We report the fatty acid profile of raw milk and of the corresponding digested milk from different sources (human milk, formula milk and donkey, bovine, ovine and caprine milk) to gain information on the nutritional quality of different milk sources in infant nutrition.

Short chain fatty acids (SC-FA) were higher in bovine and caprine milk, intermediate in ovine and donkey and lower in human and formula milk. Medium chain fatty acids (MC-FA) showed the highest values for bovine and caprine milk and the lowest for donkey and formula milk, whereas long chain fatty acids (LC-FA) were the highest in donkey and formula milk and intermediate in human milk.

The percentage distribution of fatty acids liberated after in vitro digestion did not reflect the patterns found in the corresponding milk sources. In particular, MC free fatty acids (MC-FFA) showed the highest and the lowest values in donkey and in formula milk, LC-FFA showed the highest value in human milk. The total FFA was highest in human milk, lowest in formula milk and intermediate in donkey, bovine, ovine, and caprine milk.

Current trends in the global milk market and the recent abolition of milk quotas have accelerated the trend of the European dairy industry towards larger farm sizes and higher-yielding animals. Dairy cows remain in focus, but there is a growing interest in other dairy species, whose milk is often directed to traditional and protected designation of origin and gourmet dairy products. The challenge for dairy farms in general is to achieve the best possible standards of animal health and welfare, together with high lactational performance and minimal environmental impact. For larger farms, this may need to be done with a much lower ratio of husbandry staff to animals. Recent engineering advances and the decreasing cost of electronic technologies has allowed the development of ‘sensing solutions’ that automatically collect data, such as physiological parameters, production measures and behavioural traits. Such data can potentially help the decision making process, enabling early detection of health or wellbeing problems in individual animals and hence the application of appropriate corrective husbandry practices. This review focuses on new knowledge and emerging developments in welfare biomarkers (e.g. stress and metabolic diseases), activity-based welfare assessment (e.g. oestrus and lameness detection) and sensors of temperature and pH (e.g. calving alert and rumen function) and their combination and integration into ‘smart’ husbandry support systems that will ensure optimum wellbeing for dairy animals and thereby maximise farm profitability. Use of novel sensors combined with new technologies for information handling and communication are expected to produce dramatic changes in traditional dairy farming systems.