Genomic Studies for Food and Water Efficiency in Composite Beef Cattle Based on Information Collected by an Electronic System

Abstract

The development and utilization of composite breed animals, such as the Montana® Composite, which embraces the concept of harnessing complementarity and synergy among different biological types, stand as a promising alternative for optimizing livestock production. This stems from the exploitation of heterosis retention and complementarity between the Bos taurus taurus and Bos taurus indicus subspecies. Given the intricacies of traits related to feed and water efficiency and their paramount importance for global food production sustainability amid climate changes, it becomes imperative to assess new methodologies and comprehend the implications of applying genomic predictions. This is crucial for enhancing productive efficiency and the sustainability of the national herd, by acquiring reliable information and creating opportunities to refine the production of composite animals through selective breeding. The primary aims of this study are to evaluate, from a genetic and genomic perspective, the dynamics of feed and water efficiency traits automatically recorded by an electronic system designed for measuring feed and water consumption. Additionally, the study aims to investigate the integration of genomic information in predicting genetic values in Composite Montana® cattle. The database utilized comprises 1,016 animals measured between 2018 and 2023, encompassing 4,081,703 phenotypic records of feed consumption, 396,997 live weight data from animals during feed efficiency trials, and 385,106 records of water consumption during the trials. The Montana Program incorporates approximately 370,000 animals in the pedigree, with 3,911 animals genotyped. For the estimation of genetic parameters, the ssGREML methodology (restricted maximum likelihood using genomic and pedigree information in a single-step) will be employed under a repeatability model to capt on the substantial number of phenotypes collected by the electronic system. Genomic predictions will be conducted, and the LR method (linear regression) will be applied to evaluate the accuracy, bias, and dispersion of genomic predictions for these traits.