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Thomann B, Würbel H, Kuntzer T, Umstätter C, Wechsler B, Meylan M, Schüpbach-Regula G. Development of a data-driven method for assessing health and welfare in the most common livestock species in Switzerland: The Smart Animal Health project. Front Vet Sci 2023; 10:1125806. [PMID: 37056235 PMCID: PMC10086233 DOI: 10.3389/fvets.2023.1125806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/28/2023] [Indexed: 03/30/2023] Open
Abstract
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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Affiliation(s)
- Beat Thomann
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Bern, Switzerland
- *Correspondence: Beat Thomann
| | - Hanno Würbel
- Animal Welfare Division, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | | | - Christina Umstätter
- Research Division on Competitiveness and System Evaluation, Agroscope, Ettenhausen, Switzerland
- Thünen Institute of Agricultural Technology, Braunschweig, Germany
| | - Beat Wechsler
- Centre for Proper Housing of Ruminants and Pigs, Federal Food Safety and Veterinary Office, Agroscope, Ettenhausen, Switzerland
| | - Mireille Meylan
- Clinic for Ruminants, Vetsuisse Faculty, University of Bern, Bern, Switzerland
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Lutz B, Zwygart S, Thomann B, Stucki D, Burla JB. The relationship between common data-based indicators and the welfare of Swiss dairy herds. Front Vet Sci 2022; 9:991363. [PMID: 36337183 PMCID: PMC9632483 DOI: 10.3389/fvets.2022.991363] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 10/03/2022] [Indexed: 04/03/2024] Open
Abstract
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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Affiliation(s)
- Barbara Lutz
- Centre for Proper Housing of Ruminants and Pigs, Federal Food Safety and Veterinary Office FSVO, Ettenhausen, Switzerland
- Clinic for Ruminants, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Sibylle Zwygart
- Clinic for Ruminants, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Beat Thomann
- Vetsuisse Faculty, Veterinary Public Health Institute, University of Bern, Bern, Switzerland
| | - Dimitri Stucki
- Clinic for Ruminants, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Joan-Bryce Burla
- Centre for Proper Housing of Ruminants and Pigs, Agroscope, Ettenhausen, Switzerland
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Stygar AH, Krampe C, Llonch P, Niemi JK. How Far Are We From Data-Driven and Animal-Based Welfare Assessment? A Critical Analysis of European Quality Schemes. FRONTIERS IN ANIMAL SCIENCE 2022. [DOI: 10.3389/fanim.2022.874260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
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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