Abstract
Abstract Numerous pre- and post-mortem factors, such as genotype, production system, growth promotants, diet, health events, stress, slaughter age and weight, carcass chilling, and ageing time, have been shown to impact beef production and final product quality. The objective of livestock phenomics is the systematic acquisition of high dimensional phenotypic data, which requires measuring phenomes as they change in response to genetic mutation and environmental influences. Due to the decrease in costs associated to genomics technology and related fields, researchers had to face the so called “phenomic gap”, a lack of sufficient, appropriate phenotypic data. Selecting phenotypes of interests, standardizing methodologies, developing high-throughput data collection systems, systematically recording environmental factors, and integrating bioinformatics are some of the challenges when developing a livestock phenomics program. Precision livestock farming aims at applying continuous, automated real-time monitoring systems to optimize livestock management. The information collected by these systems can be used to optimize individual animal health and welfare, reproductive traits, and productivity, as well as environmental influences. This approach requires the use of novel technologies and the management of large amounts of data. Multiple technologies and sensors are already being used, or have the potential, to monitor important individual traits. These two interdisciplinary fields share multiple objectives that could lead to significant synergies. The complexity of in-farm data collection varies depending on the species and production system, with beef cattle presenting specific challenges. In addition, data collection needs to continue after slaughter, as carcass and meat quality traits are influenced by in vivo practices, determine the final profitability of the system, and need to be taken into consideration to modify management practices. Integrating livestock phenomics and precision livestock farming approaches will lead to a faster development of both fields and an optimal use of resources.
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