Genetic analysis of efficiency traits in Austrian dairy cattle and their relationships with body condition score and lameness

Köck, Astrid; Ledinek, M.; Grüber, L.; Steininger, Franz; Fuerst-Waltl, Birgit; Egger-Danner, C.

doi:10.3168/jds.2017-13281

Cited by 20 publications

(32 citation statements)

References 22 publications

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“…On the other hand, large chest girth is associated with an increased risk for lameness (OR=1.26 [1.20;1.32]). Contrary to existing research 55,56 , animal body weight decreases the odds for lameness (OR=0.84 [0.80; 0.89]), but does not rank highly among feature importances (rank 127). The strong association of parity on the risk for lameness in our analysis (OR=2.25 [2.17; 2.33]) lies within the confidence interval reported by the meta study (OR=1.…”

Section: Lamenesscontrasting

confidence: 95%

Integrating diverse data sources to predict disease risk in dairy cattle

Lasser

Matzhold

Egger-Danner³

et al. 2021

Preprint

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Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making it impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision medicine approach, bringing together information streams from a variety of life domains of dairy cattle to predict eight common and economically important diseases. Dairy cows are part of a highly industrialised environment. The animals and their surroundings are closely monitored and environmental, behavioural and physiological observations are readily accessible yet seldomly integrated. We use random forest classifiers trained on data from 5,828 animals in 166 herds in Austria to predict occurrences of lameness, acute and chronic mastitis, anoestrus, ovarian cysts, metritis, ketosis (hyperketonemia) and periparturient hypocalcemia (milk fever). To assess the importance of specific cattle life domains and individual features for these predictions, we use multivariate logistic regression and feature permutation approaches. We show that disease in dairy cattle is a product of the complex interplay between a multitude of life domains such as housing, nutrition or climate, and identify a range of features that were previously not associated with increased disease risk. For example, we can predict anoestrus with high sensitivity and specificity (F1=0.72) and find that housing, feed and husbandry variables such as barn design and time on pasture are most predictive of this disease. We also find previously unknown associations of features with disease risk, for example humid conditions, which significantly decrease the odds for ketosis. Our findings pave the way towards data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.

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Section: Lamenesscontrasting

confidence: 95%

Integrating diverse data sources to predict disease risk in dairy cattle

Lasser

Matzhold

Egger-Danner³

et al. 2021

Preprint

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“…The recommended acceptable BCS of dairy cows ranges from 3.0 to 3.5 on a 5-point scale to ensure that health, reproduction and animal welfare are not compromised [2]. A previous study showed that cows with low BCS (LBCS) are more susceptible to metabolic diseases and claw diseases [3]. In contrast, cows with high BCS (HBCS) suffer from many metabolic disorders, such as fatty liver, oxidative stress and ketosis [4].…”

Section: Introductionmentioning

confidence: 99%

Effects of body condition on the insulin resistance, lipid metabolism and oxidative stress of lactating dairy cows

Liu

Wang

2020

Lipids Health Dis

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Background: Overconditioned dairy cows are prone to greater insulin resistance in transition to successfully adapt to negative energy balance. The associations among body condition score (BCS), insulin resistance, lipid metabolism and oxidative stress in cows during late lactation with positive energy balance remain to be elucidated. Methods: The objectives of this study were to investigate insulin sensitivity and oxidative status in late lactating dairy cows with different BCS but similar milk production, parity and days in milk. Forty-two multiparous Holstein cows were fed the same diet under the same management and divided into three groups based on BCS: low BCS (LBCS; BCS ≤ 2.75; n = 12), medium BCS (MBCS; 3.0 ≤ BCS ≤ 3.5; n = 15) or high BCS (HBCS; BCS ≥ 3.75; n = 15). Blood samples used for analysis of biochemical and hematological parameters were collected from the coccygeal vein at the end of experiment. Results: The concentrations of insulin and nonesterified fatty acid were higher and the revised quantitative insulin sensitivity check index (RQUICKI) was lower in HBCS cows than in LBCS and MBCS cows. These results suggest that insulin resistance exacerbates lipolysis in HBCS cows. Serum concentrations of very low-density lipoprotein, apolipoprotein A-I, and apolipoprotein B were lower in HBCS cows than in LBCS or MBCS cows. Although LBCS and MBCS cows had higher reactive oxygen species levels than did HBCS cows, the malondialdehyde concentration was not different among cows with different BCS. Ceruloplasmin activity was higher in MBCS and HBCS cows than in LBCS cows, but superoxide dismutase, glutathione peroxidase, and paraoxonase activities were not different among cows with different BCS. Despite the higher percentage of granulocytes in MBCS cows than in HBCS cows, no differences were found in leukocyte counts, red blood cell profiles and platelet profiles among the cows in the three groups. Conclusions: Results of this study showed that compared with MBCS and LBCS cows, HBCS cows at late lactation stage may have accumulated more hepatic triacylglycerol and lower antioxidant potential due to greater insulin resistance.

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“…Furthermore, a previous study within this project showed that larger cows required more health care (Mahoney et al, 1986). In Bavaria (Germany), BS and FV cows have increased in body size over the last decades (Krogmeier, 2009). Krogmeier (2009) also found a negative relationship between body size and longevity.…”

Section: Discussionmentioning

confidence: 85%

“…In the USA, Holstein cows were selected directly for body size to some extent, on the assumption that larger cows are able to produce more milk (Hansen, 2000). In Bavaria (Germany), increasing body size in Fleckvieh (dual-purpose Simmental) and Brown Swiss (BS) has been negatively connected to longevity (Krogmeier, 2009). In 1966, a long-term experiment with Holstein (HF) cows at the Northwest Experiment Station, University of Minnesota, concerning cow size was initiated and resulted in several studies (e.g., Mahoney et al, 1986;Hansen et al, 1999;Becker et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Analysis of lactating cows on commercial Austrian dairy farms: the influence of genotype and body weight on efficiency parameters

Ledinek

Grüber

Steininger³

et al. 2019

Arch. Anim. Breed.

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The aim of this study was twofold: first, to evaluate the influence of body weight on the efficiency of dairy cows, and second, to analyze the current state of dairy cattle populations as part of the Austrian Cattle Breeding Association's Efficient Cow project.Data of Fleckvieh (FV, dual-purpose Simmental), Fleckvieh×Red Holstein (FV×RH), Holstein (HF) and Brown Swiss (BS) dairy cows (161 farms, 6098 cows) were collected at each performance recording during the year 2014.In addition to routinely recorded data (e.g., milk yield, fertility), body weight, body measurements, body condition score (BCS) and individual feed information were also collected. The following efficiency traits were considered: body weight efficiency as the ratio of energy-corrected milk (ECM) to metabolic body weight, feed efficiency (kilogram ECM per kilogram dry-matter intake) and energy efficiency expressed as the ratio of energy in milk to energy intake.The relationship of milk yield to body weight was shown to be nonlinear. Milk yield decreased in cows above the 750 kg body weight class for HF, BS and FV×RH with 68 % RH genes, but less dramatically and later for FV at 800 kg. This resulted in an optimum body weight for feed and energy efficiency. BS and HF had the highest efficiency in a narrower and lighter body weight range (550–700 kg) due to a stronger curvature of the parabolic curve. Contrary to this, the efficiency of FV did not change as much as it did in the dairy breeds with increasing body weight, meaning that FV had a similar feed and energy efficiency in a range of 500–750 kg. The breed differences disappeared when body weight ranged between 750 and 800 kg.The average body weight of the breeds studied (FV 722 kg, BS 649 and HF 662 kg) was in the optimum range. FV was located at the upper end of the decreasing segment.In conclusion, an optimum body weight range for efficiency does exist, due to the nonlinear relationship of milk yield and body weight. Specialized dairy breeds seem to respond more intensively to body weight range than dual-purpose breeds, due to the stronger curvature. Cows with medium weights within a population are the most efficient. Heavy cows (>750 kg) produce even less milk. A further increase in dairy cows' body weights should therefore be avoided.

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Genetic analysis of efficiency traits in Austrian dairy cattle and their relationships with body condition score and lameness

Cited by 20 publications

References 22 publications

Integrating diverse data sources to predict disease risk in dairy cattle

Integrating diverse data sources to predict disease risk in dairy cattle

Effects of body condition on the insulin resistance, lipid metabolism and oxidative stress of lactating dairy cows

Analysis of lactating cows on commercial Austrian dairy farms: the influence of genotype and body weight on efficiency parameters

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