Register-based predictors of violations of animal welfare legislation in dairy herds

Are there risk factors commonly observed on Australian farms where the welfare of livestock is poor?

The objective of this study was to identify factors more commonly observed on farms with poor livestock welfare compared to farms with good welfare. Potentially, these factors may be used to develop an animal welfare risk assessment tool (AWRAT) that could be used to identify livestock at risk of poor welfare. Identifying livestock at risk of poor welfare would facilitate early intervention and improve strategies to promptly resolve welfare issues. This study focuses on cattle, sheep and goats in non-dairy extensive farming systems in Australia. To assist with identifying potential risk factors, a survey was developed presenting 99 factors about the farm, farmers, animals and various aspects of management. Based on their experience, key stakeholders, including veterinarians, stock agents, consultants, extension and animal welfare officers were asked to consider a farm where the welfare of the livestock was either high or low and rate the likelihood of observing these factors. Of the 141 responses, 65% were for farms with low welfare. Only 6% of factors had ratings that were not significantly different between high and low welfare surveys, and these were not considered further. Factors from poor welfare surveys with median ratings in the lowest 25% were considered potential risks (n = 49). Considering correlation, ease of verification and the different livestock farming systems in Australia, 18 risk factors relating to farm infrastructure, nutrition, treatment and husbandry were selected. The AWRAT requires validation in future studies.

Analysis of substantiated welfare investigations in extensive farming systems in Victoria, Australia

Substantiated incidents of poor welfare affecting cattle, sheep and goats (livestock) in non-dairy extensive farming systems continue to occur. This study sought to describe the common causes of poor welfare of livestock and the associated circumstances, by analysing 39 years of de-identified, livestock welfare investigation records. There were a total of 2179 alleged offenders (AOff), defined as individual/s that had an incident of poor welfare affecting livestock on at least one occasion. Approximately 27% of AOff were found to have poor welfare on more than one occasion. The majority of livestock welfare incidents were associated with neglect, more specifically, inadequate nutrition (56%), treatment (65%) and management/husbandry (83%). Records of malicious acts were rare (1%). In the analysis, cases were allocated to 10 animal welfare severity categories (AWSC) based on the number of incidents and visits, whether the AOff reoffended, or if the incident was ongoing and whether the welfare issue was likely to affect the whole herd. A significantly higher proportion of cases in the most severe AWSC had a failure to shear, mark, dip/drench, draft and wean/cull, were overstocked or were not providing proper and sufficient feed, compared to the least severe AWSC (P ≤ 0.05). Reoffending was significantly more likely when animals were found to be injured/unwell, recumbent, stuck in mud/yard/pen or in poor body condition, or when there was a failure to wean/cull, mark, dip/drench and draft. Some of the issues identified here may be risk factors more commonly identified on farms with poor livestock welfare.

Animal-based welfare indicators for dairy cows and their validity and practicality: a systematic review of the existing literature

Animal welfare is of increasing importance, with consumers preferring animal products made with ethical practices due to growing awareness. This shift highlights the need for reliable methods to evaluate welfare. This systematic review aims to assess the validity of current animal-based welfare indicators for dairy cows to aid farmers and agricultural professionals in evaluating and improving welfare amidst the lack of a clear legislative definition. The literature search spanned five databases: CAB Direct, PubMed, Scopus, Google Scholar and Livivo, covering publications in English and German from 2011 to 2021. Specific search terms were employed, and abstracts were screened for relevance. Publications were categorized based on exclusion criteria, with a final verification process conducted by three independent scientists. Research highlights correlations between welfare measures, farm characteristics and innovative indicators like hair cortisol concentration. Farming systems and housing methods significantly affect welfare, with pasture-based systems generally resulting in reduced lameness and improved behavior. Proper housing design and management practices are important, as they influence indicators like lameness and cleanliness. Heart rate variability and heart rate monitoring provide insights into dairy cow stress levels during milking and other stressors, making them valuable for welfare assessment. Biomarker research emphasizes the need to balance productivity and health in breeding strategies, as high milk production alone does not indicate good welfare. Behavioral studies and the human-animal relationship are key to understanding welfare. Precision Livestock Farming offers real-time assessment capabilities, although validation is needed. Stress physiology is complex, and while cortisol measurement methods are promising, further research is necessary. Assessment tools like the Animal Needs Index and routine herd data analysis are valuable for identifying welfare concerns. Key findings highlight the WQ® protocol's effectiveness and versatility, the challenge of its time demands, and the DCF protocol's promise for more practical and efficient welfare assessments. Commercial animal welfare audits should prioritize easily observable indicators and herd records due to logistical constraints in measuring biomarkers or heart rate variability. This focus on easily accessible indicators, such as body condition score, lameness, claw health, cleanliness, and somatic cell count allows effective welfare assessments, enabling prompt action to enhance wellbeing.

An analysis of substantiated complaints made about incidents of poor livestock welfare, in Victoria, Australia

Incidents of poor welfare on farm in Victoria, Australia, are generally identified during an investigation that follows receipt of a complaint. Using deidentified records of complaints received by the Victoria State Government between 2011 and 2020, this study aimed to describe the source, number and the relationship between rainfall/stock prices and substantiated welfare complaints (SWC). Only incidents involving non-dairy cattle, sheep and goats in extensive farming systems will be considered. The main source of complaints received by the Victorian Government is the general public. Almost half of all complaints were made for cattle (48%), 39% for sheep, 11% for mixed species, and 2% for goats. The number of SWC varied between months, each year and across the different regions of Victoria. The ratio of the actual mean rainfall of the last three seasons to the long-term mean of the last three seasons of rainfall (RL3SR) and livestock prices together were the best predictors of the total number of SWC (adjusted R square value for heavy lamb-RL3SR was highest (0.590), followed by merino lamb-RL3SR (0.588), goat-RL3SR (0.545) and steer-RL3SR (0.478) all were significant (p ≤ 0.05)). The rainfall by region and town were not good predictors of the number of SWC. There was a correlation between rainfall and the number of SWC, possibly due to changes in pasture availability. Favorable seasonal conditions however, were not protective of livestock welfare and it is likely a number of factors may be implicated.

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.

Is animal welfare better on smaller dairy farms? Evidence from 3,085 dairy farms in Germany

The structural change toward larger (dairy) farms is often criticized because it supposedly has a negative effect on farm animal welfare. We investigated this criticism using cross-sectional survey data from 3,085 German dairy farms. Even though our sample was a convenience sample, it closely resembled the diverse structures of dairy farming in Germany and covered a wide range of dairy farm sizes (7 to 2,900 cows per farm, mean 122). We developed an animal welfare index (AWI) in close consultation with experts along the dairy value chain (e.g., farm animal welfare scientists, farmers, dairy representatives). Regression results showed that larger farms tended to achieve a better AWI than smaller farms in our data set. However, the effect size was small. Nevertheless, in contrast to the widespread assumption in public discussion, larger dairy herds are not necessarily associated with poorer animal welfare. In all herd size classes, we found a large variation of AWI between herds. Although this study focused on the effect of herd size, it is not the only factor affecting animal welfare levels on individual farms. Other variables that we included in the regression to describe the AWI indicate that the knowledge and skills of the farm manager and the amount of time that farms can devote to animals have a positive effect on the AWI. However, as with herd size, the effect size of other explanatory variables was small in absolute terms.

Data-Based Variables Used as Indicators of Dairy Cow Welfare at Farm Level: A Review

Simple Summary

In recent years, the interest in the use of data from routine herd records in the monitoring of dairy welfare at farm level has increased. This review compiles 13 papers to outline the current potential of data-based variables for animal welfare monitoring. All the identified studies showed associations between data-based variables and farm-level dairy cow welfare and therefore provide a first indication of the possible use and suitability of data-based variables for welfare monitoring. However, we found that the definitions of animal welfare, its assessment on the farm, and the data-based variables varied considerably. Consequently, the current state of research does not allow a conclusive assessment of the potential of data-based variables for animal welfare monitoring. Therefore, future research is needed to clarify the potential of data-based variables. Harmonisation of the data-based variables and the use of valid measurements that reflect the multidimensionality of welfare could contribute to increased comparability between the studies.

Abstract

During the last years, the interest in data-based variables (DBVs) as easy-to-obtain, cost-effective animal welfare indicators has continued to grow. This interest has led to publications focusing on the relationship between DBVs and animal welfare. This review compiles 13 papers identified through a systematic literature search to provide an overview of the current state of research on the relationship between DBVs and dairy cow welfare at farm level. The selected papers were examined regarding their definition of animal welfare and classified according to this definition into three categories: (a) papers evaluating DBVs as predictors of animal welfare violations, (b) papers investigating the relationship between DBVs and animal-based measurements, and (c) papers investigating the relationship of DBVs to scores of welfare assessments like the Welfare Quality protocol or to overall welfare scores at farm level. In addition, associations between DBVs and indicators of animal welfare were extracted, grouped by the type of DBV, and examined for replications that may confirm the associations. All the identified studies demonstrated associations between DBVs and animal welfare. Overall, the first indications of a possible suitability of DBVs for predicting herds with animal welfare violations as well as good or poor animal welfare status were given. The evaluation of relationships between DBVs and animal-based measurements (ABMs) found mortality-based DBVs to be frequently associated with ABMs. However, owing to varying definitions of animal welfare, the use of different variants of DBVs, and different methods used to assess DBVs, the studies could only be compared to a limited extent. Future research would benefit from a harmonisation of DBVs and the use of valid measurements that reflect the multidimensionality of welfare. Data sources rarely investigated so far may have the potential to provide additional DBVs that can contribute to the monitoring of cow welfare at farm level.

Evaluation of the performance of register data as indicators for dairy herds with high lameness prevalence

BACKGROUND

The modern dairy industry routinely generates data on production and disease. Therefore, the use of these cheap and at times even "free" data to predict a given state of welfare in a cost-effective manner is evaluated in the present study. Such register data could potentially be used in the identification of herds at risk of having animal welfare problems. The present study evaluated the diagnostic performance of four routinely registered indicators for identifying herds with high lameness prevalence among 40 Danish dairy herds. Indicators were extracted as within-herd annual means for a one-year period for cow mortality, bulk milk somatic cell count, proportion of lean cows at slaughter and the standard deviation (SD) of age at first calving. The target condition "high lameness prevalence" was defined as a within-herd prevalence of lame cows of  ≥ 16% (third quartile). Diagnostic performance was evaluated by constructing and analysing Receiver Operating Characteristic curves and their area under the curve (AUC) for single indicators and indicator combinations. Sensitivity (Se) and specificity (Sp) of the indicators were assessed at the optimal cut-off based on data and compared to a set of predefined cut-off levels (national annual means or 90-percentile).

RESULTS

Cow mortality had the highest AUC (0.76), while adding the three other indicators to the model did not yield significant increase in AUC. Cow mortality and SD of age at first calving had highest Se (100%, 95% confidence interval (CI): 72-100%), while highest Sp was found for the proportion of lean cows at slaughter (83%, 95% CI: 66-93%). The highest differential positive rate (DPR = 0.53) optimizing both Se and Sp was found for cow mortality. Optimal cut-off points were lower than the presently used pre-defined cut-offs.

CONCLUSIONS

The selected register-based indicators proved to be able to identify herds with high lameness prevalences. Optimized cut-offs improved the predictive ability and should therefore be preferred in official control schemes.

INVITED REVIEW: Farm size and animal welfare

Critics of agricultural intensification have argued that the transition from smaller to larger farms has compromised animal welfare. To critically examine evidence relevant to this claim, we reviewed more than 150 publications that examined the relationship between farm size and at least one animal welfare indicator. Although much of this literature focuses on dairy cattle, we also reference other farmed species where appropriate. We found little evidence of any simple relationship, negative or positive, between farm size and animal welfare. Instead, the evidence suggests that larger farms provide some opportunities to improve animal welfare but may also create welfare risks. For example, larger farms permit more specialized and professional management of animal health but can make it more difficult to accommodate outdoor access that some view as integral to animal welfare. Future research should attempt to specify the underlying casual mechanisms by which statistical associations between farm size and indicators of welfare are believed to occur. We also suggest that policy and advocacy efforts aimed at reversing increases in farm size would be better directed toward improving welfare on farms of all sizes.