Advancing Sustainable Livestock and Poultry Production
The ARS food animal production research program improves food animal production efficiency, sustainability, animal welfare, and product quality while safeguarding animal genetic resources. Animal production is a critical component of the U.S. economy, yielding $440.7 billion in economic output, with $76.7 billion in earnings, $19.6 billion in income taxes, and $7.4 billion in property taxes in 2014. The following accomplishments highlight ARS advances in animal production research in FY 2019. Hyperlinked accomplishment titles point to active parent research projects.
Increases in pork carcass weight will improve tenderness of pork loin chops. Historical trends indicate the size of U.S. hogs is likely to continue to increase. ARS scientists in Clay Center, Nebraska, collaborated with University of Illinois and Kansas State University researchers to determine how increased carcass weights affect pork quality. Results indicated that the heaviest group of carcasses weighed 36 percent more than the industry average, and given current increasing weight trends, represents the expected average carcass weight by 2050. The increased carcass weight resulted in slower rates of loin muscle chilling when carcasses are cooled after slaughter. This, in turn, resulted in loin chops that retained more moisture during cooking and were juicier and more tender. Carcass weight had minimal effect on other pork quality traits, including lean color and marbling. These results show breeding for growth in pigs to improve production efficiency results in heavier market weights and improves eating quality of pork chops.
Characterization of the porcine mycobiome (fungal mycobiome). The microbial organisms found in the gastrointestinal tract of animals is recognized as a critical component of host health. Weaning is a period of stress, dietary changes, and a predisposition to infections, making it a time point of interest to industry. ARS scientists in Beltsville, Maryland, performed the first in-depth analysis of the fungal microorganisms present in the gastrointestinal tract of piglets between birth and transition to postweaning life, and demonstrated that a dynamic shift occurs in fungal populations at the time of weaning, particularly a dominance of the fungus Kazachstania slooffiae in weaned piglets. This dramatic shift in the fungal microorganisms in piglets has not been previously reported and suggests that milk may suppress fungi in the gut. Furthermore, the consequences of the dramatic onset of fungal microorganisms in the gut after weaning is not known, but the trajectory of fungal development could influence future immune competence. Further research could support the development of interventions and dietary modifications to enhance piglet performance and increase swine herd productivity.
New method improves genome assembly. A new method for assembling genetic sequencing data into more complete genomes has been pioneered by ARS researchers in Clay Center, Nebraska, and Beltsville, Maryland, and collaborators at the National Institutes of Health, University of Nebraska, and University of Kentucky. Using the new process, an individual animal resulting from the mating of a Highland breed bull and a yak cow was used to create reference-quality assemblies of the Highland breed of cattle and the yak in a single experiment. By applying the new technique to an interspecies hybrid that maximized the differences between maternal and paternal chromosome sequences, the researchers created individual sequences for both the yak and cattle. The sequences were of equal or better quality than any existing mammalian genome assembly, including those for humans or biomedical species such as mice or rats. If generally applied, the technique to generate two genome sequences from a single individual is likely to improve the accuracy of genomic selection of all livestock and many plant species and could impact a wide range of industries.
National genomic evaluations for crossbred dairy cattle. Some dairy producers have turned to crossbreeding to increase genetic diversity and reverse the decrease in reproductive fertility associated with selection for increased milk production in purebred dairy cows. However, current genomic evaluations are only available for purebred Holstein and other dairy breeds. Producers using crossbred dairy cows needed genomic evaluations that could account for the crossbreeding within their herds. Although producers had spent more than $1 million to genotype more than 50,000 crossbred animals, they had no tools to test and select their entire herds based on genomic evaluation. In collaboration with the Council on Dairy Cattle Breeding (CDCB) and São Paulo State University in Brazil, ARS researchers in Beltsville, Maryland, developed genomic evaluations for crossbred dairy cattle based on breed composition for the five dairy cattle breeds routinely evaluated (Holstein, Jersey, Brown Swiss, Ayrshire, and Guernsey). The new evaluation methodology was adopted by CDCB, and national genomic evaluations for crossbreds were released to the dairy industry for the first time in April 2019. Those evaluations help commercial producers manage their breeding programs and select tens of thousands of replacement heifers each year.
New model to predict illness using swine feeding behavior. Livestock feeding behavior is dependent on many factors, and feeding is a good proxy for assessing an animal’s health because as an animal gets sick, feed consumption often drops off even before diagnostic symptoms such as fever or difficulty breathing appear. ARS scientists in Clay Center, Nebraska, and South Dakota State University collaborators used an electronic system to monitor the feeding behavior of pigs during the grow-finishing phase and applied machine learning tools to predict swine feeding behavior based on temperature and time of day. Large deviations between predicted and observed feeding behavior before a pneumonia outbreak demonstrated the potential for the model to be used in the automated early detection of a disease outbreak and other stressful events. This work will be used to develop a computer-based modeling system for swine feeding behavior. Future work is expected to lead to the development of software that will allow swine producers to use real-time feeding behavior data as an early predictor of illness and stress events in individual animals, thereby improving both animal well-being and productivity. This is an excellent example of the potential benefits of continuous monitoring of livestock for beneficial outcomes in animal health and production.
SowPro90, a high-density swine genotyping platform. High-density swine genotyping methods rely on DNA variants within the genome sequence chosen to provide the optimum measure of differences between animals. These variants are assigned effects based on data and analyses (i.e., training) collected from ancestors of the population in which one would like to select. However, these DNA variants typically do not cause differences in traits directly, so their use in genomic selection is confined to the population used for training. To create a genotyping method that would be useful across multiple populations, several thousand DNA variants predicted to significantly alter the proteins coded by the genes were identified, increasing the likelihood that the variants cause direct changes in traits. ARS scientists in Clay Center, Nebraska, in collaboration with University of Nebraska scientists, created a genotyping product for swine that contains >90,000 single nucleotide polymorphisms targeting more than 4,000 genes and containing 676 loss-of-function variants predicted to truncate or eliminate the resulting protein produced by the gene. Gene targets were then selected from genome-wide association studies of reproductive traits and disease resistance to further enhance the utility of the genotyping platform. This genotyping product provides swine producers with a powerful new tool for informing genetic marker selection based on functional variants for critically important traits.