Genetic evaluation and best prediction of lactation persistency

Cows with high persistency tend to produce less milk than expected at the beginning of lactation and more than expected at the end. Best prediction of persistency was calculated as a function of a trait-specific standard lactation curve and a linear regression of test-day deviations on days in milk. Regression coefficients were deviations from a balance point to make yield and persistency phenotypically uncorrelated. The objectives of this study were to calculate (co)variance components and breeding values for best predictions of persistency of milk (PM), fat (PF), protein (PP), and SCS (PSCS) in Holstein cows. Data included 8,682,138 lactations from 4,375,938 cows calving since 1997, and 39,354 sires were evaluated. Sire estimated breeding values (EBV) for PM, PF, and PP were similar and ranged from −0.70 to 0.75 for PM; EBV for PSCS ranged from −0.37 to 0.28. Regressions of sire EBV on birth year were near zero (<0.003) but positive for PM, PF, and PP, and negative for PSCS. Genetic correlations of PM, PF, and PP with PSCS were moderate and favorable, indicating that increasing SCS decreases yield traits, as expected. Genetic correlations among yield and persistency were low to moderate and ranged from −0.09 (PSCS) to 0.18 (PF). This definition of persistency may be more useful than those used in test-day models, which are often correlated with yield. Routine genetic evaluations for persistency are feasible and may allow for improved predictions of yield traits. As calving intervals increase, persistency may have greater value.     

Genetic analysis of milk production traits of polish black and white cattle using large-scale random regression test-day models

Genetic parameters for milk, fat, and protein yield and persistency in the first 3 lactations of Polish Black and White cattle were estimated. A multiple-lactation model was applied with random herd-test-day effect, fixed regressions for herd-year and age-season of calving, and random regressions for the additive genetic and permanent environmental effects. Three data sets with slightly different edits on minimal number of days in milk and the size of herd-year class were used. Each subset included more than 0.5 million test-day records and more than 58,000 cows. Estimates of covariance components and genetic parameters for each trait were obtained by Bayesian methods using the Gibbs sampler. Due to the large size and a good structure of the data, no differences in estimates were found when additional criteria for record selection were applied. More than 95% of the genetic variance for all traits and lactations was explained by the first 2 principal components, which were associated with the mean yield and lactation persistency. Heritabilities of 305-d milk yield in the first 3 lactations (0.18, 0.16, 0.17) were lower than those for fat (0.12, 0.11, 0.12) and protein (0.13, 0.14, 0.15). Estimates of daily heritabilities increased in general with days in milk for all traits and lactations, with no apparent abnormalities at the beginning or end of lactation. Genetic correlations between yields in different lactations ranged from 0.74 (fat yield in lactations 1 and 3) to 0.89 (milk yield in lactations 2 and 3). Persistency of lactation was defined as the linear regression coefficient of the lactation curve. Heritability of persistency increased with lactation number for all traits and genetic correlations between persistency in different lactations were smaller than those for 305d yield. Persistency was not genetically correlated with the total yield in lactation.     

Use of multivariate analysis to extract latent variables related to level of production and lactation persistency in dairy cattle

Multivariate factor analysis and principal component analysis were used to decompose the correlation matrix of test-day milk yields of 48,374 lactations of 21,721 Italian Simmental cows. Two common latent factors related to level of production in early lactation and lactation persistency, and 2 principal components associated with the whole lactation yield and persistency were obtained. Factor and principal component scores were treated as new quantitative phenotypes related to prominent features of lactation curve shape. Genetic parameters were estimated by univariate and bivariate animal models. Estimates of heritability were moderately low for both latent factors (0.13 for persistency and yield early in lactation). Heritabilities of the principal component related to total lactation yield and 305-d yield were similar (0.19 and 0.20, respectively). Finally, heritability was quite low for the principal component related to lactation persistency (0.07). Repeatabilities between lactations were about 0.27 for both latent factors, around 0.4 for the first principal component and 305-d yield, and 0.11 for the second principal component. Moderate genetic correlation among common factors (0.26) and their high genetic correlation with total lactation yield (>0.60) suggest that selection can be used to change the shape of lactation curve as well as improve yield. Scores of the second principal component can be used to genetically improve persistency while maintaining constant total lactation yield.     

Short communication: Jersey × Holstein crossbreds compared with pure Holsteins for production, mastitis, and body measurements during the first 3 lactations

Jersey (JE) × Holstein (HO) crossbred cows (n = 76) were compared with pure HO cows (n = 73) for 305-d milk, fat, and protein production, somatic cell score (SCS), clinical mastitis, lifetime production, and body measurements during their first 3 lactations. Cows were in 2 research herds at the University of Minnesota and calved from September 2003 to June 2008. Best prediction was used to determine actual production for 305-d lactations as well as lifetime production (to 1,220 d in the herd after first calving) from test-day observations. During first lactation, JE × HO cows and pure HO cows were not significantly different for fat plus protein production; however, JE × HO cows had significantly lower fat plus protein production during second (−25 kg) and third (−51 kg) lactation than pure HO cows. Nevertheless, JE × HO cows were not significantly different from pure HO cows for lifetime production or lifetime SCS. The JE × HO cows were not significantly different from pure HO cows for SCS and clinical mastitis during first and second lactations; however, JE × HO cows tended to have higher SCS (3.79) than pure HO cows (3.40), but significantly lower (−23.4%) clinical mastitis during third lactation. The JE × HO cows had significantly less hip height, smaller heart girth, less thurl width, and less pin width than pure HO cows during the first 3 lactations. Furthermore, JE × HO cows had significantly less udder clearance from the ground and significantly greater distance between the front teats than pure HO cows during their first 3 lactations.     

Short communication: do Holstein lactations of varied lengths have different characteristics?

The calving intervals of Holstein cows in many countries have been increasing in length over recent years. Many reports of lactation characteristics refer only to a standard 305-d lactation. This paper attempts to describe the characteristics of lactations of different lengths using a large database of nationally recorded data. Three different lactation lengths were studied: 305 d (the traditional annual calving interval), 370 d (the current UK national average), and 440 d (equivalent to an 18-mo calving interval). Test-day milk yield; fat, protein, and lactose composition; and somatic cell counts were analyzed. Characteristics of each of 29,838 lactations were calculated using a biological model of lactation and were analyzed to identify which factors affected them. Maximum secretion potential was the only trait not affected by lactation length. Day of peak yield, peak yield, persistency, and total milk yield all increased with lactation length, whereas relative cell death rate and the rate of milk increase in early lactation both decreased as lactation length increased. For most weeks of lactation, the 3 lactation-length groups differed from each other in most traits, following the order of their lengths; for example, milk yield was always higher for lactations of 440 d and lower for those of 305 d. The post-peak trends in all traits were found to continue in longer lactations. Thus, daily milk production and lactose percentage continued to decrease as lactations became longer, whereas fat percentage, protein percentage, and somatic cell count continued to increase. Pregnancy was found to affect all traits, leading to an accentuation of these trends in late lactation. However, the effect of pregnancy depended on the yield at about the fourth month of pregnancy. Lactations of 305, 370, and 440 d all had different characteristics and not solely due to increasing length.     

Modeling extended lactations of dairy cows

Nine mathematical models were compared for their ability to predict daily milk yields (n = 294,986) in standard 305-d and extended lactations of dairy cows of Costa Rica. Lactations were classified by parity (first and later), lactation length (9 to 10, 11 to 12, 13 to 14, 15 to 16, and 16 to 17 mo), and calving to conception interval (1 to 2, 3 to 4, 5 to 6, 7 to 8, and 9 to 10 mo). Of the nine models, the diphasic model and lactation persistency model resulted in the best goodness of fit as measured by adjusted coefficient of determination, residual standard deviation, and Durbin-Watson coefficient. All other models showed less accuracy and positively correlated residuals. In extended lactations, models were also fitted using only test-day records before 305 d, which resulted in a different ranking. The diphasic model showed the best prediction of milk yield in standard and extended lactations. We concluded that the diphasic model provided accurate estimates of milk yield for standard and extended lactations. Interpretation of parameters deserves further attention because of the large variation observed. As expected, the calving to conception interval was found to have a negative effect on milk yield for cows with a standard lactation length. In extended lactations, these negative effects of pregnancy on milk yield were not observed.     

Genetic relationships between persistency and reproductive performance in first-lactation Canadian holsteins

The main objective of this study was to estimate genetic relationships between lactation persistency and reproductive performance in first lactation. Relationships with day in milk at peak milk yield and estimated 305-d milk yield were also investigated. The data set contained 33,312 first-lactation Canadian Holsteins with first-parity reproductive, persistency, and productive information. Reproductive performance traits included age at first insemination, nonreturn rate at 56 d after first insemination as a virgin heifer and as a first-lactation cow, calving difficulty at first calving and calving interval between first and second calving. Lactation persistency was defined as the Wilmink b parameter for milk yield and was calculated by fitting lactation curves to test day records using a multiple-trait prediction procedure. An 8-trait genetic analysis was performed using the Variance Component Estimation package (VCE 5) via Gibbs sampling to estimate genetic parameters for all traits. Heritabilities of persistency, day in milk at peak milk yield and estimated 305-d milk yield were 0.18, 0.09 and 0.45, respectively. Heritabilities of reproduction were low and ranged from 0.03 to 0.19. The highest heritability was for age at first insemination. Heifer reproductive traits were lowly genetically correlated, whereas cow reproductive traits were moderately correlated. Heifers younger than average when first inseminated and/or conceived successfully at first insemination tended to have a more persistent first lactation. First lactation was more persistent if heifers had difficulty calving (r(g) = 0.43), or conceived successfully at first insemination in first lactation (r(g) = 0.32) or had a longer interval between first and second calving (r(g) = 0.17). Estimates of genetic correlations of reproductive performance with estimated 305-d milk yield were different in magnitude, but similar in sign to those with persistency (0.02 to 0.51).     

Modeling extended lactations of Holsteins

Modeling extended lactations for the US Holsteins is useful because a majority (>55%) of the cows in the present population produce lactations longer than 305 d. In this study, 9 empirical and mechanistic models were compared for their suitability for modeling 305-d and 999-d lactations of US Holsteins. A pooled data set of 4,266,597 test-day yields from 427,657 (305-d complete) lactation records from the AIPL-USDA database was used for model fitting. The empirical models included Wood (WD), Wilmink (WIL), Rook (RK), monophasic (MONO), diphasic (DIPH), and lactation persistency (LPM) functions; Dijkstra (DJ), Pollott (POL), and new-multiphasic (MULT) models comprised the mechanistic counterparts. Each model was separately tested on 305-d (>280 days in milk) and 999-d (>800 days in milk) lactations for cows in first parity and those in third and greater parities. All models were found to produce a significant fit for all 4 scenarios (2 parity groups and 2 lactation lengths). However, the resulting parameter estimates for the 4 scenarios were different. All models except MONO, DIPH, and LPM yielded residuals with absolute values smaller than 2 kg for the entire period of the 305-d lactations. For the extended lactations, the prediction errors were larger. However, the RK, DJ, POL, and MULT models were able to predict daily yield within a ± 3 kg range for the entire 999-d period. The POL and MULT models (having 6 and 12 parameters, respectively) produced the lowest mean square error and Bayesian information criteria values, although the differences from the other models were small. Conversely, POL and MULT were often associated with poor convergence and highly correlated, unreliable, or biologically atypical parameter estimates. Considering the computational problems of large mechanistic models and the relative predictive ability of the other models, smaller models such as RK, DJ, and WD were recommended as sufficient for modeling extended lactations unless mechanistic details on the extended curves are needed. The recommended models were also satisfactory in describing fat and protein yields of 305-d and 999-d lactations of all parities.     

Phenotypic relationships of common health disorders in dairy cows to lactation persistency estimated from daily milk weights

The objective of this study was to investigate the phenotypic relationship between common health disorders in dairy cows and lactation persistency, uncorrelated with 305-d yield. The relationships with peak yield and days in milk (DIM) at peak were also studied. Daily milk weights and treatment incidence records of 991 Holstein lactations from experimental dairy herds at Virginia Tech and Pennsylvania State University were used. Persistency was calculated as a function of daily yield deviations from standard lactation curves, developed separately for first (FL) and later lactations (LL), and deviations of DIM around reference dates: 128 for FL and 125 for LL. Days in milk at peak and peak yield were computed for each lactation by using Wood's function. The disease traits studied were mastitis (MAST) only during the first 100 d (MAST1), only after 100 DIM (MAST2), both before and after 100 DIM (MAST12), and at any stage of lactation (MAST1/2), as well as metritis, displaced abomasums, lameness, and metabolic diseases. Each disease was defined as a binary trait, distinguishing between lactations with at least one incidence (1) and lactations with no incidences (0). The relationships of diseases to persistency, DIM at peak, and peak yield were investigated separately for FL and LL for all disease traits except MAST12, which was investigated across parities. The relationships of persistency to probability of the diseases in the same lactation and in the next lactation were examined using odds ratios from a logistic regression model. Metritis and displaced abomasums in FL and LL were associated with significantly higher persistencies. Metabolic diseases and MAST1 in LL were significantly related to higher persistencies. The relationships of MAST2 in both FL and LL, and MAST12 across parities were significant but negative. Cows affected by MAST tended to have less persistent lactations. Most of the diseases had a significant impact on DIM at peak in LL. In LL, metritis, metabolic diseases, and displaced abomasums tended to significantly delay DIM at peak. Mastitis only after 100 DIM was associated with significantly earlier DIM at peak in LL. Increasing persistency was associated with low MAST2 and MAST1/2 in primiparous cows. None of the diseases studied was significantly related to persistency of the previous lactation.     

Phenotypic and genetic relationships of common health disorders with milk and fat yield persistencies from producer-recorded health data and test-day yields

The objective of this study was to investigate phenotypic and genetic relationships of common health disorders in dairy cows with milk (PMY) and fat (PFY) yield persistencies. Health and production data from 398 commercial dairy herds were used. Disease traits were defined in binary form for individual lactations considering mastitis only during the first 100 d in milk (MAST1), only after 100 d in milk (MAST2), and at any stage of lactation (MAST), and reproductive disorders (REPRO), metabolic disorders (METAB), and lameness (LAME). The persistencies were defined to be uncorrelated with 305-d yields. Impact of the diseases on PMY and PFY were investigated separately in first (FL) and later (LL) lactations. Phenotypic associations of PMY and PFY with likelihood of diseases in current and subsequent lactations were examined using odds ratios from a logistic regression model. Linear-threshold sire-maternal grandsire models were used to estimate genetic correlations of displaced abomasums (DA), ketosis (KET), metritis (MET), MAST, MAST1, and MAST2 with PMY and PFY across parities. Metabolic diseases and REPRO had significantly positive relationships with PMY and PFY in both FL and LL. Significantly greater PMY and PFY were associated with MAST1 in LL. Significantly lower PMY and PFY were related to MAST2 in both FL and LL, whereas cows affected by MAST had significantly less persistent lactations. Incidence of MAST and MAST2 decreased with increasing PMY and PFY in the present and previous lactation. Heritability of disease incidences were 0.03 (DA), 0.01 (KET), 0.10 (MAST), 0.02 to 0.05 (MAST1), 0.02 (MAST2), and 0.04 to 0.10 (MET). Displaced abomasum, KET, MAST, MAST1, and MET had unfavorable genetic correlations of 0.35, 0.46, 0.17, 0.02, and 0.27 with PMY, and 0.16, 0.21, 0.07, 0.06, and 0.12 with PFY, respectively. Favorable genetic correlations were found for MAST2 with PMY (−0.24) and PFY (−0.04). Results suggest that diseases in early lactation increase persistency of milk and fat yield. Selection for greater lactation persistency must consider these antagonistic relationships.     

Relationships between milk yield and somatic cell score in Canadian Holsteins from simultaneous and recursive random regression models

Multiple-trait random regression animal models with simultaneous and recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test day were fitted to Canadian Holstein data. All models included fixed herd test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Regressions were Legendre polynomials of order 4 on a scale from 5 to 305 d in milk (DIM). Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Heterogeneity of structural coefficients was modeled across (the first 3 lactations) and within (4 DIM intervals) lactation. Model comparisons in terms of Bayes factors indicated the superiority of simultaneous models over the standard multiple-trait model and recursive parameterizations. A moderate heterogeneous (both across- and within-lactation) negative effect of SCS on milk yield (from −0.36 for 116 to 265 DIM in lactation 1 to −0.81 for 5 to 45 DIM in lactation 3) and a smaller positive reciprocal effect of SCS on milk yield (from 0.007 for 5 to 45 DIM in lactation 2 to 0.023 for 46 to 115 DIM in lactation 3) were estimated in the most plausible specification. No noticeable differences among models were detected for genetic and environmental variances and genetic parameters for the first 2 regression coefficients. The curves of genetic and permanent environmental variances, heritabilities, and genetic and phenotypic correlations between milk yield and SCS on a daily basis were different for different models. Rankings of bulls and cows for 305-d milk yield, average daily SCS, and milk lactation persistency remained the same among models. No apparent benefits are expected from fitting causal phenotypic relationships between milk yield and SCS on the same test day in the random regression test-day model for genetic evaluation purposes.     

Mastitis and the shape of the lactation curve in Norwegian dairy cows

An investigation of the shape of the lactation curve and the mastitis incidence was conducted to identify whether management interventions of the lactation curve constitute a potential for reducing incidence of mastitis at herd level. Lactation curves were estimated to describe the variation of daily milk yield during the 305-d lactation period in Norwegian Red cows. Associations between mastitis incidence at herd level and lactation curve characteristics such as production level at onset of lactation, magnitude and time of peak milk yield, and increase and decrease of milk yield rates were studied. Data from 250,303 lactations occurring during 2005 and 2006 from 14,766 herds were obtained from the Norwegian Dairy Herd Recording System. Besides veterinary treatments, the records included information on monthly test-day milk yields. The shapes of the lactation curves at herd level were parameterized using a modified Wilmink model in two separate mixed model analyses. In the first analysis a subset of lactations with no records of veterinary treatments was used. Lactation curves from herds with high (>0·31 cases/305-d lactation) and low (<0·07 cases/305-d lactation) herd mastitis incidence rate were parameterized and compared for three separate strata of parity. The result showed that high herd mastitis incidence rate was associated with a low intercept (P<0·05), a steep slope before peak milk yield (P<0·01) and a rapid decline after peak milk yield (P<0·01). In the second analysis a subset of high-yielding lactations with veterinary treatments of mastitis only and lactations with no records of veterinary treatment were compared. This was done to investigate whether the findings at herd level were also reflected at cow level. These results showed that lactation curves from lactations with mastitis cases were associated with a steep slope before peak milk yield (P<0·05) in second and later parities and a rapid decline after peak milk yield (P<0·01) in all three parity groups.     

Selecting for lactation curve and milk yield in dairy cattle

Knowledge of genetic relationships between characteristics of lactation curves and lactation yields is essential for joint selection for both. An equation, yt = atbexp(-ct), was chosen to depict individual lactation curves for 5,927 first lactations by Holsteins in 557 herds in Michigan Dairy Herd Improvement where yt is daily milk yield at day t in lactation, a is yield at time zero, b is ascent to peak, and c is decline after peak. Genetic correlations for 305-day milk yield with initial production (a), ascent to peak (b), descent after peak (c), and peak yield were -.37, .40, 0, and .91. From empirical results from applied selection indexes, selecting for both increase of ascent to peak and peak yield did not decrease 305-day milk substantially. Rankings of sires on these indexes were similar to their rankings on milk yield alone. Attempts to decrease peak yield and increase persistency decreased milk yield greatly.     

Agreement of predicted 305-day milk yields relative to actual 305-day milk weight yields

The objectives of this study were to compare the multiple trait prediction (MTP) model estimate of 305-d lactation yield with the 305-d daily milk yield data from on-farm automated meters and software and to examine the accuracy of electronic identification (ID). Twenty-four-hour milk and component yields are calculated by using milk weights and samples collected 8 to 10 times/yr by Dairy Herd Improvement (DHI) organizations. Daily milk weights were collected from cows on 20 Canadian farms that used parlor milking systems with electronic ID and that were enrolled in a regular DHI program. A total of 10,175 DHI test days from 1,103 cows with complete 305-d lactation yields were entered into the MTP model, and lactation yields were predicted. Test days were grouped into first, second, and third and greater lactations and within each lactation group, days in milk were categorized in 3 stages (5 to 60, 61 to 120, and 120 to 305 d in milk) for a total of 9 classes. Agreement analysis was used to compare the 305-d sum of daily milk to the MTP 305-d lactation yield predictions by using inputs from test days throughout the lactations. Results indicated that the MTP model overestimated lactation yields across all parity groups, ranging from 310 to 1,552 kg in parity 1, 640 to 2,000 kg in parity 2, and 567 to 1,476 kg in parity 3 and greater. A preliminary examination of electronic ID accuracy was conducted on 4 farms. Two electronic ID systems were examined for cow ID accuracy by verifying the ID number appearing in the parlor with the corresponding ear tag number. There were no ID errors on 3 of 4 farms tested and only a very small number of errors (3/80) on the fourth farm, indicating that the electronic ID systems used in milking parlors identify cows accurately.     

Bayesian analysis of lactation curves of Holstein-Friesian cattle using a nonlinear model

A Bayesian procedure was developed for fitting Wood's incomplete Gamma function to test-day milk records of Spanish Holstein Friesian cattle. Each parameter of Wood's function was considered as a dependent variable in a submodel that accounted for systematic effects and genetic relationships among animals. Marginal posterior distributions of model parameters were obtained using Gibbs sampling. Variables of economic interest, such as 305-d yield, persistency, peak yield, and days in milk at peak day were predicted as functions of Wood's function curve parameters. Heritability estimates were 0.26, 0.32, and 0.19 for parameters of Wood's function and 0.26, 0.14, 0.26, and 0.05 for 305-d yield, persistency, peak yield, and days in milk at peak yield. These estimates indicate that it is possible to modify the shape of the lactation curve through genetic selection. Genetic correlations between parameters of Wood's curve and the aforementioned functions of these parameters suggest that selection for 305-d milk yield would result in higher and later peak yield, but only a slight improvement in persistency is expected.     

Fertility and milk production on commercial dairy farms with customized lactation lengths

Drying-off, calving, and start of lactation are critical transition events for a dairy cow. As a consequence, most animal health issues occur during these periods. By extending the voluntary waiting period for first insemination after calving, calving interval (CInt) can be extended, with possible positive effects for fertility and health. Some cows might be better suited for an extended CInt than others, due to differences in milk yield level, lactation persistency, or health status, which would justify a customized CInt based on individual cow characteristics. This study aims to investigate 13 farms with customized CInt, with respect to calving to first service interval (CFSI), accomplished CInt, services per conception (SC), conception rate at first artificial insemination (CR1AI), peak yield, lactation persistency, 305-d yield, and effective lactation yield. In total, 4,858 complete lactations of Holstein Friesian cows between 2014 and 2019 from the 13 farms were grouped by parity (1 or 2+) and CFSI (CFSI class; CFSI-1 < 84; 84 ≤ CFSI-2 < 140; 140 ≤ CFSI-3 < 196; 196 ≤ CFSI-4 < 252, CFSI-5 ≥ 252 d) or CInt (CInt class; CInt-1 < 364; 364 ≤ CInt-2 < 420; 420 ≤ CInt-3 < 476; 476 ≤ CInt-4 < 532, CInt-5 ≥ 532 d). Cow inseminations, available for 11 out of 13 farms (3,597 complete lactations), were grouped by parity (1 and 2+) and CFSI class or CInt class. The fertility and milk production characteristics were analyzed with generalized and general linear mixed models. The CFSI class was not associated with SC, but extended CInt class was associated with increased SC (CInt-1–5; 1.11–3.70 SC). More than 50% of cows in the CFSI class <84 d ended up in longer than expected CInt (>364 d), showing that these cows were not able to conceive for the desired CInt. More than 50% of cows in CInt classes 3 and higher (CInt ≥ 420 d) had an earlier first insemination before successful insemination (CFSI class 1; <196 d), showing that these extended CInt classes consisted of both cows with an extended waiting period for first insemination and cows that failed to conceive at earlier insemination(s). On most farms, lactation persistency was greatest in CInt class 1 (<364 d), probably related to the low peak yield in this class. When this shortest CInt class was excluded, persistency increased with extended CInt classes on most farms. Although at the majority of farms 305-d yield was greater in CInt ≥ 532 d, effective lactation yield at most farms was greatest in CInt from 364 to 531 d, especially for multiparous cows. Based on the results of this study, a CInt between 364 and 531 days seems most optimal for milk production, when high-yielding cows were selected.     

Investigating the effect of temporal,geographic, and management factors on US Holstein lactation curve parameters

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.     

Effect of dry period length on milk yield over multiple lactations

Shortening or omitting the dry period (DP) can improve the energy balance of dairy cows in early lactation through a decrease in milk yield after calving. Little is known about the effect of a short or no DP on milk yield over multiple lactations. Our objectives were (1) to assess the effect of DP length over multiple lactations on milk yield, and (2) to assess if the prediction of milk yield in response to DP length could be improved by including individual cow characteristics before calving. Lactation data (2007 to 2015) of 16 Dutch dairy farms that apply no or short DP were used to compute cumulative milk yield in the 60 d before calving (additional yield) and in the 305 d after calving (305-d yield), and the mean daily yield over the interval from 60 d before calving to 60 d before next calving (effective lactation yield). The DP categories were no (0 to 2 wk), short (3 to 5 wk), standard (6 to 8 wk), and long (9 to 12 wk). The effect of current DP and previous DP on yields was analyzed with mixed models (n = 1,420 lactations). The highest effective lactation yield of fat- and protein-corrected milk (FPCM) was observed for cows with a standard current DP (27.6 kg per day); a daily decrease was observed of 0.6 kg for a long DP, 1.0 kg for a short DP, and 2.0 kg for no DP. Previous DP did not significantly affect the effective lactation yield. Thus, cows can be managed with short or no DP over consecutive lactations without a change in quantity of milk losses. Cows that received no DP for consecutive lactations had a lower additional yield before calving (?172 kg of FPCM), but a higher 305-d yield (+560 kg of FPCM), compared with cows that received no DP for the first time. This could lessen the improvement of the energy balance in early lactation when no DP is applied a second time compared with the first time. For the second objective, a basic model was explored to predict effective lactation yield based on parity, DP length, and first-parity 305-d yield (n = 2,866 lactations). The basic model was subsequently extended with data about recent yield, days open, and somatic cell count. Extending the model reduced the error of individual predictions by only 6%. Therefore, the basic model seems sufficient to predict the effect of DP length on effective lactation yield. Other individual cow characteristics can still be relevant, however, to make a practical and tailored decision about DP length.     

Development of an optimal index to improve lactation yield and persistency with the least selection intensity

It is important to have improvement in both lactation milk yield and persistency. Modification of the lactation curve requires severe restrictions on selection criteria designed to simultaneously improve both milk yield and persistency. As a result, manipulating the lactation curve for improved persistency requires higher selection intensity than unrestricted selection based on 305-d estimated breeding value (EBV). Our study showed that for a given restriction imposed on both milk EBV and persistency, it is possible to derive different indexes to achieve this selection constraint with different degrees of selection intensity. Of the class of indexes that meets the same restriction, it is preferable to choose the index that requires the least selection intensity because it is easier to achieve the selection goal with the use of an index that requires a lower selection intensity than a higher selection intensity. An optimal index based on random regression (RR) coefficients was developed to achieve the prespecified stage genetic gains with the lowest selection intensity. A conversion equation was derived to convert the selection index based on RR coefficients to the selection index based on stage EBV with the lowest selection intensity. A numerical example is provided to demonstrate the procedures developed compared with conventional selection based on 305-d milk EBV.     

Prediction of daily and lactation yields of milk, fat, and protein using an autoregressive repeatability test day model

We evaluated the accuracy of an autoregressive multiple-lactation test day (ATD) model to predict missing test day yields of milk, fat, and protein to obtain cumulative 305-d records for cows with incomplete or in-progress lactations. The data consisted of more than one million observations of daily yields on test days in the first 3 lactations of over 75,000 Portuguese Holstein cows. Differences between actual (estimates from complete lactations using the test interval method) and ATD-predicted 305-d yields were negligible and smaller than those predicted by the test interval method. The ATD procedure tended to slightly underestimate cumulative lactation yields, whereas the test interval method substantially overestimated them. Smaller differences obtained by the ATD procedure resulted in less biased estimates of lactation yield, which also implies greater accuracy. As expected, the correlations between actual and predicted lactation yields increased with the number of test days from 0.831 to 0.997. Average correlations (by parity) between actual and ATD-predicted yields ranged from 0.977 to 0.984. Correlations between actual test day yields and corresponding predicted yields exceeded 0.5 for up to 7 time-intervals from the last test day yield used to predict cumulative yield of projected lactations. These correlations indicate the good predictive ability of the ATD method. From a producer's viewpoint, these advantages underwrite management because most on-farm selection decisions are based on the producing abilities of cows. Implementation of ATD methodology does not require special computing capability and is easily transferable to the farm level.