Relationship between saliva and blood cortisol in handled cows

Dzviti, Melody; Mapfumo, Lizwell; Muchenje, Voster

doi:10.5713/ajas.18.0151

Cited by 14 publications

(24 citation statements)

References 33 publications

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“…One of the main potential issues for interpretation of these correlations is the difference between stress and welfare. Cortisol is a stress indicator, but welfare is not solely based on the absence of stress [ 29 , 32 ]. Cortisol is affected by social rank, especially for high-ranking cows [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, in order to collect blood samples the animal needs to be handled, which could in itself be stressful and elevate the cortisol level in the blood [ 30 , 31 ]. Cortisol or the metabolites can also be determined with non-invasive sampling methods in saliva, faeces and urine [ 32 ]. However, measurements in blood samples or non-invasively, in saliva samples, reflect an acute activation of the Hypothalamo-pituitary-adrenal axis (HPA axis).…”

Section: Introductionmentioning

confidence: 99%

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The Relation between Hair-Cortisol Concentration and Various Welfare Assessments of Dutch Dairy Farms

Eerdenburg

Hof

Doeve

et al. 2021

Animals

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Many protocols have been developed to assess farm animal welfare. However, the validity of these protocols is still subject to debate. The present study aimed to compare nine welfare assessment protocols, namely: (1) Welfare Quality© (WQ), (2) a modified version of Welfare Quality (WQ Mod), which has a better discriminative power, (3) WelzijnsWijzer (Welfare Indicator; WW), (4) a new Welfare Monitor (WM), (5) Continue Welzijns Monitor (Continuous Welfare Monitor; CWM), (6) KoeKompas (Cow Compass; KK), (7) Cow Comfort Scoring System (CCSS), (8) Stall Standing Index (SSI) and (9) a Welfare Index (WI Tuyttens). In addition, a simple welfare estimation by veterinarians (Estimate vets, EV) was added. Rank correlation coefficients were calculated between each of the welfare assessment protocol scores and mean hair cortisol concentrations from 10 cows at 58 dairy farms spread over the Netherlands. Because it has been suggested that the hair cortisol level is related to stress, experienced over a long period of time, we expected a negative correlation between cortisol and the result of the welfare protocol scores. Only the simple welfare estimation by veterinarians (EV) (ρ = −0.28) had a poor, but significant, negative correlation with hair cortisol. This correlations, however, failed to reach significance after correction of p-values for multiple correlations. Most of the results of the different welfare assessment protocols had a poor, fair or strong positive correlation with each other, supporting the notion that they measure something similar. Additional analyses revealed that the modified Welfare Quality protocol parameters housing (ρ = −0.30), the new Welfare Monitor (WM) parameter health (ρ = −0.33), and milk yield (ρ = −0.33) showed negative correlations with cortisol. We conclude that because only five out of all the parameter scores from the welfare assessment protocols showed a negative, albeit weak, correlation with cortisol, hair cortisol levels may not provide a long term indicator for stress in dairy cattle, or alternatively, that the protocols might not yield valid indices for cow welfare.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Relation between Hair-Cortisol Concentration and Various Welfare Assessments of Dutch Dairy Farms

Eerdenburg

Hof

Doeve

et al. 2021

Animals

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“… (a) The stressors were environmental heat (Alvarez & Johnson, 1973; Choshniak et al., 1982); handling and adrenocorticotropic hormone challenge (Dzviti et al., 2019); milking (Negrão et al., 2004); group relocation (Pošćić et al., 2017); biological variation between individuals without a specific challenge (Goyon et al., 2016); milk from supermarkets (no specific challenge) (Y. Yang et al., 2009); Temperament and transport (Burdick et al., 2010); Temperament (Coombes et al., 2014); Dark cutting (X. Lu et al., 2018); Inflammation (Contreras‐Aguilar et al., 2019); Transport distance and lairage time (Chulayo et al., 2016); Brief peer separation and reunion (Boissy & Le Neindre, 1997); Restraint and isolation as well as dark cutting (Apple et al., 2005); and Mastitis (Davis et al., 2004). Bracketed italicized numbers represent fold changes.…”

Section: Potential Markersmentioning

confidence: 99%

“…Moreover, in another study where the capacity of behavioral tests to predict the incidence of DC was tested, a moderate correlation ( r = 0.34; p < .05) between on‐farm cortisol levels and crush test scores (indicating behavior of the animal) was observed, while an excellent correlation between the behavior test and pHu ( r = 0.81‐0.87; p < .01) was observed, indicating merit in combining physiological measurements with behavior observations (De Freslon et al., 2014). Interestingly, high (∼15)‐fold changes in cortisol levels have been observed between baseline and response levels in saliva (Dzviti et al., 2019; Negrão et al., 2004). Moreover, a large dynamic range (∼35‐fold change) was observed for cortisol in milk (Goyon et al., 2016; Y. Yang et al., 2009) and plasma cortisol levels were clearly linked with animal temperament in some studies (Burdick et al., 2010).…”

Section: Potential Markersmentioning

confidence: 99%

Biomarkers and biosensors for the diagnosis of noncompliant pH, dark cutting beef predisposition, and welfare in cattle

Nelis

Bose

Broadbent

et al. 2022

Comp Rev Food Sci Food Safe

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Meat quality can be affected by stress, exhaustion, feed composition, and other physical and environmental conditions. These stressors can alter the pH in postmortem muscle, leading to high pH and low‐quality dark cutting (DC) beef, resulting in considerable economic loss. Moreover, the dark cutting prediction may equally provide a measure for animal welfare since it is directly related to animal stress. There are two needs to advance on‐site detection of dark cutters: (1) a clear indication that biomarker (signature compounds) levels in cattle correlate with stress and DC outcome; and (2) measuring these biomarkers rapidly and accurately on‐farm or the abattoir, depending on the objectives. This critical review assesses which small molecules and proteins have been identified as potential biomarkers of stress and dark cutting in cattle. We discuss the potential of promising small molecule biomarkers, including catecholamine/cortisol metabolites, lactate, succinate, inosine, glucose, and β‐hydroxybutyrate, and we identify a clear research gap for proteomic biomarker discovery in live cattle. We also explore the potential of chemical‐sensing and biosensing technologies, including direct electrochemical detection improved through nanotechnology (e.g., carbon and gold nanostructures), surface‐enhanced Raman spectroscopy in combination with chemometrics, and commercial hand‐held devices for small molecule detection. No current strategy exists to rapidly detect predictive meat quality biomarkers due to the need to further validate biomarkers and the fact that different biosensor types are needed to optimally detect different molecules. Nonetheless, several biomarker/biosensor combinations reported herein show excellent potential to enable the measurement of DC potential in live cattle.

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“…Communication was defined as an immediate return of vocalization (within 1 sec) by an animal devoid of other states such as estrus or hunger, solely in response to vocalization from other cows. To quantitatively assess the degree of stress level of each animal in the four physiological states, we measured salivary cortisol concentrations (Negrao et al 2004;Dzviti et al 2019). Saliva samples were collected in the morning from three randomly selected cows that had vocalizations in each of the four physiological states for cortisol analysis.…”

Section: Animals and Data Collectionmentioning

confidence: 99%

Characterization and assessment of vocalization responses of cows to different physiological states

Yoshihara

Oya

2021

Journal of Applied Animal Research

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The objective of our study was to characterize and assess the vocalization responses of cows in four different physiological states using different measurements and relate those to salivary cortisol concentrations as a possible indicator of stress. We characterized and assessed the vocalization responses of cows in four different physiological states: feed anticipation, estrus, communication between or among individuals, and parturition. We used video camera recordings of the calls made by the cattle during these four states. We measured the duration of the calls, intensity and pitch of vocalizations, and the formant, using vocalization analysis software. Two hundred and ninety calls were recorded from 32 cows during the study. Cortisol increased from the least values in communications and feed anticipation, to two-fold concentrations in estrus, and three-fold concentrations in parturition. All the formants tended to increase in the same order with the stress concentrations, whereas low intensity and lower pitched vocalizations were associated with greater cortisol concentrations during parturition. These results indicate that acoustic parameters of calls in each of the physiological states may reflect the stress level in cattle, and thus, the vocalization of animals could be an important behavioural indicator of their stress level.

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Relationship between saliva and blood cortisol in handled cows

Abstract: We conclude that, if beef cows are subjected to repeated handling, a weak association exists between serum and salivary cortisol levels in beef cows subjected to a hormonal challenge.

Cited by 14 publications

References 33 publications

The Relation between Hair-Cortisol Concentration and Various Welfare Assessments of Dutch Dairy Farms

The Relation between Hair-Cortisol Concentration and Various Welfare Assessments of Dutch Dairy Farms

Biomarkers and biosensors for the diagnosis of noncompliant pH, dark cutting beef predisposition, and welfare in cattle

Characterization and assessment of vocalization responses of cows to different physiological states

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