On-farm Welfare Research Articles

Prevalence of tail damage and ear lesions in docked and undocked pigs during trials to find alternatives to tail docking on Spanish commercial farms.

Tail docking is widely used to control tail biting in pigs, but it is a painful and stressful procedure. The impact of non-docking trials on tail damage (TD) and ear lesions (EL) from weaning to slaughter was assessed on four commercial farms. A total of 898 docked pigs (396 DP) or undocked pigs (502 UP) were assessed for tail damage and ear lesions at the beginning and end of the weaner stage, during fattening and at the abattoir during slaughter. The percentage of UP with shortened tails increased significantly from weaning to fattening, but the percentage of non-inflamed tails and tails without fresh lesions increased. In terms of severity of lesion, a lower percentage of pigs scored as 'no visible lesion' at the end of weaning compared to fattening, both for UP and DP (p<0.05). DP always had a lower percentage of TD, whereas UP had a lower percentage of EL at the end of weaning. It was not possible to statistically compare different preventive strategies trialed on farms as alternatives to docking. This study highlights that combining tail length and lesion score at slaughter could be a reliable on-farm welfare indicator.

Fermentation and fortification of Sargassum linearifolium with multi-strain probiotics improves mucosal barrier status, inflammatory response and resistance to Vibrio harveyi infection in barramundi Lates calcarifer

While aquaculture intensification increases productivity and profitability, it can lead to increased susceptibility of fish to disease outbreaks. As such, the production of aquafeeds with immunomodulatory properties is currently of significant interest to the aquaculture sector with a view of improving on-farm welfare. This study aimed to assess the effectiveness of the brown seaweed Sargassum linearifolium fortified with the probiotics Lactobacillus casei and Saccharomyces cerevisiae through submerged fermentation in improving growth, overall health and the ability to resist Vibrio harveyi infection in barramundi, Lates calcarifer. Two seaweed-based diets, probiotic-fortified S. linearifolium (referred to as PF-SL) and S. linearifolium without probiotic fortification (referred to as SL), were compared with a standard barramundi diet (control). Barramundi juveniles weighing 4.25 ± 0.09 g were fed the test diets three times daily to satiety for 56 days. The results demonstrated a significantly higher growth performance in fish fed the PF-SL diet when compared to the control. However, there were no notable differences in the apparent digestibility coefficient (ADC) of dry matter, protein, and lipid between PF-SL and control. Fish fed the PF-SL diet showed improved mucosal barrier function in both the skin and intestine, as indicated by the increased density of mucous cells. Similarly, fish fed the PF-SL diet exhibited higher glutathione peroxidase and total antioxidant activity than the control. The qRT-PCR revealed significant upregulation in the expression of anti-inflammatory genes (occludin, claudin1 and nrf2) and downregulation in the expression of proinflammatory genes (hsp70 and tnf-α) in fish fed the PF-SL diet. Furthermore, the PF-SL diet resulted in a 27% mortality in a 14-day challenge trial with V. harveyi, which was lower than the mortality rates of the SL (46%) and control (67%). Overall, this study demonstrates that feeding juvenile barramundi with probiotic-fortified brown seaweed S. linearifolium was able to improve growth, immunity, and resistance against Vibrio harveyi infection, suggesting significant potential for application across the aquaculture industry for improved fish welfare.

Preliminary study on the extention of the IT system ClassyFarm to the apiary

ClassyFarm is a new IT system of the Italian Ministry of Health for the risk categorization of livestock farms in relation to four areas: animal welfare, biosecurity, antimicrobial usage and antimicrobial resistance. Within ClassyFarm, on-farm welfare measurement is carried out using protocols based on both resource-based indicators and animal-based indicators. The aim of this study was to develop a welfare measurement protocol for the apiary. Sixteen Italian beekeeping experts were involved into a focus group to discuss a list of resource-based and animal-based indicators, previously selected by the authors by means of a review of the scientific literature, in order to identify effective and significant welfare indicators to be collected in the field. Despite the difficulties encountered related to the nature of honey bees and the lack of scientific knowledge about their welfare, 12 resource-based and 7 animal-based indicators were identified as potentially able to screen the welfare of honey bee colonies in the apiary. This study represents the first step towards the future extension of the ClassyFarm system to the apiary.

Risk Categorization in On-Farm Welfare in Different-Sized Dairy Sheep Flocks.

The objective of the present work was to categorize the risks in the on-farm welfare of dairy sheep in semi-extensive systems in order to highlight if risks in welfare vary according to the farm size. To the best of our knowledge, this study constitutes one of the few categorizations of the risks in the welfare of dairy sheep reared semi-extensively. The survey was conducted on 12 semi-extensive dairy sheep farms in Tuscany (Central Italy), which were classified according to flock size: <500, from 500 to 1000, and >1000. The results showed an adequate rating for all the farms included in the study. The most critical issues concerned the ratio between the number of stockpersons and the number of animals within the farms, the small decubitus areas, the lack of udder cleaning procedures during milking operations, and, in terms of biosecurity, the lack of prevention, control, and eradication plans for the most important infectious ovine diseases. The results show that different items in the five areas evaluated, including the assessments of management of the flock, the number of daily inspections, and the hygiene of the water supplies are associated with the farm size. In large farms, the assessment of management of the flock was better, but the number of daily inspections and the hygiene of the water supplies were worse.

Relationship between animal-based on-farm indicators and meat inspection data in pigs

BackgroundThis study aimed to validate slaughterhouse indicators collected during meat inspection as an alternative to on-farm animal welfare indicators. For this purpose, the assessments of twelve on-farm and seven slaughterhouse indicators of 628 pigs from three different farms were combined into three indices, differentiated between on-farm and slaughterhouse: (1) limb health, (2) other organ health, and (3) respiratory health. At first, an assessment at animal-level using agreement parameters was carried out to ascertain whether the same welfare or health issues were identified on-farm and at slaughterhouse, taking the production period (farrowing, rearing and fattening period) and the last weeks before slaughtering into account. Second, the connection of slaughterhouse findings on the individual on-farm health indices was examined using logistic regressions, to determine whether certain welfare issues can be better monitored using slaughterhouse indicators.ResultsAcceptable agreement was determined using the Prevalence-Adjusted Bias-Adjusted Kappa (PABAK) for the farrowing and fattening period, but not for the rearing period. A more detailed analysis of the weeks before slaughter shows that there is still a poor agreement 8 weeks before slaughter and an acceptable agreement 4 weeks before slaughter. This indicated the slaughterhouse indicators pneumonia, pleuritis and pericarditis as possible estimators of fever and deviant behavior on-farm and the slaughterhouse indicators bursitis and joint inflammations as possible estimators of lameness. In the second part of the analysis, the connection of slaughterhouse findings on the individual on-farm health indices was investigated; a significant influence of the farm on the limb and respiratory indices and no significant influence of the slaughterhouse findings could be determined, provided that all weekly assessments during the lifetime of the pigs have been taken into account. However, an influence of the slaughterhouse findings on the respiratory index and on the other organ index could be determined if only the weekly assessments four and eight weeks before slaughter, respectively, were taken into account.ConclusionsIn general, the possible suitable indicators detected by the PABAK, could replace some health-related indicators but a complete substitution of on-farm welfare assessment is not possible. In addition, the traceability over time must be investigated further.

Exploring uses for an algorithmically generated Animal Welfare Indicator for welfare assessment of dairy herds

On-farm welfare assessment is time-consuming and costly. Assessing welfare using routinely collected herd data has been proposed as a more economical alternative. The online Animal Welfare Indicator (AWI), developed by a Norwegian dairy cooperative, applies an algorithm to routinely collected health, production, and management data to "indicate" aspects of animal welfare at herd level. The overall AWI score is based on 10 AWI subindicator scores, representative of elements of animal welfare such as claw health, udder health, and mortality. Our cross-sectional study explored 2 ways in which the AWI may enable more efficient welfare assessment of Norwegian dairy herds. First, we investigated using the AWI to reduce the duration of on-farm assessments by replacing on-farm measures. Second, we examined reducing the number of on-farm welfare assessments by using the AWI to predict which herds have poorer welfare with respect to specific on-farm measures. Using Spearman rank analyses, we investigated if the AWI scores for 157 herds were associated with 24 on-farm welfare variables measured contemporaneously by Welfare Quality assessment. The mortality AWI subindicator score and the percentage mortality in the previous 12 mo were moderately correlated, as were the udder health AWI subindicator score and the percentage high somatic cell count (SCC) in the previous 3 recordings. Only negligible or weak correlations were found between the other AWI scores and the on-farm assessment variables. We built Generalized Linear Models using AWI scores as independent variables to predict herds with poorer welfare. Herds were classified as having poorer welfare based on their results in specific on-farm welfare measures. We evaluated the models' predictive ability and accuracy. Moderately accurate models were built for predicting poorer herds with respect to high SCC, mortality, and moderate or severe lameness. The other models were less accurate. The AWI scores were generally unsuitable as replacements of on-farm welfare measures. The AWI subindicators for udder health and mortality could replace the on-farm welfare measures related to those 2 topics, but there was some overlap in the data used to calculate them. Despite a lack of independence, the use of those 2 AWI subindicators may marginally reduce the duration of on-farm assessments. A prediction model based on AWI scores showed potential for identifying herds with poorer welfare in terms of moderate or severe lameness, facilitating more efficient use of resources for on-farm lameness assessment. As a consequence of the data used in the AWI, it was only reflective of health-related welfare outcomes.

Investigating the use of machine learning algorithms to support risk-based animal welfare inspections of cattle and pig farms.

In livestock production, animal-related data are often registered in specialised databases and are usually not interconnected, except for a common identifier. Analysis of combined datasets and the possible inclusion of third-party information can provide a more complete picture or reveal complex relationships. The aim of this study was to develop a risk index to predict farms with an increased likelihood for animal welfare violations, defined as non-compliance during on-farm welfare inspections. A data-driven approach was chosen for this purpose, focusing on the combination of existing Swiss government databases and registers. Individual animal-level data were aggregated at the herd level. Since data collection and availability were best for cattle and pigs, the focus was on these two livestock species. We present machine learning models that can be used as a tool to plan and optimise risk-based on-farm welfare inspections by proposing a consolidated list of priority holdings to be visited. The results of previous on-farm welfare inspections were used to calibrate a binary welfare index, which is the prediction goal. The risk index is based on proxy information, such as the participation in animal welfare programmes with structured housing and outdoor access, herd type and size, or animal movement data. Since transparency of the model is critical both for public acceptance of such a data-driven index and farm control planning, the Random Forest model, for which the decision process can be illustrated, was investigated in depth. Using historical inspection data with an overall low prevalence of violations of approximately 4% for both species, the developed index was able to predict violations with a sensitivity of 81.2 and 79.5% for cattle and pig farms, respectively. The study has shown that combining multiple and heterogeneous data sources improves the quality of the models. Furthermore, privacy-preserving methods are applied to a research environment to explore the available data before restricting the feature space to the most relevant. This study demonstrates that data-driven monitoring of livestock populations is already possible with the existing datasets and the models developed can be a useful tool to plan and conduct risk-based animal welfare inspection.

Exploring positive welfare measures: preliminary findings from a prototype protocol in loose housing dairy cattle farms.

Following the increasing interest about the development of indicators of positive welfare and affective state in farm animals, the aim of this research is to present some preliminary results on the application of a prototype protocol based exclusively on positive welfare measures and to suggest potential benefits that can promote positive welfare. The protocol was applied in 20 loose housing dairy cattle farms (6 on deep litter with straw, 14 in cubicles) and included only indicators of positive welfare and emotional states: feeding and resting synchronization, rumination during resting, comfortable lying postures, no visible eye white, relaxed ear postures, percentage of cow contacts with humans in the Avoidance Distance test. Potential benefits in terms of housing, feeding and management were then related to these variables (Mann-Whitney U test). Qualitative Behavior Assessment (QBA) was also carried out and analyzed by Principal Component Analysis to explore the effect of factors that were not evenly distributed in our sample (number of feed distributions, access to pasture, presence of paddock or environmental enrichments, automatic milking systems). When hay was included in the diet, higher feeding synchronization (93.7 ± 1.6 vs. 52.2 ± 4.7%; p < 0.01), percentage of cows with relaxed ear postures (35.8 ± 5.4 vs. 15.5 ± 2.1%; p < 0.01) and percentage of cows with no visible eye white (55.9 ± 17.0 vs. 36.6 ± 4.1%; n.s.) were recorded. A higher level of feeding synchronization was observed also when the feeding places/cow ratio was > 1 (72.1 ± 9.9 vs. 53.8 ± 5.8%), although differences were not significant (p = 0.14). Deep litter had a more positive effect than cubicles on comfort at resting, with a significantly higher percentage of ruminating cows (65.8 ± 10.2 vs. 34.2 ± 3.7%; p < 0.01), a higher percentage of cows with no visible eye white (55.6 ± 9.9 vs. 33.1 ± 3.7%; p < 0.05) and a higher percentage of cows in a more comfortable posture, with stretched legs (14.3 ± 5.1 vs. 5.6 ± 1.6%; p = 0.09). QBA highlighted the most positive emotional state in the only farm that allowed access to pasture. This study represents a first attempt to apply a protocol for on-farm welfare evaluation based exclusively on the use of positive welfare indicators and provides suggestions on possible benefits (e.g., deep litter, feeding places/cow ratio > 1, hay in the diet and access to pasture) to enhance dairy cattle welfare.

38 Application of Real-Time Location Systems for Automated Behavioral Monitoring in Sheep

Abstract Evaluating how, when, and where an animal moves in their environment can provide insights into their health and welfare states. Spatial tracking systems for monitoring sheep are less well developed than accelerometer-based systems, but have the potential to add important behavioral information to Precision Livestock Farming (PLF) applications. Global Positioning Systems (GPS) can be used to track pasture-based animals, but are not suitable to establish indoor locations. Real-Time Location Systems (RTLS) are a recent development that use radio-frequency signals emitted by tags to track the indoor location of an animal online and continuously. Raw RTLS data in the form of x, y coordinates can be used to calculate multiple spatial metrics, including velocity, acceleration, distance moved, the proportion of time an animal spends in different functional areas, social network attributes, and range size. RTLS can also be integrated with accelerometers to potentially improve predictive accuracy of behavioral algorithms. RTLS have been primarily tested and validated for indoor tracking of dairy cattle and have been used to assess behavioral time budgets and to detect space-use changes related to estrus or health status. For on-farm applications, these data must be transformed into threshold-based algorithms to generate notifications on changes in the health or welfare status of an animal. Algorithm development needs to account for confounding factors such as breed, sex, age, parity, and stage of lactation, as well as environmental variability that can influence the performance of decision-making tools under real-life conditions. In addition, independent validation studies of commercial systems are lacking, and many calculate positioning error with stationary objects rather than moving animals, potentially inflating accuracy estimates. In conclusion, there is growing empirical support for the use of RTLS as a PLF technology for on-farm animal health and welfare assessment. However, research is needed to validate these systems in sheep and identify spatial metrics that are accurate and robust predictors of health and welfare states for commercial applications.

Evaluating the efficacy of two local anesthetics to extend analgesia in Holstein calves after dehorning

The dehorning of dairy calves is a painful procedure routinely done in commercial production systems, to facilitate handling and increase human and animal safety. The American Associa­tion of Bovine Practitioners recommends the use of procedures and practices that reduce or eliminate pain associated with de­horning to improve on-farm animal welfare. This study aimed to 1) determine if the addition of bupivacaine lipsome inject­able suspension or ethanol to lidocaine would provide extended pain relief to dairy calves post-dehorning, and 2) determine which local anesthetic most effectively reduced pain biomark­ers and behaviors post-dehorning.

Use of “demonstration farm” videos to affect attitude change toward animal welfare on beef, egg, and fish farms in China

“Demonstration farms” can disseminate knowledge on farming practices and help to promote animal welfare. When on-farm visits are impractical, remote demonstrations are a feasible alternative. This study used videos of higher welfare beef, fish and free-range egg farms in China. It aimed to determine whether the videos affected attitudes and intentions toward animal welfare and whether such videos are useful training tools. Participants indicated a high acceptability of demonstration farm videos for learning about their industry and the needs of animals. Videos shifted participant attitudes toward animal welfare, but only when actively engaged in rating the farm on specific characteristics. Attitude changes suggested participants gained a greater understanding of animal welfare, a greater intention to improve on-farm welfare, and more confidence in peer support for welfare innovations after viewing the video. The findings indicate videos of demonstration farms are useful for remote training but passive viewing may be insufficient to create change, and outcomes should be monitored for success.

On-Farm Welfare Assessment of Dairy Cattle (Friesian) in Nyala City, Sudan

On-Farm Welfare Assessment of Dairy Cattle (Friesian) in Nyala City, Sudan

Welfare of dairy cows.

This Scientific Opinion addresses a European Commission's mandate on the welfare of dairy cows as part of the Farm to Fork strategy. It includes three assessments carried out based on literature reviews and complemented by expert opinion. Assessment 1 describes the most prevalent housing systems for dairy cows in Europe: tie-stalls, cubicle housing, open-bedded systems and systems with access to an outdoor area. Per each system, the scientific opinion describes the distribution in the EU and assesses the main strengths, weaknesses and hazards potentially reducing the welfare of dairy cows. Assessment 2 addresses five welfare consequences as requested in the mandate: locomotory disorders (including lameness), mastitis, restriction of movement and resting problems, inability to perform comfort behaviour and metabolic disorders. Per each welfare consequence, a set of animal-based measures is suggested, a detailed analysis of the prevalence in different housing systems is provided, and subsequently, a comparison of the housing systems is given. Common and specific system-related hazards as well as management-related hazards and respective preventive measures are investigated. Assessment 3 includes an analysis of farm characteristics (e.g. milk yield, herd size) that could be used to classify the level of on-farm welfare. From the available scientific literature, it was not possible to derive relevant associations between available farm data and cow welfare. Therefore, an approach based on expert knowledge elicitation (EKE) was developed. The EKE resulted in the identification of five farm characteristics (more than one cow per cubicle at maximum stocking density, limited space for cows, inappropriate cubicle size, high on-farm mortality and farms with less than 2 months access to pasture). If one or more of these farm characteristics are present, it is recommended to conduct an assessment of cow welfare on the farm in question using animal-based measures for specified welfare consequences.

Perceptions of farming stakeholders towards automating dairy cattle mobility and body condition scoring in farm assurance schemes

Animal welfare standards are used within the food industry to demonstrate efforts in reaching higher welfare on farms. To verify compliance with those standards, inspectors conduct regular on-farm animal welfare assessments. Conducting these welfare assessments can, however, be time-consuming and prone to human bias. The emergence of Digital Livestock Technologies (DLTs) offers new ways of monitoring farm animal welfare and can alleviate some of the challenges related to animal welfare assessments by collecting data automatically and more frequently. Whilst automating welfare assessments with DLTs may be promising, little attention has been paid to farmers’ perceptions of the challenges that could prevent successful implementation. This study aims to address this gap by focusing on the trial of a DLT (a 3D machinelearning camera) to automate mobility and body condition scoring on 11 dairy cattle farms. Semi-structured, in-depth interviews were conducted with farmers, technology developers and a stakeholder involved in a farm assurance scheme (N14). Findings suggest that stakeholders perceived important benefits to the use of the camera in this context, from building consumer trust by increasing transparency to improved management efficiency. There was also a potential for greater consistency in data collection and thus for enhanced fairness across the UK dairy sector, particularly on the issue of lameness prevalence. However, stakeholders also raised important concerns, such as a lack of clarity around data ownership, reliability, and use, and the possibility of some farmers being penalised (e.g., if the technology failed to work). More clarity should thus be given to farmers in relation to data governance and evidence provided in terms of technical performance and accuracy. The findings of this study highlighted the need for more inclusive approaches to ensure farmers’ concerns are adequately identified and addressed. These approaches can help minimise negative consequences to farmers and animal welfare, whilst maximising the potential benefits of automating welfare-related data collection.

Welfare of calves

This Scientific Opinion addresses a European Commission request on the welfare of calves as part of the Farm to Fork strategy. EFSA was asked to provide a description of common husbandry systems and related welfare consequences, as well as measures to prevent or mitigate the hazards leading to them. In addition, recommendations on three specific issues were requested: welfare of calves reared for white veal (space, group housing, requirements of iron and fibre); risk of limited cow-calf contact; and animal-based measures (ABMs) to monitor on-farm welfare in slaughterhouses. The methodology developed by EFSA to address similar requests was followed. Fifteen highly relevant welfare consequences were identified, with respiratory disorders, inability to perform exploratory or foraging behaviour, gastroenteric disorders and group stress being the most frequent across husbandry systems. Recommendations to improve the welfare of calves include increasing space allowance, keeping calves in stable groups from an early age, ensuring good colostrum management and increasing the amounts of milk fed to dairy calves. In addition, calves should be provided with deformable lying surfaces, water via an open surface and long-cut roughage in racks. Regarding specific recommendations for veal systems, calves should be kept in small groups (2-7 animals) within the first week of life, provided with ~ 20m2/calf and fed on average 1kg neutral detergent fibre (NDF) per day, preferably using long-cut hay. Recommendations on cow-calf contact include keeping the calf with the dam for a minimum of 1 day post-partum. Longer contact should progressively be implemented, but research is needed to guide this implementation in practice. The ABMs body condition, carcass condemnations, abomasal lesions, lung lesions, carcass colour and bursa swelling may be collected in slaughterhouses to monitor on-farm welfare but should be complemented with behavioural ABMs collected on farm.

Non-Invasive Methods for Assessing the Welfare of Farmed White-Leg Shrimp (Penaeus vannamei).

Gradually, concern for the welfare of aquatic invertebrates produced on a commercial/industrial scale is crossing the boundaries of science and becoming a demand of other societal actors. The objective of this paper is to propose protocols for assessing the Penaeus vannamei welfare during the stages of reproduction, larval rearing, transport, and growing-out in earthen ponds and to discuss, based on a literature review, the processes and perspectives associated with the development and application of on-farm shrimp welfare protocols. Protocols were developed based on four of the five domains of animal welfare: nutrition, environment, health, and behaviour. The indicators related to the psychology domain were not considered a separate category, and the other proposed indicators indirectly assessed this domain. For each indicator, the corresponding reference values were defined based on literature and field experience, apart from the three possible scores related to animal experience on a continuum from positive (score 1) to very negative (score 3). It is very likely that non-invasive methods for measuring the farmed shrimp welfare, such as those proposed here, will become a standard tool for farms and laboratories and that it will become increasingly challenging to produce shrimp without considering their welfare throughout the production cycle.

Positive Welfare Indicators in Dairy Animals

Nowadays, there is growing interest in positive animal welfare not only from the view of scientists but also from that of society. The consumer demands more sustainable livestock production, and animal welfare is an essential part of sustainability, so there is interest in incorporating positive welfare indicators into welfare assessment schemes and legislation. The aim of this review is to cite all the positive welfare indicators that have been proposed for dairy animals in theory or practice. In total, twenty-four indicators were retrieved. The most promising are exploration, access to pasture, comfort and resting, feeding, and behavioral synchronicity. Qualitative behavioral assessment (QBA), social affiliative behaviors, play, maternal care, ear postures, vocalizations, visible eye white, nasal temperature, anticipation, cognitive bias, laterality, and oxytocin have been also studied in dairy ruminants. QBA is the indicator that is most often used for the on-farm welfare assessment. Among all dairy animals, studies have been performed mostly on cattle, followed by sheep and goats, and finally buffaloes. The research on camel welfare is limited. Therefore, there is a need for further research and official assessment protocols for buffaloes and especially camels.

Development of a welfare assessment protocol and assessment of dairy cattle welfare in Haryana and Punjab states of Northern India

AbstractThe aim of this study was to develop an on-farm dairy cattle welfare assessment protocol at different-sized farms in two major commercial dairy farming states in India. For developing the protocol, the basic ‘Integrative Diagnostic System Welfare’ (IDSW) framework was modified to include three welfare components (animal housing and other facilities; feeds and feeding practices; and animal health, performance and behaviour) and 20 welfare indicators (ten resource- and ten animal-based). Each indicator was weighed on a value scale with an aggregate welfare score of 100. The protocol was tested for feasibility, validity and reliability using Cronbach's alpha and Guttman split-half coefficient. Using this protocol, welfare was assessed on 60 commercial farms in Punjab and 50 in Haryana, divided into three adult herd sizes: small (S &lt; 20), medium (M = 21–50) and large (L &gt; 50). Welfare scores in L (76.60 [± 1.70]) and M (68.40 [± 2.27]) sized herds in Punjab were higher than in S herds (60.80 [± 2.77]). In Haryana these were higher in L (68.1 [± 1.18]) than in S (60.50 [± 2.74]) and M (59.35 [± 2.17]) sized herds. The aggregate average welfare score was higher in Punjab (68.60 [± 1.49]) than in Haryana (62.65 [± 2.02]). Welfare at more than 75% of the farms in Punjab and more than 50% of those in Haryana was judged as ‘acceptable.’ Six welfare indicators in Punjab and eight in Haryana were most compromised. Four indicators (microclimate protection measures, availability of milking parlour, cow cleanliness and reproductive efficiency) were the most compromised indicators in both states. To improve dairy cattle welfare in these states we recommend an emphasis on improving housing and feeding conditions, especially at small and medium farms, along with heat stress amelioration measures and improving hygiene and reproductive efficiency at all farms.

Using the Herd Status Index to remotely assess the welfare status of dairy herds based on prerecorded data

The use of prerecorded data to remotely assess the herd welfare status is a promising approach to reduce the need for costly and time-consuming on-farm welfare assessments. Therefore, the objective of this study was to validate the Herd Status Index, an index developed based on Dairy Herd Improvement data from Canada, to remotely evaluate the welfare status of dairy herds. Herd-level prevalence of five animal-based welfare outcomes, measured once on 2 986 Quebec – Canada dairy herds between 2016 and 2019, were used to generate clusters with different welfare status using the algorithm partitioning around medoids. Dairy Herd Improvement data from 12 months prior to the welfare assessment were extracted and used to calculate the Herd Status Index. A linear model was used to carry out comparisons between clusters. Three stable clusters were found to best describe the data. Cluster two had the best overall welfare status since it had the lowest prevalence of all welfare issues while cluster three had the highest prevalence of most welfare issues, with the exception for the prevalence of neck lesions that was not different than cluster one. Cluster one had an overall intermediate welfare status. The Herd Status Index was higher (i.e., indicating a good welfare status) on cluster two compared to cluster three, but neither cluster three nor two differed to cluster one. In its current format, the Herd Status Index has a weak potential to identify herds with varying prevalence of welfare issues and it requires further improvements before it could be used to accurately assess the welfare status of the herds.

A "Good Life" for Dairy Cattle: Developing and Piloting a Framework for Assessing Positive Welfare Opportunities Based on Scientific Evidence and Farmer Expertise.

Simple SummaryThere is increasing appetite to understand how we can provide quality of life to farm animals. A framework to evaluate positive welfare opportunities for dairy cattle was developed using a participatory approach where farmer’s recommendations were integrated into a scientific framework and piloted on farm by vets. When provided with the opportunity to collaborate, farmers and scientists broadly agree on what constitutes “a good life” for dairy cattle and worked together to develop an assessment framework. Farmers did not agree equally on the value of each positive welfare opportunity. However, farmers supported positive welfare assessment as a means of recognition and reward for higher animal welfare, within existing farm assurance schemes, and to justify national and global marketing claims of higher animal welfare.On-farm welfare assessment tends to focus on minimising negative welfare, but providing positive welfare is important in order to give animals a good life. This study developed a positive welfare framework for dairy cows based on the existing scientific literature which has focused on developing positive welfare indicators, and trialled a participatory approach with farmers; refining the framework based on their recommendations, followed by a vet pilot phase on farm. The results revealed that farmers and scientists agree on what constitutes “a good life” for dairy cattle. Farmers value positive welfare because they value their cows’ quality of life, and want to be proud of their work, improve their own wellbeing as well as receive business benefits. For each good life resource, the proportion of farmers going above and beyond legislation ranged from 27 to 84%. Furthermore, barriers to achieving positive welfare opportunities, including monetary and time costs, were not apparently insurmountable if implementation costs were remunerated (by the government). However, the intrinsic value in providing such opportunities also incentivises farmers. Overall, most farmers appeared to support positive welfare assessment, with the largest proportion (50%) supporting its use within existing farm assurance schemes, or to justify national and global marketing claims. Collaborating with farmers to co-create policy is crucial to showcase and quantify the UK’s high welfare standards, and to maximise engagement, relevance and uptake of animal welfare policy, to ensure continuous improvement and leadership in the quality of lives for farm animals.