Animal welfare enhancement requires that problems are reliably identified and ranked in order to prioritise corrective actions. Welfare problems vary with the conditions in which animals are maintained. The objectives were to highlight major welfare problems for dairy cows on farms in France, and find out how farm characteristics (housing and milking systems, breed) could impact specific welfare aspects on these farms. We conducted a cross-sectional survey on 131 French dairy farms. We used the Welfare Quality® protocol, which addresses all aspects of welfare, and yields scores for principles and criteria that represent how well farms meet welfare requirements (from 0 for a very adverse situation to 100 for an excellent one). We used descriptive statistics to highlight low welfare scores, and variance analyses to compare farms. Most farms were found ‘Acceptable’ according to the Welfare Quality® classification. Principles scores for Health, Feeding and Behaviour ranged from 33 to 39. The median score for eight of the welfare criteria was below 50 (‘Pain’, ‘Integument alterations’, ‘Diseases’, ‘Comfort around resting’, ‘Social behaviours’, ‘Human-animal relationship’, ‘Positive emotional state’, ‘Hunger’), while the median score was above 50 for the four other criteria (‘Thirst’, ‘Lameness’, ‘Expression of other behaviours’, ‘Ease of movement’). The scores varied widely between farms, within and between systems. Farms with cubicles obtained lower scores for ‘Comfort around resting’, ‘Injuries’; farms with Holstein cows obtained lower scores for ‘Hunger’; farms using an automatic milking system obtained lower scores for ‘Expression of other behaviours’ and ‘Diseases’ in Holstein herds. This survey yields information on bottlenecks in dairy cow welfare with all dimensions of welfare considered together. The results can be used by stakeholders to prioritise corrective actions in welfare plans, focusing either on the whole population of farms or on farms with characteristics that are at high risk for specific welfare problems.

Digitalisation is an integral part of modern agriculture. Several digital technologies are available for different animal species and form the basis for precision livestock farming. However, there is a lack of clarity as to which digital technologies are currently used in agricultural practice. Thus, this work aims to present for the first time the status quo in Swiss livestock farming as an example of a highly developed, small-scale and diverse structured agriculture. In this context, the article focuses on the adoption of electronic sensors and measuring devices, electronic controls and electronic data-processing options and the usage of robotics in ruminant farming, namely, for dairy cattle, dairy goats, suckler cows, beef cattle and meat-sheep. Furthermore, the use of electronic ear tags for pigs and the smartphone usage for barn monitoring on poultry farms was assessed. To better understand the adoption process, farm and farmer’s characteristics associated with the adoption of (1) implemented and (2) new digital technologies in ruminant farming were assessed using regression analyses, which is classified at a 10% adoption hurdle. The results showed clear differences in the adoption rates between different agricultural enterprises, with both types of digital technologies tending to be used the most in dairy farming. Easy-to-use sensors and measuring devices such as those integrated in the milking parlour were more widespread than data processing technologies such as those used for disease detection. The husbandry system further determined the use of digital technologies, with the result that farmers with tie stall barns were less likely to use digital technologies than farmers with loose housing systems. Additional studies of farmers’ determinants and prospects of implementation can help identify barriers in the adoption of digital technologies.

Pig farms in the Netherlands producing boars have different levels of boar taint prevalence, as assessed by sensory evaluation with the human nose at the slaughter line. With a questionnaire to 152 Dutch pig producers (response rate 59%), farm and management characteristics were identified that are potentially associated with farm-level boar taint prevalence. Lower farm-level boar taint prevalence was associated with a smaller group size, a smaller pen surface per boar, newer housing equipment, not practicing restricted feeding in the last period before delivery, a longer fasting period before slaughter, a higher stocking weight and a lower fraction of boars from purebred dam line sows or from Pietrain terminal boars. These characteristics can be used to develop farm-level intervention strategies to control boar taint. More research effort is needed to establish causal relationships.