ORIGINAL RESEARCH published: 06 May 2022 doi: 10.3389/fanim.2022.874260 How Far Are We From Data-Driven and Animal-Based Welfare Assessment? A Critical Analysis of European Quality Schemes Anna H. Stygar 1*, Caspar Krampe 2 , Pol Llonch 3 and Jarkko K. Niemi 1 1 Bioeconomy and Environment, Natural Resources Institute Finland (Luke), Helsinki, Finland, 2 Marketing and Consumer Behaviour Group, Department of Social Science, Wageningen University, Wageningen, Netherlands, 3 Animal Welfare and Nutrition Service (SNiBA), Department of Animal and Food Science, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain 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, Edited by: breeding, production, and health recordings) for animal welfare assessments. A total Mara Miele, of 12 quality schemes, paying attention to animal welfare but not necessarily limited to Cardiff University, United Kingdom welfare, were selected for the analysis. The schemes originated from eight European Reviewed by: Anders Ringgaard Kristensen, countries: Finland, Sweden, Denmark, Ireland, the Netherlands, Germany, Austria, University of Copenhagen, Denmark and Spain. Among the studied quality schemes, we have identified 19 standards for Frida Lundmark Hedman, Swedish University of Agricultural certification: nine for dairy and 10 for pig production. Most of the analyzed standards were Sciences, Sweden comprehensive in welfare assessment. In total, 15 out of 19 standards corresponded *Correspondence: to WQ® in more than 70%. However, this high correspondence was obtained when Anna H. Stygar allowing for different information sources (environment instead of animal) than defined anna.stygar@luke.fi in WQ® . Compared to WQ® , the investigated schemes were lagging in terms of the Specialty section: number of measures evaluated based on the animals, with only five standards, out of This article was submitted to 19, using predominantly animal-based measures. The quality schemes mostly applied Animal Welfare and Policy, a section of the journal resource-based instead of animal-based measures while assessing good health and Frontiers in Animal Science appropriate behavior. The utilization of data generated along the value chain by the quality Received: 11 February 2022 schemes remains insignificant as only one quality scheme allowed the direct application Accepted: 07 April 2022 Published: 06 May 2022 of sensor technologies for providing information on animal welfare. Nevertheless, several Citation: schemes used data from farm recording systems, mostly on animal health. The quality Stygar AH, Krampe C, Llonch P and schemes rely mostly on resource-based indicators taken during inspection visits, which Niemi JK (2022) How Far Are We reduce the relevance of the welfare assessment. Our results suggest that the quality From Data-Driven and Animal-Based Welfare Assessment? A Critical schemes could be enhanced in terms of data collection by the broader utilization of data Analysis of European Quality generated along the value chain. Schemes. Front. Anim. Sci. 3:874260. doi: 10.3389/fanim.2022.874260 Keywords: cow, pig, labeling, PLF (precision livestock farming), animal-based measure Frontiers in Animal Science | www.frontiersin.org 1 May 2022 | Volume 3 | Article 874260 Stygar et al. Analysis of Welfare Quality Schemes INTRODUCTION provide a snapshot of welfare, are time-consuming and costly, and may increase biosecurity risks on farms (Sørensen et al., The European Union (EU) regulatory framework on animal 2007; Andreasen et al., 2014; Alarcón et al., 2021). One way welfare reflects the five freedoms and covers farming (Council to reduce the need for in-person assessment visits would be to of the European Union, 1998, 2008a,b), transport (Council of exploit the data continuously collected on farms or by processors, the European Union, 2005), and slaughter of animals (Council through Precision Livestock Farming (PLF) tools or management of the European Union, 2009). Yet, the EU does not regulate software. Recent literature reviews and market analyses indicate the market-based labeling of animal welfare-related product that sensor technologies have a high potential in assessing animal attributes explicitly, except to some extent for the organic and welfare at the level of an individual animal (Buller et al., 2020; eggs production, as well as poultry meat. Therefore, only the Gómez et al., 2021; Stygar et al., 2021). Routinely collected generic EU regulations on product labeling apply to animal on-farm data have already been proven to be a useful source welfare claims made on the markets (van Wagenberg et al., 2012). of welfare information in the dairy (e.g., Nyman et al., 2011; However, the EU has initiated studies and a working group in de Vries et al., 2014; Lutz et al., 2021) and pig (e.g., Stygar the recently created Animal Welfare Platform of the European et al., 2020) sectors. Several studies suggest that recordings Commission to examine the options for creating an EU-wide from slaughterhouses may provide useful information on animal animal welfare labeling scheme. wellbeing on farms (Vial, 2019). Recent years have seen the emergence of new policy initiatives Recently, the European Parliamentary Research Service for dairy and pig welfare (European Parliament, 2021). These (European Parliament, 2021) reported and compared animal include voluntary certification systems and private standards, welfare quality schemes across Europe. However, the utilization hereafter called quality schemes, often introduced by non- of routinely collected data along the value chain (e.g., farmers, governmental bodies such as food industry stakeholders or processors) by different quality schemes was beyond the scope farmers’ associations. The various quality schemes aim to of the report. The same document pointed out the need build trust between stakeholders (e.g., between producers and for a more targeted comparison of quality schemes regarding their customers and, finally, end-consumers) by providing their use of established animal welfare standards. In scientific information on legal compliance and product differentiation literature, Welfare Quality R (WQ R ) has been considered to concerning animal welfare-related attributes for meat and milk. be the most impactful assessment protocol in promoting the This goal might be achieved using different criteria (standards adoption of animal-based measures (Brscic et al., 2021). Despite for certification). some limitations of the WQ R , such as being time-consuming The methods used to assess animal welfare on the farm may and lacking the clear aggregation of all indicators, it offers vary in terms of the measures used (based on the animals, many advantages. In addition to the incorporation of animal- their handling, or their environment) and scoring systems (Main based measures, the advantages of WQ R are that it has been et al., 2003). Even though there is no one-size-fits-all solution for scientifically validated (Knierim and Winckler, 2009; Temple assessing animal welfare on farms (Main et al., 2003), various et al., 2011) and that it can be used for multiple farm animal reports advocate for the increased use of animal- rather than species including pigs and dairy cattle. Also, WQ R is possibly resource- and management-based measures, as direct indicators the most scrutinized welfare assessment protocol (Czycholl et al., of animal welfare (Welfare Quality R , 2009a; Panel EFSA on 2018; Tuyttens et al., 2021) and has been widely used as a Animal Health Welfare, 2012; European Food Safety Authority, reference protocol in scientific literature. However, the extent to 2015). The shift toward animal-based welfare assessment has also which various quality schemes utilize the animal-, management- been criticized for its impracticality, mainly due to the long time and resource-based measures defined by WQ R protocols has spent in conducting assessments and the challenges in delivering been seldom discussed (Heinola et al., 2021). Moreover, despite feedback to the farmers (Roe et al., 2011; European Food Safety the cost of collecting data that verifies animal welfare for a quality Authority, 2015). Despite the above-mentioned difficulties in scheme (Niemi et al., 2021), studies have paid little attention to incorporating animal-based measures for assessment protocols, the potential of using sensors and existing data recording systems several quality schemes introduced such measures in the to verify animal welfare in quality schemes. assessment procedures as they are considered the most valid The aim of this study was to compare the requirements set by indicators for welfare assessment (Lundmark et al., 2016; Lin animal welfare quality schemes with the WQ R scientific protocol et al., 2018; European Parliament, 2021). and to quantify how industry-wide quality schemes (1) include Data-driven welfare assessment refers to the practice of animal-based indicators to assess the welfare of pigs and dairy basing welfare evaluation, whenever possible, on different data cattle on farms and (2) utilize data generated along the value sources, namely sensors and breeding, production as well as chain, especially on farms or at processing plants. The study health recordings, collected on farms or by processors. It may objective was achieved by conducting a descriptive analysis of have benefits compared to the more traditional approaches quantitative data gathered from certification standards. Hence, to welfare assessment. In-person farm visits (e.g., inspections this study contributes to the literature on animal welfare to check compliance with audit schemes) have been used for assessment by describing the methods to assess animal welfare many years (e.g., Blokhuis et al., 2010). However, they only in the current quality schemes. Frontiers in Animal Science | www.frontiersin.org 2 May 2022 | Volume 3 | Article 874260 Stygar et al. Analysis of Welfare Quality Schemes MATERIALS AND METHODS 68% of all measures are animal-based. For the pig WQ R (the combined measures for fattening pigs, sows, and piglets), the Criteria for Selecting the Quality Schemes share of animal-based indicators is even higher, reaching 76%. In this study, we have focused on industry-wide welfare For both pig and dairy protocols, good feeding is mostly based on quality schemes concerning pig and dairy production resources or management measures. Good health, housing and in EU countries. Industry-wide, in the context of appropriate behavior, by contrast, are largely based on animal- this research, refers to systems that farmers can join based measures. For some measures (e.g., mortality rate), WQ R voluntarily, irrespective of the processor they are allows the use of farm records. affiliated to. In this study, the WQ R was used as a reference to compare Quality assessment schemes that pay attention to animal how the reviewed assessment schemes address different aspects welfare, but may not be limited to welfare only, were identified of dairy and pig welfare on farms. An Excel file with the through desk research. Desk research was conducted between list of welfare principles, criteria, and measures for growing January 2020 and May 2020, using the Google search engine pigs, sows, piglets, and dairy cows assessed on farms or at with the following search criteria: (dairy OR pork) (labeling slaughter (when used to monitor the welfare on the farms) scheme OR quality scheme) (animal welfare). The criteria were was prepared (Supplementary Tables 2, 3 for dairy and pig translated, and the search was performed in English, Spanish, schemes, respectively). For each quality scheme, the standards Italian, Dutch, Danish, Polish, Finnish, and German languages. for certification were identified (Table 1). In case the English The search languages were selected based on the language version of a standard was not available, the Google Translate tool coverage within the ClearFarm consortium. The translated was used for translations. The standards were scanned in search terms are presented in Supplementary Table 1. The first five of measures evaluating animal welfare. The measures identified pages of the results from the Google search were scanned. were categorized according to their type (animal-, resource-, or The list of quality schemes identified was further checked and, management-based) and assigned to the appropriate principle, where needed, supplemented based on the recently released criteria, and measures defined in WQ R . Measures that could report from the European Parliamentary Research Service not be assigned to any of the measures defined in WQ R (European Parliament, 2021) and other published scientific were omitted. reports (Sørensen and Schrader, 2019; Heinola et al., 2021). The correspondence of the welfare measures of the schemes to One of the quality schemes identified did not have publicly the WQ was calculated (as a percentage). Measures with the same available standards for certification and was therefore excluded source of information (animal, their handling/management, or from the analyses. their environment) as defined in WQ R were assigned direct Altogether 12 quality schemes from eight European countries correspondence. For example, direct correspondence was fulfilled the selection criteria. The schemes originated from assigned when a quality scheme was used, similarly to WQ R , Finland (Laatuvastuu), Sweden (KRAV), Denmark (Bedre an animal-based measure to indicate prolonged hunger or a Dyrevelferd, Anbefalet af Dyrenes Beskyttelse), Ireland (Origin resource-based measure to indicate prolonged thirst. Direct Green), the Netherlands (Beter Leven, Meadow Milk), Germany correspondence was assigned irrespective of the method of the (Initiative für mehr Tierwohl, Für Mehr Tierschutz), Austria measurement applied. For instance, even though the evaluation (AMA-Gütesiegel), and Spain (Welfair, IAWS) were analyzed. of the absence of prolonged hunger was based on a different scale for body condition scoring than the measure defined by WQ R , it Assessment of Similarity Between WQ® was classified as direct correspondence. Indirect correspondence and Standards Used by Quality Schemes was assigned to the measures that used a different source of The Welfare Quality R protocol (WQ R ) is a scientifically information than those defined in WQ R (e.g., environment rigorous animal welfare assessment protocol for cattle (Welfare instead of animal). An example of indirect correspondence is that Quality R , 2009a) and pigs (Welfare Quality R , 2009b) developed a quality scheme used a resource-based measure for evaluating by an EU-funded research project (Welfare Quality project, feed quality or the access to roughage to assess the absence Grant Agreement 506508). Four animal welfare principles (good of prolonged hunger. Hence, in the context of this study, the housing, good feeding, good health, and appropriate behavior) “correspondence” means that the quality scheme used similar, are further divided into 12 criteria (1. Absence of prolonged but not necessarily identical, measures to assess animal welfare as hunger, 2. Absence of prolonged thirst, 3. Comfort around WQ R . Finally, the shares of direct and indirect correspondence resting, 4. Thermal comfort, 5. Ease of movement, 6. Absence of for animal-, resource-, and management-based types injuries, 7. Absence of disease, 8. Absence of pain by management were calculated. procedures, 9. Expression of social behaviors, 10. Expression Because most schemes were initiated and operated by of other behaviors, 11. Good human-animal relationship, and the private sector (European Parliament, 2021), to avoid 12. Positive emotional state). Regarding the specific types of using our results for commercial purposes, we decided measures, WQ R bases its assessment on indicators that can be to present them in an anonymized format. Standards for observed on animals predominantly, however, in cases where certifications defined by quality schemes were assigned to animal-based indicators are not sensitive or reliable enough, a number from 1 to 19. Data on the correspondence of resource and/or management-based measures have also been the assigned numbers with the quality schemes are available defined (Welfare Quality R , 2009a). In the dairy WQ R , around upon request. Frontiers in Animal Science | www.frontiersin.org 3 May 2022 | Volume 3 | Article 874260 Stygar et al. Analysis of Welfare Quality Schemes The Use of Different Data Sources for TABLE 1 | Name, origin, additional features, and source of information concerning certification standards for analyzed quality schemes. Animal Welfare Assessment The documents describing the criteria for certifications were used Name of Country of Additional features, Source for certification to identify different data sources currently used in animal welfare quality origin besides animal standard assessment by quality schemes. In our analysis, we looked for scheme welfare, covered by quality schemes sensors and breeding, production as well as health recordings, collected on farms or by processors. The following sources of data AMA- Austria Origin (Alpine milk), Dairy (Agrarmarkt Austria were considered: sensors that provide animal-based (e.g., milking Gütesiegel feeding quality Marketing GesmbH, 2020) robots), resource-based, or management-based (e.g., ambient (GMO-free) and pigs (Agrarmarkt Austria monitoring sensors) information on farms, breeding data (e.g., Marketing GesmbH, 2018) test milking), self-reporting registries (e.g., mortality recordings, Anbefalet af Denmark Sustainability Dairy (Dyrenes Beskyttelse, Dyrenes 2021a) and pigs (Dyrenes veterinary treatments) welfare self-assessment apps, pre- and Beskyttelse Beskyttelse, 2021b) post-mortem inspections at slaughter, as well as data from dairy Bedre Denmark None Dairy and pigs (Miljø- og companies on milk quality. For the data source, the inclusion Dyrevelferd Fødevareministeriet, 2019) criterion was that the information obtained from a particular Laatuvastuu Finland Food safety and animal Pigs (Sikava, 2020) data source had to be used for actual evaluation (a threshold health was defined). For example, in the case of official registrations of Fur Mehr Germany None Dairy (Fur Mehr Tierschutz, health care and veterinary treatments, the scheme should provide Tierschutz 2021a) and pigs (Fur Mehr a method for measuring compliance (e.g., numbers of treatment Tierschutz, 2021b) registrations, and the use of antibiotics below a specified target). Mehr Tierwohl Germany None Pigs (Mehr Tierwohl, 2021) The search also involved data sources that are not yet used Origin Green Ireland Environmental Dairy (Bord Bia, 2013) for evaluation but are already included in the standards and sustainability, origin planned to be incorporated in evaluation with a known date Beter Leven Netherlands None Dairy (Beter Leven, 2021) Pigs (Beter Leven, 2018) of applicability. Meadow Milk Netherlands None Dairy (The Grazing Foundation, 2020) AWIS Spain None Pigs (Interporc Spain, 2020) RESULTS Welfair Spain None Dairy (Welfare Quality® , 2009a) and pigs (Welfare Quality® , 2009b) Among the 12 quality schemes analyzed, seven included KRAV Sweden Sustainability and Dairy and pigs (The KRAV protocols for both pig and dairy production, while the organic Association, 2021) remaining five were limited to either pigs or dairy. In total, we have identified 19 standards for certification, nine for dairy and 10 for pig production. One of the quality schemes analyzed had adopted WQ R for both dairy and pig production Figure 2 illustrates the direct correspondence to WQ R for while others had developed their criteria for evaluation. The three types of measures (animal-, resource-, and management- evaluation criteria, with relevance to WQ R , for each certification based) in the dairy and pig standards studied. The results standard, are presented in Supplementary Table 2 for dairy and indicate that only three dairy and two pig standards had an Supplementary Table 3 for pigs. elevated number of animal-based measures (equal to or above 50% in agreement with the animal-based measures defined in WQ R ). Contrary to the animal-based measures, resource and Quality Schemes vs. WQ® management practices were well-represented across different The compliance of the quality schemes analyzed to the standards standards. At least 50% agreement with both resource- and for certification in WQ R is presented in Figure 1. Of the 19 management-based measures defined in WQ R was found in 14 standards analyzed, only six directly complied with WQ R in out of the 19 standards analyzed. more than 50% of the measures (three dairy protocols and Regarding the adherence to welfare principles, 18 out of 19 three pig protocols). However, the majority of the schemes standards used at least one measure to assess good feeding analyzed indirectly address the welfare principles included in the and health. All (19/19) of the standards studied used at least WQ R protocol by using a different source of information for a one measure to assess good housing and appropriate behavior particular measure (e.g., space requirements for comfort around (Table 2). According to our analyses, good housing, health, and resting). The indirect correspondence was based on replacing feeding had good coverage in the schemes analyzed. For good information collected from animals with data gathered from their housing, in total, 18 out of 19 schemes had at least half of the handling/management or their environment. When considering measures indirectly or directly compliant with WQ R . For good both direct and indirect correspondence, most schemes were health and feeding, the number of standards agreeing with a comprehensive in animal welfare assessment. As an indicator of minimum of 50% of the measures defined by WQ R reached 16 this correspondence, 15 out of 19 standards agreed with WQ R in and 13, respectively. The lowest correspondence was obtained more than 70%. for appropriate behavior, with only eight (out of 19) standards Frontiers in Animal Science | www.frontiersin.org 4 May 2022 | Volume 3 | Article 874260 Stygar et al. Analysis of Welfare Quality Schemes FIGURE 1 | Similarity to WQ® expressed as direct and indirect correspondence in dairy (A) and pig (B) standards for certification. The direct correspondence was assigned to measures with the same source of information (animal, handling, or environment) as defined in WQ® . On the contrary, the indirect correspondence was assigned to those measures with different sources of information (e.g., environment instead of animal) as defined in WQ® . Standards 9 and 19 were based fully on WQ® . Standards listed in Table 1 were assigned to numbers 1–19. FIGURE 2 | The direct correspondence to WQ® according to the type (animal-, resource-, management-based) in dairy (A) and pigs (B) standards for certification. The direct correspondence was assigned to measures with the same source of information (animal, their handling, or their environment) as defined in WQ® . Standards 9 and 19 correspond to one certification scheme that was based fully on WQ® and represent the distribution of animal-, resource- and management-based measures in WQ® for dairy cows and pigs. Standards listed in Table 1 were assigned to numbers 1–19. agreeing directly or indirectly with at least 50% of the measures pig schemes, thermal comfort and comfort around resting defined in WQ R (Table 2). were mostly assessed using resources (type of bedding material, Regarding the adherence to the good feeding principle, ventilation, and climate check). nine out of the 19 standards studied implemented animal- The lowest share of animal-based measures was observed based measures (i.e., body condition) to evaluate the absence of for good health and appropriate behavior. According to our prolonged hunger. The schemes relying on the resources to assess analyses, only five and two standards applied at least 50% of good feeding mostly verified access to feed (e.g., roughage), feed animal-based measures as defined in WQ R for the evaluation of quality, or the condition of feeding equipment. good health and appropriate behavior, respectively. Good health For good housing, the number of standards applying at assessment, in most assessment protocols, was based on a few least 50% of animal-based measures, as defined in WQ R , was selected animal-based measures (e.g., milk somatic cell count, slightly lower than for the good feeding principle (seven out and tail biting) and supplemented with different resource-based of 19 standards). Dairy quality schemes that had developed measures (e.g., health plans designed together with veterinarians, their assessment protocols included resource-based (space antimicrobial monitoring). Regarding appropriate behavior, requirements, type of bedding) instead of animal-based measures the schemes studied mentioned social behavior (negative and (e.g., the time needed to lie down) to describe the potential positive), exploratory behavior, and fear of humans evaluated on issues with space and stocking density in dairy cattle. Regarding an animal level as part of the welfare evaluation. Frontiers in Animal Science | www.frontiersin.org 5 May 2022 | Volume 3 | Article 874260 Stygar et al. Analysis of Welfare Quality Schemes TABLE 2 | The coverage of welfare principles (good feeding, good housing, good health, and appropriate behavior) by analyzed schemes. Welfare principle Proportion of standards with at least Proportion of standards characterized Proportion of standards applying at one measure of any type (animal-, by at least half of measures compliant least half of animal-based measures resource-, and management-based) with WQ® for a given principle as defined in WQ® for a given principle complianta for a given principle Dairy Pig Dairy Pig Good feeding 18/19 6/9 7/10 4/9 5/10 Good housing 19/19 8/9 10/10 4/9 3/10 Good health 18/19 7/9 9/10 3/9 2/10 Appropriate behavior 19/19 6/9 2/10 1/9 1/10 a Includingboth direct and indirect correspondence. The direct correspondence was assigned to measures with the same source of information (animal, handling or environment) as defined in welfare quality protocol (WQ® ). Contrarily, the indirect correspondence was assigned to those measures with different source of information (e.g., environment instead of animal) as defined in WQ® . The Use of Welfare-Relevant Data pig welfare evaluation used information from meat inspections Generated on Farm or During Processing at the slaughterhouse. For health records, four standards for Regarding the data sources, the direct application of sensor certification (one dairy and three pigs) used treatment records technologies and meters (for the animal-, resource- or to verify compliance with the target value (e.g., number management-based monitoring) was identified in one of of treatments). the assessment protocols. The protocol allowed the application of accelerometer-based devices of three companies (DeLaval, Lely, and GEA) to track cow grazing time. However, we were DISCUSSION unable to identify any further direct application of sensor This study considered the question: How far are we from a technologies systematically generating data for the quality data-driven and animal-based welfare assessment? To answer schemes we have studied. this question, we identified and analyzed 12 industry-wide Nevertheless, dairy quality schemes could indirectly utilize quality schemes, seven assessing welfare for both dairy and pig data generated through automatic milking systems (AMS), production, three focusing on pig, and two on dairy production. processed by official milk recording schemes. The example of the In total, 19 standards for certification were examined, and the AMS application in official milk records includes an estimation requirements set in each standard were compared with the of daily milk yields (ICAR, 2017). Several quality schemes animal-, management- and resource-based measures defined by mentioned the possibility to obtain milk production data from a scientifically rigorous welfare assessment protocol (WQ R ). institutions carrying out official milk recordings, such as Swedish Information on the utilization of data generated along the value Kokontrollen R , Irish Cattle Breeding Federation, or LKV chain (e.g., sensor data, farm registration, and slaughterhouse Austria, but the degree to which the quality schemes utilized the data) was systematically collected. The quality schemes we AMS-generated data for welfare assessment remains unknown. studied showed variability in the animal welfare requirements The quality schemes used the breeding, production, and they evaluated. Most of the quality schemes analyzed in this study health recordings generated within the value chain to varying were comprehensive in animal welfare assessment, covering all degrees. In total, 13 standards allowed for the gathering of data on welfare principles: good feeding, good housing, good health, at least one welfare measure from breeding, production, or health and appropriate behavior. However, we also found a few quality recordings. Regarding dairy standards, data from breeding, schemes that considered selected measures (e.g., pasture time, production, or health monitoring collected by third parties intact tails, and space allowance) rather than employing a (the Dutch KoeMonitor,1 the Swedish Signaler Djurvälfärd,2 comprehensive welfare assessment protocol. Irish Cattle Breeding Federation, and dairy health improvement To date, several reports and scientific publications have associations LKV Austria) were listed as sources of relevant analyzed the coverage (European Food Safety Authority, 2015; key performance parameters (mortality rates, milk production Sørensen and Schrader, 2019; European Parliament, 2021; data, and/or prevalence of health problems such as mastitis or Heinola et al., 2021), comparison to legislative requirements ketosis). Regarding pig welfare assessment, the identified data (Annen et al., 2011; Vogeler, 2019; Heinola et al., 2021), and sources originated from farm management systems (mortality transparency (More et al., 2021) of animal welfare quality rates), health recordings (antimicrobial monitoring database), schemes. However, to the authors’ knowledge, none of these and slaughter meat inspection records (e.g., pneumonia or studies has used WQ R as a reference for the analysis of welfare lesions on the body). A total of nine out of 10 standards for standards. The Welfare Quality protocol was developed by experts on animal welfare throughout Europe and elsewhere, 1 https://www.koemonitor.nl/en/. and it is considered a valid and comprehensive protocol for 2 https://www.vxa.se/radgivning-och-kurser/analysera-nulaget/analysera- assessing animal welfare that is also independent of any market djurhalsan/signaler-djurvalfard/. organization. The comparison of our results with the previously Frontiers in Animal Science | www.frontiersin.org 6 May 2022 | Volume 3 | Article 874260 Stygar et al. Analysis of Welfare Quality Schemes published analyses might be altered by year-to-year changes in more holistic evaluation of the state of welfare (other schemes). the requirements set by the schemes, different inclusion criteria The holistic evaluation covering all four welfare principles (good for the schemes as well as the emergence of new initiatives feeding, good housing, good health, and appropriate behavior), and quality schemes. Therefore, the discussion of our results was, however, obtained by using many different types of measures will be limited to the current coverage of welfare issues by the that were mostly resource-based. An obvious consequence of this different quality schemes, their strategies for data utilization, diversification at the EU level is the lack of clarity for consumers and the potential benefits of a data-driven animal-based welfare to understand which labeled products have the highest welfare assessment that would be relevant to consumers, farmers, as well standard and/or better reveal the welfare of farm animals. as for farm animals. In this study, we have analyzed publicly available standards of certification. However, measurements used during audits Welfare Coverage of the Quality Schemes are generally more complex than indicated in the regulation The starting point for our analyses was that animal welfare paragraphs (Lundmark et al., 2016). Therefore, the results is measurable and that there is an established protocol that obtained concerning welfare coverage should be confirmed provides an approximate value for the welfare of dairy cattle in future studies using more precise information from and pigs. WQ R , at the time of being developed, was primarily assessment protocols. considered a standardized research protocol, however, it was also envisioned as a potential tool for assessing compliance with welfare legislation, EU standards for welfare labeling as well as The New Data Era—The Current and a farm management tool (Blokhuis et al., 2010). More than 10 Potential Utilization years after being published (Blokhuis et al., 2010), WQ R certainly According to our results, quality schemes are, to a great extent, has had an impact on farm animal welfare assessment, with assessing animal welfare by following environmental factors increased numbers of animal-based measures being referred to in (inputs, such as a type of bedding) rather than an animal- the scientific literature (Brscic et al., 2021). Based on our results, based response to those factors (outcomes, such as lameness or WQ R has also influenced the assessment protocols of several cleanliness). This result is expected when taking into account quality schemes, with one quality scheme fully implementing that quality schemes are assessing welfare by performing regular the WQ R protocol for welfare assessment. However, WQ R or (mostly annual) farm visits (Heinola et al., 2021). In-person any other inventory of measures, has so far not become an evaluations favor resource- or management-based measures official welfare quality assurance system at the EU level. There because they are expected to require a shorter time for farm could be several reasons for the only partial success of WQ R visits, which reduces the cost of the assessment. Furthermore, a as a welfare labeling initiative. Probably the most notorious resource-based evaluation is less prone to bias and interference is the lack of transparency related to scoring aggregation compared to an animal-based assessment because resources algorithms and the high workload associated with the assessment can often be measured without the challenges associated with (Tuyttens et al., 2021). the interpretation of animal welfare. Contrarily, collecting data Considering both direct and indirect correspondence, 15 out on animal-based measures by evaluators remains challenging. of 19 standards were in line with WQ R in more than 70% of the For example, the experience of the assessor might influence measures. Therefore, the standards used by most of the quality an animal-based evaluation and affect the robustness of the schemes build quite a comprehensive picture of animal welfare, assessment (Bokkers et al., 2012; Czycholl et al., 2017). Also, the like WQ R , providing information both on compliance with legal time required to observe a sufficient number of animals in person requirements and on consumer-desired welfare attributes which can be substantial (Knierim and Winckler, 2009), however, facilitate product differentiation. To provide information on usually in a limited observation window (a certain part of a compliance with the minimum legal requirements, 18 out of 19 day), which may affect scoring (Gutmann et al., 2015). Solutions standards used measures to assess the absence of prolonged thirst for increasing the use of animal-based measures and facilitating and hunger also required by the Council Directive 98/58/EC, real-time welfare assessment would replace subjective in-person which sets the welfare standards of all farmed animal species in evaluations, potentially resulting in more objective data-based the EU (Council of the European Union, 1998). Furthermore, all evaluations. Farms and processors are already collecting a vast the pig welfare protocols analyzed, by assessing either the animals amount of data in an automated manner, for example, data on or their environment, evaluated the provision of enrichment milk yield and composition, including indicators of subclinical material for pigs required by the EU Council Directive mastitis, is collected in a centralized manner from 30 to 93% of 2008/120/EC (Council of the European Union, 2008a) and the cows in selected central European countries (Bucek et al., Commission Recommendation (EU) 2016/336 of 8 March 2016 2014), and ∼70–97% of the cows in the Nordic countries (The European Commision, 2016), both regulating pigs’ welfare. (Wolff et al., 2012). Also, the EU legislation imposes on all Regarding the compliance with animal welfare characteristics suppliers an obligation of bulk tank somatic cell count testing that exceed the current minimum legal requirements and are for raw milk (More et al., 2013), as well as meat inspection desired by consumers (e.g., outdoor access), the quality schemes before and after slaughter for animals sent to the slaughterhouse varied in the number of measures they evaluated. The tendencies (Alban et al., 2011). The adoption of on-farm sensor systems in product differentiation are noticeable, shifting from the for monitoring animal health, fertility, and productivity may specific aspects of welfare (standards 1, 5, and 15) toward a also provide relevant information to assess animal welfare. For Frontiers in Animal Science | www.frontiersin.org 7 May 2022 | Volume 3 | Article 874260 Stygar et al. Analysis of Welfare Quality Schemes instance, in Italy and the Netherlands, over 40% of medium or Implications of Data-Driven Animal large dairy farms (>50 cows) use sensor technology (Steeneveld Assessment for Consumers and Hogeveen, 2015; Lora et al., 2020). In the Nordic countries, Earlier, consumer study results indicated that consumers have this percentage is even higher, reaching ∼70% of larger farms high expectations that the integration of PLF technology in herd in Finland (Utriainen et al., 2019). The possibility to utilize the management would lead to improvements in the health and above-mentioned and other readily collected data would benefit welfare of farm animals and the environment (Krampe et al., quality schemes because it could reduce the workload and costs of 2021). Nevertheless, consumers also fear that the integration of verification of conformity and increase the transparency of labels. technology-driven approaches will lead to more industrialization, The increasing digitalization of the livestock sector offers digitalization, and robotization, which is in contradiction to promising developments for information policy, such as product the pre-existing images they have of livestock farming. Since labeling, which has led to proposals basing the livestock consumers tend to prefer natural approaches to managing animal certification system on real-time measurements and considering production (Clark et al., 2019), value-based production concepts animal behavior as a criterion for quality labeling (Council on using state-of-the-art technology may be the less preferred Animal Affairs, 2019). However, our analyses of the existing options for obtaining animal welfare information. Consumers quality schemes indicated that the utilization of data generated also believe that data systems are vulnerable to misuse, which along the value chain remains insignificant and that industry- have an impact on the trust consumers associate with assessment wide schemes are still far from providing real-time and animal- schemes and consequently the labeled products. These aspects based welfare assessments. We were unable to identify schemes must be acknowledged when developing data-driven approaches that systematically integrate data from multiple sources (from to animal welfare assessment. sensors and breeding, production as well as health recordings). As indicated before, resource-based measures are an easy Reasons for such an underutilization of data are related to, among solution for labeling, but less informative of the real welfare others, big data characteristics (volume, velocity, variety, and of farm animals. Animal-based assessment is preferable as it veracity), issues related to data ownership, and compliance with is more reliable but would be more challenging to convert data protection regulations (Bahlo et al., 2019; Klerkx et al., into something marketable to be presented on the package, 2019; Osinga et al., 2022). Other reasons for the deficiencies in leaflet, or an innovative mobile application. Therefore, the the utilization of the existing data include the unavailability of introduction of data-driven, animal-based welfare assessment welfare classification algorithms based on automatically-collected requires new business models and intelligent communication data. The recent market reviews of PLF technologies indicated solutions, where the consumer can inter alia “visit” farms in a that farmers have access to various sensors tracking dairy and digital context, while only receiving individualized information pig behavior and productivity (Gómez et al., 2021; Stygar et al., on the desired features generated from different data sources. 2021). However, none of the solutions currently available on Using sensor and registry data, consumers could be informed the market provide an overall welfare assessment. Moreover, the on, for example, how many hours animals were outside, how currently available PLF technologies have a low potential for they were interacting with each other (playing, suckling, etc.) assessing appropriate behavior (Stygar et al., 2021). Therefore, and their current health status. Data-driven solutions for animal replacing manually collected resource-based measures, such as welfare might also play an important role in increasing food the availability of enrichment material with automatic animal- production transparency, which is a major concern of quality based evaluation provided with sensor technologies seems assurance programs (More et al., 2021). The harmonization difficult in commercial farm settings. While several models for of consumer needs and technological possibilities, therefore, detecting health issues with sensor data exist, their predictive play a pivotal role in the integration or development of performance remains a challenge (Steensels et al., 2017). Recent labeling strategies. The demands of consumers and other supply research has nevertheless shown the potential of sensor data to chain actors should be at the core of any (re)developmental estimate complex traits and predict the resilience rank of cows process of communication policies that are built on state-of- on a farm accurately (Adriaens et al., 2021). the-art technologies. Since the consumers’ level of information Previous studies indicated that business models using farm is determined to a large degree by specific product labeling advisers to interpret the information may enhance the value at the retail level (Faucitano et al., 2017), there should be of information from PLF (Rojo-Gimeno et al., 2019). A few more industrial or institutional leadership to introduce PLF examples of data use for welfare assessment identified in as the source of data and subsequently information. Finally, this study seem to indicate that farm advisers might play a the utilization of data-driven assessment schemes might help special role in strengthening the utilization of data for welfare companies, governments, and non-governmental organizations assessment. It seems that the key element for data integration to define objective standards that can be used to inform for animal welfare assessment is the cooperation between actors consumers in a harmonized way. holding the data (e.g., farmers associations, farm advisers, and technology providers) and supply chain stakeholders such as private labeling initiatives. Farm adviser organizations could Implications of Data-Driven Animal-Based contribute to distributing the benefits from the use of digital data Assessment for Farmers and Animals (Ayre et al., 2019) between consumers, farmers, and ultimately Farms with major animal welfare problems will also lag in farm animals. terms of production performance (Frondelius et al., 2020). Frontiers in Animal Science | www.frontiersin.org 8 May 2022 | Volume 3 | Article 874260 Stygar et al. Analysis of Welfare Quality Schemes Therefore, farmers have an economic interest in maintaining our approach could serve as a starting point for future high standards for animal welfare. So far, dedicated tools analyses to identify recurring trends in quality schemes in for automatically-collected data on cow behavior and milk the EU. As the range of measures shows, striving for the production have been successfully used in providing farmers development of improved animal welfare protocols requires with precise and real-time information on how small changes multidisciplinary approaches, the involvement of different in herd management, for instance, on feeding (Stygar et al., stakeholders, and also the consideration of (national and EU- 2017) and housing practices (Czubernat et al., 2020), influence wide) animal welfare legislation, which is anchored in the the production results and consequently economic profit. respective policies of the member states. To identify improved Therefore, data-driven animal-based welfare assessment can solutions for welfare assessment in quality schemes, welfare facilitate improvements and allow farmers to adjust their protocols should be analyzed by considering the perspectives strategies according to animal productivity. The utilization of of multiple stakeholders: the farm animals’, business actors’, and animal-based measures and continuously recorded digital data consumers’. Even though animals cannot provide their subjective could refine welfare evaluation so that herd management can evaluation of wellbeing, their coping abilities in challenging be improved. This may represent a significant change in welfare conditions can be assessed by measuring the endocrine response assessment for certification purposes, as continuous monitoring (Tilbrook and Ralph, 2018) or changes in behavior (Llonch et al., will allow certifying of products based on their day-to-day 2020). Therefore, future efforts should focus on studying the performance, instead of single visits once a year. However, even correlation between the biological and behavioral responses to though the PLF assessment brings important benefits for the breeding conditions (expressed as different levels of hormones transparency and refinement of animal farming, some voices or activity) with measures that are defined in welfare assessment have raised concerns that the application of PLF for farm protocols, and integrate those that are more sensitive to animal management may lead to farmer deskilling and loss of autonomy welfare, so it can be better monitored. Moreover, since welfare (Cornou, 2009; Schillings et al., 2021), whereas, as mentioned, protocols should also provide information that is aligned with consumers are worried about robotization concerning animal consumer preferences (confirming or not that the product care (Krampe et al., 2021). These concerns are, to some degree, has desirable qualities), future research should consider the related to the novelty of digitalization in animal farming, so comparison of farm rankings obtained based on objective they might disappear in the future, but they should not be welfare assessment protocols and subjective consumer/welfare ignored. They can be addressed by informing stakeholders expert rankings. (such as producers and consumers) about the benefits (and the risks) of using new technologies on the farms. For instance, Schillings et al. (2021) list potential solutions for the ethical implications of PLF utilization, namely developing technologies CONCLUSION in consultations with multiple stakeholders and advisory support for farmers. How far are we from data-driven and animal-based welfare Animal welfare can be improved by market initiatives, assessment? The results of our analysis suggest that there however, the improvement requires a range of favorable is still a long way to systematically integrate data-driven conditions, such as an appealing narrative, tying up animal animal-based welfare assessments across the EU member welfare with attributes related to human health, good states in a harmonized manner. Currently, only 26% of the product availability, labeling, and a low-price premium assessment protocols use animal-based measures as the main (Heerwagen et al., 2013). To strengthen the application source of information about welfare. Most protocols still rely of data-driven welfare assessment in the markets, we on environmental (housing conditions) or animal handling need to be more knowledgeable about the socioeconomic (management procedures) measures to provide consumers implications of technology use in animal production. For with information on animal welfare. The practice of basing instance, on how the application of sensor technologies welfare evaluation on different data sources originating could affect the economic results and animal welfare from sensors and breeding, production as well as health on a farm, or how farmers/consumers can utilize data recordings, is not widely adopted. Only one out of 19 from different sources in making informative decisions in standards included the option of using data from sensor herd management/purchase. technologies. Even though 13 analyzed standards allowed obtaining information on welfare from breeding, production, Perspectives or health records generated within the value chain, data- Animal welfare could be evaluated using different methods driven information was limited to a few selected measures. (Czycholl et al., 2015), and the selection of a suitable Multidisciplinary research can help to implement innovative protocol/method depends on the purpose for which it is used technologies and algorithms for welfare assessment. More [e.g., checking compliance with legislation, farm management efficient communication regarding animal welfare could be tools (Berg and Hedman, 2020)]. Our intention was not achieved by involving the main stakeholders (farmers, business to study the question: “Which certification standard is best actors, and consumers) in the development of new animal suited for the welfare evaluation of farm animals?” but welfare labeling schemes. Frontiers in Animal Science | www.frontiersin.org 9 May 2022 | Volume 3 | Article 874260 Stygar et al. Analysis of Welfare Quality Schemes DATA AVAILABILITY STATEMENT FUNDING The original contributions presented in the study are included This study was conducted within the ClearFarm Project aiming in the article/Supplementary Material, further inquiries can be to co-design, develop, and validate a software platform powered directed to the corresponding author. by PLF Technologies to provide animal welfare information. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement AUTHOR CONTRIBUTIONS no. 862919. AS and JN developed the study concept. AS, CK, PL, and SUPPLEMENTARY MATERIAL JN conducted quality schemes search. AS was responsible for assigning welfare criteria from analyzed schemes and wrote the The Supplementary Material for this article can be found paper with inputs from CK, PL, and JN. 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