Environmental effects on lactation curves included in a test-day model genetic evaluation

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly ( P < 0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open to maximize lifetime productivity in dairy cows. Keywords: dairy cattle, days open, days pregnant, test-day model ImplicationsDaily milk yields of cows decline as pregnancy progresses. By using a test-day model, we revealed that stage of pregnancy did not affect the estimated heritability of daily milk yield. However, 305-day milk yields were decreased in cows with fewer days open, and the rates of reduction associated with decreased days open (⩽90) in the second and third lactations were larger than that in the first lactation. These differences between the effects of stage of pregnancy in the first compared with later lactations should be included when determining the optimal number of days open for lifetime productivity in dairy cows.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effects of stage of pregnancy on variance components, daily milk yields and 305-day milk yield in Holstein cows, as estimated by using a test-day model

Yamazaki

Hagiya

Takeda

et al. 2016

“…In the lactation model of Brun-Lafleur et al (2011), genetic information was incorporated using a part of the French genetic test-day model (Leclerc et al, 2008) to simulate the expected value of milk yield under average feeding conditions. This expected value depends on the cow's characteristics (breed, lactation number, lactation and pregnancy stages) adjusted for factors such as age and month at calving, and length of the dry period.…”

Section: Introductionmentioning

confidence: 99%

Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time

Friggens

Brun-Lafleur

Faverdin

et al. 2013

In recent years, it has become increasingly clear that understanding nutrient partitioning is central to a much broader range of issues than just being able to predict productive outputs. The extent to which nutrients are partitioned to other functions such as health and reproduction is clearly important, as are the efficiency consequences of nutrient partitioning. Further, with increasing environmental variability, there is a greater need to be able to predict the ability of an animal to respond to the nutritional limitations that arise from the environment in which it is placed. How the animal partitions its nutrients when resources are limited, or imbalanced, is a major component of its ability to cope, that is, its robustness. There is mounting evidence that reliance on body reserves is increased and that robustness of dairy cows is reduced by selection for increased milk production. A key element for predicting the partition of nutrients in this wider context is to incorporate the priorities of the animal, that is, an explicit recognition of the role of both the cow's genotype (genetic make-up), and the expression of this genotype through time on nutrient partitioning. Accordingly, there has been a growing recognition of the need to incorporate in nutritional models these innate driving forces that alter nutrient partitioning according to physiological state, the genetically driven trajectories. This paper summarizes some of the work carried out to extend nutritional models to incorporate these trajectories, the genetic effects on them, as well as how these factors affect the homeostatic capacity of the animal. At present, there are models capable of predicting the partition of nutrients throughout lactation for cows of differing milk production potentials. Information concerning genotype and stage of lactation effects on homeostatic capacity has not yet been explicitly included in metabolic models that predict nutrient partition, although recent results suggest that this is achievable. These developments have greatly extended the generality of nutrient partitioning models with respect to the type of animal and its physiological state. However, these models remain very largely focussed on predicting partition between productive outputs and body reserves and, for the most part, remain research models, although substantial progress has been made towards developing models that can be applied in the field. The challenge of linking prediction of nutrient partitioning to its consequences on health, reproduction and longevity, although widely recognized, is only now beginning to be addressed. This is an important perspective for future work on nutrient partitioning.

“…MY and BCS (on a 0 to 5 scale) variations were modelled with two curves, according to lactation stage. The MY curve was modelled using the French genetic test-day model, which mainly represents the effects of breed, parity, calving month, calving age, length of dry period, lactation stage and pregnancy stage on MY, using cubic spline curves (Leclerc et al, 2008). Thus, in our model, MY was calculated from the sum of the different effects of this genetic model, which generates the shape of the curve, plus an individual constant MY level, MY indiv , which depends on the production level of cow.…”

Section: Description Of the Modelmentioning

confidence: 99%

An individual reproduction model sensitive to milk yield and body condition in Holstein dairy cows

Brun-Lafleur

Cutullic

Faverdin

et al. 2013

To simulate the consequences of management in dairy herds, the use of individual-based herd models is very useful and has become common. Reproduction is a key driver of milk production and herd dynamics, whose influence has been magnified by the decrease in reproductive performance over the last decades. Moreover, feeding management influences milk yield (MY) and body reserves, which in turn influence reproductive performance. Therefore, our objective was to build an up-to-date animal reproduction model sensitive to both MY and body condition score (BCS). A dynamic and stochastic individual reproduction model was built mainly from data of a single recent long-term experiment. This model covers the whole reproductive process and is composed of a succession of discrete stochastic events, mainly calving, ovulations, conception and embryonic loss. Each reproductive step is sensitive to MY or BCS levels or changes. The model takes into account recent evolutions of reproductive performance, particularly concerning calving-to-first ovulation interval, cyclicity (normal cycle length, prevalence of prolonged luteal phase), oestrus expression and pregnancy (conception, early and late embryonic loss). A sensitivity analysis of the model to MY and BCS at calving was performed. The simulated performance was compared with observed data from the database used to build the model and from the bibliography to validate the model. Despite comprising a whole series of reproductive steps, the model made it possible to simulate realistic global reproduction outputs. It was able to well simulate the overall reproductive performance observed in farms in terms of both success rate (recalving rate) and reproduction delays (calving interval). This model has the purpose to be integrated in herd simulation models to usefully test the impact of management strategies on herd reproductive performance, and thus on calving patterns and culling rates.Keywords: dairy cow, reproduction, model, milk yield, body condition score Implications This paper describes an up-to-date stochastic reproduction model of Holstein dairy cows. This model is sensitive to both milk production and body reserves and takes into account the recent evolutions of reproductive performance in dairy cows. This model is able to well simulate the overall reproductive performance observed in commercial farms. Therefore, it can be used in dynamic individual-based herd simulation models to anticipate the consequences of management strategies (in terms of reproduction or feeding) on dairy herd demography and global performance.