Effects of disease on milk production in the dairy cow: a review

Fourichon, Christine; Seegers, Henri H.; Bareille, Nathalie N.; Beaudeau, François

doi:10.1016/s0167-5877(99)00035-5

Cited by 135 publications

(146 citation statements)

References 42 publications

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“…Direct costs include veterinary assistance/treatment at the point of calving and increased risk of dam and calf mortality (Ettema and Santos, 2004). Indirect costs include treatment of dystocia-associated transition cow disorders (Erb et al, 1985;LeBlanc, 2012) and sequelae such as losses in genetic gains associated with mortality or culling, increased BCS loss, reduced milk production, and impaired reproductive performance (Fourichon et al, 1999(Fourichon et al, , 2000Berry et al, 2007;Fenlon et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

Fenlon¹,

O’Grady

Mee

et al. 2017

Journal of Dairy Science

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The aim of this study was to build and compare predictive models of calving difficulty in dairy heifers and cows for the purpose of decision support and simulation modeling. Models to predict 3 levels of calving difficulty (unassisted, slight assistance, and considerable or veterinary assistance) were created using 4 machine learning techniques: multinomial regression, decision trees, random forests, and neural networks. The data used were sourced from 2,076 calving records in 10 Irish dairy herds. In total, 19.9 and 5.9% of calving events required slight assistance and considerable or veterinary assistance, respectively. Variables related to parity, genetics, BCS, breed, previous calving, and reproductive events and the calf were included in the analysis. Based on a stepwise regression modeling process, the variables included in the models were the dam's direct and maternal calving difficulty predicted transmitting abilities (PTA), BCS at calving, parity; calving assistance or difficulty at the previous calving; proportion of Holstein breed; sire breed; sire direct calving difficulty PTA; twinning; and 2-way interactions between calving BCS and previous calving difficulty and the direct calving difficulty PTA of dam and sire. The models were built using bootstrapping procedures on 70% of the data set. The held-back 30% of the data was used to evaluate the predictive performance of the models in terms of discrimination and calibration. The decision tree and random forest models omitted the effect of twinning and included only subsets of sire breeds. Only multinomial regression and neural networks explicitly included the modeled interactions. Calving BCS, calving difficulty PTA, and previous calving assistance ranked as highly important variables for all 4 models. The area under the receiver operating characteristic curve (ranging from 0.64 to 0.79) indicates that all of the models had good overall discriminatory power. The neural network and multinomial regression models performed best, correctly classifying 75% of calving cases and showing superior calibration, with an average error in predicted probability of 3.7 and 4.5%, respectively. The neural network and multinomial regression models developed are both suitable for use in decision-support and simulation modeling.

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Section: Introductionmentioning

confidence: 99%

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

Fenlon¹,

O’Grady

Mee

et al. 2017

Journal of Dairy Science

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“…Ketosis and metritis are common in dairy cows in early lactation (Bar and Ezra, 2005), and are associated with decreased milk yield (Fourichon et al, 1999;Edwards and Tozer, 2004;Huzzey et al, 2007), decreased reproductive performance (Opsomer et al, 2000;Walsh et al, 2007) and increased culling rate (Dubuc et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Health problems are associated with reductions in activity (Edwards and Tozer, 2004;Walker et al, 2008;Chapinal et al, 2010), rumination (Hansen et al, 2003;DeVries et al, 2009), milk yield (Fourichon et al, 1999;Rajala-Schultz et al, 1999) and BW (Maltz, 1997). Therefore, incorporating these available sensors to detect the activity reduction that is indicative of disease is advised.…”

Section: Introductionmentioning

confidence: 99%

A decision-tree model to detect post-calving diseases based on rumination, activity, milk yield, BW and voluntary visits to the milking robot

Steensels

Antler

Bahr

et al. 2016

animal

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Early detection of post-calving health problems is critical for dairy operations. Separating sick cows from the herd is important, especially in robotic-milking dairy farms, where searching for a sick cow can disturb the other cows' routine. The objectives of this study were to develop and apply a behaviour-and performance-based health-detection model to post-calving cows in a roboticmilking dairy farm, with the aim of detecting sick cows based on available commercial sensors. The study was conducted in an Israeli robotic-milking dairy farm with 250 Israeli-Holstein cows. All cows were equipped with rumination-and neck-activity sensors. Milk yield, visits to the milking robot and BW were recorded in the milking robot. A decision-tree model was developed on a calibration data set (historical data of the 10 months before the study) and was validated on the new data set. The decision model generated a probability of being sick for each cow. The model was applied once a week just before the veterinarian performed the weekly routine post-calving health check. The veterinarian's diagnosis served as a binary reference for the model (healthy-sick). The overall accuracy of the model was 78%, with a specificity of 87% and a sensitivity of 69%, suggesting its practical value.

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“…A definição de retenção de placenta é baseada no processo fisiológico de expulsão da membrana fetal do útero de vaca (Zobel & Tkalčić 2013) e se caracteriza como a falha da expulsão da placenta dentro de 12 a 24 horas após o parto (Kelton et al 1998, Fourichon et al 1999, representando condição patológica (Zobel & Tkalčić 2013). A liberação da placenta envolve a perda da adesão materno-fetal e ocorre somente após a maturação do placentoma, sendo que as contrações uterinas ajudam mecanicamente essa liberação que ocorre fisiologicamente entre três e seis horas após o parto (Meça et al 2006).…”

Section: Introductionunclassified

“…A ocorrência de doença pode resultar em mortalidade, redução da produção de leite, redução da fertilidade e descarte (Fourichon et al 1999). Segundo LeBlanc et al (2006), aproximadamente 75% das doenças em vacas leiteiras acontecem no primeiro mês após o parto e esses problemas têm sua origem associada ao sistema imune e ao consumo de alimentos duas a três semanas antes do parto.…”

Section: Introductionunclassified

Impacto econômico da retenção de placenta em vacas leiteiras

et al. 2018

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RESUMO.-Aproximadamente 75% das doenças em vacas leiteiras acontecem no primeiro mês após o parto e esses problemas têm sua origem associada ao sistema imune e ao consumo de alimentos duas a três semanas antes do parto. Doenças relacionadas ao trato reprodutivo como, por exemplo, retenção de placenta pode afetar a eficiência reprodutiva assim como a produção de leite. Os efeitos das doenças nos processos fisiológicos dos animais se transformam em impactos econômicos passíveis de serem mensurados. Objetivou-se avaliar o impacto econômico da retenção de placenta em um rebanho composto de 900 vacas em lactação. O diagnóstico de retenção de placenta foi definido como presença da placenta 24 horas após o parto. Foi utilizado um banco de dados no estudo, do qual foram extraídas as seguintes informações: ano de parição, época de parição, ordem de lactação, presença da placenta 24 horas após o parto, e número de doses de sêmen por gestação. Para cálculo do impacto econômico foram considerados os custos Approximately 75% of diseases in dairy cows occur in the first month after parturition and these problems have their origin associated with the immune system and food consumption two to three weeks before parturition. Diseases related to the reproductive tract, such as retained placenta, can affect reproductive efficiency as well as milk production. The effects of diseases on the physiological processes of animals become economic impacts that can be measured. The objective of this study was to evaluate the economic impact of retained placenta on a herd of 900 lactating cows. The diagnosis of placental retention was defined as presence of the placenta 24 hours postpartum. A database was used in the study, from which the following information was extracted: placenta presence 24 hours after calving, year of calving, calving season, and number of semen doses per gestation. In order to calculate the economic impact, the direct costs (treatment, labor, reduction of milk production and milk discharge during the treatment period) and indirect costs (increase in the service period, increase in the number of semen doses and increase of the risk of disposal). The average values related to the costs were obtained from the veterinarian responsible for the property with reference to the year 2009 for the calculations to be carried out. The total cost per occurrence of placenta retention in primiparous cows in the rainy and dry season was

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Effects of disease on milk production in the dairy cow: a review

Cited by 135 publications

References 42 publications

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

A decision-tree model to detect post-calving diseases based on rumination, activity, milk yield, BW and voluntary visits to the milking robot

Impacto econômico da retenção de placenta em vacas leiteiras

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