The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes.
Lactation curves were estimated for Montbéliarde (MO) × ...Holstein (HO) and Viking Red (VR) × HO 2-breed crossbred cows and for MO × VR/HO and VR × MO/HO 3-breed crossbred cows and their HO herdmates from test-day observations in 7 high-performance herds that participated in a designed study. Cows calved from 2010 to 2017. Test-day observations from milk recording were used to fit the lactation curves of cows in their first 3 lactations. Lactations of cows were required to have at least 250 DIM and to have at least 6 test days ≤265 DIM. Lactation curves from random regression (RR) were compared for 305-d production (kg), peak production (kg), peak day of production, and production from 4 to 103 DIM (kg), from 104 to 205 DIM (kg), and from 206 to 305 DIM (kg) for milk, fat, and protein. Also, the persistency of production was compared. First-lactation versus second- and third-lactation cows were analyzed separately for both the 2-breed and 3-breed crossbred cows and their respective HO herdmates. Legendre polynomial RR had the best goodness of fit for the lactation curves compared with Ali–Schaeffer and Wilmink RR from the test-day observations of milk, fat, and protein production. For fluid milk production of first-lactation cows, the MO × HO 2-breed crossbreds were not different from their HO herdmates for any of the lactation-curve characteristics, except persistency. However, the VR × HO 2-breed crossbreds had less fluid milk production compared with their HO herdmates. For first lactation, the MO × HO 2-breed crossbreds had more persistency of milk, fat, and protein production compared with their HO herdmates. The first-lactation MO × VR/HO 3-breed crossbreds had more persistency of fluid milk production compared with their HO herdmates. For second and third lactations, both the MO × HO and the VR × HO 2-breed crossbreds had higher fat production compared with their HO herdmates. Furthermore, the MO × HO 2-breed crossbreds had more protein production (kg) in all 3 periods of lactation compared with their HO herdmates. Crossbred cows may have advantages over HO cows for persistency of production in high-performance herds.
The single-step genomic model has become the golden standard for routine evaluation in livestock species, such as Holstein dairy cattle. The single-step genomic model with direct estimation of marker ...effects has been proven to be efficient in accurately accounting for millions of genotype records. For diverse applications including frequent genomic evaluation updates on a weekly basis, estimates of the marker effects from the single-step evaluations play a central role in genomic prediction. In this study we focused on exploring the marker effect estimates from the single-step evaluation. Phenotypic, genotypic, and pedigree data were taken from the official evaluation for German dairy breeds in April 2021. A multilactation random regression test-day model was applied to more than 242 million test-day records separately for 4 traits: milk, fat, and protein yields, and somatic cell scores (SCS). Approximately one million genotyped Holstein animals were considered in the single-step genomic evaluations including ∼21 million animals in pedigree. Deregressed multiple across-country breeding values of Holstein bulls having daughters outside Germany were integrated into the national test-day data to increase the reliability of genomic breeding values. To assess the stability and bias of the marker effects of the single-step model, test-day records of the last 4 yr were deleted, and the integrated bulls born in the last 4 yr were truncated from the complete phenotypic dataset. Estimates of the marker effects were shown to be highly correlated, with correlations ∼0.9, between the full and truncated evaluations. Regression slope values of the marker-effect estimates from the full on the truncated evaluations were all close to their expected value, being ∼1.03. Calculated using random regression coefficients of the marker effect estimates, drastically different shapes of the genetic lactation curve were seen for 2 markers on chromosome 14 for the 4 test-day traits. The contribution of individual chromosomes to the total additive genetic variances seemed to follow the polygenic inheritance mode for protein yield and SCS. However, chromosome 14 was found to make an exceptionally large contribution to the total additive genetic variance for milk and fat yields because of markers near the major gene DGAT1. For the first lactation test-day traits, we obtained ∼0 correlations of chromosomal direct genomic values between any pair of the chromosomes; no spurious correlations were found in our analysis, thanks to the large reference population. For trait milk yield, chromosomal direct genomic values appeared to have a large variation in the between-lactation correlations among the chromosomes, especially between first and second or third lactations. The optimal features of the random regression test-day model and the single-step marker model allowed us to track the differences in the shapes of genetic lactation curves down to the individual markers. Furthermore, the single-step random regression test-day model enabled us to better understand the inheritance mode of the yield traits and SCS (e.g., variable chromosomal contributions to the total additive genetic variance and to the genetic correlations between lactations).
An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first ...genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.
This review gives an overview of the mathematical modelling of lactation curves in dairy cattle. Over the last ninety years, the development of this field of study has followed the main requirements ...of the dairy cattle industry. Non-linear parametric functions have represented the preferred tools for modelling average curves of homogeneous groups of animals, with the main aim of predicting yields for management purposes. The increased availability of records per individual lactations and the genetic evaluation based on test day records has shifted the interest of modellers towards more flexible and general linear functions, as polynomials or splines. Thus the main interest of modelling is no longer the reconstruction of the general pattern of the phenomenon but the fitting of individual deviations from an average curve. Other specific approaches based on the modelling of the correlation structure of test day records within lactation, such as mixed linear models or principal component analysis, have been used to test the statistical significance of fixed effects in dairy experiments or to create new variables expressing main lactation curve traits. The adequacy of a model is not an absolute requisite, because it has to be assessed according to the specific purpose it is used for. Occurrence of extended lactations and of new productive and functional traits to be described and the increase of records coming from automatic milking systems likely will represent some of the future challenges for the mathematical modelling of the lactation curve in dairy cattle.
Objective: The objective of this study was to estimate the lactation curves of Saanen goats by using individual milk yields in the early, mid and 0-6 months periods of lactation. For this purpose, ...Wood’s lactation curve was used to estimate lactation yields.
Material and Methods: Milk yields of Saanen goats in different periods of lactation were estimated. For this purpose, the Wood’s lactation curve model was adapted to a total of 480 milk yield measured at 14-day intervals of 40 Saanen goats. Milk yields of goats in the first three months of lactation in January-March, mid-lactation in April-June, and 0-6 months in January-June were estimated.
Results: According to the harmony of Wood's model, the determination of coefficient values of the estimations of milk yields in the first, mid-lactation and 0-6 months of lactation are 0.84, 0.87 and 0.84, respectively. The root mean square errors of Wood's model are 0.91 for the first period of lactation, 0.81 for the mid-lactation period and 0.30 for the from 0 to 6 months of lactation period.
Conclusions: According to the results obtained in this study, Wood's model was found sufficient to define the lactation curve in Saanen goats. However, it would be beneficial to conduct similar studies with larger herds and yield records.
Amaç: Çalışmada Saanen keçilerinin laktasyonun erken, orta ve 0-6 aylık dönemlerindeki bireysel süt verimleri kullanılarak laktasyon eğrileri tahmin edilmiştir. Bu amaçla, laktasyon verimlerini tahmin etmek için Wood modeli kullanılmıştır.
Materyal ve Yöntem: Saanen keçilerinin laktasyonun farklı dönemlerindeki süt verimleri tahminlenmiştir. Bu amaçla 40 baş Saanen keçisinin 14 günlük aralıklı ölçülen toplam 480 adet süt verim ölçümüne Wood laktasyon eğrisi modelinin uyumu yapılmıştır. Keçilerin Ocak-Mart aylarındaki laktasyonun ilk üç aylık dönemi, Nisan-Haziran aylarına ait orta dönemi ve 0-6 aylar arası süt verimleri tahminlenmiştir.
Araştırma Bulguları: Wood modelinin uyumuna göre laktasyonun ilk, orta ve 0-6 aylık dönemlerindeki süt verimlerinin tahminlerine ait belirleme katsayısı değerleri sırasıyla; 0.84, 0.87 ve 0.84 olarak bulunmuştur. Wood modeline ait hata kareler ortalamasının karekök değerleri ise laktasyonun ilk dönemi için 0.91; orta dönemi için 0.81 ve 0-6 aylık laktasyon dönemi için ise 0.30 olarak bulunmuştur.
Sonuç: Wood modelinin Saanen keçilerinde laktasyon eğrisini tanımlamada yeterli olduğu söylenebilir. Ancak buna benzer çalışmaların daha büyük sürü ve verim kayıtları ile yapılmasında yarar vardır.
The purpose of the research was to assess changes in the lactation activity of cows and their impact on the level of reproduction, as well as to study their fluctuations with respect to genetic and ...environmental factors. A sample of 807 cows of various ages was formed for the study. The cows were kept on a commercial farm in Kharkiv region, Ukraine. Data from DairyPlan C21 software were used in the study. It was revealed that the parameters of the Wood lactation curve model were significantly influenced by the calving season, days open, and cow's origin; lactation persistency of cows was more influenced by environmental factors (parity, calving season, days open) than by the sire. In order to improve the level of reproduction in dairy herds, it is advisable to take into account the lactation persistency of cows, since it has negative relationship with days open (r = -0.074). To improve lactation persistency it is necessary to use for insemination of cows semen of sires with high breeding values for this trait.
The aim of this paper was to study the effects of farm and parity on the shape of the lactation curve for milk yield, milk fat percentage, milk protein percentage and milk lactose percentage. ...Researches were carried out on 421 lactations obtained from a population of 260 Romanian Black and White cows reared in the South-eastern Romania. Test day data was modelled using the incomplete gamma function, and then the lactation curves were drawn taking into consideration the effect of farm (farm 1 and farm 2) and parity (lactation 1, 2 and 3+). Both factors had a significant effect (p<0.05) on the shape of the lactation curve, affecting the initial production (parameter a), the rate of increase/decrease to the peak/nadir (parameter b), as well as the rate of decrease/increase of production until the end of lactation (parameter c).
We fit the Wood's lactation model to an extensive database of test-day milk production records of US Holstein cows to obtain lactation-specific parameter estimates and investigated the effects of ...temporal, spatial, and management factors on lactation curve parameters and 305-d milk yield. Our approach included 2 steps as follows: (1) individual animal-parity parameter estimation with nonlinear least-squares optimization of the Wood's lactation curve parameters, and (2) mixed-effects model analysis of 8,595,413 sets of parameter estimates from individual lactation curves. Further, we conducted an analysis that included all parities and a separate analysis for first lactation heifers. Results showed that parity had the most significant effect on the scale (parameter a), the rate of decay (parameter c), and the 305-d milk yield. The month of calving had the largest effect on the rate of increase (parameter b) for models fit with data from all lactations. The calving month had the most significant effect on all lactation curve parameters for first lactation models. However, age at first calving, year, and milking frequency accounted for a higher proportion of the variance than month for first lactation 305-d milk yield. All parameter estimates and 305-d milk yield increased as parity increased; parameter a and 305-d milk yield rose, and parameters b and c decreased as year and milking frequency increased. Calving month estimates parameters a, b, c, and 305-d milk yield were the lowest values for September, May, June, and July, respectively. The results also indicated the random effects of herd and cow improved model fit. Lactation curve parameter estimates from the mixed-model analysis of individual lactation curve fits describe well US Holstein lactation curves according to temporal, spatial, and management factors.
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