The Revista Brasileira de Zootecnia (RBZ) is a publication dedicated to the broad field of Animal Science. We publish high-quality, original scientific research that spans across diverse areas within the discipline. The scope of RBZ encompasses a wide range of topics, including aquaculture, biometeorology and animal welfare, forage crops and grasslands, animal and forage plants breeding and genetics, animal reproduction, ruminant and non-ruminant nutrition, meat science and muscle biology, precision livestock, and animal production systems and agribusiness.
The Revista Brasileira de Zootecnia (RBZ) is a publication dedicated to the broad field of Animal Science. We publish high-quality, original scientific research that spans across diverse areas within the discipline. The scope of RBZ encompasses a wide range of topics, including aquaculture, biometeorology and animal welfare, forage crops and grasslands, animal and forage plants breeding and genetics, animal reproduction, ruminant and non-ruminant nutrition, meat science and muscle biology, precision livestock, and animal production systems and agribusiness.
The Revista Brasileira de Zootecnia (RBZ) is a publication dedicated to the broad field of Animal Science. We publish high-quality, original scientific research that spans across diverse areas within the discipline. The scope of RBZ encompasses a wide range of topics, including aquaculture, biometeorology and animal welfare, forage crops and grasslands, animal and forage plants breeding and genetics, animal reproduction, ruminant and non-ruminant nutrition, meat science and muscle biology, precision livestock, and animal production systems and agribusiness.
17/Jul/2026
Lázaro Henrique da Silva
, Caio Matheus Leite da Silva
, Erick Galani Maziero
, Daniel Rume Casagrande
, Marina de Arruda Camargo Danes
ABSTRACT The use of digital technologies and wearable sensors has advanced in precision livestock farming. However, there is still no clear definition of the most suitable models for the use of sensors in grazing animals, especially under tropical systems. Thus, we aimed to compare the performance of six predictive models for grazing cattle behavior and to evaluate different testing strategies. The study was conducted in a pasture area, using nine Tabapuã heifers monitored by triaxial accelerometers attached to the nape, […]
Keywords: animal welfare; machine learning; precision livestock; sensor; smart farming