Effects of parity and days in milk on milk composition in correlation with β-hydroxybutyrate in tropic dairy cows

Milk yield prediction in Friesian cows using linear and flexible discriminant analysis under assumptions violations

BACKGROUND

The application of novel technologies is now widely used to assist in making optimal decisions. This study aimed to evaluate the performance of linear discriminant analysis (LDA) and flexible discriminant analysis (FDA) in classifying and predicting Friesian cattle's milk production into low ([Formula: see text]4500 kg), medium (4500-7500 kg), and high ([Formula: see text]7500 kg) categories. A total of 3793 lactation records from cows calved between 2009 and 2020 were collected to examine some predictors such as age at first calving (AFC), lactation order (LO), days open (DO), days in milk (DIM), dry period (DP), calving season (CFS), 305-day milk yield (305-MY), calving interval (CI), and total breeding per conception (TBRD).

RESULTS

The comparison between LDA and FDA models was based on the significance of coefficients, total accuracy, sensitivity, precision, and F1-score. The LDA results revealed that DIM and 305-MY were the significant (P < 0.001) contributors for data classification, while the FDA was a lactation order. Classification accuracy results showed that the FDA model performed better than the LDA model in expressing accuracies of correctly classified cases as well as overall classification accuracy of milk yield. The FDA model outperformed LDA in both accuracy and F1-score. It achieved an accuracy of 82% compared to LDA's 71%. Similarly, the F1-score improved from a range of 0.667 to 0.79 for LDA to a higher range of 0.81 to 0.83 for FDA.

CONCLUSION

The findings of this study demonstrated that FDA was more resistant than LDA in case of assumption violations. Furthermore, the current study showed the feasibility and efficacy of LDA and FDA in interpreting and predicting livestock datasets.

The prevalence and factors associated with mastitis in dairy cows kept by small-scale farmers in Dodoma, Tanzania

Bovine mastitis is among the major diseases of economic importance in the dairy industry worldwide. Hygienic conditions during milking and housing are important determinants of mastitis infections. However, arid and semi-arid areas have a scarcity of water, which is an essential component of cleanliness. Thus, the study determined the prevalence of mastitis and associated factors in Dodoma, a semi-arid region in Tanzania. Three hundred and sixty-eight lactating cows were selected randomly from 114 farms in the Dodoma urban district for mastitis determination based on clinical signs and the California Mastitis Test. About 59.8 % of the cows had mastitis, and the subclinical type dominated. Factors associated with mastitis prevalence were cleanliness (hand and udder washing before milking), farmers' awareness of mastitis, the cow's parity and lactation stage, and herd size. Milk yield was not associated with the mastitis status of the cow but was related to the breed of the cow, whether warm or cold water was used to wash the cow's udder, parity, and lactation stage. Improvement in education on the importance of hygiene is necessary, as 38.6 % of the farmers were unaware of mastitis. Further detailed studies on microbiology, such as bacteriologic culture and polymerase chain reaction, are recommended to formulate interventions.

Heat Stress Effects on Physiological and Milk Yield Traits of Lactating Holstein Friesian Crossbreds Reared in Tanga Region, Tanzania

Global warming caused by climate change is a challenge for dairy farming, especially in sub-Saharan countries. Under high temperatures and relative humidity, lactating dairy cows suffer from heat stress. The objective of this study was to investigate the effects and relationship of heat stress (HS) measured by the temperature-humidity index (THI) regarding the physiological parameters and milk yield and composition of lactating Holstein Friesian crossbred dairy cows reared in the humid coastal region of Tanzania. A total of 29 lactating Holstein Friesian x Zebu crossbred dairy cows with 50% (HF50) and 75% (HF75) Holstein Friesian gene levels in the second and third months of lactation were used. The breed composition of Holstein Friesians was determined based on the animal recording system used at the Tanzania Livestock Research Institute (TALIRI), Tanga. The data collected included the daily temperature, relative humidity, daily milk yield, and physiological parameters (core body temperature, rectal temperature, respiratory rate, and panting score). THI was calculated using the equation of the National Research Council. The THI values were categorized into three classes, i.e., low THI (76-78), moderate THI (79-81), and high THI (82-84). The effects of THI on the physiological parameters and milk yield and composition were assessed. The effects of the genotype, the parity, the lactation month, and the interaction of these parameters with THI on the milk yield, milk composition, and physiological parameters were also investigated. The results show that THI and its interaction with genotypes, parity, and the lactation month had a highly significant effect on all parameters. THI influenced (p ˂ 0.05) the average daily milk yield and milk fat %, protein %, lactose %, and solids-not-fat %. As the THI increased from moderate to high levels, the average daily milk yield declined from 3.49 ± 0.04 to 3.43 ± 0.05 L/day, while the fat % increased from 2.66 ± 0.05% to 3.04 ± 0.06% and the protein decreased from 3.15 ± 0.02% to 3.13 ± 0.03%. No decline in lactose % was observed, while the solid-not-fat % declined from 8.56 ± 0.08% to 8.55 ± 0.10% as the THI values increased from moderate to high. Also, the THI influenced physiological parameters (p ˂ 0.05). The core body temperature (CBT), rectal temperature (RT), respiratory rate (RR) and panting score (PS) increased from 35.60 ± 0.01 to 36.00 ± 0.01 °C, 38.03 ± 0.02 to 38.30 ± 0.02 °C, 62.53 ± 0.29 to 72.35 ± 0.28 breaths/min, and 1.35 ± 0.01 to 1.47 ± 0.09, respectively, as the THI increased from low to high. The THI showed a weak positive correlation with the average daily milk yield and fat percentage, whereas the protein, lactose, and solids-not-fat percentages showed negative relationships with THI (p ≤ 0.05). CBT, RT, RR, and PS showed positive relationships (p ≤ 0.05) with THI. These negative relationships indicate that there is an antagonistic correlation between sensitivity to HS and the level of production. It is concluded that the THI, the genotype, the parity, and the lactation month, along with their interactions with THI, significantly influenced the milk yield, milk composition, and physiological parameters of lactating Holstein Friesian dairy crosses at THI thresholds ranging from 77 to 84.

Influence of production level, number, and stage of lactation on milk quality in compost barn systems

This study evaluated the influence of milk production, number of lactations, and days in milk (DIM) on the quality and composition of milk from dairy cows housed in a compost barn (CB) system. The study was carried out using a six-year database, counting 31,268 observations from 2,037 cows of European breeds. Multiparous cows showed higher fat and protein production. Lactose showed high levels for primiparous and the initial stage of lactation (4.65%) and was negatively influenced by somatic cell count (SCC). Milk urea nitrogen was higher (14.01%) from 106 to 205 days in milk, and the other components were higher at >305 days. Therefore, the solids content was higher in the first and second lactations due to the high contents of lactose, fat, and milk protein, but lactose was reduced over lactations. In contrast, high DIM increased SCC and concentrated solids due to lower milk production. The effect of milk production, stage, and lactation order on the composition and milk quality of herds housed in CB showed the same pattern as in other production systems.

Influence of repeated mastitis on the milk production and metabolic status in the subsequent lactation period of dairy cattle

Recurrent mastitis poses a common challenge on dairy farms. While the impact of repeated mastitis within the same lactation has been investigated, the difference from one lactation to the next, particularly concerning the change of milk and blood metabolites, remains unclear. This study aimed to examine the difference in milk yield, milk composition, and metabolic status in the subsequent lactation between healthy and repeated mastitis in the previous lactation. The study population comprised 50 cows chosen from 400 cows, with 25 having no history of mastitis and 25 experiencing mastitis more than three times during the last lactation. Following dry-off and calving, all cows initiated a new lactation, during which no mastitis was diagnosed until the sample collection period. In the group exposed to repeated mastitis, a significant decrease in milk fat levels was observed in the subsequent lactation, while no change was observed in milk somatic cell count (SCC). Milk collected from cows that had experienced repeated mastitis in the previous lactation exhibited significant increases in the levels of free amino acids, namely valine, proline, and alanine. However, no difference in plasma levels of these amino acids was noted. These results indicate that individuals exposed to repeated mastitis have persistent milk quality changes even after dry-off. Biomarker analysis suggested that the milk valine and proline showed a moderate biomarker potential on Kappa coefficients to characterize cows that have experienced repeated mastitis. Furthermore, the results of biomarker combinations for valine and proline provided the highest specificity (100 %), positive likelihood ratio (infinity), and substantial biomarker potential on kappa coefficients (0.68). These findings significantly enhance our understanding of the pathobiology and etiology of recurrent mastitis and provide a biomarker to characterize cows that have experienced repeated mastitis in the past.

Metabolic Changes in Serum and Milk of Holstein Cows in Their First to Fourth Parity Revealed by Biochemical Analysis and Untargeted Metabolomics

The performance of dairy cows is closely tied to the metabolic state, and this performance varies depending on the number of times the cows have given birth. However, there is still a lack of research on the relationship between the metabolic state of Holstein cows and the performance of lactation across multiple parities. In this study, biochemical analyses and metabolomics studies were performed on the serum and milk from Holstein cows of parities 1-4 (H1, N = 10; H2, N = 7; H3, N = 9; H4, N = 9) in mid-lactation (DIM of 141 ± 4 days) to investigate the link between performance and metabolic changes. The results of the milk quality analysis showed that the lactose levels were highest in H1 (p = 0.036). The total protein content in the serum increased with increasing parity (p = 0.013). Additionally, the lipase activity was found to be lowest in H1 (p = 0.022). There was no difference in the composition of the hydrolyzed amino acids in the milk among H1 to H4. However, the free amino acids histidine and glutamate in the serum were lowest in H1 and highest in H3 (p < 0.001), while glycine was higher in H4 (p = 0.031). The metabolomics analysis revealed that 53 and 118 differential metabolites were identified in the milk and serum, respectively. The differential metabolites in the cows' milk were classified into seven categories based on KEGG. Most of the differential metabolites in the cows' milk were found to be more abundant in H1, and these metabolites were enriched in two impact pathways. The differential metabolites in the serum could be classified into nine categories and enriched in six metabolic pathways. A total of six shared metabolites were identified in the serum and milk, among which cholesterol and citric acid were closely related to amino acid metabolism in the serum. These findings indicate a significant influence of blood metabolites on the energy and amino acid metabolism during the milk production process in the Holstein cows across 1-4 lactations, and that an in-depth understanding of the metabolic changes that occur in Holstein cows during different lactations is essential for precision farming, and that it is worthwhile to further investigate these key metabolites that have an impact through controlled experiments.

Assessing the relationship between somatic cell count and the milk mid-infrared spectrum in Chinese Holstein cows

BACKGROUND

Milk produced by dairy cows is a complex combination of many components, but the effect of mastitis has only been investigated for a few of these components. Milk mid-infrared (MIR) spectra can reflect the global composition of milk, and this study aimed to detect the relationships between milk MIR spectral wavenumbers and milk somatic cell count (SCC)-a sensitive biomarker for mastitis.

METHODS

Pearson correlation analysis was used to calculate the correlation coefficient between somatic count score (SCS) and spectral wavenumbers. A general linear mixed model was applied to investigate the effect of three different classes of SCC (low, middle and high) on spectral wavenumbers.

RESULTS

The mean correlation coefficient between the 'fingerprint region' (wavenumbers 925-1582 cm-1 ) and the SCS was higher than that for other regions of the MIR spectrum, and the specific wavenumber with the strongest correlation with the SCS was within the 'fingerprint region'. SCC class had a significant (p < 0.05) effect on 639 spectral wavenumbers. In particular, some spectral wavenumbers within the 'fingerprint region' were highly affected by the SCC class.

LIMITATION

The data were collected from only one province in China, so the generalisability of the findings may be limited.

CONCLUSION

SCC had close relationships with milk spectral wavenumbers related to important milk components or chemical bonds.

A survey report on the donkey original breeding farms in China: Current aspects and future prospective

Introduction

The number of the large-scale donkey breeding farms in China has increased dramatically. However, information regarding the situation of a Chinese donkey population under large-scale donkey breeding farms is limited.

Methods

This survey report was conducted using questionnaires online to investigate the current situation of the donkey original breeding farms in China, in terms of donkey stock, local breeds, reproductive parameters, growth and lactation performance, and future perspectives. China has developed the donkey reserve system based on national, provincial and non-governmental (self-own) donkey original breeding farms.

Results

In the present study, a total of 38 donkey original breeding farms concentrated in Northern of China were studied, and 52% of them keep their donkeys with a stocking density of 100-500 donkeys. China is rich in various local donkey breeds, and 16 local donkey breeds including large-sized, medium-sized and small-sized breeds were collected in our survey. Dezhou donkey are prevalent with a percentage of more than 57% of the total donkeys, while the Cullen donkeys belong to small-sized donkey breeds are scare. The reproductive efficiency and donkey productivity were different across donkey farms, indicating potential differences in management and breeding practices between different donkey original breeding farms. The artificial insemination has been performed in these donkey farms with an average proportion of 73%. Regarding the donkey productivity, the national and provincial donkey original breeding farms showed a higher birthweight and fat content in donkey milk than self-own farms. Furthermore, our results indicate that the donkey breeds with different body size also have important influence on the reproduction parameters and donkey productivity, with the large-sized donkeys had better performance compared to the small-sized donkeys.

Discussion

In summary, our survey provided valuable baseline information on the situation of donkey population dynamics in the donkey original breeding farms. However, further study is required in the future to investigate the factors such as donkey health care, management and nutrition during breeding, fattening and lactation that influence donkey productivity under large-scale farm systems.