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

Adriaens, Ines; Brulle, Igor Van Den; Geerinckx, Katleen; D’Anvers, Lore; Vliegher, Sarne De; Aernouts, Ben

doi:10.1016/j.prevetmed.2021.105420

Cited by 19 publications

(10 citation statements)

References 31 publications

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“…Mastitis disease is a parenchymal inflammation of the dairy cattle mammary gland that is one of the most expensive problems in global milk production [ 1 ]. In which, mastitis caused significant economic losses for the global dairy industry [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic in silico analysis of bovine Escherichia coli mastitis highlights its immune-related expressed genes as an effective biomarker

Farmanullah

Liang

Khan

et al. 2021

Journal of Genetic Engineering and Biotechnology

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Background Mastitis is one of the major diseases causing economic loss to the dairy industry by reducing the quantity and quality of milk. Thus, the objective of this scientific study was to find new biomarkers based on genes for the early prediction before its severity. Methods In the present study, advanced bioinformatics including hierarchical clustering, enrichment analysis, active site prediction, epigenetic analysis, functional domain identification, and protein docking were used to analyze the important genes that could be utilized as biomarkers and therapeutic targets for mastitis. Results Four differentially expressed genes (DEGs) were identified in different regions of the mammary gland (teat cistern, gland cistern, lobuloalveolar, and Furstenberg’s rosette) that resulted in 453, 597, 577, and 636 DEG, respectively. Also, 101 overlapped genes were found by comparing 27 different expressed genes. These genes were associated with eight immune response pathways including NOD-like receptor signaling pathway (IL8, IL18, IL1B, PYDC1) and chemokine signaling pathway (PTK2, IL8, NCF1, CCR1, HCK). Meanwhile, 241 protein-protein interaction networks were developed among overlapped genes. Fifty-seven regulatory events were found between miRNAs, expressed genes, and the transcription factors (TFs) through micro-RNA and transcription factors (miRNA-DEG-TF) regulatory network. The 3D structure docking model of the expressed genes proteins identified their active sites and the binding ligands that could help in choosing the appropriate feed or treatment for affected animals. Conclusions The novelty of the distinguished DEG and their pathways in this study is that they can precisely improve the detection biomarkers and treatments techniques of cows’ Escherichia coli mastitis disease due to their high affinity with the target site of the mammary gland before appearing the symptoms. Graphical abstract

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Section: Introductionmentioning

confidence: 99%

Transcriptomic in silico analysis of bovine Escherichia coli mastitis highlights its immune-related expressed genes as an effective biomarker

Farmanullah

Liang

Khan

et al. 2021

Journal of Genetic Engineering and Biotechnology

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“…Mastitis-associated milk losses have a large impact on milk yield and herd sustainability. Adriaens et al (2021a) indicated that milk losses ranged from 38.4 to 215.6 kg within -5-30 days around the first treatment of mastitis. Resilience indicators based on variability in milk yield might reflect resistance to mastitis.…”

Section: Resilience In Holstein Cattlementioning

confidence: 99%

“…The pattern of milk losses differs among cows and events and can last for long periods. For instance, mastitis events could affect milk yield for more than 30 days, with a milk loss of 50-300 kg per event (van Soest et al, 2016;Adriaens et al, 2021a). Milk fever can result in lower milk yield for up to 6 weeks with milk losses ranging from 1.1 to 2.9 kg per day (Rajala-Schultz et al, 1999b).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it becomes a mainstream approach to calculate ELC from the actual daily milk yield. Compartment model (Ben et al, 2021), fourth-order polynomial quantile regression model (Poppe et al, 2020), nonparametric trend model (Poppe et al, 2020), and Wood model incorporating iterative procedures (Adriaens et al, 2021a; have been used to fit ELC. An important limitation of these approaches is the generalization of the ELC to a single model, thus ignoring differences in lactation trends among cows, which is a topic interest of this current study.…”

Section: Introductionmentioning

confidence: 99%

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Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle

et al. 2022

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Genetic selection for resilience is essential to improve the long-term sustainability of the dairy cattle industry, especially the ability of cows to maintain their level of production when exposed to environmental disturbances. Recording of daily milk yield provides an opportunity to develop resilience indicators based on milk losses and fluctuations in daily milk yield caused by environmental disturbances. In this context, our study aimed to explore milk loss traits and measures of variability in daily milk yield, including log-transformed standard deviation of milk deviations (Lnsd), lag-1 autocorrelation (Ra), and skewness of the deviations (Ske), as indicators of general resilience in dairy cows. The unperturbed dynamics of milk yield as well as milk loss were predicted using an iterative procedure of lactation curve modeling. Milk fluctuations were defined as a period of at least 10 successive days of negative deviations in which milk yield dropped at least once below 90% of the expected values. Genetic parameters of these indicators and their genetic correlation with economically important traits were estimated using single-trait and bivariate animal models and 8,935 lactations (after quality control) from 6,816 Chinese Holstein cows. In general, cows experienced an average of 3.73 environmental disturbances with a milk loss of 267 kg of milk per lactation. Each fluctuation lasted for 19.80 ± 11.46 days. Milk loss traits are heritable with heritability estimates ranging from 0.004 to 0.061. The heritabilities differed between Lnsd (0.135–0.250), Ra (0.008–0.058), and Ske (0.001–0.075), with the highest heritability estimate of 0.250 ± 0.020 for Lnsd when removing the first and last 10 days in milk in a lactation (Lnsd2). Based on moderate to high genetic correlations, lower Lnsd2 is associated with less milk losses, better reproductive performance, and lower disease incidence. These findings indicate that among the variables evaluated, Lnsd2 is the most promising indicator for breeding for improved resilience in Holstein cattle.

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“…Disease traits are used to select against occurrence of the disease, but are not informative about differences between cows in how much they are affected and how quickly they recover. Studies on genetic selection for recovery rate of mastitis (Franzén et al, 2012;Welderufael et al, 2017) have been performed and other studies characterized degree of mastitis infection (Højsgaard and Friggens, 2010) and yield loss due to infection (Adriaens et al, 2021). However, as far as known, no genetic selection on such traits is performed in practice.…”

Section: What Do the Resilience Indicators Explain About Resilience T...mentioning

confidence: 99%

Genetic improvement of resilience in dairy cattle using longitudinal data

Poppe¹

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Poppe, M. (2022). Genetic improvement of resilience in dairy cattle using longitudinal data. PhD thesis, Wageningen University, the Netherlands Resilience is the ability of cows to be minimally affected by, and to quickly recover from environmental disturbances, such as pathogens, extreme weather, or changes in feed quality. Resilience is important for welfare of cows and work pleasure of farmers, especially given an expected increase of disturbances in the future. This thesis focuses on developing indicators of resilience from longitudinal data, based on the expectation that longitudinal data records show response patterns to disturbances, informative about resilience. Resilience indicators based on response patterns can potentially assist in selecting the genetically most resilient animals for breeding, and in identifying resilience at herd level. The main focus of this thesis was on the resilience indicators 'variance' and 'autocorrelation' of daily deviations from expected values of milk yield and step count. Resilience is indicated by low variance (small deviations from baseline) and low lag-1 autocorrelation (weak dependency between subsequent deviations -quick recovery). Variance and autocorrelation of daily deviations from expected milk yield were heritable (0.21 and 0.09, respectively) and both were genetically similar within and between lactations. Variance seems informative about vulnerability and strength of response to disturbances, based on genetic correlations with milk loss upon reallife disturbances and with health, longevity and fertility. Autocorrelation had a favorable genetic correlation with recovery rate upon disturbances. Furthermore, variance was associated with lifetime gross margin, which confirms its economic importance. Levels of variance and autocorrelation differed considerably between herds, suggesting herd management plays a role in resilience. When based on step count instead of milk yield, variance and autocorrelation were heritable (0.14 and 0.04), and autocorrelation was favorably genetically correlated with health traits and fertility. However, variance had unfavorable genetic correlations with health. Additional traits based on step count were also heritable (especially mean step count; 0.45), but more research is needed into their relations with resilience. Genetic selection on a resilience index based on variance and autocorrelation of milk yield deviations, and potentially autocorrelation based on step count deviations, is expected to result in more resilient cows, that are less affected by disturbances and that recover more quickly. Contents

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

Cited by 19 publications

References 31 publications

Transcriptomic in silico analysis of bovine Escherichia coli mastitis highlights its immune-related expressed genes as an effective biomarker

Transcriptomic in silico analysis of bovine Escherichia coli mastitis highlights its immune-related expressed genes as an effective biomarker

Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle

Genetic improvement of resilience in dairy cattle using longitudinal data

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