Beef cattle have experienced massive gains in virtually every measure of efficiency over the last 50 years. Much of this progress can be attributed to improvements to the genetics of these populations. Tools for performing more accurate selection (EPDs, genomically-enhanced EPDs) have further accelerated genetic progress. Despite this improvement, further increases in efficiency will be critical for meeting the global demand for protein while continuing to reduce the beef industry's environmental footprint. Our group is interested in identifying areas where we can use genetics to improve the efficiency of forage-based cow-calf production. To address these challenges, we use genomics and high-throughput phenotyping to identify and select more efficient cows. This work relies on accurate phenotypes from large groups of of animals. As a result, I am interested in using high-dimensional information collected through imaging, sensors, or molecular markers to develop phenotypes for hard-to-measure traits like forage-based feed efficiency, health status, fertility, and greenhouse gas emissions. Our work also attempts to identify genetic variants that underlie these phenotypes and others using genome-wide association studies (GWAS). Beyond mapping trait-associated variants, we are interested in layering multiple forms of "omic" data (genomic, transcriptomic, metabolomic) to identify important functional variants and determine how they modulate complex biology. Finally, we want to understand how environmental adaptation and robustness in cattle can help equip animals for an increasingly volatile climate.