Evaluation of body condition score measured throughout lactation as an indicator of fertility in dairy cattle

Body condition score (BCS) records of primiparous Holstein cows were analyzed both as a single measure per animal and as repeated measures per sire of cow. The former resulted in a single, average, genetic evaluation for each sire, and the latter resulted in separate genetic evaluations per day of lactation. Repeated measure analysis yielded genetic correlations of less than unity between days of lactation, suggesting that BCS may not be the same trait across lactation. Differences between daily genetic evaluations on d 10 or 30 and subsequent daily evaluations were used to assess BCS change at different stages of lactation. Genetic evaluations for BCS level or change were used to estimate genetic correlations between BCS measures and fertility traits in order to assess the capacity of BCS to predict fertility. Genetic correlation estimates with calving interval and non-return rate were consistently higher for daily BCS than single measure BCS evaluations, but results were not always statistically different. Genetic correlations between BCS change and fertility traits were not significantly different from zero. The product of the accuracy of BCS evaluations with their genetic correlation with the UK fertility index, comprising calving interval and non-return rate, was consistently higher for daily than for single BCS evaluations, by 28 to 53%. This product is associated with the conceptual correlated response in fertility from BCS selection and was highest for early (d 10 to 75) evaluations.</p

Genetic profile of total body energy content of Holstein cows in the first three lactations

Weekly total body energy content (TBEC) was calculated for 444 Holstein cows in their first 3 lactations. These calculations were based on body lipid and protein changes predicted from weekly changes in body condition score and live weight of each cow. In first lactation, cows lost TBEC during the initial 8 wk, regained it by wk 22, and continued to build up their reserves until wk 37. Cows started lactations 2 and 3 with considerable reserves from the dry period that they used during the first 13 wk of lactation. Variance components for TBEC were estimated using random regression analysis allowing for heterogeneous residual variance. The genetic variance increased within each lactation, suggesting that the genetic component becomes more important as lactation progresses. The genetic correlations between very early ( wk 1 to 4) and later stages of first lactation were near zero but they increased considerably between later lactation stages. Genetic correlations between TBEC on wk 5 of first lactation and the remainder of this lactation ranged from 0.64 for the more distant weeks to 0.99 for the immediately subsequent weeks. Genetic correlations with TBEC in second lactation were moderately high (0.68 to 0.70) for the early weeks ( 1 to 8) and decreased gradually to 0.56 for weeks at the end of lactation. For third lactation, these estimates ranged from 0.53 to 0.63. Genetic correlation estimates of TBEC in wk 12 of first lactation with subsequent first-lactation weeks varied from 0.79 to 0.99, whereas they ranged from 0.65 to 0.77 and from 0.57 to 0.68 in second and third lactations, respectively. The genetic correlation between TBEC in later weeks of first lactation and the rest of productive life increased as first lactation progressed, but the improvement diminished. Weekly genetic evaluations for first-lactation TBEC were used to predict second- and third-lactation energy content. The accuracy of these predictions increased with progressing weeks in first lactation, but about three-fourths of the improvement occurred by wk 5. Our results suggest that TBEC calculated after a month from the first calving may give useful information about the future energy content of a cow.</p

Prenatal maternal effects on body condition score, female fertility, and milk yield of dairy cows

In this study, maternal effects were described as age of dam at first and second calving, first-lactation body condition score (BCS) of the dam during gestation, and milk yield of the dam. The impact of these effects on first-lactation daughter BCS, fertility, and test-day milk yield was assessed. The effect of milk yield of dam on daughter 305-d yield in the latter's first 3 lactations was also investigated. The proportion of total phenotypic variance in daughter traits accounted for by maternal effects was calculated. Dams calving early for the first time (18 to 23 mo of age) had daughters that produced 4.5% more first-lactation daily milk, had 7% higher BCS, and had their first service 3 d earlier than cows whose dams calved late (30 to 36 mo). However, daughters of dams that calved early had difficulties conceiving as they needed 7% more inseminations and had a 7.5% higher return rate. Cows from second calvings of relatively young (36 to 41 mo) dams produced 6% more first-lactation daily milk, had 2% higher BCS, and showed a significantly better fertility profile than cows whose dams calved at a late age (47 to 55 mo) High maternal BCS during gestation had a favorable effect on daughter BCS, nonreturn rate, and number of inseminations per conception. However, it was also associated with a small decrease in daughter daily milk yield. Changes in dam BCS during gestation did not affect daughter performance significantly. Maternal effects of milk yield of the dam, expressed as her permanent environment during lactation, adversely affected daughter 305-d milk, fat, and protein yield. However, although the effect was significant, it was practically negligible (&lt;0.3% of the mean). Finally, overall maternal effects accounted for a significant proportion of the total phenotypic variance of calving interval (1.4 +/- 0.6%) and nonreturn rate (1.1 +/- 0.5%).</p

Genetic aspects of growth of Holstein-Friesian dairy cows from birth to maturity

In general, genetic selection is applied after first calving to traits that manifest themselves during the animal¿s productive life, mostly during the early part of productive life. This selection policy has had undesirable correlated responses in other economically important traits, such as health and fertility, and may also have had an effect on the growth of animals both during productive life and before first calving. In this study, we analyzed the growth trajectory of dairy heifers that had been selected for maximum production of combined fat and protein (measured in kg; select line) or for average production (control line) in the United Kingdom. Before first calving, these divergent lines were managed as a single group. Select line heifers grew faster than did control line heifers. They were also heavier at first calving, but by the end of 3 lactations, the lines were not significantly different in live weight. Selection primarily for yield and for other traits has led to heifers that grow faster and reach higher growth rates earlier in life. A genetic analysis of birth, weaning, and calving weights yielded heritability estimates of 0.53 (birth weight), 0.45 (weaning weight), and 0.75 (calving weight). Confidence intervals for the genetic correlations between the traits indicated that these BW traits are not under the same genetic control

Calculation of multiple-trait sire reliability for traits included in a dairy cattle fertility index

The advent of genetic evaluations for fertility traits in the UK offers valuable information to farmers that can be used to control fertility problems and safeguard against involuntary culling. In addition to estimated genetic merit, proof reliabilities are required to make correct use of this genetic information. Exact reliabilities, based on the inverse of the coefficient matrix, cannot be estimated for large data sets because of computational restrictions. A method to calculate approximate reliabilities was implemented based on a six-trait sire model. Traits considered were interval between first and second calving, interval between first calving and first service, non-return rate 56 days post first service, number of inseminations per conception, daily milk yield at test nearest day 110 and body condition score. Sire reliabilities were calculated in four steps. Firstly, the number of effective daughters was calculated for each bull, separately for each trait, based on total number of daughters and daughter distribution across herd-year-seasons. Secondly, multiple-trait reliabilities were calculated, based on bull daughter contribution, applying selection index theory on independent daughter groups. Thirdly, (great-) grand-daughter contribution was added to the reliability of each bull, using daughter-based reliability of sons and maternal grandsons. An adjustment was made to account for the probability of bull and son or grandson having daughters in the same herd-year-season. Without the adjustment, reliabilities were inflated by proportionately 0·15 to 0·25. Finally, parent (sire and maternal grandsire) contribution was added to the reliability of each bull. The procedure was first tested on a data subset of 28 061 cow records from 285 bulls. Approximate reliabilities were compared with exact estimates based on the inverse of the coefficient matrix. Mean absolute differences ranged from 0·014 to 0·020 for the six traits and correlation between exact and approximate estimates neared unity. In a full-scale application, sire reliability for the fertility traits increased by proportionately 0·47 to 0·79 over single-trait estimates and the number of bulls with a reliability of 0·60 or more increased by 42 to 115%