Lactation Curves In Dairy Cattle Research Articles

Research Article Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models

Knowledge of lactation curves in dairy cattle is essential for understanding the animal production in milk production systems. Genomic prediction of lactation curves represents the genetic pattern of milk production of the animals in the herd. In this context, we made genomic predictions of lactation curves through genome-wide selection (GWS) to characterize the genetic pattern of lactation traits in Girolando cattle based on parameters estimated by nonlinear mixed effects (NLME) models. Data of 1,822 milk control records from 226 Girolando animals genotyped for 37,673 single nucleotide polymorphisms were analyzed. Nine NLME models were compared to identify the equation with the best fit. The lactation traits estimated by the best model were submitted to GWS analysis, using the Bayesian LASSO method. Then, based on the genomic estimated breeding values (GEBVs) obtained, genomic predictions of lactation curves were constructed, and the genetic parameters were calculated. Wood's equation showed the best fit among the evaluated models. Heritabilities ranged from 0.09 to 0.29 for the seven lactation variables (initial production, rates of increase and decline, lactation peak, time to peak yield, persistence and total production). The correlations among GEBVs ranged from -0.85 to 0.98. The concordances between the best animals selected according to the selected traits were greater when the correlations between GEBVs for these traits were also high. Consequently, the methodology allowed us to identify the best nonlinear model and to construct the genetic lactation curves of a Girolando cattle population, as well as to assess the differences between animals and the association between lactation variables.

A study of lactation curves in dairy cattle using the optimal design of experiments methodology

In this paper, we have worked with some of the most popular models to fit the lactation curve. Lactation curves have received considerable attention in modelling dairy cattle milk production. These curves express the evolution over time of a cattle head’s milk yield. Observations of milk production over a cycle have usually been carried out periodically (i.e. daily, weekly or monthly). Optimal experiment design allows one to know the times when a herd’s milk production must be recorded. An experiment design is optimal when it achieves the best parameter estimates for the curve model. This work aims to study optimal designs for certain models such as quadratic polynomial, hyperbolic, exponentials and Wilmink models (WMs). Designs are observed to depend on models and must be compared to get to know if a design for a specific model has a good behaviour with another model. Design efficiency is the tool used for this comparison. Efficiency is a value between 0 and 1 – a design is good if its efficiency is close to 1. The WM designs perform well and are an improvement on those designs that are routinely used in daily observations.HighlightsOptimal experiment design allows one to know the times when a herd‘s milk production must be recorded.The use of optimal design methodology greatly reduces the number of instances when dairy production has to be recorded.Optimal designs reduce the costs of experimentation by allowing parameters to be estimated with fewer experimental runs.

Using geometric morphometrics for the genetics analysis of shape and size of lactation curves in Israeli first-parity Holstein cattle

Our objective was to combine the methods of geometric morphometrics and multivariate quantitative genetics to genetic evaluation of the size and shape of lactation curves of milk of 3,492 Israeli first-parity Holstein cattle. Lactation records were treated as morphological data, for which 2 different lactation shape functions were evaluated, one depicted by a line graph and the other by an orbital graph. The lactation curves from both shape functions were represented by 2-dimensional Cartesian landmark coordinates. The 2 sets of landmarks were then analyzed individually for each shape function with geometric morphometrics to separate variation into components of size and shape. The analysis yielded 2 size measures and 2 sets of shape variables, and they were the inputs to estimate variance components using the MTC REML individual animal model program. Variance components were also estimated for the 305-d lactation production as a reference. Shape variables showed negligible correlation with 305-d production, providing evidence of size and shape of lactation curve as separate characters. The size measure derived from the orbital-depicted lactation curve had equal heritability (0.39 ± 0.01; ± standard error) and complete genetic and environmental correlations with 305-d production, whereas the size measure derived from the line-depicted lactation curve showed low heritability (0.09 ± 0.01) and environmental correlation (0.02 ± 0.004) and relative high genetic correlation with 305-d production (0.48 ± 0.04). This may validate both the orbital graph to depict lactation records and the use of geometric morphometrics to split variation of lactation curve into size and shape components. The maximal heritability for shape of lactation curve was 0.55 for orbital- and 0.56 for line-depicted lactation curves. The respective patterns of variations were visualized as shape changes from the mean shape in the data set. Geometric morphometrics are well grounded within the theory of shape analysis and can be paired with conventional methods in the field to characterize the patterns of phenotypic and genetic variation of shape and size of lactation curve in dairy cattle.

Climate change trend and influence of heat stress on lactation curve in dairy cattle in Korea

Climate change trend and influence of heat stress on lactation curve in dairy cattle in Korea

0139 Genetic parameters and impact of postpartum diseases on lactation curves in dairy cattle

0139 Genetic parameters and impact of postpartum diseases on lactation curves in dairy cattle

Study of the Lactation Curve in Dairy Cattle on Farms in Central Mexico

Accurate knowledge of lactation curves has an important relevance to management and research of dairy production systems. A number of equations have been proposed to describe the lactation curve, the most widely applied being the gamma equation. The objective of this work was to compare and evaluate candidate functions for their predictive ability in describing lactation curves from central Mexican dairy cows reared under 2 contrasting management systems. Five equations were considered: Gaines (exponential decay), Wood (gamma equation), Rook (Michaelis-Menten ×exponential), and 2 more mechanistic ones (Dijkstra and Pollott). A database consisting of 701 and 1283 records of cows in small-scale and intensive systems, respectively, was used in the analysis. Before analysis, the database was divided into 6 groups representing first, second, and third and higher parity cows in both systems. In all cases except second and above parity cows in small-scale systems, all models improved on the Gaines equation. The Wood equation explained much of the variation, but its parameters do not have direct biological interpretation. Although the Rook equation fitted the data well, some of the parameter estimates were not significant. The Dijkstra equation consistently gave better predictions, and its parameters were usually statistically significant and lend themselves to physiological interpretation. As such, the differences between systems and parity could be explained due to variations in theoretical initial milk production at parturition, specific rates of secretory cell proliferation and death, and rate of decay, all of which are parameters in the model. The Pollott equation, although containing the most biology, was found to be over-parameterized and resulted in nonsignificant parameter estimates. For central Mexican dairy cows, the Dijkstra equation was the best option to use in describing the lactation curve.

Models for estimating typical lactation curves in dairy cattle

SummaryMilk yields from monthly tests of 59474 Holsteins cows were used to estimate model parameters of the incomplete Gamma, Y(t) = atbe−ct, Compartmental, Y(t) = me−nt (1 ‐ e−p(t‐q)), and Modified Compartmental, Y(t) = ge−ht/(1+e−s(t‐r)), models to describe the shape of typical first lactations, where Y(t) is daily milk production on day t. Gamma parameters were estimated by both linear (L) and non‐linear (NL) procedures. Models were evaluated on the basis of average bias (B) of the estimated 305‐day (305‐d) milk yield compared with that reported under the Agriculture Canada official milk recording program (ROP). Gamma (L) underestimated milk yield more than Gamma (NL) (p < 0.05), and Compartmental more than Modified Compartmental (p < 0.05). When the same cow lactations were used in fitting all three models, the Compartmental model had least bias (p < 0.05). Heritabilities for parameters of the Compartmental model were small (from 0.02 to 0.18). Genetic and phenotypic correlations of parameters from this model with 305‐d milk yield ranged from −0.11 to 0.17. Parameters associated with the beginning of the lactation were positively correlated with 305‐d yield, while the parameter representing decrement of production had a negative correlation.ZusammenfassungModelle zur Schätzung typischer Laktationskurven von MilchrindernMonatliche Milchtestergebnisse von 59474 Holsteinkühen wurden zur Schätzung von Modellparametern vom kompletten Gamma y(t) = atb e−ct, Compartmentalen, [Y(t) = me−nt (1 ‐ e −p(t‐q),] und modifiziert Compartmentalen, Y(t) = ge −ht/(1+e−s(t‐r)) Modell wurden zur Beschreibung der Formen der typischen Erstlaktationskurven verwendet, wobei y(t) die tägliche Milchproduktion am Tag t symbolisiert. Gamma Parameter wurden zur Schätzung sowohl linearer (L) wie auch nichtlinearer (NL) Methoden geschätzt. Modelle wurden auf der Basis der durchschnittlichen Verzerrung (B) des geschätzten 305 d Milchertrages im Vergleich zu den vom offiziellen Testprogramm berichteten (ROP) bewertet. Gamma (L) unterschätzte den Milchertrag mehr als Gamma (NL) (p < 0,05), und Compartmentales Modell mehr als modifiziert Compartmentales (p < 0,05). Bei Verwendung derselben Kuhlaktationen zur Schätzung der drei Modelle zeigte das Compartmentale Modell die geringste Verzerrung (p < 0,05). Heritabilitätswerte für Parameter des Compartmentalen Modells waren klein (0,02‐0,18). Genetische und phänotypische Korrelationen der Parameter des Modells mit 305 d Milchleistung schwanken von —0,11 bis 0,17. Die mit dem Beginn der Laktation asoziierten Parameter waren positiv mit 305 d Leistung korreliert, während die Parameter zur Beschreibung des Produktionsabfalls eine negative Korrelation zeigten.

Comparison of Alternative Diphasic Lactation Curve Models Under Bovine Somatotropin Administration

The multiphasic logistic function has recently been proposed as a model for lactation curves in dairy cattle. The applicability of several forms of this function was examined under three levels of recombinant bST administration beginning 100 d postpartum. Lactation curve models were fit to mean daily yields for each of the treatment groups and a control group.The traditional diphasic function was unable to model adequately the steep ascent and early peak in daily milk yield occurring immediately after calving in both treatment and control groups. The early portion of the lactation was more appropriately modeled using a diphasic function with a power transformation of time in the first phase, such that this phase became asymmetric. This modified diphasic function also resulted in smaller residuals and less autocorrelation than the triphasic function, which was used as a reference model, when both models were applied to the control group data.The increase in daily milk yield because of bST administration was modeled by the addition of a term consisting of a rising exponential function. Such a model may be useful for examining dosage effects in long-term bST studies in which injections begin in midlactation or later.Time series procedures reduced autocorrelation among residuals in both the control and treatment groups, and such models could be useful for removing autocorrelation in lactation curve applications that involve daily or weekly yield observations.

Multiphasic Analysis of Lactation Curves in Dairy Cattle

A multiphasic function that considers milk yield to result from an accumulation from more than one phase of lactation was used to describe a lactation curve. Estimated mean milk yields from purebred Dutch Friesian Black and White cows for 15 test d with 20-d intervals, starting from 10 d in milk, were fitted by nonlinear regression using the sum of logistic functions: yt=∑i=1n{aibi[1−tanh2(bi(t−ci))]} where yt is milk yield (kg) at time t (t = days in milk); n is number of lactation phases (n = 1, 2, 3); tanh is the hyperbolic tangent; for each phase i, a function of parameters aibi is maximum (peak) yield, ci is time of peak yield, and 2bi−1 is duration, defined as days required to attain about 75% of asymptotic total yield during that phase. Initial yield and 305-d yield were additional functions of parameters.The diphasic function (n = 2) was chosen to estimate parameters for lactation curves for purebred Dutch Friesian Black and White and purebred Meuse-Rhine-Yssel Red and White cows. Effects of breed, parity, and length of lactation on functions of estimates for these parameters were analyzed. Relationships between the two phases of lactation were examined using correlations between functions of estimates for parameters within and between phases.

A comparison of different models of the lactation curve in dairy cattle

ABSTRACTA study of 468 lactations in two herds of British Friesian cows sampled weekly between 1976 and 1979 was undertaken to investigate seasonal variations in milk yield and to compare four models of the lactation curve: (i) logey(n)=loge a + b logen—cn; (ii) y(n) = anbe-cn; (iii) y(n)= ae-pn—ae-qn; and (iv) y(n) = a—p'n—ae-q‘n.Model (i) was fitted by unweighted linear regression and the remainder by maximum likelihood. Average peak yield was 29 kg/day and 305-day yield was 5720 kg.Model (ii) fitted the data slightly better on average than models (iii) and (iv), and all were better than model (i). Compared with model (i), model (ii) reduced the average residual mean square proportionally by 0-10 in cows and 004 in heifers (first calvers). Model (iv) described the initial rise in milk yield up to week 5 better than models (i) or (ii) but reached a maximum value slightly early. Models (i), (ii) and (iv) slightly underestimated and (iii) slightly overestimated maximum milk yield, but model (ii) provided the best estimate of the position of maximum yield.Peak yields for heifers occurred on average at approximately 10 weeks post calving compared with 7 weeks for cows. Mean persistency of lactation, calculated as s= -(b+1) logec, did not vary from the second lactation onwards, whereas the average rate of decline p' in model (iv) increased with parity.When milk yields were adjusted for stage of lactation, significant variations were found that were related to the week of recording but this seasonal variation in milk production was smaller than that found nationally in the United Kingdom.

乳牛における泌乳曲線パラメータの表型および遺伝的特性

乳牛における泌乳曲線パラメータの表型および遺伝的特性