Annual herd turnover rates and reported reasons for culling may be misleading, and the timing of culling during the lactation can be a more useful indicator of potential management problems on a given farm. Conformation traits are often used to select for improved cow longevity, but these traits account for only a modest proportion of differences in cow survival. Significant genetic variation exists between sires in the length of productive life of their daughters, as well as susceptibility to specific health disorders. Genetic improvement of cow survival should focus on direct measures of longevity, fertility, and health.
Dairy cow survival is influenced by many factors. Non-genetic factors include stall size, bedding type, degree of overcrowding, heat abatement devices, nutrition, veterinary care, herd expansion plans, milk quota restrictions, and availability and affordability of replacement heifers. As shown below, genetic improvement of longevity involves breeding animals that can produce a live calf without assistance; cycle normally, show visible heat, and conceive when inseminated; maintain adequate body condition and resist metabolic disorders; avoid udder injuries and fight off infection by mastitis pathogens; walk and stand comfortably without frequent hoof trimming, and efficiently produce milk of desirable composition. Many cows fail to complete these tasks and leave the herd prematurely. In some cases, the cow is genetically flawed, while in other cases her environment is lacking. Significant genetic variation exists between sire families for longevity, fertility, calving performance, and disease resistance. Therefore, we can improve longevity directly by selecting families that resist culling, or we can improve longevity indirectly by selecting families that excel for each of its component traits.
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Much negative attention has been given to the annual turnover or replacement rates on commercial dairy farms. However, the overall replacement rate for a given herd doesnt provide enough information to indicate whether or not a problem exists; one needs to know which cows left the herd, as well as the reasons for and timing of their removal. Suppose that 35% of the milking cows in each of two dairies were replaced last year. In one herd, the majority of these were low-producing, older animals that were culled late in lactation and subsequently replaced by younger, genetically superior heifers grown from within the operation. In the other herd, the majority were young, high-producing cows that were culled or died early in lactation due to calving problems, mastitis, lameness, ketosis, and other metabolic disorders, and the owner was forced to buy springing heifers from a cattle dealer at the prevailing market price. Thus, even though the replacement rates on these farms are identical, ones interpretation of each herd's management success is vastly different. Replacement rates can also be misleading in herds that have an excess of replacement heifers. Suppose one herd sells its extra animals as springing heifers prior to first calving, while another herd calves all of its heifers and culls stringently for low production within the milking herd. Both may be successful in terms of managing the health, fertility, and productivity of their cattle, but the former will have a substantially lower annual replacement rate. In general, herds with lower replacement or turnover rates tend to be more successful in terms of cow comfort, health, and productivity. However, replacement rates can be misleading, and more detailed information about the reasons for culling and the timing of culling is needed. Furthermore, herd turnover rates are influenced heavily by external factors, such as expansion plans and replacement heifer prices.
Dairy producers routinely report reasons for disposal as part of the national milk recording program. Animals can be recorded as died, sold for dairy, or sold for beef, with the latter category broken down into low production, mastitis, infertility, and so on. These descriptive reasons for disposal can be useful when studying the general demographics of a national dairy herd. For example, one might use such data to conclude that mastitis and infertility are the most common causes of culling on most dairy farms. However, reported reasons for disposal can be misleading when one attempts to compare the management level of various dairy farms, or when one attempts to draw conclusions about the genetic merit of certain animals or sire families. Many animals are culled for multiple offenses. For example, a cow might have a difficult calving, followed by a case of ketosis, and a displaced abomasum. She may then fail to breed back in a timely manner and be culled from the herd when her daily milk production drops below a profitable level. The farmer might code her as sold for low production, sold for infertility, or sold due to disease. Thus, the reported reason for disposal is often a vague indicator of the actual problem. Furthermore, inconsistencies may exist between reported reasons for disposal and the actual health and reproductive history of the culled animals.
Stewart (Steve Stewart, Univ. of Minnesota, 2002, unpublished) proposed the idea of using the timing of culling within the lactation as an indirect indicator of the reason for disposal. He constructed a graph showing the proportion of total culled animals that were removed within certain time periods during the lactation. An example of this type of graph is shown below, where the percentage of culled cows that left during each 3-week period from calving to 440 d postpartum is shown for 59,390 cows that calved in 2001-2003 and were subsequently culled from 151 herds that participate in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program. One can hypothesize that cows that were culled between 0 and 62 d postpartum may have left due to calving problems or early lactation metabolic disorders, while cows that were culled between 63 and 293 d postpartum may have left due to mastitis or lameness, and cows that were culled after 294 d postpartum may have left due to infertility. Exceptions to the rule exist, as a cow that is culled today may actually reflect the outcome of a do not breed decision that occurred many months earlier. Nonetheless, an analysis of the timing of culling events within a dairy herd may provide a more objective description of management on a given farm than one can obtain by inspecting the overall turnover rate or reported reasons for disposal. Furthermore, genetic evaluation of dairy sires based on the proportion of daughters that were culled during each period of the lactation may provide a useful indicator of differences in susceptibility to various diseases or disorders. For example, one could compute sire predicted transmitting abilities for early lactation survival, and this might identify bulls whose daughters avoid calving complications and resist early postpartum metabolic disorders.
Initial attempts to improve dairy cow longevity through artificial selection began in the 1970s and early 1980s when breed associations and AI studs first developed linear type appraisal programs. For the next two decades, type and longevity were considered as synonymous. Bulls that sired daughters with high, wide rear udders, strong median suspensory ligaments, well-attached fore udders, and correct teat placement were considered to transmit superior longevity. Likewise, bulls that sired daughters with strong pasterns, a steep foot angle, and correct set to the hock were expected to improve longevity.
Numerous studies have addressed the genetic relationships between linear type traits and longevity (e.g., Caraviello et al., 2004a; Sewalem et al, 2004; Short and Lawlor, 1992). Early studies relied on the estimation of genetic correlations between longevity and linear type traits, and these studies typically invoked a 60-, 72-, or 84-month opportunity period for longevity. However, these studies suffered from two major limitations. First, the use of genetic correlation parameters to assess trait-longevity relationships limited these studies to measurement of linear relationships only. Few traits have a strictly linear relationship with longevity, and the role of traits with intermediate optima or traits that offer diminishing returns as scores increase cannot be evaluated properly using genetic correlations. Second, the use of a long opportunity period, to allow each cow to fully express her genetic potential for longevity, resulted in a tremendous time lag between the birth of animals in the study group and eventual publication of results. For example, the youngest cows in the Short and Lawlor (1992) study were born in 1982, so more than a decade of additional genetic and management improvements occurred prior to publication of results of the study. The use of survival analysis methodology in more recent studies (e.g., Caraviello et al., 2004b) alleviated several of the aforementioned limitations. In many of these studies, linear type scores were grouped into categories, and no restrictions were placed on the form of the trait-longevity relationship. Furthermore, because survival analysis allows proper modeling of censored records from animals that are still alive (Ducrocq, 1994), these studies used much more timely data than previous studies that invoked a long opportunity period. Results indicated that many type traits, such as rear leg set, rump angle, or dairy form, have intermediate optima, while many others, such as udder support, teat placement, or foot angle, seem to display a pattern of diminishing returns. More importantly, these studies demonstrated that udder depth, fore udder attachment, rear udder attachment, and udder support were of primary importance with respect to longevity, while rear leg set and foot angle were of secondary importance, and stature had no importance. Despite the importance of physical conformation, a significant proportion of the genetic variation in longevity remained unexplained by existing type or production traits well into the 1990s. Numerous examples were noted of bulls that transmitted outstanding production and type to their daughters but whose daughters nonetheless tended to leave the herd prematurely. Thus, type traits can be used as an indirect indicator of the expected longevity of a bull's daughters, and actual culling and fertility data are needed to explain the rest of the story.
In 1994, the USDA Animal Improvement Programs Laboratory (Beltsville, MD) introduced national genetic evaluations for length of productive life (PL), which was measured as the total number of months in milk from first calving until 84 mo of age, with a limit of 10 mo per lactation (VanRaden and Klaaskate, 1993). Because the vast majority of cows are culled by 84 mo of age, this seems to provide a reasonable opportunity period. In fact, the additional gain in accuracy that could be achieved by waiting for a few, highly selected daughters to complete 8, 10, or 12 yr of PL would be negligible. Because the starting point of the opportunity period (date of first calving) can vary, it is possible that this definition of PL may favor animals that calve at a young age. However, the phenotypic variation in age at first calving on most commercial dairies is rather limited. The limit of 10 mo of PL per lactation was applied for two reasons: because it seemed desirable to penalize cows that have a long dry period and an extended calving interval and because test-day production data beyond 305 d postpartum were unavailable historically. However, this restriction may have some unintended consequences, in terms of the genetic relationships between PL and other traits in the breeding goal. Tsuruta et al. (2005) showed that the genetic correlation between milk yield and PL changed from -0.11 with a 305 d limit, to +0.08 with a 500 d limit, and to +0.14 with a 999 d limit. Corresponding genetic correlations between days open and PL were -0.62, -0.36, and -0.27 for per lactation PL limits of 305 d, 500 d, and 999 d, respectively, while genetic correlations with dairy form were -0.25, -0.12, and -0.08, respectively. An extension of the current 305 d limit to a value that is more closely aligned with the management of modern commercial dairies, such as 365 d or 400 d, has been discussed. An additional argument for extending, or even removing, a limit on PL credits per lactation is that national dairy sire evaluations for daughter pregnancy rate (DPR) are now routinely available, and indirect selection for fertility using PL information is no longer necessary. An important aspect of current national genetic evaluations for PL is that records of cows that are still alive can be extended or projected to obtain an estimate of the total months in milk that such animals will accumulate by culling or 84 mo of age (VanRaden and Klaaskate, 1993). Thus, the 84-mo opportunity period does not cause a major delay or time lag in computing sire PTA for PL. On the other hand, the accuracy of such projections is low much lower than the accuracy of projected 305 d milk production records based on the first two or three test-day yields.
The primary concern with regard to genetic evaluation of PL is the substantial period of time required to obtain complete culling data for daughters of a given bull. Because a short generation interval is desirable in genetic selection programs, AI studs and pedigree breeders like to make sire selection decisions as quickly as possible. However, when a recently tested sire is being considered as a sire of sons or as an embryo transfer sire, most of his daughters are still alive. Therefore, reliability of PL evaluations is often low at the most critical points in life. This can lead to errors in selection decisions, particularly for bulls whose daughters fail to follow a typical maturity pattern. If a particular bull's daughters mature more or less gracefully than daughters of an average bull, his PTA can change significantly over time. Weigel et al. (1998) developed a procedure by which correlated traits, such as type, production, and somatic cell score, can be used to compute an indirect prediction of a bulls PTA for PL. The indirect prediction for a given bull can then be combined with his direct prediction, which is based on actual culling data, using weights that depend on the REL of direct and indirect predictions. The combined PTA will have higher REL than either the direct or indirect prediction, especially early in life. Although the combined PL predictions tend to be more accurate than direct predictions for the majority of sires, there are exceptions. The most disturbing cases are bulls whose daughters appear promising in first lactation but mature less gracefully than daughters of an average bull. These bulls tend to have a high indirect prediction early in life, but their combined PTA tends to decrease over time, as daughters mature and get culled from the herd more rapidly than anticipated.
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Genetic Improvement of Dairy Cow Longevity - eXtension