Periodic Reporting for period 4 - TechCare (Integrating innovative TECHnologies along the value Chain to improve small ruminant welfARE management)
Período documentado: 2024-09-01 hasta 2025-08-31
Although precision livestock farming (PLF) and innovative digital technologies (DT) are widely adopted in management of high-value animals, this is not the case for small ruminants (SR), despite the very significant welfare and production efficiency advantages that can be achieved by applying PLF and DT in these contexts. Animal welfare is about the individual sum of suffering or pleasure that accrues to the individual over the course of its life. SR, however, are often managed as a group and only average welfare state might be considered. A PLF approach to welfare management in SR could allow the individual animal to be identified within the group, so that nutrition, health care or other welfare aspects can be individualized and managed.
The overall objective of TechCare was to explore and demonstrate DT to develop early warning systems (EWS) to improve welfare management in SR farming systems, with the added challenge of using innovative and low-cost DT, adapted to the husbandry of SR systems across the EU.
TechCare did:
1. Prioritise SR welfare issues in a spectrum of systems and conditions.
2. Co-design approaches with stakeholders, to improve welfare management using innovative DT and EWS.
3. Identify, prototype and test different DT for monitoring prioritised welfare issues and risks.
4. Establish integrated data management approaches and design EWS.
5. Validate a set of ‘ready-to- use’ DT under large scale farm conditions in different operational environments.
6. Propose business models to boost innovative DT uptake.
7. Establish communication and dissemination of project’ outputs and ensure an efficient exploitation of results.
The work led to a better understanding of SR welfare and the identification of several DT that were acceptable to farmers and affordable. Most of them were animal-based, focused on the identification of the individual animal, but some covered environmental risks. Testing in real conditions has been successful. Farmers showed high level of satisfaction and great confidence in the information allowing a good monitoring of animal welfare. The interest from the small ruminant industry to the project has been very encouraging.
The overall objective of TechCare was to explore and demonstrate DT to develop early warning systems (EWS) to improve welfare management in SR farming systems, with the added challenge of using innovative and low-cost DT, adapted to the husbandry of SR systems across the EU.
TechCare did:
1. Prioritise SR welfare issues in a spectrum of systems and conditions.
2. Co-design approaches with stakeholders, to improve welfare management using innovative DT and EWS.
3. Identify, prototype and test different DT for monitoring prioritised welfare issues and risks.
4. Establish integrated data management approaches and design EWS.
5. Validate a set of ‘ready-to- use’ DT under large scale farm conditions in different operational environments.
6. Propose business models to boost innovative DT uptake.
7. Establish communication and dissemination of project’ outputs and ensure an efficient exploitation of results.
The work led to a better understanding of SR welfare and the identification of several DT that were acceptable to farmers and affordable. Most of them were animal-based, focused on the identification of the individual animal, but some covered environmental risks. Testing in real conditions has been successful. Farmers showed high level of satisfaction and great confidence in the information allowing a good monitoring of animal welfare. The interest from the small ruminant industry to the project has been very encouraging.
The project relied on stakeholder engagement to ensure relevant outcomes to the industry. At each major step, stakeholders have been asked for their feedback. Across the 9 countries, 8 series of workshops have been organised, with over 1350 stakeholders participating. A consumer perception survey towards welfare issues in SR systems and their behaviour towards welfare certified dairy and meat products was conducted across 13 countries and attracted 2,863 respondents.
An inventory of the welfare issues relevant to the systems studied in the project was developed, resulting in a list of 80 potential welfare issues per species. Stakeholders prioritised welfare issues relevant to them. 13 welfare issues for meat and dairy sheep, 8 for lambs and 9 for dairy goats were ranked. Welfare assessment protocols were developed for meat sheep, dairy sheep, dairy goats and lambs covering the animal-based measures (ABMs) required to assess the prioritised welfare issues previously identified. 4 broad categories of indicators based on those welfare issues have been subsequently defined.
In parallel, a review of existing PLF tools and DT was conducted, with 12 promising digital tools that could measure/inform any of the 4 categories of welfare indicators being selected.
7 pilot farms tested a selection of the 12 DT for validation, collecting welfare and data to develop early warning systems (EWS). 56 large-scale study farms were identified in 5 countries, to further test 4 out of the 12 technologies: weather stations and indoors environmental sensors, RFID readers and automatic weigh-crates, individual milk meters, milk tank weighing scales, alongside welfare assessments using a tailored app. Feedback from the farmers has been sought, and guidelines on how to use those tools have been developed for further dissemination.
Other trials, conducted under more controlled conditions, focused on prototyping and/or adaptation of some promising PLF tools and approaches (e.g. Bluetooth, GPS collars, Walk-over-Weigh).
Data exchange and analysis to build algorithms led to three main EWS being investigated:
1) Milk yield (can be reduced by undernutrition, heat stress, disease, poor environmental conditions – measured with milk meters/daily tank weight) and milking parlour order (changes indicative of lameness/disease - recorded with RFID tags or milking counters)
2) THI/Thermal stress (risk factors of heat/cold stress, respiratory diseases – measured by indoor sensors in shed and from outdoor weather stations) – leading to the THIcare App development.
3) Liveweight change (slower growth due to disease, poor maternal relationship, parasitism, nutrition – measured with weigh-crates and RFID antennas).
These algorithms and datasets collected during the project are available on a GitLab platform. Work on transport to market or abattoir (adult sheep on boats and kid/lambs on trailers) has also been undertaken.
Business models around the 4 tested technologies on large-scale identified both the subscription and freemium models as relevant approaches.
For a productive dissemination, communication and exploitation of results, a TechCare platform has been created (www.techcare-project.eu) with social media accounts, alongside a zenodo site (www.zenodo.org/communities/techcare) and EUFarmbook site. 40 practice abstracts are uploaded on EUCAP network, 16 newsletters have been distributed. Over 130 presentations were done at conferences, workshops and webinars. Collaboration with other EU projects during conferences and webinars was also sought after. A final conference has been organised in Brussels in 2025, with videos of the presentations and panel discussions available on YouTube (TechCare project - YouTube). Popular articles, podcasts and 14 scientific publications have also been published or submitted. Over 8000 people participated to various open days and on-site visits showcasing the project’s results.
An inventory of the welfare issues relevant to the systems studied in the project was developed, resulting in a list of 80 potential welfare issues per species. Stakeholders prioritised welfare issues relevant to them. 13 welfare issues for meat and dairy sheep, 8 for lambs and 9 for dairy goats were ranked. Welfare assessment protocols were developed for meat sheep, dairy sheep, dairy goats and lambs covering the animal-based measures (ABMs) required to assess the prioritised welfare issues previously identified. 4 broad categories of indicators based on those welfare issues have been subsequently defined.
In parallel, a review of existing PLF tools and DT was conducted, with 12 promising digital tools that could measure/inform any of the 4 categories of welfare indicators being selected.
7 pilot farms tested a selection of the 12 DT for validation, collecting welfare and data to develop early warning systems (EWS). 56 large-scale study farms were identified in 5 countries, to further test 4 out of the 12 technologies: weather stations and indoors environmental sensors, RFID readers and automatic weigh-crates, individual milk meters, milk tank weighing scales, alongside welfare assessments using a tailored app. Feedback from the farmers has been sought, and guidelines on how to use those tools have been developed for further dissemination.
Other trials, conducted under more controlled conditions, focused on prototyping and/or adaptation of some promising PLF tools and approaches (e.g. Bluetooth, GPS collars, Walk-over-Weigh).
Data exchange and analysis to build algorithms led to three main EWS being investigated:
1) Milk yield (can be reduced by undernutrition, heat stress, disease, poor environmental conditions – measured with milk meters/daily tank weight) and milking parlour order (changes indicative of lameness/disease - recorded with RFID tags or milking counters)
2) THI/Thermal stress (risk factors of heat/cold stress, respiratory diseases – measured by indoor sensors in shed and from outdoor weather stations) – leading to the THIcare App development.
3) Liveweight change (slower growth due to disease, poor maternal relationship, parasitism, nutrition – measured with weigh-crates and RFID antennas).
These algorithms and datasets collected during the project are available on a GitLab platform. Work on transport to market or abattoir (adult sheep on boats and kid/lambs on trailers) has also been undertaken.
Business models around the 4 tested technologies on large-scale identified both the subscription and freemium models as relevant approaches.
For a productive dissemination, communication and exploitation of results, a TechCare platform has been created (www.techcare-project.eu) with social media accounts, alongside a zenodo site (www.zenodo.org/communities/techcare) and EUFarmbook site. 40 practice abstracts are uploaded on EUCAP network, 16 newsletters have been distributed. Over 130 presentations were done at conferences, workshops and webinars. Collaboration with other EU projects during conferences and webinars was also sought after. A final conference has been organised in Brussels in 2025, with videos of the presentations and panel discussions available on YouTube (TechCare project - YouTube). Popular articles, podcasts and 14 scientific publications have also been published or submitted. Over 8000 people participated to various open days and on-site visits showcasing the project’s results.
After 5 years, the project has identified and validated:
• 4 ready to use innovative digital solutions for SR welfare management (TRL 8-9): i) EID weigh crate + reader(s); ii) electronic milk meter; iii) milk tank weight; iv) weather station/indoor sensors; coupled with 2 applications (THIcare App, Breedr App), 1 alert based on milking order and 1 on weight change.
• 5 technologies not yet acceptable to the whole of the SR sector (TRL 7-8), but promising for innovative farmers (Walk-over-Weigh, TroughAI, UHF platform and readers, Abinsula platform, GPS collars).
• 3 promising technologies (TRL 6) needing more development (Bluetooth Low Energy, accelerometers, proximity loggers).
The project has also identified welfare priorities lists for SR, 4 sets of welfare assessment guidelines, 4 broad categories of welfare indicators, and indicators for transport.
The project thus provided a better understanding of animal welfare and associated animal behaviour, a broader range of animal welfare management strategies and tools, assisted in developing innovative approaches to animal welfare monitoring and potentially increased the sustainability of the SR sector.
• 4 ready to use innovative digital solutions for SR welfare management (TRL 8-9): i) EID weigh crate + reader(s); ii) electronic milk meter; iii) milk tank weight; iv) weather station/indoor sensors; coupled with 2 applications (THIcare App, Breedr App), 1 alert based on milking order and 1 on weight change.
• 5 technologies not yet acceptable to the whole of the SR sector (TRL 7-8), but promising for innovative farmers (Walk-over-Weigh, TroughAI, UHF platform and readers, Abinsula platform, GPS collars).
• 3 promising technologies (TRL 6) needing more development (Bluetooth Low Energy, accelerometers, proximity loggers).
The project has also identified welfare priorities lists for SR, 4 sets of welfare assessment guidelines, 4 broad categories of welfare indicators, and indicators for transport.
The project thus provided a better understanding of animal welfare and associated animal behaviour, a broader range of animal welfare management strategies and tools, assisted in developing innovative approaches to animal welfare monitoring and potentially increased the sustainability of the SR sector.