Lactation Curve Research Articles

Sensor-based behavioral patterns can identify heat-sensitive lactating dairy cows

Heat stress impairs the health and performance of dairy cows, yet only a few studies have investigated the diversity of cattle behavioral responses to heat waves. This research was conducted on an Italian Holstein dairy farm equipped with precision livestock farming sensors to assess potential different behavioral patterns of the animals. Three heat waves, defined as at least five consecutive days with mean daily temperature-humidity index higher than 72, were recorded in the farm area during the summer of 2021. Individual daily milk yield data of 102 cows were used to identify “heat-sensitive” animals, meaning the cows that, under a given heat wave, experienced a milk yield drop that was not linked with other health events (e.g., mastitis). Milk yield drops were detected as perturbations of the lactation curve estimated by iteratively using Wood’s equation. Individual daily minutes of lying, chewing, and activity were retrieved from ear-tag-based accelerometer sensors. Semi-parametric generalized estimating equations models were used to assess behavioral deviations of heat-sensitive cows from the herd means under heat stress conditions. Heat waves were associated with an overall increase in the herd’s chewing and activity times, along with an overall decrease of lying time. Heat-sensitive cows spent approximately 15 min/days more chewing and performing activities (p < 0.05). The findings of this research suggest that the information provided by high-frequency sensor data could assist farmers in identifying cows for which personalized interventions to alleviate heat stress are needed.

Genetic parameters and genome-wide association studies for mozzarella and milk production traits, lactation length, and lactation persistency in Murrah buffaloes

Genetic and genomic analyses of longitudinal traits related to milk production efficiency are paramount for optimizing water buffaloes breeding schemes. Therefore, this study aimed to: 1) compare single-trait random regression models under a Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) setting based on alternative covariance functions (i.e., Wood - WD, Wilmink - WL, and Ali-and-Schaeffer - AS) to describe milk (MY), fat (FY), protein (PY), and mozzarella (MZY) yields, fat-to-protein ratio (FPR), somatic cell score (SCS), lactation length (LL), and lactation persistency (LP) in Murrah dairy buffaloes (Bubalus bubalis); 2) combine the best functions for each trait under a multiple-trait framework; 3) estimate time-dependent SNP effects for all the studied longitudinal traits; and, 5) identify the most likely candidate genes associated with the traits. A total of 323,140 test-day records from the first lactation of 4,588 Murrah buffaloes were made available for the study. The model included the average curve of the population nested within herd-year-season of calving, systematic effects of number of milkings per day, and age at first calving as linear and quadratic covariates, and additive genetic, permanent environment, and residual as random effects. The WD model had the best goodness of fit based on the deviance information criterion and posterior model probabilities for all traits. Moderate heritabilities were estimated over time for most traits (0.30 ± 0.02 for MY; 0.26 ± 0.03 for FY; 0.45 ± 0.04 for PY; 0.28 ± 0.05 for MZY; 0.13 ± 0.02 for FPR; and 0.15 ± 0.03 for SCS). The heritability estimates for LP ranged from 0.38 ± 0.02 to 0.65 ± 0.03 depending on the trait definition used. Similarly, heritabilities estimated for LL ranged from 0.10 ± 0.01 to 0.14 ± 0.03. The genetic correlation estimates across DIM for all traits ranged from -0.06 (186 to 215 DIM for MY-SCS) to 0.78 (66 to 95 DIM for PY-MZY). The SNP effects calculated for the random regression model coefficients were used to estimate the SNP effects throughout the lactation curve (from 5 to 305 d). Numerous relevant genomic regions and candidate genes were identified for all traits, confirming their polygenic nature. The candidate genes identified contribute to a better understanding of the genetic background of milk-related traits in Murrah buffaloes and reinforce the value of incorporating genomic information in their breeding programs.

Genetic-parameter estimation of milk yield in White Maritza sheep breed using different test day models.

The aims of this study were to estimate the genetic parameters of the test day milk yield (TDMY) of the White Maritza sheep breed population and to choose the most appropriate linear models for genetic-parameter estimation of test day milk yield. The White Maritza sheep breed is a multipurpose native sheep breed in Bulgaria. Test day milk yield data were collected from 1992 to 2015 (24years). Milk yield recordings were made in 18 flocks according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test day models (TDMs) were formulated and tested for the estimation of the genetic parameters of milk yield. The first three models were repeatability models (REP models), the second three were random regression models (RRMs), and the last three models were also random regression models with an added Ali and Schaeffer regression to describe the lactation curve using first-, second- and third-order polynomials. The average TDMY was 764.47 mL. There were no significant differences in the values of heritability () calculated by the three REP models: REP1 0.355 0.060, REP2 0.344 0.047 and REP3 0.347 0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 0.065, 0.376 0.051 and 0.378 0.065, respectively. Based on REP model 1, three models with random regression RRM1, RRM2 and RRM3 were constructed, which is associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The trajectories of calculated by the three RRMs were not similar and demonstrated some differences, both at the beginning and in the middle of the milking period. The RRM with third-order polynomials demonstrated more genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL) estimates were higher. The regression models with first- and second-degree polynomials were insufficient to reveal genetic diversity to a higher degree than REP model 1. The trend in the trajectories of calculated by the three random regression models with Ali and Schaeffer regression models (ASRMs) was similar to that of random regression models without the Ali and Schaeffer regression incorporated. Although the noted advantages of the random regression models revealed, to a greater extent, the genetic diversity of test day milk yield, AIC, BIC and LogL estimates indicated that repeatability models achieved a better balance between complexity and fitness and a smaller prediction error compared to random regression models.

The marker effects of a single-step random regression model for 4 test-day traits in German Holsteins

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).

Fitting mathematical functions to extended lactation curves and forecasting late-lactation milk yields of dairy cows

A 305-d lactation followed by a 60-d dry period has traditionally been considered economically optimal, yet dairy cows in modern intensive dairy systems are frequently dried off while still producing significant quantities of milk. Managing cows for an extended lactation has reported production, welfare, and economic benefits, but not all cows are suitable for an extended lactation. Implementation of an extended lactation strategy on-farm could benefit from use of a decision support system, based on a mathematical lactation model, that can identify suitable cows during early lactation that have a high likelihood of producing above a target milk yield (MY) at 305 d in milk (DIM). Therefore, our objectives were (1) to compare the suitability of 3 commonly used lactation models for modeling extended lactations (Dijkstra, Wood, and Wilmink) in primiparous and multiparous cows under a variety of lactation lengths, and (2) to determine the amount of early-lactation daily MY data needed to accurately forecast MY at d 305 by using the most suitable model and determine whether this is sufficient for identifying cows suitable for an extended lactation before the end of a typical voluntary waiting period (50-90 d). Daily MY data from 467 individual Holstein-Friesian lactations (DIM >305 d; 379 ± 65-d lactation length [mean ± SD]) were fitted by the 3 lactation models using a nonlinear regression procedure. The parameter estimates of these models, lactation characteristics (peak yield, time to peak yield, and persistency), and goodness-of-fit were compared between parity and different lactation lengths. The models had similar performance, and differences between parity groups were consistent with previous literature. Then, data from only the first i DIM for each individual lactation, where i was incremented by 30 d from 30 to 150 DIM and by 50 d from 150 to 300 DIM, were fitted by each model to forecast MY at d 305. The Dijkstra model was selected for further analysis, as it had superior goodness-of-fit statistics for i= 30 and 60. The data set was fit twice by the Dijkstra model, with parameter bounds either unconstrained or constrained. The quality of predictions of MY at d 305 improved with increasing data availability for both models and assisting the model fitting procedure with more biologically relevant constraints on parameters improved the predictions, but neither was reliable enough for practical use on-farm due to the high uncertainty of forecasted predictions. Using 90 d of data, the constrained model correctly classified 66% of lactations as being above or below a target MY at d 305 of 25 kg/d, with a probability threshold of 0.95. The proportion of correct classifications became smaller at lower targets of MY at d 305 and became greater when using more lactation days. Overall, further work is required to develop a model that can forecast late-lactation MY with sufficient accuracy for practical use. We envisage that a hybridized machine learning and mechanistic model that incorporates additional historical and genetic information with early-lactation MY could produce meaningful lactation curve forecasts.

Application of Machine Learning Algorithms to Describe the Characteristics of Dairy Sheep Lactation Curves.

In recent years, machine learning (ML) algorithms have emerged as powerful tools for predicting and modeling complex data. Therefore, the aim of this study was to evaluate the prediction ability of different ML algorithms and a traditional empirical model to estimate the parameters of lactation curves. A total of 1186 monthly records from 156 sheep lactations were used. The model development process involved training and testing models using ML algorithms. In addition to these algorithms, lactation curves were also fitted using the Wood model. The goodness of fit was assessed using correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and relative root mean square error (RRSE). SMOreg was the algorithm with the best estimates of the characteristics of the sheep lactation curve, with higher values of r compared to the Wood model (0.96 vs. 0.68) for the total milk yield. The results of the current study showed that ML algorithms are able to adequately predict the characteristics of the lactation curve, using a relatively small number of input data. Some ML algorithms provide an interpretable architecture, which is useful for decision-making at the farm level to maximize the use of available information.

Milk productivity and reproduction function of black-and-white holland cows of different genotypes

Relevance. The black-and-white breed of cattle in our country has been improved by the Holstein breed for several decades. Along with its positive impact, there are also negative aspects of the process. Thus, the duration of the use of cows was reduced, resistance and reproduction function decreased.In order to find breeding methods to increase the safety of cows and improve the function of reproduction, studies were carried out in herds of three regions with high bloodliness for the Holstein breed (97% HL, 3% BW) and descendants from Montbéliarde bulls (50% MB, 49% HL, 1% BW) and promising genotypes of cows were identified.Experimental studies have been carried out since 2016 with loose and tethered animals.In the experiment, with loose keeping in daughters from Montbéliarde bulls (genotype 50% MB, 49% HL, 1% BW), with almost equal productivity, the age of 1 calving was 41 days less, the service period was 25 days less, the safety to 2 lactations was higher, because by its beginning, 16.1% fewer cows dropped out than peers of the genotype 97% HL, 3% BW.In herds with tethered housing and year-round exercise of cows, similar data were revealed regarding cows with loose housing, in particular, the age of the first calving, which in cows of the genotype 50% MB, 49% HL, 1% BW was 49 and 23 days less in comparison with peers of the genotype 97 % HL, 3% BW. The lactation curves of cows of two genotypes, which have almost equal milk yield per lactation, differ: in the daughters of Montbéliarde bulls, it is smoother, which favorably affects the stabilization of the reproduction function after calving.

Proposed methods for estimating loss of saleable milk in a cow-calf contact system with automatic milking

Cow-calf contact (CCC) systems, although beneficial in many respects, introduce additional challenges to collect reliable data on milk production, which is important to assess individual cow efficiency and dairy farm profitability. Apart from weighing calves before and after each feeding, the amount of saleable milk lost due to calf suckling is practically impossible to measure. Here, we assess 2 indirect methods for estimating loss of saleable milk when housing cows and calves together in a robotic milking unit. In our study, treatment (CCC) cows and calves were kept together full-time until the calves were 127 ± 6.6 d old (mean ± SD). Control cows were separated from their calves within 12 h of birth and then kept in the same unit as the treatment cows but with no access to either their own or treatment calves. Milk yield recording of both groups was performed from calving until pasture release at 233 ± 20 d in milk. The first estimation method relied on observed post-separation milk yield data, which were fed into a modified Wilmink regression model to determine the best-fitting lactation curve for the pre-separation period. The second method was based on the cows' daily energy intake post-separation, calculated by measuring the daily feed intake and analyzing the energy content of the ration. The calculated energy intake was used to determine the average ratio between energy intake and the observed milk yield the following day for each individual cow, assuming constant rates of mobilization and deposition of body fat. The obtained ratio was then used to calculate the expected daily milk yield based on daily energy intake data during the pre-separation period. In this paper, we analyzed data from 17 CCC cows kept together with their calves and 16 control cows; both groups calved from September to October 2020 and were followed up until release to pasture in May 2021. Saleable milk yield was lower in CCC cows than in control cows, both before and after separation. The 2 methods were used on data for control cows and showed milk yield loss using the lactation curve method (average of -3.4 ± 2.8 kg/d) and almost no loss using energy intake data (average of -1.4 ± 2.7 kg/d). Milk yield loss for CCC cows was estimated at average 11.3 ± 4.8 and 7.3 ± 6.6 kg milk/d, respectively. The proposed lactation curve estimation method tends to overestimate milk yield loss, whereas the method based on energy intake is more accurate. However, collecting detailed energy intake data per individual cow requires additional effort and equipment, which is not always feasible on commercial farms. Further research is needed to improve milk loss estimation and to better understand trade-offs in CCC systems.

Study of persistency of lactation and survival of Iranian Holstein dairy cattle using random regression model

The aim of this paper is to investigate whether characteristics of the first lactation (FL) curve of Iranian Holstein cows are associated with survival. Cows with least 10 test-days of milk production in their FL were used. The persistency of lactation (PL) and survival were estimated using a random regression model by restricted maximum likelihood with the ECHIDDNA software. We also used the Wood model to parameterize each individual lactation curve and then analyzed various curve characteristics using an animal model. The predicted breeding value (EBV) of the characteristics of the lactation curve of the cows from day 40 to 305 was predicted. The EBV of the production range (PR) and the slope of line in increasing phase ( m40,Peak) of production curve of sires with higher survival EBV were lower than other sires ( P &lt; 0.05). The estimates of PL were independent of survival estimate. Therefore, the PR from 40th day after calving can be considered as a definition of PL because the lower the PR, the flatter is the milk production curve. Genetic evaluation of young bulls for survival needs the data of death or culling of their daughters. Therefore, the bulls can genetically be evaluated for survival according to the PL and m40,Peak of FL information of their daughters.

Does modeling causal relationships improve the accuracy of predicting lactation milk yields?

This study compared 3 correlational (best prediction, linear regression, and feed-forward neural networks) and 2 causal models (recursive structural equation model and recurrent neural networks) for estimating lactation milk yields. The correlational models assumed associations between test-day milk yields (health conditions), while the casual models postulated unidirectional recursive effects between these test-day variables. Wood lactation curves were used to simulate the data and served as a benchmark model. Individual Wood lactation curves provided an excellent parametric interpretation of lactation dynamics, with their prediction accuracies depending on the coverage of the lactation curve dynamics. Best prediction outperformed other models in the absence of mastitis but was suboptimal when mastitis was present and unaccounted for. Recurrent neural networks yielded the highest accuracy when mastitis was present. Although causal models facilitated the inference about the causality underlying lactation, precisely capturing the causal relationships was challenging because the underlying biology was complex. Misspecification of recursive effects in the recursive structural equation model resulted in a loss of accuracy. Hence, modeling causal relationships does not necessarily guarantee improved accuracies. In practice, a parsimonious model is preferred, balancing model complexity and accuracy. In addition to the choice of statistical models, the proper accounting for factors and covariates affecting milk yields is equally crucial.

GENETIC POTENTIAL FOR MILK AND CHARACTERISTICS OF LACTATION ACTIVITY OF COWS OBTAINED BY PUREBRED BREEDING AND CROSSING

GENETIC POTENTIAL FOR MILK AND CHARACTERISTICS OF LACTATION ACTIVITY OF COWS OBTAINED BY PUREBRED BREEDING AND CROSSING

Dairy productivity of Swiss cows of different origin

The article presents the results of a comparative assessment of the dairy productivity of Swiss cows of different origin. The superiority in milk yield of the first heifers of the brown Swiss breed of Austrian breeding in all months of lactation over the peers of other groups has been established. At the same time, the lactation curves of imported heifers and their hybrids with local Shvits indicate that the maximum levels of productivity were observed in earlier periods – for 2-3 months lactation, whereas in individuals of the local population – for 3-4 months. After the peak of the productivity of the experimental livestock, the Austrian and crossbred Swiss heifers, whose lactation curve was relatively equalized, were the best preserved at the achieved milk yield level, while the local Swiss heifers had a sharp decline. The highest level of milk yield was distinguished by the first heifers of imported breeding, which surpassed both half-blooded peers (by 686 kg, P&gt;0,999) and individuals of the local population (by 1656 kg, P&gt;0,999). Imported animals, as well as crossbreeds, exceeded the yield of milk fat by 37,8-62,7 kg (P&gt;0,999) and milk protein by 31,8–54,1 kg (P&gt;0,999) of the peers of the brown Swiss breed of the local population.

Shape of curve lactation affects the fitting of empirical and mechanistic models applied to dairy sheep lactations in Mexico

The ability of mathematical models to represent the lactation process varies according with their mathematical structure and database characteristics. The aim of the current study was evaluated the goodness of fit of empirical and mechanistic models applied to dairy sheep lactation curves with different shapes. A total of 4,494 weekly test day records were analyzed. All lactations were individually fitted using two empirical (Wood and Wilmink) and two mechanistic (Dijkstra, and Pollott) models. The Dijkstra model showed the best performance to typical curves and Wood model to atypical curves (without peak lactation). Therefore, the selection of the mathematical model to fit sheep lactation curves must consider the specific patter of milk production.

Thermoregulatory, behavioral, and productive responses and physical integrity of primiparous and multiparous cows on compost barn in Brazilian tropical conditions.

The compost barn is presented as a system which is capable of providing a suitable environment for dairy cows, but this must be assessed for different climatic regions. Few studies have been carried out evaluating the physics of the thermal environment of this system under tropical conditions. In this study, we evaluated thermoregulatory, behavioral, and productive responses and physical integrity in primiparous and multiparous cows housed in a compost barn system under tropical conditions. From a total of 121 clinically healthy dairy cows aged 3 to 6years, 30 Girolando cows (7/8) were randomly selected, divided into two groups, according to calving order (primiparous and multiparous), body weight, lactation curve and milk production for the evaluations. Thus, group 1 (primiparous) with an average weight of 524kg and production of 30kg was characterized, and group 2 (multiparous) with an average weight and production of 635kg and 36kg, respectively. The enthalpy was higher (P < 0.05) in the internal environment of the shed at the three evaluated times (3:30 a.m., 11:30 a.m., and 6:30 p.m.), but the humidity did not vary (P > 0.05) between the internal and external environments at the evaluated times. Respiratory rate was higher (P < 0.0001) in multiparous cows at 11:30 a.m., but was similar at 3:30 a.m. and 6:30 p.m. when compared with primiparous cows. The coat surface temperature was higher (P < 0.001) at 3:30 a.m., but similar at the other two times. For the variables lameness and dirtiness, the vast majority of animals presented scores considered adequate (1 and 2), indicating that it was able to provide an ideal physical environment. Regarding animal behavior, panting (O) and lying idle (OD) were higher (P < 0.05) in multiparous cows. Multiparous cows have higher (P < 0.0001) milk production. Milk production has a negative correlation with enthalpy. The CB system was not able to provide a suitable thermal environment for the animals. Multiparous cows present higher heat stress with change in behavioral responses, especially at midday, but with higher milk production when compared to primiparous cows in compost barn under tropical conditions.

Modelling Lactation Curve for Genetic Evaluation of Crossbred Cattle

The present study was conducted on first lactation 4415 monthly test-day milk yield records of 466 crossbred (CB) cattle sired by 89 bulls during 2000–2018 (19 years) maintained at Directorate of Livestock Farms, GADVASU, Ludhiana, Punjab, India. The crossbred cattle with lactation length of minimum 100 days were considered for the study. The aim was to develop the best lactation curve model and to compare the breeding values of sires based on actual and predicted first lactation 305-day milk yield (FL305DMY). The data were classified and coded according to different season and age at first calving groups for first lactation 305-day milk yield. Monthly test-day milk yields of first lactation were used to develop the best lactation curve model. The Polynomial Regression Function (PRF) model was the best model amongst all the models based on both R2 and RMSE values. The breeding values of 60 HF crossbred sires with two or more daughters were estimated from the actual and predicted FL305DMY using Polynomial Regression Function by applying two sire evaluation methods viz. least squares method (LSQ) and restricted maximum likelihood method (REML). The effectiveness of LSQ and REML methods of sire evaluation was compared on the basis of Spearman’s rank correlations, coefficients of determination (R2) and coefficients of variation (CV). LSQ was found most efficient and accurate method for sire evaluation using actual and predicted first lactation 305-day milk yield in HF crossbred cattle.

Validation of single-step genomic BLUP random regression test-day models and SNP effects analysis on milk yield in French Saanen goats

The shape of the lactation curve is linked to an animal's health, feed requirements, and milk production throughout the year. Random regression models (RRM) are widely used for genetic evaluation of total milk production throughout the lactation and for milk yield persistency. Genomic information used with the single-step genomic BLUP method (ssGBLUP) substantially improves the accuracy of genomic prediction of breeding values in the main dairy cattle breeds. The aim of this study was to implement an RRM using ssGBLUP for milk yield in Saanen dairy goats in France. The data set consisted of 7,904,246 test-day records from 1,308,307 lactations of Saanen goats collected in France between 2000 and 2017. The performance of this type of evaluation was assessed by applying a validation step with data targeting candidate bucks. The model was compared with a nongenomic evaluation and a traditional evaluation that use cumulated performance throughout the lactation model (LM). The incorporation of genomic information increased correlations between daughter yield deviations (DYD) and estimated breeding values (EBV) obtained with a partial data set for candidate bucks. The LM and the RRM had similar correlation between DYD and EBV. However, the RRM reduced overestimation of EBV and improved the slope of the regression of DYD on EBV obtained at birth. This study shows that a genomic evaluation from a ssGBLUP RRM is possible in dairy goats in France and that RRM performance is comparable to a LM but with the additional benefit of a genomic evaluation of persistency. Variance of adjacent SNPs was studied with LM and RRM following the ssGBLUP. Both approaches converged on approximately the same regions explaining more than 1% of total variance. Regions associated with persistency were also found.

"Application of mathematical equations to describe lactation curves in buffaloes"

"Application of mathematical equations to describe lactation curves in buffaloes"

Investigating lactation curve characteristics of dairy cows

The main goal of this study was to analysis of the main characteristics (latent variables) of lactation curve in dairy cows using multivariate Principal Component Analysis. This work used primary database from the milk production of Holstein cows (n = 238 heads) in the PJSC “Pedigree farm ‘Stepnoy’ Kamianka-Dniprovska Raion of Zaporizhzhia Oblast over a 4-yr period (2014-2017). Recording is done with an interval of 30 days for 10 test-days (TD1-TD10), i.e., TD1 is milk production recorded on milking day 30th, TD2 is day 60th, TD3 is day 90th, etc., and 305-day milk yield records (Y305) were used also. High significant correlations were found between daily milk yields for certain test-days. The Principal Component Analysis performed on the variance-correlation matrix of TD1-TD10 records are able to explain about 90.33 % of the total variance. The first principal component (PC1) explained 66.32 % of the total variance and was highly-positively correlated with TD2-TD10 records. Thus, PC1 were defined as “total milk production”. The second principal component (PC2) explained 19.06 % of the total variance and was highly-positively correlated with TD1-TD2 records and highly-negatively correlated with TD9-TD10 records. Thus, PC2 were defined as “lactation curve persistency”. Finally, the third principal component (PC3) explained 4.95 % of the total variance and was highly-positively correlated with TD1 and TD10 records and highly-negatively correlated with TD4-TD5 records. Thus, PC3 were defined as “lactation curve type”. The use of a multivariate method (namely, the PCA) for the analysis of lactation curve characteristics based on monthly test-day records gave very close results of the analysis of milk productivity in different groups of domestic animals (cattle, goats and sheep). In all cases, the first principal component (PC1) described the absolute level of milk productivity during lactation, and the second principal component (PC2) described the persistency of the lactation curve. Significant influence on the PC1-PC3 factor scores was revealed to the greatest extent for such non-genetic factors as age of cow (in lactations), year and month of calving. Of the genetic factors, the greatest influence on the shape of the lactation curve was not so much the differences between the bull lines (Bell, Valiant, Elevation, Starbuck and Chief), but differences between individual bulls within some lines.

Random regression models to estimate genetic parameters for milk yield, fat, and protein contents in Tunisian Holsteins.

This study aimed to find the parsimonious random regression model (RRM) to evaluate the genetic potential for milk yield (MY), fat content (FC), and protein content (PC) in Tunisian Holstein cows. For this purpose, 551,139; 331,654; and 302,396 test day records for MY, FC, and PC were analysed using various RRMs with different Legendre polynomials (LP) orders on additive genetic (AG) and permanent environmental (PE) effects, and different types of residual variances (RV). The statistical analysis was performed in a Bayesian framework with Gibbs sampling, and the model performances were assessed, mainly, on the predictive ability criteria. The study found that the optimal model for evaluating these traits was an RRM with a third LP order and nine classes of heterogeneous RV. In addition, the study found that heritability estimates for MY, FC, and PC ranged from 0.11 to 0.22, 0.11 to 0.17, and 0.12 to 0.18, respectively, indicating that genetic improvement should be accompanied by improvements in the production environment. The study also suggested that new selection rules could be used to modify lactation curves by exploiting the canonical transformation of the random coefficient covariance (RC) matrix or by using the combination of slopes of individual lactation curves and expected daily breeding values.

Metabolic and physiological adaptations to first and second lactation in Holstein dairy cows

Huge differences exist between cow yields and body sizes during their first and second lactations. The transition period is the most critical and investigated phase of the lactation cycle. We compared metabolic and endocrine responses between cows at different parities during the transition period and early lactation. Eight Holstein dairy cows were monitored at their first and second calving during which they were reared under the same conditions. Milk yield, dry matter intake (DMI), and body weight (BW) were regularly measured, and energy balance, efficiency, and lactation curves were calculated. Blood samples were collected on scheduled days from -21 d relative to calving (DRC) to 120 DRC for the assessment of metabolic and hormonal profiles (biomarkers of metabolism, mineral status, inflammation, and liver function). Large variations in the period in question for almost all variables investigated were observed. Compared with their first lactation, cows during their second lactation had higher DMI (+15%) and BW (+13%), their milk yield was greater (+26%), lactation peak was higher and earlier (36.6 kg/d at 48.8 DRC vs. 45.0 kg/d at 62.9 DRC), but persistency was reduced. Milk fat, protein, and lactose contents were higher during the first lactation and coagulation properties were better (higher titratable acidity, faster and firmer curd formation). Postpartum negative energy balance was more severe the during the second lactation (1.4-fold at 7 DRC) and plasma glucose was lower. Circulating insulin and insulin-like growth factor-1 were lower in second-calving cows during the transition period. At the same time, markers of body reserve mobilization (β-hydroxybutyrate and urea) increased. Moreover, albumin, cholesterol, and γ-glutamyl transferase were higher during second lactation, whereas bilirubin and alkaline phosphatase were lower. The inflammatory response after calving was not different, as suggested by the similar haptoglobin concentrations and only transient differences in ceruloplasmin. Blood growth hormone did not differ during the transition period but was lower during the second lactation at 90 DRC, whereas circulating glucagon was higher. These results agree with the differences in milk yield and confirmed the hypothesis of a different metabolic and hormonal status between the first and second lactation partly related to different degrees of maturity.