Short communication: Early modification of the circadian organization of cow activity in relation to disease or estrus

Veissier, Isabelle; Mialon, Marie-Madeleine; Sloth, Karen Helle

doi:10.3168/jds.2016-11853

Cited by 50 publications

(36 citation statements)

References 14 publications

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“…An LPS challenge nevertheless does not reflect on-farm conditions because, unlike our experimental procedure, LPS-associated mastitis does not involve live bacteria. Rather, our findings reflect the typical decrease in activity observed in sick animals (Veissier et al, 2017) as described following an E. coli challenge (Fogsgaard et al, 2012). Cows may also have moved less to limit the pressure on the udder when changing posture (e.g., from standing to lying; Stewart et al, 2017).…”

Section: Discussionsupporting

confidence: 57%

Behavioral and patho-physiological response as possible signs of pain in dairy cows during Escherichia coli mastitis: A pilot study

Roches

Fauré

Lussert

et al. 2017

Journal of Dairy Science

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Bovine mastitis is one of the most common diseases in the dairy industry and it is a major welfare problem. Pain during mastitis is generally assessed through behavior but a combination of indicators would increase the chances of detecting pain and assessing its intensity. The aim of this study was to assess behavioral and patho-physiological responses as possible signs of pain experienced by cows after experimental intramammary challenge (mastitis) with Escherichia coli. Six Holstein-Friesian cows received an inoculation of E. coli P4 in one healthy quarter. Evolution of the disease was assessed using bacteriological growth and somatic cell counts (SCC). Cows' response to the challenge was monitored by direct behavioral and clinical observations, data loggers, rumen temperature sensors, and indicators of inflammation, stress, and oxidative status. From all data recorded, the variables that contributed most to the discrimination of mastitis phases were obtained by factorial discriminant analysis. Baseline levels of all indicators corresponded to values before challenge. Specifically, we weighted data relating to lying behavior by the observations at the same hour of the day before challenge to eliminate the circadian rhythm effect. We identified 3 phases that were discriminated by factorial discriminant analysis with good performance. Nine indicators varied according to the phase of the disease: cows' attitude toward their surroundings, tail position, clinical signs, ear position, variation of postural changes, concentrations of haptoglobin and serum amyloid A (SAA), cortisol blood levels, and rumen temperature (as a surrogate for body temperature). In phase 1 (4 to 8 h postinoculation), E. coli proliferated exponentially in milk but inflammation indicators remained at baseline levels. Cows were less attentive toward their surroundings (median score, 0.63), and postural changes (lying/standing) were less frequent (0.75 times from baseline). In phase 2 (12 to 24 h postinoculation), bacterial concentrations peaked around 12 h and then began to decrease concomitantly with a sharp SCC increase. Cows were less attentive toward their surroundings (score, 0.54), had high plasma cortisol (31.3 ng/mL) and SAA (100.3 µg/mL) concentrations, and rumen temperature was increased (40.3°C). In phase 3 (32 to 80 h postinoculation), bacterial concentrations decreased concomitantly with high SCC levels. Cows had high levels of haptoglobin (0.57 mg/mL) and SAA (269 µg/mL) but showed no behavioral changes. Dairy cows displayed changes of behavioral, inflammatory, and stress parameters after E. coli mammary inoculation. Our results suggest that cows may have experienced discomfort in the preclinical phase (phase 1) and pain in the acute phase (phase 2) but neither discomfort nor pain in the remission phase (phase 3). Although larger controlled studies are needed to confirm our findings, this knowledge could be useful for early detection of E. coli mastitis and for decision-making regarding the initiation of pain-relief treatment durin...

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Section: Discussionsupporting

confidence: 57%

Behavioral and patho-physiological response as possible signs of pain in dairy cows during Escherichia coli mastitis: A pilot study

Roches

Fauré

Lussert

et al. 2017

Journal of Dairy Science

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“…As in Veissier et al (2017), we performed a factorial correspondence analysis with hours of the day as observations and proportion of cows in each activity as variables. We used the weights of activities on the first component to build a new summary variable called activity level.…”

Section: Observationmentioning

confidence: 99%

“…For instance, cows are less reactive to their environment and change activity less often a few hours before fever due to Escherichia coli-induced mastitis (De Boyer Des Roches et al, 2017). In addition, the circadian rhythm of cattle activity can be less marked from as early as two days before clinical symptoms are detected by caretakers (Veissier et al, 1989b;Veissier et al, 2017). There is therefore scope to use such behaviours as early signs of a disease in order to detect and remedy health disorders very early on, and thus reduce medicine intakes and improve animal welfare.…”

Section: Introductionmentioning

confidence: 99%

Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis

Wagner

Antoine

Mialon

et al. 2020

Computers and Electronics in Agriculture

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et al.. Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis. A B S T R A C TSickness behaviour is characterised by a lethargic state during which the animal reduces its activity, sleeps more and at times when normally awake, reduces its feed and water intake, and interacts less with its environment. Subtle modifications in behaviour can materialise just before clinical signs of a disease. Recent sensor developments enable continuous monitoring of animal behaviour, but the shift to abnormal animal activity remains difficult to detect. We explored the use of Machine Learning (ML) to detect abnormal behaviour from continuous monitoring. We submitted 14 cows (Bos taurus) to Sub-Acute Ruminal Acidosis (SARA), a disease known to induce changes in behaviour. Another 14 control cows were not submitted to SARA. We used a ruminal bolus to monitor pH and detect when a cow experienced SARA. We used a positioning system to infer an animal's activity based on its position in relation to specific elements in the barn (feeder, resting area, and alleys). We tested several ML algorithms: K Nearest Neighbours for Regression (KNNR); Decision Tree for Regression (DTR); MultiLayer Perceptron (MLP); Long Short-Term Memory (LSTM); and an algorithm where activity is assumed to be similar from one day to the next. First, we developed ML models to predict activity on a given day from the previous 24 h, considering all cows together. Then, we calculated the error between observed and predicted values for a given cow. Finally, we compared the error to a threshold chosen to optimise the distinction between normal and abnormal values. KNNR performed best, detecting 83% of SARA cases (true-positives), but it also produced 66% of false-positives, which limits its use in practice. In conclusion, ML can help detect anomalies in behaviour. Further improvements could probably be obtained by applying ML on very large datasets at animal rather than group level.

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“…Les activités de jeux, particulièrement présentes chez les jeunes animaux (jeux de contact avec des objets, jeux locomoteurs avec l'animal qui sautille…) et le toilettage (animal se léchant) sont souvent diminués lors d'épisodes de douleur ou de fièvre (Mintline et al, 2013 ;Mandel et al, 2017 ;Mandel et al, 2018). Les changements dans le rythme journalier d'activité semblent également des signes précoces de maladies (Veissier et al, 1989(Veissier et al, , 2017.…”

Section: Introductionunclassified

“…C'est pourquoi, au-delà de la détection de la maladie, l'élevage de précision pourrait servir à évaluer le niveau de mal-être induit par cette maladie, à condition que les modifications subtiles des comportements soient intégrées dans les systèmes de décision. Des travaux sont conduits pour enrichir les outils actuels de l'EdP afin de prendre en compte le rythme d'activité, le toilettage ou encore la réaction des animaux à l'approche d'un observateur (Johansson et al, 2015 ;Veissier et al, 2017 ;Meunier et al, 2018).…”

Section: Introductionunclassified

Élevage de précision et bien-être en élevage : la révolution numérique de l’agriculture permettra-t-elle de prendre en compte les besoins des animaux et des éleveurs ?

et al. 2019

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Les techniques d'élevage de précision ont été développées essentiellement pour augmenter la rentabilité et réduire la charge de travail en appliquant des processus automatiques de surveillance des animaux et de leur environnement. Par exemple la détection de l'œstrus permet une insémination rapide, tandis que la détection des boiteries à un stade précoce ou d’un déséquilibre nutritionnel ou même des paramètres d'ambiance anormaux dans l'étable peuvent aider à prendre des mesures correctives rapidement. Les données générées par les capteurs pourraient également contribuer au bien-être des animaux. Un système détectant les problèmes de santé (par exemple, mammite ou cétose chez les vaches laitières) peut faire partie de la gestion du bien-être. En plus et surtout, certains dispositifs de l’élevage de précision sont basés sur la détection du comportement animal directement ou indirectement par la position des animaux : temps passé à se nourrir, ruminer, se reposer, marcher, etc. Des changements subtils de comportement peuvent indiquer l'état mental d'un animal : hyper-réactivité vs apathie, isolement social, modification du rythme quotidien d'activité, réduction du comportement de jeu ou du toilettage, hyper-agressivité. Ces changements peuvent être autant de signes de malaise dus à la maladie, au stress, à l'instabilité sociale, etc. Ainsi les techniques de l’élevage de précision offrent un large éventail de possibilités d'utiliser des signes de comportement animal pour aborder le bien-être dans des élevages modernes, qu’il s’agisse du bien-être lié à l'état de santé, aux relations sociales, aux relations homme-animal ou à un environnement quelconque stressant. À l'heure actuelle, ces possibilités sont peu explorées. Par ailleurs, l’élevage de précision modifie le travail des agriculteurs et potentiellement leurs interactions avec les animaux. Il est nécessaire que les animaux restent au centre de l'attention si l'on veut respecter leur bien-être et ce en harmonie avec celui de l’éleveur.

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Short communication: Early modification of the circadian organization of cow activity in relation to disease or estrus

Cited by 50 publications

References 14 publications

Behavioral and patho-physiological response as possible signs of pain in dairy cows during Escherichia coli mastitis: A pilot study

Behavioral and patho-physiological response as possible signs of pain in dairy cows during Escherichia coli mastitis: A pilot study

Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis

Élevage de précision et bien-être en élevage : la révolution numérique de l’agriculture permettra-t-elle de prendre en compte les besoins des animaux et des éleveurs ?

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