In the current context of reduced antibiotic use, climate change emergency and limited resource availability, the efficiency and sustainability of animal production requires more robust and resilient animal populations, which use food in a better way.
In the Animal Breeding and Genetics program, we develop selection strategies to improve feed efficiency in pigs and rabbits and to reduce greenhouse gas emissions. In the case of pigs, we also study the genetic basis of their immune capacity to improve immunocompetence and disease resistance. We also study the role of the microbiota on immunity, efficiency and animal welfare.
Likewise, in a scenario marked by digital transformation, we also work in the field of digitization, 4.0 technologies and data science applied to animal production and improvement. On the one hand, we manage BDporc, a system that collects and analyzes the productive data of Spanish swine with reference information and tools that help to make decisions. Through technology, we develop systems for monitoring the behavior, health and welfare of animals using artificial intelligence.
Immunometabolism in pigs
We study the genetic bases and molecular physiology to select the most robust and resilient animal populations in terms of health.
Microbiome
We study the interrelationships between intestinal microbiota, immunity and animal behavior in pigs for a greater productive efficiency through improvements in animal welfare.
BDporc
We carry out the digitalization, sensorization and augmented analytics so that the pork sector work with efficiency and sustainability.
Optimization of resources in swine
We study the resources used in different pig genetics to achieve more efficient production with less environmental impact.
Food efficiency
We use genetic selection and animal management to improve feed efficiency in rabbits and pigs, promoting animal welfare and environmental sustainability.
Animal genetics and genomics
We conduct studies of the transcriptome, epigenome and metagenome in different animal species to improve genomic prediction models and reduce environmental impact.
Data science and artificial intelligence
We use massive data systems (e.g. machine vision systems, sensors or electronic feeders) and artificial intelligence tools for monitoring, phenotyping and prediction in production systems and in the framework of breeding programs.
