São Paulo School of Advanced Science on Precision Livestock Farming

In the pursuit of sustainable solutions for livestock production, new systems and technologies have been developed and proposed in recent years. And the emergence of Precision Livestock Farming (PLF) has been a game-changer. PLF aims to optimize production management by monitoring and controlling aspects such as animal productivity, environmental impacts, and animal health and welfare in a continuous and automated way.

While cutting-edge equipment propels PLF forward, the biggest challenge lies in processing vast amounts of collected information. This is where multidisciplinary teams play an important role, as the correct interpretation of biological responses is critical for harnessing the full potential of PLF.

With the São Paulo School of Advanced Science (SPSAS) on Precision Livestock Farming (SPSAS-PLF), we intend to make a qualitative leap in PLF research. By bringing together worldwide known scientists, we seek to inspire graduate students and young scientists to explore various aspects of PLF research in-depth. The goal of this program is to foster great interest in PLF research, empowering young scientists to play a global role in advancing science and bridging the gap between fundamental and applied research in Precision Livestock Farming.

Students at SPSAS-PLF can expect in-depth courses on Machine Learning, Statistical Tools, and Database Systems, ensuring that participants are “on the same page” with important content that supports PLF. Also, practical applications of PLF in Brazil and abroad will be showcased by means of engaging workshops.

  • Institution

    Faculdade de Ciências Agrárias e Veterinárias da Universidade Estadual Paulista (FCAV-Unesp)

  • Field of Knowledge

    Animal science

  • Academic Director

    Luciano Hauschild

  • Grant Number


  • Date

    2024-10-15 to 2024-10-24

  • Registration Deadline


  • Site


  • City


  • Keywords

    Livestock Production, Precision Livestock, Animal Health, Machine Learning