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

Lutz, Barbara; Zwygart, Sibylle; Rufener, Christina; Burla, Joan-Bryce; Thomann, Beat; Stucki, Dimitri

doi:10.3390/ani11123458

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…In 2011, a review observed that only a few studies reported relationships between DBIs and measurements of resting comfort and animal behavior ( 38 ). On one hand, this was explained by a general lack of studies examining correlations between DBIs and behavior or resting comfort, as was also reported recently ( 25 ). On the other hand, the potential of herd data to detect problems in resting comfort and behavior was questioned ( 38 ).…”

Section: Discussionmentioning

confidence: 83%

“…In addition, the DBIs had to be calculable using data routinely available in Switzerland. To identify the DBIs, results from previous scientific literature ( 25 ) were used. These DBIs were supplemented with DBIs that are currently being used in other animal welfare projects or assessments, such as Q-check ( 26 ) or AssureWel ( 27 ), even if no peer-reviewed reports have yet been published for these projects or assessments.…”

Section: Methodsmentioning

confidence: 99%

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The relationship between common data-based indicators and the welfare of Swiss dairy herds

Lutz

Zwygart²,

Thomann³

et al. 2022

Front. Vet. Sci.

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The assessment of dairy cow welfare has become increasingly important in recent years. Welfare assessments that use animal-based indicators, which are considered the most direct indicators, are time consuming and therefore not feasible for assessments on a large number of farms. One approach to reducing this effort is the use of data-based indicators (DBIs) calculated from routine herd data. The aim of this study was to explore the relationship between common DBIs and the welfare of 35 dairy herds to evaluate the feasibility of a data-based welfare prediction method. For this purpose, the WelfareQuality® (WQ) protocol was used to assess the welfare of dairy cows on 35 Swiss farms, for each of which 10 commonly used DBIs were calculated from herd data. Spearman's rank correlations were used to investigate the relationship between DBIs and WQ criteria and measurements. The study found only a few statistically weak associations between DBIs and animal welfare, with no associations for measurements or criteria of resting comfort and appropriate behavior. Thus, the multidimensional welfare definition is insufficiently covered, and the present publication does not support the approach of a purely data-based prediction of dairy welfare status at the farm level. Instead, the regular calculation of DBIs that are indicative of isolated animal welfare problems or metrics of animal health could allow monitoring of these specific areas of animal welfare.

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Section: Discussionmentioning

confidence: 83%

Section: Methodsmentioning

confidence: 99%

The relationship between common data-based indicators and the welfare of Swiss dairy herds

Lutz

Zwygart²,

Thomann³

et al. 2022

Front. Vet. Sci.

Self Cite

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“…For literature identification, search queries were made in the scientific databases PubMED, Web of Science, Scopus, CAB Direct and Science Direct, and identified publications were analyzed based on the PRISMA guidelines ( 17 ). Detailed methods of the literature review for the different animal species are described in Gebhardt-Henrich and Schlapbach ( 18 ), Lutz et al ( 19 ), Minning et al ( 20 ), and Zufferey et al ( 21 ). Subsequently, a preliminary set of promising indicators was compiled and discussed with stakeholders.…”

Section: Methodsmentioning

confidence: 99%

Development of a data-driven method for assessing health and welfare in the most common livestock species in Switzerland: The Smart Animal Health project

Thomann

Würbel

Kuntzer³

et al. 2023

Front. Vet. Sci.

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Improving animal health and welfare in livestock systems depends on reliable proxies for assessment and monitoring. The aim of this project was to develop a novel method that relies on animal-based indicators and data-driven metrics for assessing health and welfare at farm level for the most common livestock species in Switzerland. Method development followed a uniform multi-stage process for each species. Scientific literature was systematically reviewed to identify potential health and welfare indicators for cattle, sheep, goats, pigs and poultry. Suitable indicators were applied in the field and compared with outcomes of the Welfare Quality® scores of a given farm. To identify farms at risk for violations of animal welfare regulations, several agricultural and animal health databases were interconnected and various supervised machine-learning techniques were applied to model the status of farms. Literature reviews identified a variety of indicators, some of which are well established, while others lack reliability or practicability, or still need further validation. Data quality and availability strongly varied among animal species, with most data available for dairy cows and pigs. Data-based indicators were almost exclusively limited to the categories “Animal health” and “Husbandry and feeding”. The assessment of “Appropriate behavior” and “Freedom from pain, suffering, harm and anxiety” depended largely on indicators that had to be assessed and monitored on-farm. The different machine-learning techniques used to identify farms for risk-based animal welfare inspections reached similar classification performances with sensitivities above 80%. Features with the highest predictive weights were: Participation in federal ecological and animal welfare programs, farm demographics and farmers' notification discipline for animal movements. A common method with individual sets of indicators for each species was developed. The results show that, depending on data availability for the individual animal categories, models based on proxy data can achieve high correlations with animal health and welfare assessed on-farm. Nevertheless, for sufficient validity, a combination of data-based indicators and on-farm assessments is currently required. For a broad implementation of the methods, alternatives to extensive manual on-farm assessments are needed, whereby smart farming technologies have great potential to support the assessment if the specific monitoring goals are defined.

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“…Recent literature reviews and market analyses indicate that sensor technologies have a high potential in assessing animal welfare at the level of an individual animal (Buller et al, 2020;Gómez et al, 2021;Stygar et al, 2021). Routinely collected on-farm data have already been proven to be a useful source of welfare information in the dairy (e.g., Nyman et al, 2011;de Vries et al, 2014;Lutz et al, 2021) and pig (e.g., Stygar et al, 2020) sectors. Several studies suggest that recordings from slaughterhouses may provide useful information on animal wellbeing on farms (Vial, 2019).…”

Section: Introductionmentioning

confidence: 99%

How Far Are We From Data-Driven and Animal-Based Welfare Assessment? A Critical Analysis of European Quality Schemes

et al. 2022

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Within the European Union, there is no harmonization of farm animal welfare quality schemes for meat and dairy products. Instead, there are several industry-driven initiatives and voluntary schemes that seek to provide information on animal welfare for attentive consumers. This study had two aims. First, we quantified how selected industry-wide quality schemes cover the welfare of pigs and dairy cattle on farms by comparing the evaluation criteria selected by schemes with the animal-, resource- and management-based measures defined in the Welfare Quality protocol (WQ®). Second, we identified how these quality schemes use the data generated along the value chain (sensors, breeding, production, and health recordings) for animal welfare assessments. A total of 12 quality schemes, paying attention to animal welfare but not necessarily limited to welfare, were selected for the analysis. The schemes originated from eight European countries: Finland, Sweden, Denmark, Ireland, the Netherlands, Germany, Austria, and Spain. Among the studied quality schemes, we have identified 19 standards for certification: nine for dairy and 10 for pig production. Most of the analyzed standards were comprehensive in welfare assessment. In total, 15 out of 19 standards corresponded to WQ® in more than 70%. However, this high correspondence was obtained when allowing for different information sources (environment instead of animal) than defined in WQ®. Compared to WQ®, the investigated schemes were lagging in terms of the number of measures evaluated based on the animals, with only five standards, out of 19, using predominantly animal-based measures. The quality schemes mostly applied resource-based instead of animal-based measures while assessing good health and appropriate behavior. The utilization of data generated along the value chain by the quality schemes remains insignificant as only one quality scheme allowed the direct application of sensor technologies for providing information on animal welfare. Nevertheless, several schemes used data from farm recording systems, mostly on animal health. The quality schemes rely mostly on resource-based indicators taken during inspection visits, which reduce the relevance of the welfare assessment. Our results suggest that the quality schemes could be enhanced in terms of data collection by the broader utilization of data generated along the value chain.

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

Cited by 5 publications

References 30 publications

The relationship between common data-based indicators and the welfare of Swiss dairy herds

The relationship between common data-based indicators and the welfare of Swiss dairy herds

Development of a data-driven method for assessing health and welfare in the most common livestock species in Switzerland: The Smart Animal Health project

How Far Are We From Data-Driven and Animal-Based Welfare Assessment? A Critical Analysis of European Quality Schemes

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