Relationship between milk constituents from milk testing and health, feeding, and metabolic data of dairy cows

Comprehensive genetic analysis and predictive evaluation of milk electrical conductivity for subclinical mastitis in Chinese Holstein cows

BACKGROUND

Bovine mastitis significantly impacts the dairy industry, causing economic losses through reduced milk production, lower milk quality, and increased health risks, and early detection is critical for effective treatment. This study analyzed milk electrical conductivity data from 9,846 Chinese Holstein cows over a two-year period, collected during three daily milking sessions, alongside smart collar data and dairy herd improvement test results. The aim was to conduct a comprehensive genetic analysis and assess the potential of milk electrical conductivity as a biomarker for the early detection of bovine subclinical mastitis.

RESULTS

The results revealed significant phenotypic and strong genetic correlations (-0.286 to 0.457) between milk electrical conductivity, somatic cell score, milk yield, activity quantity, and milking speed. Logistic regression models yielded area under the curve values ranging from 0.636 to 0.697 and odds ratio values from 9.70 to 10.69, demonstrating a certain predictive capability of milk electrical conductivity for identifying subclinical mastitis. Various factors influencing milk electrical conductivity, including lactation stage, environmental conditions, age at first calving, parity, and body condition score, were identified. The random regression model demonstrated moderate to high heritability of milk electrical conductivity (0.458 to 0.487), particularly during the early to mid-lactation periods, with all estimates exceeding 0.35 However, after day 275 of lactation, the heritability decreased to below 0.2. Notably, shifts in genetic factors affecting milk components were observed around 60 and 270 days into lactation, with increased environmental sensitivity to milk electrical conductivity during these periods.

CONCLUSIONS

This study demonstrates that milk electrical conductivity is influenced by multiple factors, such as age at first calving, parity, and body condition score, and exhibits significant phenotypic associations with somatic cell score, milk yield, activity quantity, and milking speed. Although milk electrical conductivity showed moderate to high heritability and potential as a predictor for subclinical mastitis, its low genetic correlations with SCS limit its effectiveness as a standalone indicator. Future research should focus on combining EC with other indicators to improve the accuracy of mastitis detection.

Influences of fat-protein-ratio and udder health parameters on the milk urea content of dairy cows

The milk urea content is influenced by a large variety of factors, including the quantity and quality of protein fed, its balance with energy, diurnal fluctuations, management, season, analysis method, and also individual cow factors which include the health status of the cow. Aim of this study was to investigate the effects of metabolic disorders (ketosis, ruminal acidosis; indicated by the fat-protein-content of the milk) as well as high somatic cell counts and udder diseases on the milk urea content of dairy cows from different regions and farms across Germany. For this purpose, 5 independent data sets which contain information derived from monthly milk recordings (data sets A (6,140,342 test-data in 2015), data set D (439,767 test-data in 2020-2023), data set E (399,279 test-data in 2019-2020)) in combination with the differential somatic cell count (DSCC) in data set D and E, or individual recordings of daily feed and energy intake and milk analysis (data set B (58,235 test-data in 2014-2017) and data set C (352,346 test-data in 2018-2021)), were analyzed. The group of cows with severe energy deficiency showed a 11.0 to 20 mg/l higher milk urea content than cows with a demand-orientated energy supply. The results for the effect of a very high energy supply are inconsistent across the 5 data sets. Furthermore, the milk urea content of cows with the highest somatic cell count are observed to be 9.0 to 13.0 mg/l lower in comparison to cows with a healthy udder. Moreover, the milk urea content is 14 mg/l lower in cows diagnosed with mastitis compared with those without a diagnosis. While this may have impact on judgements for the individual cow, in groups of cows, individual incidences of a disease will not have a significant impact on the average milk urea content. However, this should be taken into account for herds with a high prevalence of sick cows.

Canonical Correlation of Milk Composition Parameters and Blood Biomarkers in High-Producing Dairy Cows During Different Lactation Stages

This study explores milk composition and blood markers in cows across lactation stages. Holstein cows were divided into four groups: beginning of lactation (BL; n = 21), peak of lactation (PL; n = 21), middle of lactation (ML; n = 21), and end of lactation (EL; n = 20). Blood (1 × 15 mL) and milk samples (1 × 100 mL) were collected for biomarker analysis. Blood chemistry profiles were determined using a clinical chemistry analyser, and milk lactose, fat, and protein levels (%) were determined using an infrared absorbance analyser. Minerals (Ca, P, and Mg) in milk were determined by atomic absorption spectrometry after mineralizing the samples. Glucose was higher in the EL group than in the BL group (p < 0.01), whereas D-beta-hydroxybutyrate (D-BHB) was higher in the BL group than in the PL and ML groups (p < 0.001). Cholesterol was higher in the PL, ML, and EL groups than in the BL group (p < 0.001). Gamma-glutamyl transferase was increased in the PL group compared to the BL group. Phosphorus levels were lower in the PL than in the BL group, whereas protein levels were higher in the EL than in the PL group. Spearman and partial correlation analysis showed several significant associations between the observed variables. Using canonical correlation analysis were identified three significant correlations (rc1 = 0.853; rc2 = 0.823; rc3 = 0.739). The main canonical correlation identified blood TG and milk urea as the strongest variables. According to the canonical loading, the biomarkers TG, Mg, urea, cholesterol, and alkaline phosphatase (U1) are the primary variables associated with milk parameters (V1), specifically with milk urea, milk Mg and P, protein, and lactose.

Relationship of body condition and milk parameters during lactation in Simmental cows in Bavaria, Germany

In dairy cows the body condition forms a reflection of the energy reserves of the organism. Health, welfare and productivity of dairy cows are strongly associated with changes in body condition. As lactation puts substantial demands on the metabolism of dairy cows, farm management aims at avoiding either a deficient body condition or a substantial loss of body condition within a short period of time. A body condition higher or lower than recommended (over- and underconditioning in the following) compromises dairy cow productivity. While the body condition of Holstein Friesian cows has been thoroughly explored, few is known about the consequences of deviations from a target body condition for health and productivity of cows from other breeds. This study explores the percentage of over- and underconditioned cows at different days post partum [dpp] and their association with production parameters i.e., milk yield, milk fat and milk protein content of Simmental cows on Bavarian farms, categorized by parity (primi- or multiparous). Our study displays that in Simmental cows, overconditioning is more prevalent than underconditioning. While the middle of lactation (dpp = 100-199) resulted in higher percentage of overconditioning, the dry period (dpp = < 0 & > 299) indicated a higher percentage of underconditioned cows. The dry period and the middle of lactation are therefore the most challenging lactation stages for Simmental cows. We found milk protein content to have the strongest association with over- and underconditioning in Simmental cows. The probability of overconditioning was higher with higher milk protein content for every lactation stage and the probability of underconditioning was lower with higher milk protein content in every lactation stage. This study provides a theoretical basis for potential improvements in stockbreeding, which, if implemented, could improve not only the milk yield of Simmental dairy cows, but also their health and welfare.

Association between Fatty Acid Composition in Hair and Energy Availability during Early Lactation in Simmental and German Holstein Cows

This study examined (1) if fatty acids in bovine hair are influenced by dietary energy levels and (2) if the relationship between energy availability and fatty acids in hair persists across breeds and farms. Sixty-two and 59 Fleckvieh (Simmental), and 55 German Holstein cows from three farms, respectively, were fed two levels of energy concentration of roughage (6.1 and 6.5 MJ net energy for lactation/kg dry matter) and two levels of concentrate supply (150 and 250 g/kg energy-corrected milk). The average body weight was 727 kg (Simmental) and 668 kg (Holstein). The average lactation number was 3.1. Hair samples were taken in lactation weeks 4 and 8. In Simmental cows, a lower energy deficit due to a relatively higher energy intake from high energy concentration of the roughage was associated with higher C18:2n-6 and C18:3n-3 contents in hair at week 8. In cows from all three farms, higher energy intake between lactation weeks 2 and 6 correlated with higher content of C18:2n-6 in hair samples taken in lactation weeks 4 and 8. No correlation was found for C12:0. These results provide the first evidence that increased energy intake increases the contents of C18:2n-6 in hair.

The Use of Multilayer Perceptron Artificial Neural Networks to Detect Dairy Cows at Risk of Ketosis

Simple Summary

Ketosis is a serious metabolic disease in high-yield dairy cows, that affects productive herds throughout the world. Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during early lactation, so early detection and prevention are important for both economic and animal welfare reasons. Neural networks, which offer a high degree of accuracy in predicting various phenomena and processes where there is no clear causal correlation or there are no rules that allow the establishment of a logical cause-and-effect relationship, can be used to address problems related to prediction, classification, or control. A Multi-Layer perceptron (MLP) is a feedforward artificial neural network model that takes input data for a set of proper output. This study investigated the performance of four algorithms used to train MLP networks. The experimental results demonstrate that the MLP network model improved the accuracy of process recognition of subclinical ketosis in dairy cows. The received artificial model’s results were saved in the predictive model markup language (PMML) and can be used to describe the learning set, the algorithm used in the data mining application and related information.

Abstract

Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during lactation. Cows suffering from ketosis experience elevated ketone body levels in blood and milk, including β-hydroxybutyric acid (BHB), acetone (ACE) and acetoacetic acid. Ketosis causes serious financial losses to dairy cattle breeders and milk producers due to the costs of diagnosis and management as well as animal welfare reasons. Recent years have seen a growing interest in the use of artificial neural networks (ANNs) in various fields of science. ANNs offer a modeling method that enables the mapping of highly complex functional relationships. The purpose of this study was to determine the relationship between milk composition and blood BHB levels associated with subclinical ketosis in dairy cows, using feedforward multilayer perceptron (MLP) artificial neural networks. The results were verified based on the estimated sensitivity and specificity of selected network models, an optimum cut-off point was identified for the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). The study demonstrated that BHB, ACE and lactose (LAC) levels, as well as the fat-to-protein ratio in milk, were important input variables in the network training process. For the identification of cows at risk of subclinical ketosis, variables such as BHB and ACE levels in milk were of particular relevance, with a sensitivity and specificity of 0.84 and 0.61, respectively. It was found that the back propagation algorithm offers opportunities to integrate artificial intelligence and dairy cattle welfare within a computerized decision support tool.

Associations between Dairy Herds' Qualitative Behavior and Aspects of Herd Health, Stockperson and Farm Factors-A Cross-Sectional Exploration

The affective state is an integrated aspect of farm animal welfare, which is understood as the animals' perception of their living environment and of their internal biological functioning. The aim of this cross-sectional study was to explore animal-internal and external factors potentially influencing dairy cows' affective state. For this purpose, qualitative behavior assessments (QBA) describing the animals' body language were applied at herd level on 25 dairy farms. By means of principal component analysis (PCA), scores of PC1 (QBAscores) were determined for further analyses. From monthly milk recordings (MR) one year retrospectively, prevalences of udder and metabolic health impairments were calculated. Factors of housing, management, and human-animal contact were recorded via interviews and observations. A multivariable regression was calculated following a univariable preselection of factors. No associations were found between MR indicators and QBAscores. However, more positive QBAscores were associated with bedded cubicles or straw yards compared to raised cubicles, increased voluntary stockperson contact with the cows, and fixation of cows during main feeding times, the latter contributing to the explanatory model, but not being significant. These results underline the importance of lying comfort, positive human-animal relationship and reduction of competition during feeding for the well-being of dairy cows.

Genetic analysis of milk urea concentration and its genetic relationship with selected traits of interest in dairy cows

The aim of this study was to estimate genetic parameters of milk urea concentration (MU) and its genetic correlations with milk production traits, longevity, and functional traits in the first 3 parities in dairy cows. The edited data set consisted in 9,107,349 MU test-day records from the first 3 parities of 560,739 cows in 2,356 herds collected during the years 1994 to 2020. To estimate the genetic parameters of MU, data of 109 randomly selected herds, with a total of 770,016 MU test-day records, were used. Genetic parameters and estimated breeding values were estimated using a multiple-trait (parity) random regression model. Herd-test-day, age-year-season of calving, and days in milk classes (every 5 d as a class) were used as fixed effects, whereas effects of herd-year of calving, permanent environment, and animal were modeled using random regressions and Legendre polynomials of order 2. The average daily heritability and repeatability of MU during days in milk 5 to 365 in the first 3 parities were 0.19, 0.22, 0.20, and 0.48, 0.48, 0.47, respectively. The mean genetic correlation estimated among MU in the first 3 parities ranged from 0.96 to 0.97. The average daily estimated breeding values for MU of the selected bulls (n = 1,900) ranged from -9.09 to 7.37 mg/dL. In the last 10 yr, the genetic trend of MU has gradually increased. The genetic correlation between MU and 11 traits of interest ranged from -0.28 (milk yield) to 0.28 (somatic cell score). The findings of this study can be used as the first step for development of a routine genetic evaluation for MU and its inclusion into the genetic selection program in the Walloon Region of Belgium.

The Effect of Humic Mineral Substances from Oxyhumolite on the Coagulation Properties and Mineral Content of the Milk of Holstein-Friesian Cows

The study was conducted to determine the effect of humic mineral substances from oxyhumolite added to the diet of Holstein-Friesian cows on the coagulation properties, proximate chemical composition, and mineral profile of milk. The experiment was conducted on 64 cows divided into two groups of 32 each, control (CON) and experimental (H). The group H cows received the humic mineral substances as feed additive, containing 65% humic acids, for 60 days (100 g cow/day). Milk samples were collected twice, after 30 and 60 days. After 30 days no significant changes were observed in the chemical composition, somatic cell count (SCC), mineral content (except potassium), or curd texture parameters. However, the coagulation properties improved. The milk from group H after both 30 and 60 days coagulated significantly (15%) faster on average (p < 0.05), and the curd was about 36% and 28% firmer after 30 and 60 days, respectively (p < 0.05). After 60 days there was an increase in the content of fat (by 0.27 p.p.; p = 0.041), protein (by 0.14 p.p.; p = 0.012), and casein (by 0.12 p.p.; p = 0.029). SCC decreased by 20% (p = 0.023). The curds were significantly harder and less fracturable compared to the control. Calcium and iron content increased as well. The results indicate that humic mineral substances from oxyhumolite in the diet of cows can improve the suitability of milk for cheese production.

Genetic parameters for milk urea and its relationship with milk yield and compositions in Holstein dairy cows

The aim was to estimate genetic parameters for milk urea (MU) concentration and its relationship with milk yield and compositions in Holstein dairy Cows. Edited data were 90,594 test-day records of milk yield and composition collected during 2015 to 2018 on 13,737 lactations obtained from 7,850 Holstein cows in 50 herds. Random regression test-day model was used to estimate genetic parameters. (Co)variance components were estimated with the Bayesian Gibbs sampling method using a single chain of 400,000 iterates. The first 50,000 iterates of each chain were regarded as a burn-in period. Mean (SD) of MU was 23.03 (5.99) and 22.41 (5.74) mg/dl in primiparous and multiparous cows, respectively. Average heritability estimates for daily MU was 0.33 (SD = 0.02) ranged 0.29 to 0.36 and 0.32 (SD = 0.03) ranged 0.27 to 0.34, respectively, for primiparous and multiparous cows. The mean (SD) genetic correlation between MU and milk yield, fat yield, protein yield, lactose yield, fat percentage, protein percentage, lactose percentage, and somatic cell score was, respectively, -0.02 (0.03), -0.02 (0.01), 0.01 (0.04), 0.01 (0.03), 0.00 (0.07), -0.03 (0.04), 0.00 (0.01), -0.11 (0.06) in primiparous cows. The corresponding values in multiparous cows were -0.01 (0.02), -0.01 (0.03), -0.04 (0.04), -0.04 (0.04), 0.04 (0.04), 0.04 (0.07), -0.03 (0.09), 0.06 (0.11), respectively. The results indicate that selection on MU is possible with no effect on milk yield or compositions, however, relationships between MU and other important traits such as longevity, metabolic diseases, and fertility are needed.

Effects of Energy Supply from Roughage and Concentrates and the Occurrence of Subclinical Ketosis on Blood Chemistry and Liver Health in Lactating Dairy Cows during Early Lactation

The objectives of this study were to examine the effects of varying dietary energy supply as well as the impacts of subclinical ketosis (SCK) on blood chemistry and liver health. A total 63 German-Holstein cows were housed from three weeks antepartum until sixteen weeks postpartum. After calving, cows were assigned to one of four treatment groups receiving either moderate or high energy concentrations in roughage and secondly moderate or high amounts of concentrates. Retrospectively, cows were additionally grouped according to their β-hydroxybutyrate concentration (SK: cows with SCK vs. CON: cows without SCK). The different energy supply of treatment groups had little effects on blood and liver variables; greater differences occurred between SK and CON cows. Liver fat content of SK cows was 34% higher compared to CON cows. Also, the activity of aspartate aminotransferase and γ-glutamyl transferase, bilirubin concentration, and percentage of granulocytes were increased in SK cows. The results indicate that cows were able to adjust their metabolism to different dietary energy supplies without having a clearly increased risks for metabolic disorders. However, individual animals of all groups developed a metabolic derailment during the postpartum period resulting in SCK, which is closely connected with impaired liver function, compromised immune-responsiveness, and elevated oxidative stress.