Some aspects of on-farm assessment of social behaviour and avoidance distance were investigated on 20 Austrian dairy farms. The avoidance distance of at least 75% of cows was assessed. Social behaviour of the cows was observed for one hour and the number of animals standing was recorded every 10 min. Lameness of each animal was scored, and Spearman correlations were calculated. Generally, the avoidance distance of the cows was very low. Lameness did not correlate significantly with avoidance distance. The number of agonistic interactions with body contact per cow correlated negatively with the percentage of lame animals (rs = -0.49; P = 0.029). However, this correlation was no longer found (rs = -0.22; not significant) when calculated on the basis of standing animals only. The total number of social interactions correlated highly with the number of social interactions when interactions in the feeding rack were disregarded. The present study suggests that lameness confounds the assessment of social behaviour but not that of avoidance distance of cows, and that social interactions of animals standing in the feeding racks can be disregarded without decreasing the reliability of the assessments.

It is not enough to study animal welfare; our responsibility is to promote it. To this end, we need to step out of our laboratories and develop robust protocols for assessing welfare in groups of animals on farms or in the laboratory. While these protocols must incorporate principles derived from detailed scientific study they will, in practice, need to be based on clinical observations and records that an assessor can acquire at a single visit. Such assessments must also be comprehensive: approaches based only on behaviour, or motivational state, or physical appearance, or performance records, can never tell the full story. The ‘Five Freedoms’ provide a comprehensive template that incorporates the different elements that define welfare state. However they only describe welfare at the time of observation. Protocols, for example, for on-farm assessment need to include measurements and records that provide evidence of long-term consequences of the quality of husbandry practices (eg body condition, chronic injuries). This paper reviews approaches developed at Bristol for the on-farm evaluation of welfare in dairy cows and free-range hens. The criteria used to assess welfare from clinical evidence were developed through expert consultation using the ‘Delphi’ review process. Experts were also asked to make value judgements as to the seriousness of different elements of poor welfare by indicating the point at which intervention would be necessary to resolve specific problems. This study identified a serious limitation of Quality Assurance schemes that seek to encompass many different elements of welfare into a single index that ranks overall welfare as acceptable or unacceptable. Specific farms had specific welfare problems and these required specific solutions.

Tests for assessing the animal-human relationship in tied dairy cows have been used in experimental research, but the reliability and feasibility of the measures in the on-farm context has not been studied yet. Therefore we investigated the between-experimenter repeatability of avoidance reactions in dairy cows housed in tie-stall systems. Nine farms in Austria with 15-60 tethered dairy cows were visited. Two to three out of four experimenters were testing the avoidance reaction of the individual cows in a balanced order. Cows were approached slowly from the front and the reactions recorded on an 11-point score. The avoidance reaction of each cow was assessed twice by each experimenter and averaged. Also, a farm value per experimenter was calculated (median). Repeatability between experimenters was assessed within farms and at farm level by calculating Spearman rank correlation coefficients. The average between-experimenter correlations within farms ranged from 0.37 to 0.88. At farm level, median of scores ranged from 2.5 to 6.75 and correlated moderately to highly between experimenters from 0.65 to 0.80. Testing the animals on all farms was relatively simple.

The between-experimenter repeatability within farms differed substantially and reasons for this merit further investigation. At farm level repeatability was moderate to sufficiently high. Due to the high feasibility, this measure seems to be promising and should be developed further.

It was the purpose of this study to assess the consistency of selected animal-based welfare parameters for dairy cattle throughout a one-year period. Eight cubicle-housed dairy herds were visited five times, at two-monthly intervals. At each visit, lameness, injuries to the carpal and tarsal joints, cleanliness, social behaviour and the avoidance distance towards an unknown person were assessed by the same observer in a random sample.

At herd level, lesions of the carpal joints, udder cleanliness and frequencies of agonistic and cohesive behaviour showed low consistency. However, correlations between consecutive recordings as well as between single visits and the average were moderate to satisfactory for lameness prevalence, lesions of the tarsal joints, cleanliness of the hind leg and avoidance distances towards an unknown person in two different locations. The integration of these parameters into on-farm welfare assessment protocols seems to be justified.

The modern dairy industry involves close contact between the stockperson and their animals and thus complex relationships develop between stockperson and cow. This study examines the assessment of stockmanship quality on commercial dairy farms and aims to develop useable protocols for on-farm assessment of stockmanship for inclusion in a quality-assurance scheme. In this study the behaviour of cows was used to assess the quality of stockmanship on fifteen commercial dairy farms, which varied in level of production and intensification. The behavioural reactions of cows to a novel human and the behaviour of the stockperson before, during and after milking were scored, and stockpersons completed a fifty-question psychometric attitude questionnaire, which was made up of seven subgroups of questions. Preliminary results indicated that stockpersons differ in the behaviour they use when handling cows. Stockpersons on zero-grazing farms appeared to use fewer positive tactile behaviours and more severe negative behaviour. The behavioural responses of cows in a novel human approach test differed between farm types. Cows on straw-court farms appeared to be more flighty and less confident in the presence of a novel human. Differences were observed in mean attitude scores for the seven subgroups of questions. Job type appears to have an effect on the extent of the stockperson's positive attitude toward cows, animals in general, job satisfaction and farm economics. The results indicate that there are differences in quality of stockmanship between farms and that the three methods chosen do identify these. They show that the human-animal relationship is a potential source of fear for cows in dairy production and therefore can be used to identify poor stockmanship.

The aim of this study was to develop a set of tests to assess the reaction of laying hens towards humans in non-cage systems and to investigate the repeatability within and between days.

Based on a literature survey six tests were developed or modified, which were examined for practicability and adapted in a first phase. Reactions of hens were recorded directly or with a video camera. These included the reaction towards a stationary person: stockperson (SPs); experimenter: (SPe); a moving person: (moving through flock [MP]; approaching individual hens measuring avoidance distance [AD]) and a complex test including stationary and moving elements: (arm moving to touch hens [touch test] in the litter [Tl] and on the slatted area [Ts]). Six flocks of five farms were visited twice, one week apart. At each visit the six tests were performed during morning and afternoon hours. The order of the tests differed between farms.

Repeatability of flock reactions within as well as between days was high for SPs, AD and Tl. SPe and especially MP showed slightly reduced correlations. Touching hens at the droppings pit (Ts) was less repeatable than in the litter area (Tl).

To sum up, first experiences show that most tests can be practically used in groups of hens in non-cage systems and seem to have good repeatability.

Feasibility and validity of protocols used to assess welfare were evaluated in two fattening rooms in twenty farms, chosen according to the group size, pen shape, floor and space per animal. The two rooms contained animals at the beginning or at the end of the fattening period, respectively. Behaviour was recorded either through direct observations of the pens (3 times per minute, at 5 min intervals), 15 minutes after entering the room (B1) or after the removal of an object given at the end of the visit (B3). The third protocol consisted of a 2 minute observation inside the pen (B2). To evaluate the human-animal interaction, the time to adapt to the presence of the operator in the corridor (HA1) was measured, as well as the reaction of the group when the operator entered the pen (HA2) or walked slowly through the pen (HA3), the time taken by the first five pigs to approach the operator (HA4) and the reaction of the animals when the operator tried to catch an ear (HA5). Lesion scoring was carried out inside the pen on a sample of 60 individual fatteners or at pen level on most of the pigs from the corridor. The occurrence of the main active behaviours is assessed similarly over time by B1 and B3. Behavioural observations are then possible in an on-farm welfare assessment. Lesion scoring from the corridor tends to under estimate the number of lesions (scratches, tail wounds). The HA3 test appears to be the best one to evaluate the relationship towards humans but has to be validated.

The Welfare Quality® (WQ) protocols are increasingly used for assessing welfare of farm animals. These protocols are time consuming (about one day per farm) and, therefore, costly. Our aim was to assess the scope for reduction of on-farm assessment time of the WQ protocol for dairy cattle. Seven trained observers quantified animal-based indicators of the WQ protocol in 181 loose-housed and 13 tied Dutch dairy herds (herd size from 10 to 211 cows). Four assessment methods were used: avoidance distance at the feeding rack (ADF, 44 min); qualitative behaviour assessment (QBA, 25 min); behavioural observations (BO, 150 min); and clinical observations (CO, 132 min). To simulate reduction of on-farm assessment time, a set of WQ indicators belonging to one assessment method was omitted from the protocol. Observed values of omitted indicators were replaced by predictions based on WQ indicators of the remaining three assessment methods, resources checklist, and interview, thus mimicking the performance of the full WQ protocol. Agreement between predicted and observed values of WQ indicators, however, was low for ADF, moderate for QBA, slight to moderate for BO, and poor to moderate for CO. It was concluded that replacing animal-based WQ indicators by predictions based on remaining WQ indicators shows little scope for reduction of on-farm assessment time of the Welfare Quality® protocol for dairy cattle. Other ways to reduce on-farm assessment time of the WQ protocol for dairy cattle, such as the use of additional data or automated monitoring systems, should be investigated.

This paper presents an account of a Welfare Quality® assessment of 92 dairy farms carried out by seven experienced assessors. The aim was to evaluate the potential of the Welfare Quality® assessment protocol with respect to its uptake by UK farm assurance schemes. Data collection, and measure aggregation were performed according to the Welfare Quality® protocol for dairy cows. This study examined the data itself, by the testing of how hypothetical interventions might be reflected in changes in the aggregated scores, and also investigated human-related aspects, through inter-assessor standardisation sessions to evaluate reliability, and an assessor focus group to collect feedback. Overall, three main ‘challenges’ were identified. The first challenge related to the large amount of missing data. Unexpectedly, this was such that it was only possible to calculate an overall classification for 7% of farms. The second challenge concerned the way in which aggregated scores did not always reflect hypothetical interventions. The final challenge was inter-assessor reliability, where not all assessors were found to achieve acceptable levels of agreement on a number of outcome measures by the third training session. Suggestions for managing these challenges included, follow-up to assessor training, the use of multiple imputation methods to fill in missing data, and, where applicable, not aggregating the scores. The conclusion of the study was that the protocol provided useful information from which to make an informed selection of measures, but that the challenges, combined with the lengthy assessment time, were too great for its use as a certification tool.

Lameness is one of the most serious health and welfare problems faced by dairy cattle. The aim of this study was to assess how aware Czech farm managers were of this problem. The project was carried out on 14 Czech dairy farms. The proportion of lame cows observed (ie prevalence of moderate and severe lameness) on a farm varied between 9 and 64% while the farm managers’ estimation ranged between 0 and 20%, showing that lameness prevalence was under-perceived by the Czech farm managers. There were no correlations between the farm managers’ estimations and the observed total or severe lameness on each farm. Also, the observed prevalence of lameness did not differ between farms with managers who considered lameness to be a major problem in their herds and managers who did not, although their estimations did differ. The large variation in observed lameness prevalence between farms indicates that there is a large potential for reduction, which must start with increasing the farm managers’ awareness of dairy cow lameness.

The mortality and morbidity of unweaned dairy calves and management practices that may impair calf health and welfare were surveyed on 115 farms in Canada (Quebec) and 60 farms in Central Europe (Austria and Germany) to examine whether outcome-based measures of calf health could be used to identify farms that use management practices that place calf health at risk. Quebec herds had higher juvenile mortality incidence than those in Central Europe. Juvenile mortality was poorly estimated by producers. Low levels of mortality did not include low levels of morbidity in the same herds. Health status was not necessarily associated with management practices generally recommended for health and welfare. Many management practices that may impair calf health and welfare were found in Quebec while only some were found in Central Europe; these were related to calving management and care of the newborn, colostrum management, calf-dam separation, calf feeding, weaning and calf housing. Inadequate recording of calf morbidity and mortality can be a problem in using recorded measures to assess the level of calf health on a farm. The recorded mortality and morbidity do not necessarily show the extent that producers use management practices that pose a risk to calf health.

On-farm scoring of behavioural indicators of animal welfare is challenging but the increasing availability of low cost technology now makes automated monitoring of animal behaviour feasible. We discuss some of the issues with using automated methods to measure animal behaviour within the context of assessing animal welfare. Automated feeders (eg for dairy calves) can help measure the degree that animals are hungry and have potential to identify sick animals even in group housing. Such equipment is best used for longitudinal studies of individual animals rather than making comparisons between farms. Devices attached to animals (eg accelerometers or GPS devices) can help measure the activity levels of animals with a high degree of accuracy and can easily be transported between farms, making them best suited for welfare assessment at the group level. Automated image analysis has great potential to assess movement within groups of animals, but following individual animals can be difficult. The techniques have been validated against traditional methods (eg direct observation). The accuracy of measures taken automatically varies between methods but can be increased by combining measures. Technological developments have provided us with a variety of tools that can be used to monitor behaviour automatically, and these have great potential to improve our ability to monitor animal welfare indicators on-farm. However, it is important that methods be developed to measure a wider range of behaviour patterns. Animal welfare assessment schemes should not place undue emphasis on behavioural indicators solely on the basis that they can be monitored automatically.

Animal welfare is generally referred to as the quality of an animal's life as experienced by the individual animal. On-farm welfare assessment, however, usually relies on both individual and group measures. As regards the latter, individual animals are not identified (eg incidence of stereotypic behaviour in a pen) or features of the whole group (eg score obtained from qualitative behaviour assessment) are used. This raises the question whether our current approaches to on-farm assessment sufficiently consider the individual nature of animal welfare. Measures assessed at the group level bear the disadvantage that distribution across group members may be skewed and the most affected individuals are not necessarily identified. However, the importance of knowing about the welfare state of individual animals depends on the purpose of the assessment. If the primary aim is farm assurance, the individual animal is of lesser importance, but non-compliance with thresholds at herd/farm level or comparison with peer farms may induce change. Using individual measures in a sample of animals means that animals not sampled but requiring intervention, eg for treatment of lameness, would have to be identified subsequently. Measures truly taken at the group level make individual interventions difficult, but interventions implemented at the group level (eg reducing stocking density) do not necessarily require information on the individual animal. Automatic detection of welfare-relevant states has received increased attention and identifying individual animals with impaired welfare seems to be promising. Automated early detection of problems may also reduce the ethical dilemma that traditional assessments at the end of the production cycle, eg in broiler chickens, may identify welfare impairments but not directly benefit the affected animals. Reflection on individual and group measures and their consequences for animal welfare may help in interpreting the outcomes of the assessments and stimulate future developments in the field.

Welfare assessment protocols using primarily animal-based measures are believed to give valid information about the welfare of animals. However, they tend to be time consuming and therefore costly, thus in practice there is often considerable reluctance to use them. In the present study, the relatively quick to use, simple but non-validated welfare assessment protocol for dairy cattle developed by the Danish Cattle Federation and the validated comprehensive Welfare Quality® protocol were compared in Danish conditions. In total, 44 Danish dairy herds were evaluated using the two protocols. The protocols were correlated on four sub-levels (corresponding to the ‘Principles’ in the Welfare Quality® protocol) and on the Overall welfare score. They correlated significantly with regard to the Principles ‘Good health’ and ‘Appropriate behaviour’. Significant correlations were not found for ‘Good feeding’, ‘Good housing’, and the Overall score. On the basis of this we changed the Danish Cattle Federation protocol by introducing six new measures, changing three measuring procedures and omitting two measures. This extended protocol was found to correlate significantly with the Welfare Quality® protocol in all four Principles and on the Overall score. The extended protocol still has the advantage of the original Danish Cattle Federation protocol whereby under Danish field conditions it will take only 2 h to apply as opposed to 7-8 h for the Welfare Quality® protocol. We believe that the extended protocol balances, in a good way, the demands of practicability against its value as a diagnostic test.

This study sought to investigate whether animal health and welfare state and changes were associated with technical efficiency in a sample of 34 Austrian dairy farms. Health and welfare were assessed twice using the Welfare Quality® (WQ) assessment protocol for dairy cattle. Following a baseline welfare assessment, farm-specific health and welfare planning was conducted on the farms. This included the identification, selection and implementation of measures in housing and management that aimed at improving health and welfare states. One year after implementation, farms were reassessed to evaluate changes in health and welfare states and consequences for farms’ technical efficiency were analysed using the Malmquist index. Our results indicated that farms with a higher health state (WQ principle score ‘Good health’) achieved higher technical efficiencies. However, we could not show that changes in the welfare state within a one-year period affected technical efficiency: across all farms, technical efficiency remained stable and Malmquist indices (indicating efficiency and technological change) could not be explained by the different welfare scores. Nevertheless, our study showed data envelopment analysis to be a valuable method for analysing the relationship between animal welfare and farm success and our results indicate substantial potential synergies between these two aspects.

For on-farm sheep welfare assessment, a reliable, simple and robust method is required to assess the level of flock lameness. This study examined the level of test agreement for two binary lameness scoring systems for sheep. The first was a group-level lameness assessment of sheep performed on ungathered sheep at pasture and was termed group observation method (GOM). The second method of lameness assessment was performed after gathering of the sheep and involved close observation of the gait of individual sheep in a handling pen and was termed individual animal gait assessment (IAGA). Following individual gait assessment, each sheep was also examined for the presence of specific foot and limb lesions: white line lesions (WL); inter-digital dermatitis (ID); footrot (FR); contagious digital dermatitis (CODD); toe granuloma (TG); and joint swellings (JS). A total of 3,074 sheep were assessed from 40 flocks in North England and Wales by one assessor. Test agreement between the assessment methods was found to be good as judged by linear regression and Bland-Altman plots. The method of group observation identified a slightly higher proportion of lame sheep compared to the individual animal examination and also appeared to be a more feasible on-farm method of observation. Over half of the sample sheep were identified with WL but this did not appear to be associated with a high level of lameness (as assessed by IAGA) with just under 12% of sheep with WL being identified as lame. In contrast, the percentage of lame sheep was most closely associated with CODD and over 80% of animals with this lesion were scored as lame.

Despite increased concerns about dairy cattle welfare, there is a paucity of knowledge regarding their welfare and the attitudes of farmers and veterinarians in the Western Balkan region. This is the first on-farm study to address dairy cattle welfare and the attitudes of farmers and veterinarians towards animal welfare in Kosovo. Thirty tie-stall dairy farms across seven Kosovo regions were assessed twice with an interval of 10 to 12 months. During the first visit, the Welfare Quality® assessment protocol was applied, whilst the second visit focused on clinical animal-based indicators and interviews with the farmers regarding intervention thresholds for a number of welfare indicators. Additionally, such thresholds were obtained from 15 veterinarians via an online questionnaire. The main areas of concern that were highlighted relate to comfort around resting (soiling of animals, restriction of lying down movements) and injuries, including lameness. Farmers and veterinarians agreed on the intervention thresholds for the majority of the indicators (eg animals with dirty udders, animals with lesions/swellings) but differences were found for important health and welfare issues (eg farmers suggesting a higher threshold for lameness compared to veterinarians). Compared to the on-farm prevalences, both farmers and veterinarians suggested lower intervention levels for welfare issues indicating an awareness of problems. In conclusion, investments into close co-operation between farmers, veterinarians and other advisors regarding awareness-building and inducing changes in daily management routines are considered necessary to improve dairy cow welfare.

Little information is available on the phenotypic performance of perennial ryegrass varieties when exposed to grazing conditions on commercial grassland farms. Grass varieties are classically evaluated in mechanically defoliated plot systems which, although designed to mimic grazing conditions, do not fully capture the range of stresses or interactions that a sward is subjected to under commercial settings or over any period longer than 4 years. The evolution of technology in the form of PastureBase Ireland has led to agronomic data of individual paddocks being made available for analysis over multiple years. Data used in the current study consisted of dry matter (DM) production and ground score data across a 7-year period from ten perennial ryegrass varieties grown as monocultures in 559 paddocks on 98 commercial farms. The results demonstrated how perennial ryegrass variety is associated with a range of agronomic performance traits on commercial farms; including total and seasonal DM production, grazing DM production and number of grazing events. Varieties with the highest total DM production also had the highest spring and mid-season DM production; autumn DM production was associated with the interaction between variety and year. The highest producing variety in the study, AberGain, produced 1342 kg DM/ha/year more than the mean of all other varieties. Variety differences manifested themselves as swards aged, with some varieties increasing in total DM production while others reduced in total DM production. The current work provides a basis for the consideration of on-farm variety assessment in the composition of future variety evaluation protocols.

Sample sizes of welfare assessment protocols must warrant to reflect prevalences on-farm properly – regardless of farm size. Still, solely a fixed sample size was specified for the Welfare Quality® protocol for sows and piglets. The present study investigated whether animals may be assessed from only one body side as applied in the protocol and whether the pre-set sample size of 30 animals mirrors the prevalences of the animal-based indicators on-farm in the gestation unit considering different farm sizes. All indicators were assessed for both sides of an animal’s body by one observer on 13 farms in Germany, which were visited five times within 10 months. The farm visits were treated as independent since different animals were housed in the gestation units. The number of sows in the gestation units varied between 18 and 549 animals. The comparison of sides was carried out calculating exact agreement between animals’ sides and a Wilcoxon signed-rank test (W). The results signified that it is sufficient to assess the animal from one side (exact agreement: 88.3% to 99.5%, except for bursitis (70.0%); W: P-values 0.14 to 0.92). However, if side preferences existed in the indicator bursitis a potential bias must be considered. In the following, the sample size was evaluated by comparing samples’ prevalences against true prevalence, that is, the prevalence of all observed animals in the gestation unit in each farm visit. Therefore, subsets of data were generated by applying simple random sampling without replacement. The samples randomly included the animals’ right or left sides. Linear regression was rated as appropriate provided: coefficient of determination R2 ≥ 0.90, slope = 1 and intercept = 0 signifying exact agreement. The results revealed that the sample size required by the protocol and the application of calculation formulas are solely appropriate to mirror the prevalences of frequent indicators in the gestation unit, for example, bursitis (mean prevalence 34.4%). Using a proportion of animals, for example, a sample of 30% of all observed animals in a farm visit, pointed out that proportions must increase with indicators’ underlying prevalence narrowing 0.00%. Local infections (mean prevalence 13.3%) needed samples including 60% of all observed animals in each farm visit, whereas vulva lesions (mean prevalence 7.28%) only reached accuracy with the inclusion of 70% of the animals. Indicators with a mean prevalence of <1% were not analysed but can most likely only be ascertained by the assessment of all animals.

The animal health and welfare status in European organic dairy production does not in all aspects meet the organic principles and consumers’ expectations and needs to be improved. To achieve this, tailored herd health planning, targeted to the specific situation of individual farms could be of use. The aim of this study was to apply herd health planning in a structured participatory approach, with impact matrix analysis, not previously used in this context, in European organic dairy farms and to assess changes in animal health and welfare. Herd health planning farm visits were conducted on 122 organic dairy farms in France, Germany and Sweden. The farmer, the herd veterinarian and/or an advisor took part in the farm discussions. The researcher served as facilitator. Baseline data on the animal health status of the individual farm, collected from national milk recording schemes, were presented as an input for the discussion. Thereafter a systematic impact matrix analysis was performed. This was to capture the complexity of individual farms with the aim to identify the farm-specific factors that could have a strong impact on animal health. The participants (i.e. farmer, veterinarian and advisor) jointly identified areas in need of improvement, taking the health status and the interconnected farm system components into account, and appropriate actions were jointly identified. The researcher took minutes during the discussions, and these were shared with the participants. No intervention was made by the researcher, and further actions were left with the participants. The number of actions per farm ranged from 0 to 22. The change in mortality, metabolic diseases, reproductive performance and udder health was assessed at two time points, and potential determinators of the change were evaluated with linear regression models. A significant association was seen between change in udder health, as measured by the somatic cell count, and country. At the first follow-up, a significant association was also found between change in the proportion of prolonged calving interval and the farmers’ desire to improve reproductive health as well as with an increase in herd size, but this was not seen at the second follow-up. The degree of implementation of the actions was good (median 67%, lower quartile 40%, upper quartile 83%). To conclude, the degree of implementation was quite high, improvement of animal health could not be linked to the herd health planning approach. However, the approach was highly appreciated by the participants and deserves further study.