Leveraging milk mid-infrared spectroscopy to authenticate animal welfare, farming practices, and dairy systems of Parmigiano Reggiano cheese.

Increasing consumer concerns underscore the importance of verifying the practices and origins of food, especially certified premium products. The aim of this study was to evaluate the ability of Fourier-transform mid-infrared (FT-MIR) spectroscopy to authenticate animal welfare parameters, farming practices, and dairy systems. Data on farm characteristics were obtained from the Parmigiano Reggiano Consortium in northern Italy.

Mind the Step: An Artificial Intelligence-Based Monitoring Platform for Animal Welfare.

We present an artificial intelligence (AI)-enhanced monitoring framework designed to assist personnel in evaluating and maintaining animal welfare using a modular architecture. This framework integrates multiple deep learning models to automatically compute metrics relevant to assessing animal well-being. Using deep learning for AI-based vision adapted from industrial applications and human behavioral analysis, the framework includes modules for markerless animal identification and health status assessment (e.

Associations between milk infrared-predicted plasma biomarkers of stress resilience and fertility in dairy cattle: insights for enhancing breeding programs and herd management.

Fertility is a crucial aspect of dairy herd efficiency and sustainability. Among factors influencing fertility in dairy cattle, metabolic stress and systemic inflammation of animals are of main relevance, especially in the postpartum stage when ovarian activity begins and cows are inseminated. Our study aimed to infer the associations between milk infrared-predicted blood biomarkers of stress resilience and fertility traits, namely the interval from calving to first service (iCF), days open (DO), and the pregnancy rate at first service (PRF) in a multi-breed population of 89,097 dairy cows.