86,649 research outputs found
Qualitative behaviour assessment of dairy buffaloes (Bubalus bubalis)
Full text linkThis study applies qualitative behaviour assessment (QBA) for the first time to dairy buffaloes,
using three groups of observers with different cultural backgrounds and different
levels of experience in animal behaviour observation and buffalo farming. Eight buffalo
heifers aged 16–18 months were subjected to two isolation tests, one performed in the
indoor part of their home environment, and one in a novel outdoor paddock. Animals
were filmed individually for 2.5 min, and the resulting 16 video clips were shown to
three observer panels, consisting of 11 applied animal behaviour scientists from 6 European
countries, 11 Italian animal scientists with a background in buffalo farming but no
experience in behavioural observation, and 14 Italian undergraduate animal science students
with no particular experience. A free choice profiling method was used to instruct
observers in QBA, and data for the three panels were analysed separately using Generalised
Procrustes Analysis. All three panels showed significant inter-observer agreement
(p < 0.001) and generated two main consensus dimensions characterised as ‘calm-agitated’
and ‘curious-shy’. There were significant correlations between buffalo scores provided by
each of the three observer panels on both these dimensions (dim1: Kendall W = 0.96, n = 3,
2 = 43.28, p < 0.001; dim2: W = 0.68, n = 3, 2 = 30.73, p < 0.01). Buffaloes viewed in the familiar
indoor pen were assessed by all three panels as more calm and less agitated (dimension
1) than animals viewed in the novel outdoor pen (Wilcoxon z =
−2.52, p < 0.01, z =
−2.52,
p < 0.01, z =
−2.38, p < 0.01 for Panels 1, 2, and 3, respectively). Scores on dimension 1 for the
same animals viewed in either indoor or outdoor pen were correlated at r = 0.60 (p < 0.10),
0.74 (p < 0.05) and 0.71 (p < 0.05) for Panels 1, 2, and 3, respectively. Quantitatively, buffalo
in the outdoor pen displayed longer bouts of running and higher frequencies of sniffing
(both p < 0.05) than those in the indoor pen. Principal component analysis showed meaningful
associations between qualitative and quantitative assessments, allowing qualitative
dimensions to play a valuable role in interpreting the animals’ state. The main outcomes of
this study are that QBA can be usefully applied to scientific studies of dairy buffalo, and that
substantial differences in observer background do not appear to diminish the reliability of
QBA
Qualitative behaviour assessment
No full textQualitative Behaviour Assessment (QBA) is a method that relies on the ability of human observers to integrate perceived details of behaviour, posture, and context into descriptions of an animal’s style of behaving, or ‘body language’, using descriptors such as ‘relaxed’, ‘tense’, ‘frustrated’ or ‘content’. Such terms have an expressive, emotional connotation, and provide information that is directly relevant to animal welfare and could be a useful addition to information obtained from quantitative indicators. Previous research with pigs, cattle, sheep and poultry consistently showed QBA to have high inter- and intra-observer reliability and to be coherent with quantitative behavioural and physiological measures, both when animals were assessed individually and at group level. Previous QBA work however was based on a Free-Choice-Profiling methodology that asks observers to develop their own descriptive terminologies, and is unsuitable for on-farm inspection. The aim of this study therefore was to design, and test the inter-observer reliability of, a fixed rating scale for QBA of cattle expression. This work was carried out with beef cattle, dairy cattle and veal calves. On the basis of previous QBA research with cattle and consultation with cattle experts, we designed a rating scale of 29 descriptors (32 for beef cattle). The rating scales were tested by three cohorts of four assessors, on 22 groups of veal calves and 22 groups of dairy cattle in Northern and Southern Italy, and on 21 groups of beef cattle in Southern Scotland. Assessors were given detailed instructions on the procedures of assessment and the use of the rating scale. The inter-observer reliability of the QBA scores attributed to the different cattle groups was tested using Kendall Correlation Coefficient W, and for beef cattle showed satisfactory reliability (W ≥ 0.70) for 20 out of 32 descriptors. For dairy cattle and calves this criterion was reached with only a few descriptors. However, comparison of Principal Component Analyses (PCA) of assessor scores within the three cattle groups showed remarkably similar emergent patterns of cattle expression, in which the first principal component (PC1) distinguished between positive and negative mood, and the second (PC2) differentiated these moods in low and high levels of arousal. These patterns were reproduced when descriptors with low loadings, low apparent welfare relevance, or with synonyms on the list, were removed from the assessor data sets (leaving 20 descriptors for each cattle group). For beef cattle, PC1 of the ‘reduced’ PCAs showed satisfactory inter-observer reliability (Kendall W=0.73; p<0.001) and explained 26-51% of the total variation between groups (depending on the assessor). PC1 scores also showed a high mean correlation (0.80; p<0.001) to the assessors’ scores of the descriptor ‘welfare overall’, which suggests that the distinction between positive and negative mood made by PC1 is directly relevant to beef cattle welfare. However, for dairy cattle and veal calves these emergent patterns, though present, were quantitatively weak. A subsequent video-based assessment of dairy cattle by 14 assessors using this rating scale found satisfactory reliability (Kendall W=0.73; p<0.001).We propose that PC1 may provide an integrative measure of positive and negative cattle emotion, to be accorded to single farm units through PCA of assessors’ scores on a 20-term rating scale. To calibrate the QBA measures of single farms, testing the QBA scale on a large sample of farm units is required to create a ‘benchmark’ data base. This in turn will allow identification of cut-off points on PC1 for unacceptable levels of negative mood/welfare. The application of QBA on farms is highly feasible and easy to learn. However, assessors must be experienced in observing cattle, and be given additional training in recognising cattle expression if required
Qualitative behaviour assessment of dairy buffaloes (Bubalus bubalis)
No full textThis study applies qualitative behaviour assessment (QBA) for the first time to dairy buffaloes,
using three groups of observers with different cultural backgrounds and different
levels of experience in animal behaviour observation and buffalo farming. Eight buffalo
heifers aged 16–18 months were subjected to two isolation tests, one performed in the
indoor part of their home environment, and one in a novel outdoor paddock. Animals
were filmed individually for 2.5 min, and the resulting 16 video clips were shown to
three observer panels, consisting of 11 applied animal behaviour scientists from 6 European
countries, 11 Italian animal scientists with a background in buffalo farming but no
experience in behavioural observation, and 14 Italian undergraduate animal science students
with no particular experience. A free choice profiling method was used to instruct
observers in QBA, and data for the three panels were analysed separately using Generalised
Procrustes Analysis. All three panels showed significant inter-observer agreement
(p < 0.001) and generated two main consensus dimensions characterised as ‘calm-agitated’
and ‘curious-shy’. There were significant correlations between buffalo scores provided by
each of the three observer panels on both these dimensions (dim1: Kendall W = 0.96, n = 3,
χ2 = 43.28, p < 0.001; dim2: W = 0.68, n = 3, χ2 = 30.73, p < 0.01). Buffaloes viewed in the familiar
indoor pen were assessed by all three panels as more calm and less agitated (dimension
1) than animals viewed in the novel outdoor pen (Wilcoxon z =
−2.52, p < 0.01, z =
−2.52,
p < 0.01, z =
−2.38, p < 0.01 for Panels 1, 2, and 3, respectively). Scores on dimension 1 for the
same animals viewed in either indoor or outdoor pen were correlated at r = 0.60 (p < 0.10),
0.74 (p < 0.05) and 0.71 (p < 0.05) for Panels 1, 2, and 3, respectively. Quantitatively, buffalo
in the outdoor pen displayed longer bouts of running and higher frequencies of sniffing
(both p < 0.05) than those in the indoor pen. Principal component analysis showed meaningful
associations between qualitative and quantitative assessments, allowing qualitative
dimensions to play a valuable role in interpreting the animals’ state. The main outcomes of
this study are that QBA can be usefully applied to scientific studies of dairy buffalo, and that
substantial differences in observer background do not appear to diminish the reliability of
QBA
Quantitative and qualitative assessment of the response of foals to the presence of an unfamiliar human
Full text linkThis work aimed to apply a combined qualitative and quantitative approach to the interpretation of an on-farm behaviour test for horses, and to examine whether 1 month of handling would affect the response of yearlings to an unfamiliar stationary human in their home environment. Throughout a 1-month period, 14 Thoroughbred Yearlings (16±0.22 months old) that had formerly experienced minimal contact with humans, were handled daily for about 45min. The yearlings were tested twice, just before and just after the handling period. The behaviour of the horses during the tests was both video-recorded and directly recorded by the experimenter using an instantaneous time sampling recording method. Quantitative analysis of these data was achieved using principal component analysis (PCA). Qualitative analysis took place from video clips using a free choice profiling (FCP) methodology that requires observers to generate their own qualitative descriptors of behaviour, and in a second phase instructs these observers to quantify their personal descriptors on a Visual Analogue Scale. Observers were 21 veterinarians who were unaware that the horses had been handled in half of the clips and not in the other half. The data generated through FCP assessment were analysed using generalised procrustes analysis (GPA). Any differences in behaviour that may have occurred before and after the handling period were evaluated by comparing horse scores on the main PCA and GPA factors using a Wilcoxon matched-pairs test. To compare qualitative and quantitative assessments, both the quantitative behaviour measures and the qualitative behaviour scores were correlated to the main PCA factors obtained from the quantitative analysis using Spearman's rank correlation. PCA analysis revealed three main factors (explaining 30%, 23% and 21% of the total variation between horses, respectively). The first factor showed high-negative loadings for immobile behaviour and high-positive loadings for contact and nibbling behaviour, and indicated that the horses tended to be more inclined to approach and contact the experimenter after handling (p=0.08). GPA analysis revealed two main factors of expression (explaining 51.4% and 10.2%, respectively). Both factors indicated significant qualitative differences in the behavioural style of yearlings before and after handling (p<0.05 and <0.01, respectively), characterising yearlings as ‘suspicious/nervous’ and ‘impatient/reactive’ before handling, and as ‘explorative/sociable’ and ‘calm/apathetic’ after handling. The correlation between GPA factor 1 scores with PCA factor 1 scores was highly significant (Spearman's r=0.75; p<0.001), while those between GPA factor 2 scores with PCA factor 2 and 3 scores were not significant (r=−0.255; ns and r=0.251; ns, respectively). On the whole a meaningful relationship was found to exist between the quantitative and qualitative behavioural assessments of the horses’ behaviour, indicating that these methods may be usefully combined in interpreting a behavioural test involving the presence of an unfamiliar human person
The qualitative assessment of responsiveness to environmental challenge in horses and ponies
Full text linkThe responsiveness of 10 horses and 10 ponies to environmental challenge (represented by an open field
test) was assessed using a qualitative approach based on free choice profiling methodology (FCP), which
gives observers complete freedom to choose their own descriptive terms. Data were analysed with
generalised Procrustes analysis (GPA), a multivariate statistical technique associated with FCP. A
cross-validation of the outcomes of this approach to data recorded through quantitative behaviour analysis,
and through a questionnaire given to the animals’ owner/riding instructor, was also performed using
principal component analysis (PCA). Twelve undergraduate students generated their own descriptive
vocabularies, by watching 20 horse/pony video clips lasting 2.5 min each. GPA showed that the consensus
profile explained a high percentage of variation among the 12 observers, and differed significantly from the
mean randomised profile ( p < 0.001). Two main dimensions of the consensus profile were identified,
explaining 60% and 5.2% of the variation between animals, respectively. The 12 observer word charts
interpreting these dimensions were semantically consistent, as they all converged towards the same
meaning, albeit using different terms. The most used term to describe the positive end of axis 1 was
‘‘quiet’’, whereas ‘‘attentive’’ was the best positive descriptor of axis 2. The most frequently used
descriptors for the negative ends of axes 1 and 2 were ‘‘nervous’’ and ‘‘bored’’, respectively. Thus, axis
1 was labelled as ‘‘quiet/nervous’’ and axis 2 was named as ‘‘attentive/bored’’. A marked effect of animal
category was observed on the scores of the animals on the first dimension ( p < 0.001). Horses received
significantly higher scores, and were thus assessed as more quiet and calm, than ponies. Conversely, ponies
tended to receive lower scores on the second dimension ( p < 0.12), therefore they appeared less curious and attentive. The results of the PCA showed that the variables from different types of measurement clearly had
meaningful relationships. For instance, the variables with the highest loading on the positive end of axis 1
were all indicative of tractable and docile animals, whereas axis 2 showed high loadings on the positive end
for variables indicating attentive animals. Qualitative behaviour assessment proved to be an appropriate
methodology for the study of horse behavioural responsiveness, in that it provided a multifaceted
characterisation of horse behavioural expression that was in agreement with other quantitative and
subjective assessments of the animals’ behaviour
The qualitative assessment of responsiveness to environmental challenge in horses and ponies.
Full text linkThe responsiveness of 10 horses and 10 ponies to environmental challenge (represented by an open field
test) was assessed using a qualitative approach based on free choice profiling methodology (FCP), which
gives observers complete freedom to choose their own descriptive terms. Data were analysed with
generalised Procrustes analysis (GPA), a multivariate statistical technique associated with FCP. A
cross-validation of the outcomes of this approach to data recorded through quantitative behaviour analysis,
and through a questionnaire given to the animals’ owner/riding instructor, was also performed using
principal component analysis (PCA). Twelve undergraduate students generated their own descriptive
vocabularies, by watching 20 horse/pony video clips lasting 2.5 min each. GPA showed that the consensus
profile explained a high percentage of variation among the 12 observers, and differed significantly from the
mean randomised profile ( p < 0.001). Two main dimensions of the consensus profile were identified,
explaining 60% and 5.2% of the variation between animals, respectively. The 12 observer word charts
interpreting these dimensions were semantically consistent, as they all converged towards the same
meaning, albeit using different terms. The most used term to describe the positive end of axis 1 was
‘‘quiet’’, whereas ‘‘attentive’’ was the best positive descriptor of axis 2. The most frequently used
descriptors for the negative ends of axes 1 and 2 were ‘‘nervous’’ and ‘‘bored’’, respectively. Thus, axis
1 was labelled as ‘‘quiet/nervous’’ and axis 2 was named as ‘‘attentive/bored’’. A marked effect of animal
category was observed on the scores of the animals on the first dimension ( p < 0.001). Horses received
significantly higher scores, and were thus assessed as more quiet and calm, than ponies. Conversely, ponies
tended to receive lower scores on the second dimension ( p < 0.12), therefore they appeared less curious and attentive. The results of the PCA showed that the variables from different types of measurement clearly had
meaningful relationships. For instance, the variables with the highest loading on the positive end of axis 1
were all indicative of tractable and docile animals, whereas axis 2 showed high loadings on the positive end
for variables indicating attentive animals. Qualitative behaviour assessment proved to be an appropriate
methodology for the study of horse behavioural responsiveness, in that it provided a multifaceted
characterisation of horse behavioural expression that was in agreement with other quantitative and
subjective assessments of the animals’ behaviour
Integrating parameters to assess on-farm welfare
No full textGiven the absence of a 'Golden Standard' for the objective determination of welfare, the collection and interpretation of data involving different parameters is essential for assessing the well-being of farm animals. The choice of parameters and the relative weights assigned to each of them are crucial for the outcome of the assessment. Both elements involve a certain degree of subjectivity. In this paper we discuss the basics of different methods used to integrate welfare parameters, focussing on the issue of scientific objectivity. We begin by addressing parameter selection, the assignment of parameter weightings or rankings and the qualifications necessary for 'experts' designing and applying the methodology. Five different approaches to integrating parameters are then discussed. The paper does not state a preference for any method, but aims to encourage discussion of key elements involved with the on-farm assessment of welfare
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