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

Genetic variants associated with two major bovine milk fatty acids offer opportunities to breed for altered milk fat composition

Background

Although bovine milk is regarded as healthy and nutritious, its high content of saturated fatty acids (FA) may be harmful to cardiovascular health. Palmitic acid (C16:0) is the predominant saturated FA in milk with adverse health effects that could be countered by substituting it with higher levels of unsaturated FA, such as oleic acid (C18:1cis-9). In this work, we performed genome-wide association analyses for milk fatty acids predicted from FTIR spectroscopy data using 1811 Norwegian Red cattle genotyped and imputed to a high-density 777k single nucleotide polymorphism (SNP)-array. In a follow-up analysis, we used imputed whole-genome sequence data to detect genetic variants that are involved in FTIR-predicted levels of C16:0 and C18:1cis-9 and explore the transcript profile and protein level of candidate genes.

Results

Genome-wise significant associations were detected for C16:0 on Bos taurus (BTA) autosomes 11, 16 and 27, and for C18:1cis-9 on BTA5, 13 and 19. Closer examination of a significant locus on BTA11 identified the PAEP gene, which encodes the milk protein β-lactoglobulin, as a particularly attractive positional candidate gene. At this locus, we discovered a tightly linked cluster of genetic variants in coding and regulatory sequences that have opposing effects on the levels of C16:0 and C18:1cis-9. The favourable haplotype, linked to reduced levels of C16:0 and increased levels of C18:1cis-9 was also associated with a marked reduction in PAEP expression and β-lactoglobulin protein levels. β-lactoglobulin is the most abundant whey protein in milk and lower levels are associated with important dairy production parameters such as improved cheese yield.

Conclusions

The genetic variants detected in this study may be used in breeding to produce milk with an improved FA health-profile and enhanced cheese-making properties.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00731-9.

Prevention of mastitis in multiparous dairy cows with a previous history of mastitis by oral feeding with probiotic Bacillus subtilis

Mastitis is a very common inflammatory disease of the mammary gland of dairy cows, resulting in a reduction of milk production and quality. Probiotics may serve as an alternative to antibiotics to prevent mastitis, and the use of probiotics in this way may lessen the risk of antibiotic resistant bacteria developing. We investigated the effect of oral feeding of probiotic Bacillus subtilis (BS) C‐3102 strain on the onset of mastitis in dairy cows with a previous history of mastitis. BS feeding significantly decreased the incidence of mastitis, the average number of medication days and the average number of days when milk was discarded, and maintained the mean SCC in milk at a level substantially lower than the control group. BS feeding was associated with lower levels of cortisol and TBARS and increased the proportion of CD4⁺ T cells and CD11c⁺ CD172a^high dendritic cells in the blood by flow cytometry analysis. Parturition increased the migrating frequency of granulocytes toward a milk chemoattractant cyclophilin A in the control cows, however, this was reduced by BS feeding, possibly indicating a decreased sensitivity of peripheral granulocytes to cyclophilin A. These results reveal that B. subtilis C‐3102 has potential as a probiotic and has preventative capacity against mastitis in dairy cows.

Influence of environmental factors and parity on milk yield dynamics in barn-housed dairy cattle

We investigated the effects of environmental factors on average daily milk yield and day-to-day variation in milk yield of barn-housed Scottish dairy cows milked with an automated milking system. An incomplete Wood gamma function was fitted to derive parameters describing the milk yield curve including initial milk yield, inclining slope, declining slope, peak milk yield, time of peak, persistency (time in which the cow maintains high yield beyond the peak), and predicted total lactation milk yield (PTLMY). Lactation curves were fitted using generalized linear mixed models incorporating the above parameters (initial milk yield, inclining and declining slopes) and both the indoor and outdoor weather variables (temperature, humidity, and temperature-humidity index) as fixed effects. There was a higher initial milk yield and PTLMY in multiparous cows, but the incline slope parameter and persistency were greatest in primiparous cows. Primiparous cows took 54 d longer to attain a peak yield (mean ± standard error) of 34.25 ± 0.58 kg than multiparous (47.3 ± 0.45 kg); however, multiparous cows yielded 2,209 kg more PTLMY. The best models incorporated 2-d lagged minimum temperature. However, effect of temperature was minimal (primiparous decreased milk yield by 0.006 kg/d and multiparous by 0.001 kg/d for each degree increase in temperature). Both primiparous and multiparous cows significantly decreased in day-to-day variation in milk yield as temperature increased (primiparous cows decreased 0.05 kg/d for every degree increase in 2-d lagged minimum temperature indoors, which was greater than the effect in multiparous cows of 0.008 kg/d). Though the model estimates for both indoor and outdoor were different, a similar pattern of the average daily milk yield and day-to-day variation in milk yield and milk yield's dependence on environmental factors was observed for both primiparous and multiparous cows. In Scotland, primiparous cows were more greatly affected by the 2-d lagged minimum temperature compared with multiparous cows. After peak lactation had been reached, primiparous and multiparous cows decreased milk yield as indoor and outdoor minimum temperature increased.

Milk losses linked to mastitis treatments at dairy farms with automatic milking systems

Mastitis-associated milk losses in dairy cows have a massive impact on farm profitability and sustainability. In this study, we analyzed milk losses from 4 553 treated mastitis cases as recorded via treatment registers at 41 AMS dairy farms. Milk losses were estimated based on the difference between the expected and the actual production. To estimate the unperturbed lactation curve, we applied an iterative procedure using the Wood model and a variance-dependent threshold on the milk yield residuals. We calculated milk losses both in a fixed window around the first treatment day of each mastitis case and in the perturbations corresponding to this day, at the cow level as well as at the quarter level. In a fixed time window of day -5 to 30 around the first treatment, the absolute median milk losses per case were 101.5 kg, highly dependent on the parity and the lactation stage with absolute milk losses being highest in multiparous cows and at peak lactation. Relative milk losses expressed in percentage were highest on the first treatment day, and full recovery was often not reached within 30 days from treatment onset. In 62 % of the cases, we found a perturbation in milk yield at the cow level at the time of treatment. On average, perturbations started 8.7 days before the first treatment and median absolute milk losses increased to 128 kg of milk per perturbation. Mastitis is not expected to have equal effects on the four quarters so this study additionally investigated losses in the individual udder quarters. We used a data-based method leveraging milk yield and electrical conductivity to project the presumably inflamed quarter. Next, we compared losses with the average of presumably non-inflamed quarters. Median absolute losses in a fixed 36-day window around treatment varied between 50.2 kg for front and 59.3 kg for hind inflamed quarters compared to respectively 24.7 and 26.3 kg for the median losses in the non-inflamed quarters. Also here, these losses differed between lactation stages and parities. Expressed proportionally to expected yield, the relative median milk losses in inflamed quarters on the treatment day were 20 % higher in inflamed quarters with a higher variability and slower recovery. In 86 % of the treated mastitis cases, at least one perturbation was found at the quarter level. This analysis confirms the high impact of mastitis on milk production, and the large variation between quarter losses illustrates the potential of quarter analysis for on-farm monitoring at farms with an automated milking system.

Fourier transform infrared spectroscopy of milk samples as a tool to estimate energy balance, energy- and dry matter intake in lactating dairy cows

The objective of the study was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) analysis of milk samples to predict body energy status and related traits (energy balance (EB), dry matter intake (DMI) and efficient energy intake (EEI)) in lactating dairy cows. The data included 2371 milk samples from 63 Norwegian Red dairy cows collected during the first 105 days in milk (DIM). To predict the body energy status traits, calibration models were developed using Partial Least Squares Regression (PLSR). Calibration models were established using split-sample (leave-one cow-out) cross-validation approach and validated using an external test set. The PLSR method was implemented using just the FTIR spectra or using the FTIR together with milk yield (MY) or concentrate intake (CONCTR) as predictors of traits. Analyses were conducted for the entire first 105 DIM and separately for the two lactation periods: 5 ≤ DIM ≤ 55 and 55 < DIM ≤ 105. To test the models, an external validation using an independent test set was performed. Predictions depending on the parity (1st, 2nd and 3rd-to 6th parities) in early lactation were also investigated. Accuracy of prediction (r) for both cross-validation and external test set was defined as the correlation between the predicted and observed values for body energy status traits. Analyzing FTIR in combination with MY by PLSR, resulted in relatively high r-values to estimate EB (r = 0.63), DMI (r = 0.83), EEI (r = 0.84) using an external validation. Only moderate correlations between FTIR spectra and traits like EB, EEI and dry matter intake (DMI) have so far been published. Our hypothesis was that improvements in the FTIR predictions of EB, EEI and DMI can be obtained by (1) stratification into different stages of lactations and different parities, or (2) by adding additional information on milking and feeding traits. Stratification of the lactation stages improved predictions compared with the analyses including all data 5 ≤ DIM ≤105. The accuracy was improved if additional data (MY or CONCTR) were included in the prediction model. Furthermore, stratification into parity groups, improved the predictions of body energy status. Our results show that FTIR spectral data combined with MY or CONCTR can be used to obtain improved estimation of body energy status compared to only using the FTIR spectra in Norwegian Red dairy cattle. The best prediction results were achieved using FTIR spectra together with MY for early lactation. The results obtained in the study suggest that the modeling approach used in this paper can be considered as a viable method for predicting an individual cow's energy status.

Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm

Simple Summary

A lactation curve (LC) provides valuable insights in planning appropriate management strategies related to health, nutrition, and breeding in dairy cows. A clustering based approach on LC patterns analysis is presented. The k-medoids algorithm is adopted for the clustering. This approach generates several clusters which have similar milking characteristics of total milk yield, peak milk yield, and days in milk at peak yield. The LCs of some groups represent characteristics of atypical milking patterns which are not considered much in previous approaches, whereas LCs of the other groups show the typical LC patterns similar to the results of previous methods. This approach could be used as a tool to manage an abnormal herd of cows.

Abstract

The aim of the study was to group the lactation curve (LC) of Holstein cows in several clusters based on their milking characteristics and to investigate physiological differences among the clusters. Milking data of 330 lactations which have a milk yield per day during entire lactation period were used. The data were obtained by refinement from 1332 lactations from 724 cows collected from commercial farms. Based on the similarity measures, clustering was performed using the k-medoids algorithm; the number of clusters was determined to be six, following the elbow method. Significant differences on parity, peak milk yield, DIM at peak milk yield, and average and total milk yield (p < 0.01) were observed among the clusters. Four clusters, which include 82% of data, show typical LC patterns. The other two clusters represent atypical patterns. Comparing to the LCs generated from the previous models, Wood, Wilmink and Dijsktra, it is observed that the prediction errors in the atypical patterns of the two clusters are much larger than those of the other four cases of typical patterns. The presented model can be used as a tool to refine characterization on the typical LC patterns, excluding atypical patterns as exceptional cases.

Dynamic forecasting of individual cow milk yield in automatic milking systems

Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.

Effect of mastitis on lactation curves in purebred Jersey cows

Mastitis is a most frequently occurring disease in dairy cattle which causes severe losses in milk production. In our study, we had collected 9960 weekly test day milk yield (WTDMY) records over a period of five years (2010– 2015) of 130 purebred Jersey cows reared at Central Cattle Breeding Farm, Sunabeda, Odisha under Ministry of Agriculture, Government of India. To study the lactation pattern of above milk data, we used six different lactation curve models, viz. Wilmink (WK), Wood (WD), Brody (BRD), Morant and Gnanasakthy (MG), Mitscherlich × Exponential (ME) and Ali and Schaeffer (AS). It was observed that in healthy and cows affected with mastitis, Ali and Schaeffer (AS) model showed best fit giving highest value of adjusted coefficient of determination (R2 adj.= 0.963) and lowest value of root mean square of error (0.303), Akaike’s information criterion (–97.887) and Schwartz Bayesian Information Criterion (–89.081). Testing of residuals was carried out by several tests, viz. the Shapiro- Wilk’s test, the run test and the Durbin-Watson (DW). Summary measures revealed that the loss of milk production due to mastitis with respect to healthy cows was 4.43%. Lactation persistency was estimated by ratio method and Mahadevan method. Higher persistency was observed in healthy cows.

Unravelling genetic variation underlying de novo-synthesis of bovine milk fatty acids

The relative abundance of specific fatty acids in milk can be important for consumer health and manufacturing properties of dairy products. Understanding of genes controlling milk fat synthesis may contribute to the development of dairy products with high quality and nutritional value. This study aims to identify key genes and genetic variants affecting de novo synthesis of the short- and medium-chained fatty acids C4:0 to C14:0. A genome-wide association study using 609,361 SNP markers and 1,811 animals was performed to detect genomic regions affecting fatty acid levels. These regions were further refined using sequencing data to impute millions of additional genetic variants. Results suggest associations of PAEP with the content of C4:0, AACS with the content of fatty acids C4:0-C6:0, NCOA6 or ACSS2 with the longer chain fatty acids C6:0-C14:0, and FASN mainly associated with content of C14:0. None of the top-ranking markers caused amino acid shifts but were mostly situated in putatively regulating regions and suggested a regulatory role of the QTLs. Sequencing mRNA from bovine milk confirmed the expression of all candidate genes which, combined with knowledge of their roles in fat biosynthesis, supports their potential role in de novo synthesis of bovine milk fatty acids.

Lactation Curve Pattern and Prediction of Milk Production Performance in Crossbred Cows

Data pertaining to 11728 test-day daily milk yields of normal and mastitis Karan Fries cows were collected from the institute herd and divided as mastitis and nonmastitis and parity-wise. The data of lactation curves of the normal and mastitis crossbred cows was analyzed using gamma type function. FTDMY in normal and mastitis cows showed an increasing trend from TD-1 to TD-4 and a gradual decrease (P < 0.01) thereafter until the end of lactation (TD-21) in different parities. The FTDMY was maximum (peak yield) in the fourth parity. Parity-wise lactation curve revealed a decrease in persistency, steeper decline in descending slope (c), and steeper increase in ascending slope (b) from 1st to 5th and above parity. The higher coefficient of determination (R (2)) and lower root mean square error (RMSE) indicated goodness and accuracy of the model for the prediction of milk prediction performance under field conditions. Clinical mastitis resulted in a significantly higher loss of milk yield (P < 0.05). The FTDMY was maximum (P < 0.05) in the fourth parity in comparison to the rest of parity. It is demonstrated that gamma type function can give the best fit lactation curve in normal and mastitis infected crossbred cows.

On-farm evaluation of the effect of metabolic diseases on the shape of the lactation curve in dairy cows through the MilkBot lactation model

The effects of metabolic diseases (MD) occurring during the transition period on milk production of dairy cows have been evaluated in many different ways, often with conflicting conclusions. The present study used a fitted lactation model to analyze specific aspects of lactation curve shape and magnitude in cows that avoided culling or death in the first 120 d in milk (DIM). Production and health records of 1,946 lactations in a 1-yr follow-up study design were collected from a transition management facility in Germany to evaluate both short- and long-term effects of MD on milk production. Milk production data were fitted with the nonlinear MilkBot lactation model, and health records were used to classify cows as healthy (H), affected by one MD (MD), or by multiple MD (MD+). The final data set contained 1,071 H, 348 MD, and 136 MD+ cows, with distinct incidences of 3.7% twinning, 4.8% milk fever, 3.6% retained placenta, 15.4% metritis, 8.3% ketosis, 2.0% displaced abomasum, and 3.7% mastitis in the first 30 DIM. The model containing all healthy and diseased cows showed that lactations classified as H had milk production that increased faster (lower ramp) and also declined faster (lower persistence) compared with cows that encountered one or more metabolic problems. The level of production (scale) was only lowered in MD+ cows compared with H and MD cows. Although the shape of the lactation curve changed when cows encounter uncomplicated (single) MD or complicated MD (more than one MD), the slower increase to a lower peak seemed to be compensated for by greater persistency, resulting in the overall 305-d milk production only being lowered in MD+ cows. In the individual disease models, specific changes in the shape of the lactation curve were found for all MD except twinning. Milk fever, retained placenta, ketosis, and mastitis mainly affected the lactation curve when accompanied by another MD, whereas metritis and displaced abomasum affected the lactation curve equally with or without another MD. Overall, 305-d milk production was decreased in complicated metritis (10,603 ± 50 kg vs. 10,114 ± 172 kg). Although care should be taken in generalizing conclusions from a highly specialized transition management facility, the current study demonstrated that lactation curve analysis may contribute substantially to the evaluation of both short- and long-term effects of metabolic diseases on milk production by detecting changes in the distribution of production that are not apparent when only totals are analyzed.

A semi-parametric model for lactation curves: development and application

We propose a semi-parametric model for lactation curves that, along with stage of lactation, accounts for day of the year at milk recording and stage of gestation. Lactation is described as having 3 different phases defined by 2 change points of which the second is a function of gestation stage. Season of milk recording is modelled using cosine and sine functions. As an application, the model is used to estimate the association between intramammary infections (IMI) dynamics as measured by somatic cell count (SCC) over the dry period and the shape of the lactation curve. Milk recording data collected in 2128 herds from England and Wales between 2004 and 2007 were used in the analysis. From a random sample of 1000 of these herds, smoothed milk production was used to test the behaviour of the model and estimate model parameters. The first change point was set at 60 days in milk. The second change point was set at 100 days of gestation or 200 days in milk when the latter was not available. Using data from the 1128 remaining herds, multilevel models were then used to model individual test-day milk production within lactations within herds. Average milk production at 60 days in milk for cows of parities 1, 2, 3 and greater than 3 were 26.9 kg, 31.6 kg, 34.4 kg and 34.7 kg respectively and, after this stage, decreases in milk production per 100 days milk of lactation were 3.1 kg, 5.1 kg, 6.3 kg and 6.7 kg respectively. Compared to cows that had an SCC below 200,000 cells/mL on both the last milk recording in a lactation and the first milk recording in the following lactation, cows that had an SCC greater than 200,000 cells/mL on their first milk recording after calving had an estimated loss of milk production of between 216 and 518 kg depending on parity. These estimates demonstrate the impact of the dynamics of SCC during the dry period on milk production during the following lactation.