Mastitis is one of the most common diseases of high-producing dairy cows with significant economic and animal welfare implications.

Mastitis is a multifactor disease and results from the inflammation of the mammary gland. The severity of the inflammation can be classified into either sub-clinical or clinical mastitis. In the clinical form, milk alterations, mammary gland modifications and changes in animal status are easily visible. but no visible symptoms are observed in subclinical mastitis.

The actual ability to identify early stages of mastitis or predict the likelihood of mastitis in healthy herds is limited, and it can be expensive.

Cows with clinical mastitis may show sick behaviours and/or changes in their behavioural patterns (i.e. changes in feed and water intake, and feeding, ruminating and lying time). Identification of these behavioural changes at the onset of mastitis can be useful as an early detection system.

Precision livestock farming (PLF) (e.g. accelerometers’ technology), permits non-invasive real-time data collection providing continuous information about behaviours related to individual activity (i.e. feeding, ruminating, lying behaviour, standing, walking, etc.). from which the health of the cow can be estimated.

The implementation of accelerometery collars used in the ClearFarm project, recorded the daily hours spent ruminating, eating and lying down in dairy cows. Behaviour was monitored all time. For the study 5 days prior to diagnosis of mastitis, the day of diagnosis and 3 days after were considered. All cows were treated with antibiotics at the day of diagnosis.

Our results seem to demonstrate that cows with mastitis showed a significant reduction in time spent ruminating four days before the diagnosis and that this behaviour recovers after antibiotic treatment. Suggesting that rumination may be a proxy behaviour to early detection of mastitis. An early diagnosis of clinical mastitis can improve the welfare of cows and can reduce the costs, treatments, and the cow’s replacement rate.

ClearFarm will advance the understanding of the possible role of PLF technology in monitoring dairy cows’ behaviours to identify automatically sick cows and to predict the onset of diseases that threaten animal welfare in the dairy industry.

EU Regulation 1169/2011 on the provision of food information to consumers establishes a labelling system with a list of indications that each food label is obliged to include (name, list of ingredients and allergens, etc.).

In addition to these obligatory elements, Article 36 provides for the possibility to include additional information on the label on a voluntary basis: in this case, the information must not mislead the consumer, must not be ambiguous or confusing, and, where appropriate, must be based on "relevant scientific data".

Uneven labelling schemes

The reference to animal welfare only appears on the label through the voluntary channel (as this parameter is not included among the declarations indicated as mandatory).

This has led to the development of different animal welfare labelling schemes in the different Member States, each based on different criteria, mostly unknown to the consumer, which often carry the risk of having a misleading impact on them.

The ClearFarm project is based on the development of a software platform capable of collecting data from various types of sensors, integrating them and extrapolating information on animal welfare to make it available to producers and consumers (by entering the data on the label) to help them in their decision-making.

Thus, the project creates a digital system powered by precision livestock technologies that allows not only to monitor the welfare of each animal but also to constantly verify in real time the supply chain's compliance with current regulations, reinforcing the final consumer's confidence. Therefore, everything is based on objective and scientific analysis methods, i.e., detectors (IoT [Internet of Things] sensors, optical readers, NFC [Near-Field Communication] devices, etc.) based on animal-based measurements (ABM) —such as behavioural, physiological, pathological, and production parameters—which are constantly updated with the latest scientific evidence.

The use of the latest precision livestock farming (PLF) technologies confers absolute reliability, transparency, and immediacy to the system, drastically reducing the possibility of consumer deception and confusion and the risk of providing potentially misleading information.

ClearFarm system is, therefore, not only fully in line with European provisions on voluntary food labelling, but also capable of adding value to European agri-food communication, providing consumers with essential information to guide them towards more informed consumption choices.

Play behaviour has been proposed to be an indicator of animal welfare. As part of the ClearFarm research, the validity of play behaviour in weaner pigs is currently investigated as an animal welfare measure. To do so, the team explores the relationship between play behaviour and broadly used welfare measures, such as weight gain, stress hormones, presence of disease and injuries, and feeding behaviour. The researchers also assess the relationship of play behaviour with novel behavioural measures related to emotions such as tail posture and tail motion.

More stress, less play

In nature, sows decrease their nursing activity gradually and pigs are naturally weaned when they are 4-to-5 months old. However, pigs raised in modern production systems shift from milk to solid feed much earlier, as they are abruptly weaned from their sow at approximately 3 to 5 weeks of age.

A ClearFarm study demonstrated that pigs weaned at approximately 26 days of age showed a drastic reduction in time spent playing in the first 24 h after weaning compared to before weaning. However, the study also showed that weaning stress was reduced by keeping litters socially intact in their familiar environment after weaning, as indicated by a less pronounced reduction in time spent playing from the day before weaning to the day after weaning and a stepper increase in time spent playing on the second day after weaning.

These findings illustrate a suppression of play behaviour when pigs experience hunger and social instability, and a higher engagement in playing when social conditions are more stable. In fact, early weaning is a management practice known to inflict nutritional, physiological, and psychological stress in pigs, constituting a suitable context for examining the relationship of play behaviour with other animal welfare measures.

With this, ClearFarm will advance the understanding and validation of play behaviour as an animal welfare indicator and potentially promote the use of play behaviour in on-farm welfare assessment protocols.

Farm animal welfare is increasingly emphasised as a quality attribute of food and a growing number of EU citizens would like to have more information about how farmed animals are treated. Product labelling can inform consumers effectively about food quality and sustainability, increase the transparency of farming and provide better protection to EU producers who apply a high animal welfare standard.

Animal welfare labels reviewed by ClearFarm mostly measured animal welfare by using resource- rather than animal-based measures. Only few of them referred to the technical specification ISO/TS 34700:2016 on animal welfare management.

Generic EU product labelling regulations apply also to animal welfare labelling. For instance: 1) product must not be marketed with properties it does not have or that can be assumed to be shared by all other products, such as minimum legal requirements, 2) the marketing must not be misleading, and 3) comparative claims should be verifiable.

All packing labels should also be easily noticeable, readable, and comprehensible. In addition, the European Commission has provided best practice guides for voluntary certification schemes for agricultural products and foodstuffs.

The key factors to successful animal welfare labelling are 1) business operators’ desire for openness, 2) the participation of industry, retailers, and interest groups in designing and implementing the labelling, as well as 3) consumers’ awareness of the label and its benefits. Moreover, 4) the transparency of the labelling scheme and 5) wide involvement of actors in decision-making were identified as additional important factors. Particularly farmers appreciate the possibility to take part in the label’s decision-making. Informing consumers requires adequate, correct, and coherent communication so that they understand the benefits of the label. It is recommended that the verification of conformity is reliable, made by an independent body, and that regular inspections with clear, understandable, and realistic criteria take place.

Financial viability of labelling is important. A commercial label must provide adequate value to all relevant actors, including animals, consumers, farmers, food business operators and other business entities involved.

Studies (Yang & Renwick, 2019; Cicia & Colantuoni, 2010) suggest that consumers are typically willing to pay some 15-30% price premium for products originating from high animal welfare farming systems. However, products are different, and consumers are a heterogeneous group, with dynamical changing shopping habits. While some are interested in animal welfare and willing to pay a price premium for welfare improvements, others may not be.

Digitalisation offers new opportunities to change the way labels are used in a business environment. It allows innovative technologies to be used for the benefit of consumers, which can both change the way of communication and give rise to new business models based on precision livestock farming technologies and data.

Quality labels are recommended to adhere to the principle of continuous improvement. The technical standard of animal welfare label affects how much impact the label can generate. Enhanced quality is not delivered for free. Rather, it requires effort at the different stages of the value chain. As the price rises, some consumers become excluded as the buyers of welfare-labelled products. Thus, the additional cost of delivering high quality cannot be too high.

Life Cycle Assessment or LCA is a standardized methodology (ISO 14040 ff) that assesses the environmental impacts of a product, process, or service throughout its entire life cycle. When applied to livestock systems such as the pork and dairy value chains, the LCA informs about the impact of these production systems on global warming, land use, eutrophication, acidification, water use, ecotoxicity, among other indicators. It can also help identify hotspots (phases in the value chain that contribute most to the pollution or resource use) and to define strategies to reduce the environmental impact of animal-derived products.

The amount and the composition of feed, water, the energy consumption on- and off-farm, or manure management, for instance, are examples of relevant input data needed for an LCA.

The required primary data is collected directly from farmers and producers such as integrators or cooperatives. Countries included in ClearFarm LCA are Spain, Italy, Germany, Finland, and The Netherlands, representing different regions and production systems throughout Europe. Based on the completeness and quality of data, some assumptions and secondary data sources of information are required to fill data gaps. The ClearFarm consortium has a good combination of diverse expertise, especially veterinary physicians, which helps to make such assumptions based on their knowledge of the system.

An important aspect when performing an LCA is the definition of the system boundaries (i.e. what processes to include). Another important aspect in animal-derived products is the assessment of animal-derived systems considering co-products as butter, cheese, and meat from the dairy farms. The environmental impact has then to be allocated to different products leaving the farm. Since there are different allocation methods, the outcome may also be different.

Once all data are gathered and the system boundaries are defined, the potential environmental impacts of the pork and milk production can be calculated. The calculation is based on existing data repositories such as Ecoinvent, GaBi, as well as databases with local inventories as the LCADB® developed by ICTA-UAB. To make such calculations, a series of characterization factors have been defined and agreed. For example, one of the most used impact categories in LCIA is the global warming potential (GWP). The reference substance to account for the GWP is carbon dioxide (CO2). All substances contributing to GWP –e.g., methane (CH4) and nitrous oxide (N2O), gases commonly emitted in livestock production through enteric fermentation or manure management–, are accounted for in kg or g of CO2 equivalent.

In ClearFarm, performing an LCA of the pork and dairy value chain from over five different countries helps comparing differences due to day-to-day practices and the implementation of diverse farm technologies across countries, and how they affect the environment.

One of the most important challenges in ClearFarm is understanding the potential contribution of LCA results to the assessment of animal welfare in the various farms assessed. There is still limited literature linking how a change in the farm management can benefit or constraint animal welfare. Identifying potential benefits and trade-offs between animal welfare and environmental performance would be a major outcome of the project. To achieve such goal, the ClearFarm team develop a unique platform and concentrate a high level of expertise to develop scores that reflect both the environmental and the animal welfare domains.

The Five-Domains Model for animal welfare assessment inspires the methodology of welfare assessment used in ClearFarm. This model is an integrative approach for animal welfare assessment based on the assumption that animal welfare is structured into five different domains including (1) nutrition, (2) physical environment, (3) health, (4) behavioural interactions and (5) mental state. Each of these domains can be assessed by a list of indicators that provide a quantitative outcome and that can be monitored continuously, 24 hours a day and 7 days a week.

For instance, the time a cow spends lying is an indicator that can be used to monitor her physical comfort, which pertains to the physical environment domain. ClearFarm will identify the existing Precision Livestock Farming (PLF) tools (sensors) available on the market that can effectively monitor these indicators, in order to know the welfare status of an animal at any time.

Following on with the example of laying time and comfort, activity sensors may help monitoring the time a cow spends lying. With the data from sensors on the farm, ClearFarm will develop a digital platform that will integrate the welfare indicators providing precise and continuous information. Farmers will have continuous access to precise information about the welfare status of their animals with regard to different domains.

ClearFarm will inform farmers when a cow is sick, and whether sickness has implications for other domains such as nutrition, comfort, or else. Real-time monitoring will not only allow to identify the problem (and to put in place the most appropriate remedy) but also to monitor the recovery, so treatments could be refined according to each animal’s needs. This approach will facilitate a comprehensive welfare assessment and help farmers to identify any critical aspect that compromises animal welfare at any time, which should be used to refine the corrective measures to improve the animal’s quality of live.

Animal welfare is crucial to ensure the maximum efficiency of livestock production. So, the maximum profit will only be achieved if animals are in the finest conditions. Having the ability to control the status of animals on farm continuously will help farmers to refine livestock production and achieve the highest efficiency in their production system.

ClearFarm interviewed all the relevant actors of the pig and dairy value chains about their needs and preferences about Precision Livestock Farming.



Consumers agreed that Precision Livestock Farming could lead to many advantages for farm animals, farmers, producers and consumers. For them, technology can help to satisfy short-term needs pertaining to consumption, like taste and food safety. It also can help to satisfy their needs pertaining to animal-welfare and the environmental impact and importantly it may respond to an unfulfilled need for greater transparency and trustworthiness of the animal production chains.



Their main concern was that PLF would become a form of robotization of livestock farming at the expense of animals and farmers with even more intensified production systems. Consumers were also worried about how the technology would increase prices and they highlighted the importance of transparency, reliability and trustworthiness.



Farmers associated PLF with the opportunities to take care of every individual animal, to stay competitive on the market and to offer new sales opportunities because of an improved product segmentation. At the same time, it was indicated that integrating PLF technologies requires an increased acceptance of innovation and that the perceived benefits need to outweigh the risks.



For farmer cooperatives, PLF was expected to allow to produce sufficient quantity of meat and dairy products to satisfy the global market and that it allows to directly respond to individual animals’ needs. They also considered privacy as is a key point that needs to be taken into account.



Processors & slaughterhouses indicated the opportunity that PLF has the possibility to evaluate the entire lifetime cycle of animals. In addition, PLF can provide an approach to harmonise welfare standards and labelling across Europe. Their requirement was that PLF technologies should be non-invasive for the animals.



Retailers mentioned that PLF is an opportunity to access new market segments and to trade more transparent products. They also suggested that PLF can increase the transparency of the value chain and help to harmonise labelling approaches across the EU.



For technology providers, PLF has the capability of optimising routine processes whilst reducing the farmers’ workload. However, they warned of the requirement to have a robust and secure data (storage) system.



Consultants and researchers indicated that PLF can optimise the whole value chain, integrating innovative technology, algorithms and data management tools. The requirement that PLF has to cope with the claims of modern livestock farming was mentioned.



Finally, animal interest groups pointed that PLF has the opportunities to assure animal-friendly treatment of animals, that it facilitates the process of setting standards for animal welfare and that PLF might fosters public discussions, which may contribute to a critical evaluation of the own consumption behaviour.

The acceptance of producers and consumers is essential for the adoption of technological solutions to improving animal welfare. Therefore, using the methodology of Design Thinking, ClearFarm gathered different profiles involved in the value chains of dairy cattle and pig products so that their needs and sensitivities were fully represented to ensure that the newly developed technological solution fit into their requirements.



In this sense, a sequence of events, both focus groups and co-creation workshops, were organized with consumers, producers, retailers, regulators, academics, animal welfare organisations and policy makers. After understanding the consumer’s needs, the requirements and opportunities for companies and the technological and institutional constraints, ClearFarm designed market-based solutions, based on Precision Livestock Farming, to provide easy to understand information on animal welfare status, as well as other environmental and economic sustainability information, to producers and consumers.



The proposed solutions included measurable indicators and standards, innovative technology integration, value proposition towards consumers, data management and data protection measures, development of ethical and systematic decision-making approaches, connections to animal welfare labelling organisations and should consider the impact of the system itself.