• Title: “DigitWelfare - a hybrid modeling approach to characterize the activity profiles of dairy goats associated with welfare” ; INRAE.
  • PhD student: Sarah Mauny.
  • Affiliated unit: INRAE, UMR 0791 – MoSAR.
  • Co-supervision:
    • Masoomeh Taghipoor (INRAE).
    • Christine Duvaux-Ponter (AgroParisTech).
  • Doctoral school: École doctorale Agriculture, Alimentation, Biologie, Environnement, Santé.
  • Project duration: 2022–2025.

Project summary:

In the current context of agroecological transition, selecting animals that are more resilient to climate change is encouraged, and societal demand for livestock systems that respect animal welfare is increasing. Simultaneously, the concept of "One Welfare," inspired by "One Health," emphasizes the importance of the interrelation between animal and human welfare, suggesting that it should be considered among other disciplines to support the sustainability of animal production systems. Considering all these elements, incorporating animal welfare into future selection strategies seems crucial. Meanwhile, Precision Livestock Farming (PLF) technology provides access to longitudinal and dynamic data on animal activity and performance, which can be correlated with environmental conditions and farming practices. Interdisciplinary scientific communities are emerging to analyze this data to detect potential issues that could affect animal health. However, the issue of improved animal welfare has been less addressed in this context. Additionally, literature on this subject generally considers animal welfare at the herd level, partly due to difficulties in determining and quantifying welfare indicators at the individual level in relation to the animal's perception of a situation. In this project, we will characterize activity profiles in various farming contexts and develop a hybrid modeling process to detect deviations in animal activity profiles, correlating them with disturbances in production performance, used as early signs of alterations in health and welfare.