An overview of the national cattle health- and welfare-related information systems in Estonia, Finland, Sweden, and Germany

The digitalization of cattle husbandry has increased the cattle health- and welfare-related data creation and collection into multiple information systems (IS). We describe nationally the widely used cattle health and welfare data-containing IS in Estonia, Finland, Germany, and Sweden by studying their ownership and the funding, developmental views, data access, data ownership, and data integration between other IS. In addition, we describe the IS with experts and stakeholders in interviews and living labs (LLs). There are two statutory IS in each study country, while there is one non-statutory IS in Estonia and Sweden and four in Finland and Germany. The IS providers and the data integration possibilities between systems differed by study countries, while the type of data collected and the IS funding policies remained similar. In LLs, congruent views about the importance of the standardization of the collected data and the usability of these data for farm decision-making and improvement in management practices were mentioned as the main challenges for future development. To gain full benefit from the collected data, data from automatic milking systems should be integrable with the data collected into other IS. In the future, data integration should be improved for a more user-friendly approach of cattle health- and welfare-related IS, and utilization of the collected data for animal health and welfare improvement should be implemented within the cattle value chain to ensure the sustainability of the industry. Common will between public and private operators, as well as cross-border multidisciplinary cooperation, is required to fulfill these goals.

1 Introduction

The new era of digitalization of agriculture has started in the previous decades and is continuously strengthening. In cattle husbandry, this has increased data creation and collection for assurance of food safety and the improvement of animal welfare and the overall herd management and productivity (Bahlo et al., 2019; Cabrera and Fadul-Pacheco, 2021; Patel et al., 2023). Traditionally, cow health and welfare has been evaluated through regular on-farm visits, during which visual examination of the animals and scoring of their health and welfare conditions have been used as the main health and welfare assessment tool (Vasseur, 2017). At present, herd-specific data, such as cow milk production, reproductive performance, and longevity, are regularly collected at animal and herd levels into national information systems (IS) through dairy processors, breeding enterprises, milk quality monitoring systems, or governmental actors (de Vries et al., 2011; Thomann et al., 2023). Some of these data are collected uniformly with preregistered requirements, enabling the evaluation and comparison of cattle health and welfare (Brouwer et al., 2015). Several studies have demonstrated that the collected data could be effectively and inexpensively utilized to screen the herd-level welfare and health, reducing the number of farm visits and improving the comparison of health and welfare in cattle herds (Nyman et al., 2011; de Vries et al., 2014; Brouwer et al., 2015; Thomann et al., 2023). However, previous studies have also demonstrated farmer concerns related to herd-level data collection and utilization, as well as data ownership and security issues (Forney and Epiney, 2022; Reissig et al., 2024), which should be considered when data utilization schemes are developed within the value chain.

The complete data utilization for cattle health and welfare evaluation is still underachieved as there is no consensus which variables of the cow or herd data are associated with the overall on-farm cattle health. In addition, the welfare indicators and the collected data parameters can vary in different countries (de Vries et al., 2011; Barkema et al., 2015). The data integration and utilization processes are hampered by multiple IS administrators, as data can be, for example, in multiple data formats, in closed IS, or are managed through different communication protocols (Antle et al., 2017; Cabrera and Fadul-Pacheco, 2021; Thomann et al., 2023). These factors complicate the large-scale integration of the collected data and their use for animal welfare and health improvement, together with the fact that predictive analytic methods are not commonly used in livestock environments (White et al., 2018).

The current European Union (EU) legislation reflects the potential that cattle husbandry could benefit from more effective data utilization for enhanced animal health and welfare as the European Commission has approved measures for a fair and innovative data economy in a Data Act in November 2023. The Data Act allows, for example, users of connected devices to gain access to data generated by them and to share these with third parties (European Parliament, 2023a). However, these actions do not support greater usage of the data for cattle health and welfare improvements if the data integration or analysis processes are technically not manageable. Similarly, the use of predictive analytic methods and achievement of a consensus on data indicators that reflect good on-farm cattle health and welfare must be met to fully utilize the data collected for animal health and welfare assessment and improvement.

By analyzing the current cattle health- and welfare-related IS, as well as the relationships around them, a better understanding of the requirements for greater data utilization for improvement of cattle health and welfare can be achieved. Surprisingly, to our knowledge, no research studies or systematic insights into the technical aspects of cattle health- and welfare-related IS in Europe are available. The purpose of this paper was to describe the current cattle health- and welfare-related IS and the ecosystems around them in Estonia, Finland, Germany, and Sweden. We also describe the functions of the IS, their interactions and integration with other IS, and the availability of the data from these IS, as well as forecast and recommend future developments. As this paper describes the IS in different EU countries, a thorough comparison of the IS holding cattle health and welfare data could be achieved. This highlights strategic opportunities for data collection, analysis, and usage to improve cattle health and welfare not only in the study countries but also at the EU level.

2 Materials and methods

2.1 Study design

We conducted a qualitative study by collecting information through desk studies, expert interviews, and living labs (LLs) to describe the current cattle health- and welfare-related IS in four study countries: Estonia, Finland, Germany, and Sweden. The IS included in this study were both the statutory ones required by the EU or national legislation and the non-statutory ones that are nationally widely used and not commercialized by or related to farming device manufacturers or individual companies of the dairy and meat processing industry. IS provided by device manufacturers or individual companies of processing industries, such as dairy or meat companies, were not in the scope of this research as these IS are commonly closed, with data available only to farmers and the device manufacturers. The IS studied in Germany revealed complex structures between different federal states. Some IS were mandated by the EU or national legislation, but are maintained by a specific federal ministry or managed by a regional office despite nationwide data collection. The IS not legislatively regulated could cover several federal states of Germany or be operated only in some of them.

The study countries formed a diverse cattle production system structure, enabling the study of cattle health and welfare IS in different cattle value chains. Dairy herds in Estonia are large, with an average number of cows per herd in milk recording of 229 cows in 2023 (Estonian Livestock Performance Recording Ltd, 2024). In Finland and Sweden, the average sizes of dairy herds in milk recording were 60 and 99 dairy cows in 2023, respectively (ProAgria Keskusten Liitto ry, 2024; Växa, 2024). In Bavaria, the average size of dairy herd in milk recording was 57.3 dairy cows, while that throughout Germany was 95.6 dairy cows per herd (LKV Bayern, 2024). In 2023, the average milk yield values per dairy cow were 10,162 kg in Finland, 10,584 kg in Sweden, and 10,628 kg in Estonia (Estonian Livestock Performance Recording Ltd, 2024; ProAgria Keskusten Liitto ry, 2024; Växa, 2024). In Germany, the average milk yield per dairy cow in 2023 was 9,379 kg (German Livestock Association, 2023). The main dairy cow breeds in 2023 were Holstein cows in Estonia and Finland, Holstein and Swedish Red and White cows in Sweden, and German Fleckvieh in Germany (Estonian Livestock Performance Recording Ltd, 2024; LKV Bayern, 2024; ProAgria Keskusten Liitto ry, 2024; Växa, 2024). In 2023, 53.3% (n = 1,692) and 34.7% (n = 649) of the dairy herds in milk recording used tie stalls in Finland and Sweden, respectively (ProAgria Keskusten Liitto ry, 2024; Växa, 2024). In Germany, the number of dairy herds with a whole year tie stall system was 3,164 herds (20.3%) in 2023 (LKV Bayern, 2024). Tie stalls were also used in Estonia; however, their exact number was not available. For suckler cows in Finland, in 2023, 24.0% (n = 487) of the herds had one to nine cows per herd, 47.2% (n = 958) had 10–40, 25.3% (n = 513) had 41–99, and 3.5% (n = 71) had more than 100 cows per herd (Natural Resources Institute Finland, 2025). In Estonia, there were, in total, 14,130 suckler cows in 429 herds belonging to beef performance recording in 2023 (Estonian Livestock Performance Recording Ltd, 2024). The average numbers of suckler cows per herd in Sweden was 35 cows in organic herds and 18 cows in conventional herds (Jordbruksverket, 2024). In 2022, the total number of suckler cow herds in Sweden was 9,909 (Jordbruksverket, 2023).

Firstly, structured desk studies were implemented to determine the relevant IS for the study and to collect background information, as well as promote understanding of the current state-of-art in the research and development of the topics about construction and function of the IS in the study countries. Secondly, experts from the IS-providing organizations and the users of these systems in all four countries were interviewed to collect the main body of information about the systems, including their structure, functions, financing, and needs for future development. After expert interviews, a summary of the IS observed in the study countries was created. Finally, the IS and their functionality were discussed in LLs in each study country, where members contributed to the collection of the required research data and commented on the summary of the IS. Moreover, the discussions in the LLs investigated the main challenges and expectations about the development of these IS. After the LLs, experts were interviewed or contacted again for more information, if any issues remained open. Thereafter, a conclusive summary about the IS was written. All three steps of the study (desk studies, expert interviews, and LLs) were implemented separately in the study countries by researchers and, as a final step, the country-specific results aggregated for description and comparison.

The ethical statement in this qualitative research included, among others, elucidation of the interview and LL contents, the voluntary participation of stakeholders (or any provided incentives), the termination of participation at any time, the anonymization in any analyses and non-public data storage, and any requests for producing videos, transcripts, and audio or text files.

2.2 Terminology

In this study, we used the term information systems (IS) collectively to describe the cattle health and welfare data systems in the study countries. IS consist of database, database management system (database management system is also known as database system), application programming interfaces (APIs), applications, and information and communication technology (ICT) infrastructures for the collection, storage, processing, and transmission of the data (NIST, 2023). The term “database” is used only for the structured collection of electronically collected and stored data without any application to analyze or manipulate these data (ExactData, 2019; Oxford English Dictionary, 2023). The cattle health and welfare data were collected into databases within a system integrated with one or more separate systems in the study countries. Hence, we used the term IS, which adequately describes that these systems have the database and database management system and the applications for the integration of the collected data.

Furthermore, we categorized the IS as statutory or non-statutory, depending on the legislative requirements for data collection. Those IS required by the EU or national legislation are considered as statutory, while the non-statutory ones refer to those IS without any legislative regulations behind their existence.

2.3 Desk studies

Desk studies were performed separately in each study country to gather basic information on the current cattle health- and welfare-related IS. To meet this purpose, an Internet search was performed through Google using appropriate keywords, including “cattle” OR “dairy cattle” AND/OR “animal health” AND/OR “animal welfare” AND/OR “database” AND/OR “management systems” AND/OR “data collection” AND/OR “data flow” AND/OR “data transmission” AND the country of the researcher. At least three of the keywords were included in one search. The Internet search through Google was done both in English and the native language of the researcher. Furthermore, desk studies were continued by a research study and an information search using Scopus, ScienceDirect, Web of Science, and Google Scholar, or an Internet search for professional articles and information with Google. The keywords used for the scientific search were those named in the Google search, but were used only in English. The requirement for the inclusion of research articles for the desk studies was that they describe data on cattle husbandry in relation to databases or IS. In addition, for inclusion in the desk studies, every matching search word had to be used in a relevant context and meaning.

The Internet and scientific searches were performed from April to May 2021, with results in English, Finnish, Swedish, Estonian, or German included for further inspection. No research articles relevant to this study were found during the scientific search or article preparation. In total, 31 Internet sources were reached. These included websites of IS-holding organizations and other relevant Internet sources or articles (n = 6 in Finland, n = 2 in Estonia, n = 13 in Germany, and n = 10 in Sweden).

After selecting relevant Internet sites, these were thoroughly reviewed and other information searched from the Internet with key information, if needed. During the further steps of this study, desk studies were used to examine some detailed information obtained in the interviews or in LLs, or to gain information still missing after the discussions.

2.4 Expert interviews

After the desk studies, interviews with relevant experts from the observed IS-holding organizations were performed to collect specific information on the existing IS. The main idea was to contact the experts from key organizations managing and owning the IS included in the study. Alternatively, the key organizations had the opportunity to suggest interviewees from their organization. To be interviewed, experts from the organizations managing the studied IS were eligible if their working position covered the maintenance, development, administrative, or supervisory tasks related to the IS and they were at least in management level at the organization. Moreover, other professional experts outside the key organizations (e.g., veterinarians and researchers) with high expertise in the use of IS within the cattle value chain were included in the sample if they were known to be familiar with the studied IS based on their previous work. Finally, the selected experts were interviewed and invited to take part in the further step of the study in the LLs.

A template for the interviews in the form of an Excel table (Supplementary Table S1) was used for data collection, which included questions about the owner of the IS, the owner of the data, and the funding of the data collection and IS development. In addition, the connections with other IS were mapped. The next set included questions related to the clients of the IS, their access to the data, the access rights procedures, and the client support activities. In addition, the main costs and revenues related to the IS were determined. Finally, the processes for the development of the IS services, as well as the main recent developments and subsequent plans, were queried. The interviews followed the drawn template. However, during the discussion, further questions and discourses were made to gather more information not covered in the template, considering, for example, the usability of the IS and their use in the transparency of the food value chain.

The interviews were conducted country specifically by both male and female researchers from the Estonian University of Life Sciences, Halmstad University, University of Oulu, and Technical University of Munich in the native language of the researcher and the expert. There were no previous relationships with conflicts of interest between the researchers and the interviewed experts. Basic information about the aim of the study and the researcher was provided for the experts at the beginning of the interview. Interviews with the experts were conducted as private phone calls or by using online meeting services, such as MS Teams (Microsoft Corporation, Redmond, WA, USA) or Zoom (Zoom Video Communications, San Jose, CA, USA), between March and December 2021. The duration of the interviews was approximately 1–2 h. Interviews were recorded when online meeting services were used. If the interview was done through a private phone call, handwritten notes were used to record the interview outcomes. From each interview, a thorough transcription was made. More questions after the interviews were also asked from the experts via an e-mail, when more detailed information on the IS was needed. The transcriptions were then translated into English and the translations describing the IS shared and discussed with the other researchers from each partner country. Corrections to the translations were made, if any obscurities about the details of the IS appeared in the discussions among researchers. An information technology expert confirmed the final professional terminology used in the English reports describing the IS. The results of the expert interviews and the details of the IS were further introduced in each national LL of the partner countries, the structure of which is described in Section 2.5. This provided the opportunity for participants to complement and comment on the data collected about the IS of their country and to compare the IS of the different study countries. After LLs, any complementary data with regard to the studied IS were added into the English reports.

In total, 24 people were interviewed in the study countries. Seven persons were interviewed in Estonia, seven in Finland, nine in Germany, and one person in Sweden. The single interview conducted in Sweden was with a management-level representative of the only non-statutory IS observed in the country. All relevant information on the observed IS was gathered during that interview, making additional interviews unnecessary. The positions of the experts included chief executive officer (n = 2), director (n = 3), manager (n = 12), head of department (n = 2), research agronomist (n = 1), associate professor (n = 1), expert veterinarian in IS management (n = 1), veterinarian (n = 1), and leading expert (n = 1).

2.5 Living lab discussions

LLs are multidisciplinary phenomena and cover various research domains. They can be seen simultaneously as landscapes, real-life environments, and methodologies, suggesting that they include multifarious stakeholders and apply various methods, tools, and approaches (Hossain et al., 2019). LLs focus on the co-creation, prototyping, and the testing of innovations or businesses supplying joint value to the involved stakeholders. Hence, LLs work as intermediaries among citizens, research organizations, companies, and government organizations (European Network of Living Labs, 2023). In this study, the LLs were organized after information was gathered in the desk studies and expert interviews to further analyze the issues considering the cattle health- and welfare-related IS. In the LLs, the invited experts could express their opinions and work together to analyze the IS and the solutions needed with the guidance of the participating research team members. The LLs concentrating on cattle health and welfare IS are part of a LL continuum implemented within the project SustainIT.

The structure of the LLs was modified from two earlier methodologies, the Botnia Living Lab and the FormIT LL methodology, both of which focused on the open innovation process, a specific research format, and the involvement of users for co-design purposes (Ståhlbröst and Bergvall-Kåreborn, 2008; Bergvall-Kåreborn and Ståhlbröst, 2009). To create a common and structured LL basis to be implemented in all study countries, all research team members from all partner countries participated in an LL training in June 2021. The main elements of the LL structure, i.e., user involvement and co-creation (Malmberg et al., 2017), were implemented in the LLs with standardized LL practices. The LLs started with a topic and research question introduction, which was followed by problem identification with the participants. Furthermore, the problem was validated with the LL participants. The second phase of the LLs concentrated on creating solutions to the identified challenges and evaluating these solutions. The final concept regarding the problems and solutions was summarized and discussed with the participants at the end of the LLs.

The LLs concentrating on cattle health and welfare IS took place once in each participating country organized by the country research team between July and November 2021. An exception was Germany, where two LL meetings were organized with different participating experts. The LLs were held as a hybrid model in Estonia and Finland, where the participants were able to attend via MS Teams (Microsoft Corporation, Redmond, WA, USA) or on the meeting premises. In Germany and Sweden, the LLs were organized only as virtual meetings via Zoom (Zoom Video Communications, San Jose, CA, USA).

Research questions already set in the research proposal and the general purposes of the study constructed the basis for the issues discussed in the LLs. In addition, country-related subjects and questions were addressed. These included, for example, the interest of farmers for data collection; the interest of the public sector; the needs, opportunities, and barriers faced in information technology in strengthening cattle welfare and health; national differences in data storage into IS; and the ethics of data use. At the start of the LLs, a brief introduction to the topic and a description of the IS were provided by the organizers, followed by discussions about the topics either generally or in smaller groups, and finally a summary of the opinions and ideas of the current IS. The discussions in the LLs were recorded in every study country. From the recording, a memo about the course of the discussion was written by country-specific researchers and translated into English for further data analysis. The memo did not include the identification of the participants in the discussion. Furthermore, a summary of the discussion was written for LL participants and was sent to them for additional comments and for confirmation that they agreed with the presented information. There were no additional comments to the summaries in the study countries. Section 3 of this study contains excerpts from the LL memos of the study countries.

The LLs had participants from the following professional fields and background: i) veterinarians; ii) researchers on animal husbandry and welfare; iii) holders of the current animal health- and welfare-related IS; iv) the public sector; v) the advisory/extension service; vi) sustainability experts; vii) the food industry; viii) dairy and beef farmers; ix) breeding associations; x) food industry associations; and xi) technology and input providers. Included in this group were the experts interviewed after the desk studies and other representatives from stakeholder organizations recognized as important for cattle health and welfare data collection or utilization in the value chain. For the participants in the LLs, there were 10 in Estonia, 13 in Finland, eight in Sweden, and varied between three and eight in Germany.

2.6 Data handling and analysis

The information gathered from the desk studies and expert interviews about the studied IS was inserted in English into an MS Excel table separately for each study country. From the MS Excel table, two researchers from Finland summarized the information on the studied IS and shared this with the researchers from other countries for review and revision.

The English translations of the country-specific LL memos were shared with one Finnish researcher for a summary of all the LL discussions. Firstly, the statements about the problems and the solutions identified for the current IS during the LL discussions were recorded on a country basis. For this purpose, the memos were thoroughly read from sentence to sentence, and the statements found were listed in sentences. The context of the statement and its overall meaning had to be clear and reveal relevantly the opinion or vision for it to be included in the list. If the meaning of the statement remained unclear, it was excluded from the list. The country-specific LL summaries were shared with the researchers from other countries for review.

After the country-specific summaries, the statements about the problems and solutions of the current IS were summarized for all study countries. For this purpose, the country-specific lists were reviewed and the opinions about the current IS discussed in the LLs recorded binomially (yes = 1, no = 0) by the study countries. To be recorded as “yes” in the summary, the basic idea and message behind the statement had to be clearly conveyed from the sentence in the memos of the different countries, even though the words used would have differed. To be recorded as “no” in the summary, the wording of the statements in the memos had to be clearly interpreted so that the LL participants did not mention that in the country-specific discussions.

The data handling and the analysis were made with MS Excel (Microsoft Corporation, Redmond, WA, USA).

3 Results

3.1 Description of the information systems

3.1.1 Statutory information systems

Two statutory IS required by EU legislation (European Parliament, 2000, 2018) were the cattle register and the register saving the data on the use of antimicrobial treatments in animals (Table 1). The statutory IS are provided and funded by the government or regional administrations of the study countries (Table 1). The aim of cattle registers is to store the animal identification number, birth, removal, and location to ensure the traceability of the animals within the food value chain. The register saving the data on the use of antimicrobials stores the data on the farm basis, veterinarian basis, and animal species basis in each member country (Table 1). Reporting these data to the two databases is mandatory, which led to the supposition that the databases should have data on the cattle population and the antimicrobial usage in the study countries in their totality.

Table 1

Table 1. Information systems (IS) required by European Union (EU) legislation.

In addition, mandatory registration of cases of set infectious animal diseases is required by EU legislation in Regulation 429/2016 (European Parliament, 2016). National legislation may also require the registration of other infectious cattle diseases, for example, in Finland (Ministry of Agriculture and Forestry in Finland, 2021). The statutory data on infectious diseases are routinely available only for governmental representatives, and the structure of the data collection and holding process was not further reviewed in this study.

3.1.2 Non-statutory information systems

The number of non-statutory IS differed by study country and ranged between one in Estonia and Sweden to four in Finland and Germany (Table 2). However, the list of non-statutory IS can be incomplete as the IS studied in this research were preselected and new IS can be developed by different projects, for which access to developmental data might not be available. The Swedish Växa IS had several applications using the same databases and APIs. In both Finland and Sweden, the cooperatives or associations with farmers’ membership provide and manage majority of the national cattle production and health monitoring IS. In Germany, the significant role in the holding and management of these IS is with advisory boards and breeding organizations, together with the companies and economic associations formed by these organizations. In Estonia, however, the cattle production and health monitoring IS is mainly owned and administered by the state, as the Estonian Livestock Performance Recording Ltd. is a 93.3% state-owned company (Table 2).

Table 2

Table 2. Non-statutory cattle health and welfare information systems (IS) in Estonia (EE), Finland (FI), Germany (GE), and Sweden (SE).

The non-statutory IS contain data related to accredited milk recording results, cattle treatments and disease records, breeding and reproduction metrics, herd welfare metrics, and hoof health records (Table 3). Depending on the country, the same data could be available in one or more IS (Table 3). In Estonia and Finland, cattle treatment and disease records are entered into one or more IS, but hoof trimming records were not available in the Estonian IS (Table 3). The Naseva IS in Finland enabled access to carcass quality, herd infectious diseases status, and overall laboratory analysis results (Table 3). In Germany, cattle health characteristics, including hoof health data and disease and treatment data, could be obtained via Rinder Daten Verbund (RDV) (cattle data network) and Vereinigte Informationssysteme Tierhaltung (VIT) (United Livestock information systems). The QS Database and Qualifood IS in Germany include data on carcass quality and weight, meat rejections, and other possible data on slaughter, together with data on antimicrobial treatments. In Sweden, the Växa IS application includes comprehensive data, for example, on hoof trimming, treatments and diseases, breeding values, milk recording results, carcass quality, and bulk tank milk amounts (Table 3). However, the data on infectious diseases in the FriskKo application were limited to four infectious diseases (Salmonella, Mycoplasma bovis, Streptococcus agalactiae, RS virus, and bovine coronavirus) compared with information on the overall herd infectious disease situation, including specific notifications for several infectious diseases (n = 11), in the Naseva IS in Finland (Table 3).

Table 3

Table 3. Data inserted to non-statutory information systems in Estonia (EE), Finland (FI), Germany (GE), and Sweden (SE).

3.1.3 Data input and transmission to the information systems

The official registration of cows into the cattle register requires a manual registration process by farmers in every study country. In Estonia and Finland, a farmer could register the newborn cattle via non-statutory IS, Livestock Performance Recording, or MTech/MyFarm, from which the data are transferred into the cattle register (Figures 1 and 2). Veterinarians enter the data on antimicrobial treatments into the country-specific statutory IS manually, or additionally, at least in Finland, via data interface with veterinary patient IS. All non-statutory IS receive the animal identification number and movement data from the central cattle register.

Figure 1

Figure 1. Studied cattle health- and welfare-related information systems in Estonia and the exemplary data input and transmission between them. Automatic interfacial data input indicates one-way data transmission, while automatic interfacial data transmission is used as a description of the back-and-forth transmission of data between information systems. Automatic means data transmission or input without any manual approval for the process. The data color and arrow indicate in which information system data are inserted. The information systems presented in the figure were preselected and do not represent all available information systems in the country. The data input and transmission process is described exemplary with the available information. All data transfer between the information systems is based on agreements between the client (farmer) and the information system provider. ^aClient (farmer) authorization is needed once for interfacial data input from the Estonian Livestock Performance Recording information system to the cattle register.

Figure 2

Figure 2. Studied cattle health- and welfare-related information systems in Finland and the exemplary data input and transmission between them. Automatic interfacial data input indicates one-way data transmission, while automatic interfacial data transmission is used as a description of the back-and-forth transmission of data between the information systems. Automatic means data transmission or input without any manual approval for the process. The data color and arrow indicate in which information system data are inserted. The information systems presented in the figure were preselected and do not represent all available information systems in the country. The data input and transmission process is described exemplary with the available information. All data transfer between the information systems is based on agreements between the client (farmer) and the information system provider. ^aThe veterinarian can enter all data into the register for antimicrobial use or into Naseva manually or with application programming interfaces between the information system and the veterinary patient information system.

Figures 1 and 2 illustrate the data input process and the data transmission between the IS in Finland and Estonia by way of example for IS ecosystems with four and one non-statutory IS, respectively. For the IS in Germany and Sweden, the data input into the IS and the transmission between them were not illustrated due to incomplete understanding of the data flow detail within the IS ecosystem.

In Estonia, where there is only one non-statutory IS, majority of the data are entered manually by the farmer or by other experts, except for the milk recording results, which are provided by the Estonian Livestock Performance Recording laboratory (Figure 1). In addition, data on the milking process could be entered into the Estonian Livestock Performance Recording IS from the milking systems in the form of csv-file.

The data input and the transmission to the IS in Finland are shown in detail in Figure 2. Manual data input into IS is required by farmers, veterinarians, inseminators, advisors, and hoof trimmers, but a set type of inserted data is then transmitted between the IS automatically and daily, i.e., in real time or every night. However, automatic data transmission requires agreement from the farmer and could also be done manually. For the Naseva IS, veterinarians or farmers enter the disease and treatment data manually, or for veterinarians also via data interface between Naseva and the veterinary patient IS (Animal Health ETT, 2024). In addition, veterinarians manually fill out herd welfare evaluation forms and herd infectious disease status into the Naseva IS. If a farmer had a contract with a veterinarian regarding medicine delivery and usage at the farm, the farmer could then start the medication for a cow. In this case, the farmer enters the treatment data into Naseva as a manual operation. Alternatively, the farmer could manually enter the treatment data into the Mtech/MyFarm IS, from which data are automatically transmitted into the Naseva IS (Figure 2).

3.1.4 Data ownership and access to the information systems

In Tables 1 and 2, the ownership of the data inserted into IS is summarized. Data ownership belongs to the IS-providing organization, in the case of statutory IS (Table 1). For the Estonian Livestock Performance Recording, the Swedish Växa, and the Finnish FABA digital services IS, ownership of the inserted data belongs to the data-producing client. With regard to the other IS, ownership of the data is transferred to the IS holder or owner after data entry (Table 2). Data ownership, usage rights, transmission, and accessibility are determined by user contracts between the farmer and the organization that provides or owns the non-statutory IS.

Non-statutory IS users and the requirements for their access to these systems are provided in Table 4. In all study countries, farmers gain access to the IS via license or membership in the organization managing the IS. Third parties (e.g., research institutions, veterinarians, etc.) might have access to the farm-specific data in the IS with authorization from the farmer and the IS holder (Table 4).

Table 4

Table 4. Users and access requirements to cattle health- and welfare-related non-statutory information systems in the study countries Estonia (EE), Finland (FI), Germany (GE), and Sweden (SE).

3.2 Results of the living lab discussions

In brief, an easier data collection process and better transmission of the data between the IS, improvement of their user-friendliness, and ensuring data security were the challenging issues discussed in the LLs of every participating study country (Table 5). Hence, the ideas about future development of the IS were strongly linked to these issues, but also covered other concepts. These statements about developmental needs for IS are summarized for all study country LL discussions in Figure 3. The statements about the developmental needs for the current IS can be categorized into four groups: i) functionality of the IS; ii) data analytics; iii) data security; and iv) others (Figure 3). To a lesser extent, the LL discussions covered the challenges in IS finances, educational aspects, and weak digital infrastructure (Table 5). The excerpts presented in the following paragraphs are direct quotes from the LL memos of the study countries.

Table 5

Table 5. Information system-related challenging issues discussed in the living labs of the study countries Estonia (EE), Finland (FI), Germany (GE), and Sweden (SE).

Figure 3

Figure 3. Summary of the developmental needs for the information systems identified in the living labs conducted in Estonia, Finland, Germany, and Sweden. ^aAutomatic milking system. ^bArtificial intelligence.

Within functionality, easy data collection process and access to the IS, as well as functioning APIs of these IS, were mentioned in the LL discussions (Figure 3). For example, in the Estonian LL, it was stated that:

“The Program must be very precisely prepared and technically designed so that when the user is in the barn and holding it (e.g. mobile phone), he can enter the data into it easily with just a few clicks and without having to write anything in it.”

Similarly, in the Finnish LL, the issue was discussed as:

“The vision for 2030 is to see user-friendly databases utilising data more efficiently, reports are broader and more comprehensive than they are today.”

As well as:

“It would be easier to access databases if it were possible to log in to different databases at one location.”

Furthermore, the need for a user-friendly IS was summarized in the Finnish LL, as follows:

“User-friendliness needs to be developed.”

In addition,

“From the farmer’s point of view, it is important that the programmes are easy to use and the programmes talk to each other and that there is no need to ensure the transfer of information from another programme separately.”

The ease and user-friendliness of the IS was also raised in the discussion in the Swedish LL:

“The farmers also stated that it is crucial that the IT-solutions are user friendly. Otherwise, it will be hard to get the farmers to use them.”

As a functional approach, LL discussions also covered manual data collection, IS interfaces, and fragmentation into several IS holding the same cattle health and welfare data (Figure 3). In the Estonian LL, the issue was summarized as:

“Manual data collection, including some service providers use physical notebooks. One issue is that everyone wants a digital summary of the data, but they don’t want to enter the data. Someone’s data can also be in Excel, but solution must be found on how those databases are aggregated together to a central database.”

Moreover, in the Finnish LL, manual data collection was included in the discussion together with the variety of IS collecting cattle health and welfare data as:

“The fragmentation of data in different databases and the manual collection of data were highlighted.”

In the German LLs, the issue was summarized as follows:

“The main challenges for data exchange along the value chain identified by the participants of the first country living lab were the lack of interfaces along value chain, which still often lead to the need for manual data transfer.” In addition, “At the system level, as already mentioned, new data interfaces must be created so that continuous communication along the value chain can be realised.”

As a further example, in the Estonian LL, hoof trimming and animal health records were taken as an example for the non-standardized data hampering IS functionality (Figure 3), being described as follows:

“The problem today is that different methods of hoof trimming are used and data entry is not standardized. It is not known whether the trimmers use the same diagnoses, with common names, how they enter the results. This is also question for other animal health data, e.g., difficult calving, lameness, how it is defined or written down may be different.”

In the LL discussions, data analytics and its improvement were widely discussed with different perspectives (Figure 3). For example, in the Finnish LL, it was discussed that the data collected by automatic milking systems (AMS) and agricultural devices should be analyzed together with the data from other IS. This was reported for the Finnish LL as:

“The information generated by milking robots, and sensor and camera technologies should be integrated with other well-being information.”

In addition, the LL discussions revealed that further processing and statistical analysis of the collected cattle health and welfare data should be better used for creating action plans to improve farm management practices and for evaluating the effect of cattle health and welfare metrics on farm economic productivity and in economic terms. For example, in the Finnish LL, it was stated that:

“A lot of data is collected, but its analysis should be made of more efficient and further developed.//From the point of view of usability, e.g., the producer should receive feedback on the production chain, in which case the work at the farm can be developed on the basis of the feedback.”

That

“In Sweden and Estonia, the presenting of data in economic terms (what data means for productivity in Euros) is more common than in Finland and this model should be imported to Finland more.”

And that

“It would be easier to use different systems if AI (= artificial intelligent) could extract and combine the desired information from different systems. In addition, the data should be accessible directly to an easy-to-read format, such as a diagram or image.”

The help of artificial intelligence (AI) in analyzing the cattle health and welfare data was also reported in the German LL as:

“With the help of artificial intelligent (AI) it is necessary to provide more concrete instructions for action and recommendations for the use of their generated animal health data.”

In the Finnish LL discussion, it was mentioned that comparison of cattle health and welfare should be possible with the available data nationally and internationally (Figure 3). In the Finnish LL, these discussions stated that:

“The international comparison of farms and farm data would bring broader context and comparative information to Finnish farmers, and it would also help to verify national circumstances and differences in other EU countries.”

Furthermore, it was stated in the LL discussions that education for farmers should be available and that the costs should remain moderate in the data collection and processing (Figure 3). In the Swedish LL, these discussions revealed that:

“The IT-consultant said that many farms are growing larger and getting more complex. Hence, there is a growing need for the use of IT at the farms. However, we are all influenced by our old habits and need education and other incentives to change our behaviour, to use IT-solutions in a higher degree.”

That

“It is also very important that all IT-solutions are designed to be very user friendly. Their opinion was that digitalization can lead to efficiency but there is also a risk that it creates more work and costs for the farmer.”

And that

“However, it is important for the smaller producers and refiners that the IT-solutions does not generate high costs.”

In the German LL discussions, the issue about the costs of data collection was summarized as:

“It is also very important to make farmers aware of the monetary added value of health monitoring and thus increase participation. One possibility here would be to ‘market’ the data fed in or the effort of data collection and to have it monetarily rewarded.”

In the Finnish LL, the statements about IS funding perceived that:

“As regards the further development of databases, the challenges of funding were identified as an obstacle.”

Finally, the data security and the benefits of data collection, particularly for farmers, encouraged discussions in the LLs (Figure 3). In the German LL, the data security improvements were discussed as follows:

“Clearly defined regulations regarding data protection and data security guarantee security for all market participants and increase trust.//Farmers must overcome exaggerated prejudices and fears regarding data protection and show more willingness to make data available.”

In the Estonian LL as:

“Data security and usage. In the case of databases, farmers have concerns that they will not be misused. It may occur from time to time.”

In the Swedish LL, the data security was discussed together with the utility of the data collection, being summarized as:

“The farmers also raised the question of ownership and security of the gathered data. They said that it seems relevant to merge some of the collected data in order to strengthen the system for animal health and welfare. However, the farmers also experience that some data gathering and inspections are done without any real reasons behind it.”

4 Discussion

In this descriptive study, we identified several cattle health- and welfare-related IS in the study countries Estonia, Finland, Germany, and Sweden. The IS studied are commonly known as the most widely used nationally, as confirmed in the expert interviews and LLs. However, the list of non-statutory IS might be incomplete as several actors contribute to their development, management, and operation, leading to differences in their accessibility. In addition, the IS provided by device manufacturers or individual companies of the processing industry, such as dairy or meat companies, were not in the scope of this research and were therefore not described in this study. As different sensor technologies and devices are strongly linked to cow health and welfare data collection and evaluation (Rutten et al., 2013), and as their usage may increase in the future, future studies should investigate the data economy around these IS.

As in our study, in earlier studies, multiple public and private sector IS providers were identified (Antle et al., 2017). While the statutory IS are provided by the public sector in all study countries, the non-statutory IS are strongly operated by privately owned agricultural advisory, breeding, or farmer benefit organizations in Finland, Germany, and Sweden. This has promoted the development of a fairly smooth data integration between the IS, particularly in Finland, where one technical operator provides the technology for all the non-statutory IS with the help of a long history of collaboration among IS providers. On the other hand, in Germany due to the fragmentation into federal states, not all IS are available nationwide, inhibiting the integration of cattle health and welfare data.

The ecosystem of cattle health- and welfare-related IS is simple and functioning also in Estonia, where a state-owned company provides the non-statutory IS. In Estonia, the IS ecosystem was developed beginning from the independence at the 1990s, whereupon knowledge from abroad could have been effectively used for the developmental processes, helping to avoid common problems. On the one hand, state-owned actors could organize effectively and in a controlled manner the technology and management around the cattle welfare and health data collection. On the other hand, the private sector could be more flexible in the development of the ecosystem compared with the public sector, which mostly concentrates on legislatively mandatory data collection for public health and international agreements. Despite the differences in number, administrative aspects, and data integration of the non-statutory IS in the study countries, the overall ecosystem, as well as the purposes around them, remains comprehensive. This highlights the possibilities of extrapolating the knowledge from other countries and determining the best practices in the development of the current IS ecosystems. However, a common will between the private and public operators supplying the IS is needed, when data ecosystems are developed for complete use and integration of the collected cattle health and welfare data, particularly in countries divided administratively into states.

One of the most discussed topics in the LLs was the better use of the collected data, also from the IS outside this study, for the farm’s everyday decision-making process and for improvement of the farm productivity and animal health. The data collected from AMS should be used more effectively, as currently these cannot be integrated very easily with the other collected data due to the closed IS behind the AMS data. There is a promising beginning for the better use of these data with the establishment of iDDEN (International Dairy Data Exchange Network) and the choice of the NCDX (Nordic Cattle Data eXchange) interface as the International Committee for Animal Recording (ICAR)-certified technology base for AMS data integration (Kyntäjä et al., 2018; iDDEN, 2020). These solutions could effectively promote the use of the collected cattle health and welfare data and their integration into AMS data for farm production and animal welfare improvement. While the integration of the data collected from agricultural devices with the data types examined in this study could help farmers improve herd management and cattle health at the farm level, previous research has highlighted farmers’ concerns with regard to the sharing and transfer of device-generated data with third parties, such as with industry partners or for governance purposes (Forney and Epiney, 2022; Reissig et al., 2024). In this study, the LL discussions revealed a concern of farmers about unclear data ownership. Reissig et al. (2024) highlighted a discrepancy between farmers’ roles as data producers and owners and the terms of data ownership in agricultural device user contracts. These contracts often contain ambiguous ownership clauses, leading farmers to accept them without fully understanding their implications for data ownership. Kritiskos (2017) has also remarked that, despite the statements that farmers own the data they produce, in practice, this statement may not be valid. Sustainable data utilization in the cattle value chain requires open and constructive discussions between the IS providers and the farmers about data ownership and integration. This is now supported legislatively by the EU Data Act, which must be applied from September 2025 onwards and which enables farmers to have a more active role as a decision-maker about use of the data produced by agricultural machines and about sharing the data with third parties (European Parliament, 2023b).

The farmer is the most important actor in improving cattle health and welfare (Adler et al., 2019). Some of the studied non-statutory IS provided benchmarks or overviews for farmers about cattle health and welfare remarks or interacted with the herd management systems to create summaries or recommended actions for the management and improvements of herd production and health. However, the effective utilization of these tools in practice is dependent on the farmers’ motivation to use these tools for cattle herd health and welfare management. With regard to data use and integration, standards for data collection are needed to fully utilize the collected data for animal welfare improvement and herd productivity at the farms. As the collected cattle health and welfare data could serve as an effective tool for herd-level welfare evaluation (Nyman et al., 2011; de Vries et al., 2014; Brouwer et al., 2015; Thomann et al., 2023), a standardized and reliable data collection should be generated to improve comparison of animal welfare in cattle herds. International standards for data collection may be difficult to create, even though it has been implemented in ICAR-accredited milk recording data collection (ICAR, 2023). In this study, an example of data standardization and between-herd welfare evaluation is the herd-level welfare data collection into the ISO 9001-accredited Finnish Naseva IS (Animal Health ETT, 2023). While herd-level welfare data collection and evaluation help farmers improve cattle health and welfare, it could also serve as a value base for the whole cattle value chain. For example, in Finland, herd welfare data collected into Naseva are strongly linked to product flow from farms to dairy or beef processing companies; in Germany, meat inspection data are used for the certification of meat produced in an acceptable standard based on the data in the QS-Databank. Hence, standardized data collection and highlighting their use through the whole value chain may help instill the improvement of data-driven cattle health and welfare and support the sustainability of cattle husbandry by responding to current political demands and consumer expectations of animal-based products. However, this process requires trust and respect across all the actors within the value chain, starting from the data producer, the farmer.

The aim of this study was to investigate the current cattle health- and welfare-related IS in Estonia, Finland, Germany, and Sweden. This is a novel research studying IS and their data transfer around the cattle value chain in different countries with different production systems and including several actors in order to gather their visions and opinions about the utilization of the systems within the cattle value chain. The visions and sentiments of the experts in the interviews and the LLs may influence the results of this study, even though these resulted in congruent views in some aspects. Furthermore, as the research was based on interviews and discussion sessions, versatile comments and discourses were included. Hence, not all of the issues were addressed with the same intensity in the interviews or the LL discussions, and country-specific topics were discussed, making the comparison of the results difficult in some parts. On the other hand, as the interviews and the LLs involved multiple experts in different countries, a good overview of the data collection process and the IS structure in the participating countries was achieved. Although the LLs were held separately for each study country, congruent views about the disadvantages and ideas for development of the current IS were identified. These were the driving forces in the LLs for the innovation of future developmental needs, which were user-oriented and with practical origin. Hence, the need for IS development found in our study could be extrapolated to other countries with similar data ecosystems around the cattle value chain. In addition, the information gathered in this study could be extrapolated to cover the IS collecting information on other animal species and to help in the development of animal health- and welfare-related IS within those species and regions, where no such systems are currently available.

5 Conclusions

A lot of cattle health- and welfare-related data are collected into multiple publicly or privately operated IS in the study countries Estonia, Finland, Germany, and Sweden. Expert and user opinions highlighted the importance of the usability of the collected data for farm decision-making, management, and improvement practices. For the best utilization of the collected data for animal health and welfare improvement at the farm level, the standardization and the integration of these data are the key elements to be developed in the future. Further studies are also needed to evaluate the data economy among the IS not included in this study and to evaluate how data from these systems could be integrated with other cattle health- and welfare-related data. In addition, common will and multidisciplinary cooperation are needed to determine solutions to the challenging issues within data utilization raised in this study. It is likely and desirable that renewed EU legislation will support this development to enhance the data economy and the overall sustainability of cattle husbandry by allowing more comprehensive possibilities to utilizing the cattle health and welfare data generated on farms, for example, by analyzing the same primary data from several IS providers.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Ethics statement

Ethical approval was not required for the studies involving humans because the study was performed in accordance to local legislation and institutional requirements. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements because participants received the information about the purpose of the study and the aim to publish the results anonymously before the voluntary participation to living labs. Hence, participation to living labs was noted to be a seal of approval to participate to the study.

Author contributions

TK: Conceptualization, Formal Analysis, Investigation, Methodology, Writing – review & editing. AT: Formal Analysis, Investigation, Visualization, Writing – original draft, Writing – review & editing. HT: Conceptualization, Funding acquisition, Investigation, Methodology, Writing – review & editing. AP: Investigation, Methodology, Writing – review & editing, Formal Analysis. MK: Investigation, Methodology, Writing – review & editing. NS: Investigation, Methodology, Writing – review & editing. PU: Conceptualization, Investigation, Methodology, Project administration, Writing – review & editing. P-OU: Conceptualization, Investigation, Methodology, Project administration, Writing – review & editing. ET: Investigation, Writing – review & editing. PK: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – review & editing. HB: Funding acquisition, Investigation, Methodology, Project administration, Writing – review & editing, Conceptualization. GA: Funding acquisition, Investigation, Methodology, Project administration, Writing – review & editing, Conceptualization. AV: Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – review & editing, Conceptualization.

Funding

The author(s) declare financial support was received for the research and/or publication of this article. This paper was written as part of the Horizon 2020 ERA-NET Cofund ICT-AGRIFOOD project “SustainIT – Releasing the Potential of ICT for Sustainable Milk and Beef Cattle Value Chains.”

Acknowledgments

The authors would like to thank all the experts and living lab participants as well as Sanna Keski-Nisula for review of the ICT terminology.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fanim.2025.1400288/full#supplementary-material

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