Sustainable production of high-quality animal protein for a growing world population is the key challenge for livestock industry in the future. The aim of Breed4Food is to develop and apply innovations that utilize the genetic potential of cattle, pigs and poultry to breed production animals that are suited to meet future needs. The Breed4Food goals focus on: (1) providing tools to enable efficient breeding for sustainability traits, and (2) efficient utilization of DNA information and different sources of phenotypic data in breeding programmes. The anticipated innovations will generate effective breeding programmes and broad breeding goals that capture traits related to resource efficiency, health and welfare.
In this PPP key technologies, including genomic prediction, are innovated to increase the impact of animal breeding worldwide and its contribution towards three MMIP missions (A, B,and D).
In this PPP fundamental and applied research is performed for the development of generic key technologies for the breeding companies. Moreover, it develops concepts and the underlying tools to apply these key technologies in breeding programs in the mid-long term to contribute to A Circular agriculture B. Climate-neutral agriculture and food production D. Healthy, safe and appreciated food.
In animal breeding, the most important aspect is not to develop a new breed, but to select the best animals from the current population to produce the next generation. As current livestock production systems need to adjust, animal breeding needs to develop new generations of animals that will better fit these new production circumstances.
New phenotype recording techniques, such as high-throughput robotic sensors, offer opportunities to collect complex and detailed animal data at a high or even real-time frequency. These data can be used to define and predict new phenotypes for sustainability traits, that were previously difficult and/or expensive to measure and analyse.
Likewise, recent advances in genomics and molecular biology, such as massive sequencing and genotyping technologies, offer unprecedented possibilities to create enormous genomic datasets.
Simultaneously, novel data mining techniques facilitate the translation of Big Data into useful measures of animal performance by using combinations of observations or detection of specific patterns that yield accurate predictions for target traits. Practical examples include the utilization of longitudinal sensor data to collect information on individual and group behaviour to predict health, resilience and welfare. The utilization of new methods to collect and analyse phenotypic and genomic data will increase the efficiency of genetic improvement and herd management. Indeed, the unique combination of advanced techniques for recording and defining new phenotypes and genomics offers great opportunities to include traits in breeding goals that were unattainable with the classical approach.