A Living Lab approach to understanding dairy farmers' technology and data needs to improve herd health: Focus groups from 6 European countries.

For successful development and adoption of technology on dairy farms, farmers need to be included in the innovation process. However, the design of agricultural technologies usually takes a top-down approach with little involvement of end-users at the early stages. Living Labs offer a methodology that involve end-users throughout the development process and emphasize the importance of understanding users' needs. Currently, exploration of dairy farmers' technology needs has been limited to specific types of technology (e.g., smartphone apps) and adult cattle. The aim of this study was to use a Living Lab approach to identify dairy farmers' data and technology needs to improve herd health and inform innovation development. We conducted 18 focus groups with a total of 80 dairy farmers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the United Kingdom. Data were analyzed using Template Analysis, and 6 themes were generated representing the fundamental needs of autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers favored technologies that provided them with convenience, facilitated their knowledge and understanding of problems on farm, and allowed them to be self-reliant. Issues with data sharing and accessibility and usability of software were barriers to technology use. Furthermore, farmers were facing problems around recruitment and management of labor and needed ways to reduce stress. Controlling aspects of the barn environment, such as air quality, hygiene, and stocking density, were particular concerns in relation to youngstock management. Overall, the findings suggest that developers of farm technologies may want to include farmers in the design process to ensure a positive user experience and improve accessibility. The needs identified in this study can be used as a framework when designing farm technologies to strengthen need satisfaction and reduce any potential harm toward needs.

Output-based assessment of herd-level freedom from infection in endemic situations: Application of a Bayesian Hidden Markov model.

Countries have implemented control programmes (CPs) for cattle diseases such as bovine viral diarrhoea virus (BVDV) that are tailored to each country-specific situation. Practical methods are needed to assess the output of these CPs in terms of the confidence of freedom from infection that is achieved. As part of the STOC free project, a Bayesian Hidden Markov model was developed, called STOC free model, to estimate the probability of infection at herd-level. In the current study, the STOC free model was applied to BVDV field data in four study regions, from CPs based on ear notch samples. The aim of this study was to estimate the probability of herd-level freedom from BVDV in regions that are not (yet) free. We additionally evaluated the sensitivity of the parameter estimates and predicted probabilities of freedom to the prior distributions for the different model parameters. First, default priors were used in the model to enable comparison of model outputs between study regions. Thereafter, country-specific priors based on expert opinion or historical data were used in the model, to study the influence of the priors on the results and to obtain country-specific estimates. The STOC free model calculates a posterior value for the model parameters (e.g. herd-level test sensitivity and specificity, probability of introduction of infection) and a predicted probability of infection. The probability of freedom from infection was computed as one minus the probability of infection. For dairy herds that were considered free from infection within their own CP, the predicted probabilities of freedom were very high for all study regions ranging from 0.98 to 1.00, regardless of the use of default or country-specific priors. The priors did have more influence on two of the model parameters, herd-level sensitivity and the probability of remaining infected, due to the low prevalence and incidence of BVDV in the study regions. The advantage of STOC free model compared to scenario tree modelling, the reference method, is that actual data from the CP can be used and estimates are easily updated when new data becomes available.

Assessment of concurrent infection with bovine viral diarrhoea virus (BVDV) and Mycobacterium bovis: A herd-level risk factor analysis from Northern Ireland.

Bovine viral diarrhoea virus (BVDV) is a significant pathogen of cattle, leading to severe economic and animal-welfare impacts. Furthermore, the pathogen has been associated with impacting the progression or spread of other pathogens (e.g. Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB)). During this study we investigated (i) risk factors for BVDV at a herd-level and (ii) whether there was any association between BVDV and herd-level bTB risk. The data for this study were gathered from a voluntary BVDV control programme in Northern Ireland (2013-2015) based on the identification of virus positive animals through tissue tag testing of calves. We assigned a herd-level BVDV status to 2827 participating herds, where a herd was assumed "infected" if one or more animals tested positive for BVDV. Two model suites were developed. Firstly, we assessed risk factors for BVDV herd status using multivariable logit random-effects modelling, aggregating to the calendar year level (2013-2015; n=4828; model 1). Secondly, we aggregated data across the three years of the study to give an overall status for the whole study period (n=2827; logistic model 2). Risk factors included year, herd-type, herd size, number of births, inward trade moves, calf mortality, and region. Furthermore, the herd-level bovine tuberculosis status (based on the single intradermal comparative cervical tuberculin (SICCT) test outcomes, or confirmation at post-mortem), or the size of bTB breakdowns (number of SICCT test positive animals), of herds was also investigated to assess whether there was an association (co-infection) with herd BVDV status. The final models suggested that BVDV herd status was positively associated with increased levels of calf mortality, herd size, number of births, the number of BVDV tests undertaken and the number of animals introduced to the herd. There was a significant univariable positive association between BVDV status, and SICCT breakdown risk, breakdown size and confirmed bTB status in model 2. However, there was no evidence of significant associations between bTB status (using SICTT status, confirmed status or herd breakdown size) and BVDV status in final multivariable models when controlling for other significant confounders. These results provide information for action for the future control and eradication of BVDV in Northern Ireland, though these data provide little support for the hypothesised association between BVDV and bTB status at herd-level. Further animal-level analyses are necessary to investigate whether there is support for a BVD-bTB co-infection association, including the impact of co-infection on the severity of infection.