetic parameters for PL for several definitions of PL using recent data, and to update equations for obtaining an early prediction of a cow’s PL.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
Data
A data file for production traits (305-d milk, fat, and protein yields), SCS, days open (DO), and DIM was provided by Animal Improvement Programs Laboratory-USDA, and another data file for 18 linear type traits and final scores was obtained from the Holstein Association USA, Inc. (Brattleboro, VT). Days open was calculated by the USDA based on the presentation of VanRaden et al. (2003) (i.e., for lactations with no reported services, DO = calving interval – mean gestation length (280 d); for lactations with no next calving date, DO = last service date – current calving date). Records of cows born between 1995 and 1997 were extracted from both files and merged. Cows born after 1997 were not used to avoid censored records for PL. The data set comprised 392,800 records with 17,345 sires including 3 generations. Means and standard deviations for the variables analyzed are presented in Table 1. Several studies have proposed that longevity traits such as functional PL or herd life (HL) adjusted for milk production should be used to account for voluntary culling on milk (Hudson and Van Vleck, 1981; Ducrocq et al., 1988; Allaire and Gibson, 1992; Boldman et al., 1992; Dekkers et al., 1994). However, our preliminary analysis indicated that no significant differences existed between genetic correlations of PL with other traits estimated using true unadjusted PL and functional PL adjusted with linear and quadratic regressions for milk yield, suggesting that low milk production may not be a primary reason for voluntary culling. Therefore, to simplify the interpretation of results, the PL was not adjusted for any traits in this study.

Model
The following multiple trait sire model was used:

where y = vectors of observations for 25 traits (PL, milk, fat, protein, SCS, DO, 18 linear type traits, and final score); ß = vectors of effects for registration status (registered or grade), herd-year, age group, month of calving, and stage of lactation x year at classification (type traits only); u = vectors of sire effects; e = vectors of residual effects; and X and Z = incidence matrices for corresponding effects. The variances were defined as:

where G = a 25 x 25 sire (co)variance matrix for 25 traits; = the direct matrix product; A_n = the additive genetic relationship matrix for n sires; R = a 25 x 25 residual variance matrix for 25 traits; and I = the identity matrix. Variance components were estimated with GIBBS2F90 (Misztal et al., 2002), which is a Fortran 90 program using a Bayesian approach via the Gibbs sampling algorithm. After 10,000 Gibbs samples were discarded as burn-in, 50,000 samples were used to calculate posterior means and standard deviations for (co)-variance components, heritabilities, and correlations.

Weights and Maximum Reliability
For indirect PTA of PL, weights or partial regressions of PL on genetically correlated traits were calculated as follows:

where u_PL = a vector of sire effects for PL, and U = a matrix of sire effects for other traits. Using the (co)variance matrix:

the formula for the weight can be written as

where G_PL,PL = the genetic variance for PL; G_PL,j and G_j,PL = the genetic covariances between PL and other traits; and G_i,j = the genetic (co)variance for other traits. These weights were calculated using standardized traits (divided by standard deviation of each trait) to be able to compare among traits. Maximum reliabilities for the indirect PTA of PL were calculated as Boldman et al. (1992) described:

The maximum reliability is an upper limit to the reliability of indirect PL, assuming that sire effects for other traits are known with no error. The largest value among those maximum reliabilities for all combinations can be determined to select optimum traits for the indirect prediction of PL.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
Culling Rates and Lactation Length
Culling rate was small in the first parity, but greater than 50% in the third parity, and increased to >95% in the fifth parity (Table 2). Over the past 20 yr, the age distribution of US Holsteins has shifted towards younger ages, relative to a study by Nieuwhof et al. (1989). One of the consequences of this change is that there are now fewer potential replacements per cow. The cows in this study had 2.8 parities on average, compared with 3.4 parities 20 yr ago (Nieuwhof et al., 1989). The current numbers are also underestimated somewhat, as the cows in this study are only those of 84 mo of age.

Culling rates have also changed dramatically (Table 3) in the past 20 yr. Previously, the risk of being culled rose gradually with each successive lactation (Nieuwhof et al., 1989). In this study, the risk of being culled started low (17%) within the first lactation, rose rapidly over the next 2 lactations (35 and 47%, respectively), and then dramatically increased to the point (71%) where very few cows remained after 5 lactations. Culling rates increased quickly after the first lactation and essentially no cows now survive beyond 5 lactations. This result does not mean that the higher rates of culling observed now are associated with more profit. As Van Arendonk (1985) indicated, there must be an optimum length of HL and culling rate.

View larger version (16K): [in this window] [in a new window]   Figure 1. Distribution of months-in-milk for culled cows and survivors.

Genetic correlations (with posterior standard deviations ranging from 0.03 to 0.04) between the several measures of PL and the other traits are presented in Table 5. Several characteristics of the cow had a consistent relationship with PL regardless of the length of the productive period considered. Lower SCC, more capacious and better-attached udders, shorter teats, smaller body size, straighter legs, steeper foot angle, and higher overall conformation scores were consistently related to increased longevity. Other traits such as milk yield, DO, BCS, and dairy form, which are dependent upon the partitioning of nutrients, were strongly influenced by the length of the productive period evaluated. The 2 largest changes in genetic correlation with the different measures of PL were for reproduction and production traits. These 2 types of traits had an antagonistic relationship with each other. An increase in milk production was associated with an increase in DO; the genetic correlations were from 0.49 to 0.50. As the length of the productive period measured increased, the correlation of PL with DO changed by .35 (–0.62 to –0.27). Increased fertility is always desirable, but it is of critical importance when no credit is given to those cows that are still productive after 305 DIM. These changes can be explained partially by increase of average lactation length over time and the limit of 84 mo as used for PL. The average number of lactations within 84 mo of life decreases even though the total number of DIM may not decrease as much or none at all. With longer lactations, most cows milk for at least 305 d. In this case, PL₃₀₅ becomes the number parities up to 84 mo of age x 305 d. Assuming constant days dry and gestation length, longer lactations are due longer DO. Consequently, for cows that survive to 84 mo, PL₃₀₅ becomes an inverse function of DO. As more productive cows are generally allowed a higher voluntary waiting period (and thus longer DO), this may create a negative correlation of PL₃₀₅ with production and explain the change in correlation between production and PL of various definitions. There may be several ways to avoid the strong dependency of DO and the PL measure currently used (i.e., PL₃₀₅). The first one is to use a model that does not require an 84-mo limit. The survival model (Ducrocq, 1988) is one such model, although survival analysis required more demanding computations and may lose some accuracy by ignoring female relationships. The other possibility is to increase the credit in PL for longer lactations. The use of PL with some limit would decrease the influence of outliers, but the limit is going to be arbitrary. Another possibility is to eliminate any limit and just use HL. This trait has similar properties to PL₅₀₀ and PL₉₉₉, and the interpretation of HL is simple and less controversial.

The critical point is to select a PL measure that corresponds to the productive period under which the cows are exposed. Because most cows are milking for longer than 305 DIM, the productive period evaluated needs to include this additional time.

The relationships found within this study for dairy form and BCS with the different measures of PL sheds light on some important health issues of today’s cows. Dairy form and BCS had a genetic correlation of –0.66 (not shown). A simple explanation is that more dairy character means a thinner cow if these 2 traits have a linear relationship. Farmers refer to dairy form as an indicator of a cow’s "will" to milk. What is meant by this expression is that dairy form is not only a predictor of milk yield but also an indicator of a cow’s propensity to convert body fat into metabolizable energy (Dechow et al., 2004a, b). In early lactation, high yielding cows will go into a negative energy balance because they cannot consume enough feed to meet their energy needs. The negative correlation (–0.25) of dairy form with PL₃₀₅ indicates that the cows that are forced to convert body reserves into energy put themselves at a higher risk level of being culled.

The negative correlation of dairy form with all measures of PL indicates that today’s cows may be too effective at converting their body reserves into usable energy, whereby they are at an elevated risk level of being culled throughout their entire life. Dairy form and BCS are similar traits genetically; this study found a genetic correlation of –0.66, whereas Dechow et al. (2004b) reported –0.72, and Dechow et al. (2004c) reported –0.74. Understanding the relationship that dairy form has with health and fertility traits of cows can be aided by a review of its relationship with BCS. Lassen et al. (2003) noted that BCS could be used as a management tool to estimate energy balance at specific periods of a lactation or in a breeding program as an indicator of energy balance. They referred to BCS as "a subjective method of assessing the amount of metabolizable energy stored in fat and muscle." Rogers et al. (1999) found a genetic correlation of 0.73 between dairy form measured in the United States and disease other than mastitis measured in Denmark. Dechow et al. (2004c) did a follow up study with Danish and US data and found that dairy form had a stronger correlation with disease incidence than did BCS when both traits were measured by US classifiers. Correlations between BCS and disease tended to be nonsignificant when adjusted for dairy form whereas genetic correlation estimates between dairy form and disease were not reduced significantly by adjustment for BCS. They hypothesized that the relationship between both dairy form and BCS with metabolic and digestive diseases was likely due to differences in early lactation negative energy balance. Dechow et al. (2004a) reported that the phenotypic scores of dairy form and BCS closely followed the cow’s lactation curve but in opposite directions. This suggests that both traits are visual measures of fat (energy reserves). Additionally, cows that are genetically inclined to have a high dairy form score (high angularity) at calving are likely to have the most change in dairy form during the first 2 mo of lactation. This means that cows that are genetically predisposed to score higher on dairy form will appear even sharper, more angular, and thinner at the peak of lactation than the other cows. In another study of US data, Dechow et al. (2004b) reported that dairy form was more highly correlated with both production and fertility than was BCS. The importance of dairy form as an indicator trait of potential changes in energy balance is further illustrated by the fact that it explains important variation in fertility above and beyond its association with production. It appears that dairy form may be a better indicator of potential changes in energy reserves than BCS. Dechow et al. (2004c) explained that BCS reflects variation in fat deposition among cows. Dairy form, as it is currently evaluated, may be more highly correlated with differences in total internal and external body fat than BCS. Whereas dairy form looks towards a future event, i.e., it is a predictor of a cow’s ability to convert body fat into energy, BCS measures a current state, how much fat does the cow carry at the moment when the observation is made. The phenotypic and environmental correlations between BCS and PL with any definitions were not significantly different from zero (–0.02 to 0.00).

Fatter cows have lower risk of being culled when a shorter time period is evaluated and a higher risk of being culled over a longer period. Over the entire life-time of the cow, there appears to be no need to change a cow’s propensity for getting fat. The genetic correlation between BCS and HL did not differ significantly from zero. Genetic correlations of BCS with DO and production were negative (–0.26 and –0.30, respectively). As cows maintain more body reserves (become fatter), fertility improves (DO goes down), and production goes down. Therefore, as the length of the productive period lengthens, production gains in importance and reproduction declines in importance, BCS goes from having a positive relationship with PL to a negative relationship.

Indirect Prediction of PL
Formulas for an indirect prediction of PL from correlated traits were developed (Table 6). The weights for the other traits, with the exception of DO, strength, and body depth, to predict the various measures of PL were similar. The weights are partial regression coefficients, which are dependent upon the other traits in the analysis. The change in DO represents a real change in importance of this trait with the various measures of PL. The change in weights for strength and body depth is more of a reflection of several traits describing body size being analyzed together in a multivariate analysis. The changes in the weights of these 2 traits tend to cancel each other out rather than representing any true change in the relationship of body size with the different measures of PL.

Increasing the number of traits used to predict PL from 1 to 10 correlated traits increased the maximum reliability from 0.39 to 0.65 for PL₃₀₅ and from (0.11 to 0.14) to (0.49 to 0.51) for the other measures of PL (Table 7). The prediction equation using a reduced model with 10 traits in the current study is substantially different from the one obtained by Weigel et al. (1998). This result may be due to changes in genetic correlations over time or the additional traits (DO, SCS, udder tilt, BCS, and final score) available in this analysis.

Improving profitability depends upon the environment, resources, management system, and economic values of the time. The goal is not necessarily to just lengthen the productive period of a cow but rather improve the overall efficiency. It is well documented that production levels have increased tremendously in the last 20 yr. However, perhaps less appreciated has been the improvement in lifetime production and production per day of life.

A comparison of lifetime productivity of today’s cows with cows born 20 yr earlier can be made by looking at the results of Short and Lawlor (1992). They measured HL in the same way as the current study (Table 8). Age at first calving has decreased by 33 d. A comparison of total HL favors the past generation of cows by 55 d. In the current study, the mean for lifetime DIM is 881 d, as measured by PL₉₉₉. Unfortunately, Short and Lawlor (1992) did not report this value. However, if we take into account the difference in number of parities (3.4 vs. 2.8) and a dry period of 60 d, then the difference in the productive period drops to 12 d. This difference may even be less if we adjust for changes in the length of the dry period that have taken place over time (average number of dry days have decreased from 61 to 54 d during this period; T. J. Lawlor, personal communication, 2004). Even with the assumption that the largest change (55 d) is correct, the increase in production that has occurred during this time more than compensates for the loss. The difference in lifetime production is 8338 kg. Today’s cows produce at a much higher level, resulting in more production per day and more total lifetime milk. In Table 8, we observed large increases in production with little increase in actual DIM. This supports the reported small correlation between production and total HL (Powell and VanRaden, 2003). Lifetime production continues to increase because of large increases in production per day with little increase in number of days.

It is essential that PL be correctly defined for the conditions under which the selection will take place. This is necessary in order to capture the true reasons behind the culling decisions of dairy cows. It is believed that the current restriction of 305 DIM per lactation, which is placed upon the length of the productive period being evaluated, is the primary cause for the large changes in the genetic correlations between PL and several traits related to production and reproduction that have been observed in recent years. Truncating the productive period to only 305 d penalizes the survivors much more than it does the culled cows, giving a distorted record of the culling decisions.

Weigel et al. (1998) calculated the genetic correlation between PL₃₀₅ and HL as 0.985. That means that all 4 measures of PL will rank animals similarly. Although HL has the appealing feature that it encompasses the entire lifetime of the cow, it also includes the dry period, which is an unproductive time period, and can vary over time or from cow to cow. Although PL₉₉₉ encompasses the entire productive period of the cow, extremely long lactations may not give a true indication of the desirable characteristics of the cow. Therefore, PL₅₀₀ has the advantage that it captures the complete productive period for most of the cows without any complications of extensively long lactations.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
Using the measurement PL₃₀₅ is expected to have caused some additional indirect selection emphasis to be placed upon fertility. This was reasonable in a situation where no information of fertility was available. Now the situation has changed. Genetic evaluations for daughter pregnancy rate were introduced in 2003, and complete lactation lengths are now being routinely recorded and stored, making it possible to utilize a longer productive period per lactation. Estimated genetic correlations of PL with other traits indicate that PL with a longer limit of lactation length has more desirable properties than PL₃₀₅. Indirect predictors of PL can be obtained replacing PL₃₀₅ by PL₅₀₀ and updating the equation being used as the routine measurement of PL. In that case, more weight on fertility should be placed in the Total Merit Index because PL₅₀₀ or HL puts relatively more emphasis on production.

	ACKNOWLEDGEMENTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 
Financial support from Holstein Association USA Inc. is greatly appreciated. We are grateful to Melvin Tooker of USDA for providing the DHI data.

Received for publication September 3, 2004. Accepted for publication November 24, 2004.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES

 


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