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Understanding beef cattle enteric emissions on extensive western U.S. rangeland systems is critical for implementing sustainable grazing practices that reduce greenhouse gas (GHG) emissions. These practices must also improve livestock and plant productivity and climate resiliency while maintaining or reducing production costs. However, enteric emissions of grazing cattle under different stocking rates and grazing management strategies are largely unknown for western U.S. rangelands. Thus, our objective was to evaluate the differences in enteric emissions of stocker steers grazing native rangeland. Yearling Angus steers (n = 127) were stratified by initial body weight (BW) to six groups, then randomly assigned to one of six pastures grazed from June to September 2023 at the South Dakota State University Cottonwood Field Station (Cottonwood, SD) as part of a long-term stocking rate trial (80+ yr). Each pasture was equipped with a GreenFeed (C-Lock Inc, Rapid City, SD) pasture system to measure individual animal CH4 and CO2 emissions for continuous grazing (CG) and rotational grazing (RG) strategies in three stocking rates (Light, Moderate, and Heavy, 0.32, 0.40, and 0.72 animal unit months, respectively) in a 2 x 3 factorial design. Differences in enteric emissions among stocking rate and grazing strategy were analyzed using a linear mixed effects model in R, using the lme4 package, with animal as a random effect, and a post-hoc contrast among treatments was assessed using the lsmeans package. Steer initial and final BW were similar (P > 0.05) among treatments. For CH4 emissions, there was a significant interaction between stocking rate and grazing strategy (P < 0.05). Specifically, CH4 emissions for moderate-RG (195 ± 4.3 g/d) were less than those from heavy-CG (221 ± 4.5 g/d) and light-RG (221 ± 4.7 g/d) over the study period (P < 0.05). For CO2 emissions, light-CG steers respired greater CO2 emissions (6892 ± 101 g/d) than heavy-RG (6398 ± 107 g/d; P < 0.05). All other treatment lsmeans were similar (P > 0.05) and intermediate to these treatment effects. The assessment of GHG emission values for cattle grazing native rangeland is essential for informing climate mitigation strategies, improving animal efficiency, and understanding changes in GHG emissions over production phases and across animal classes. [ABSTRACT FROM AUTHOR]

Solving complex livestock production problems is a pressing issue for achieving long-term sustainability and profitability and often requires modeling techniques. The use of mathematical models is undoubtedly a daily practice in the livestock industry, especially nutrition, and has improved animal performance, productivity, and environmental sustainability while maintaining or reducing costs. Although many students and professionals use spreadsheets and existing empirical models for nutrition and management [NASEM (2016)], there is still a need to understand the complexity of livestock systems and the utility of flight simulator models. At the same time, more complex models (although robust) may fail to provide new insights for experienced nutritionists due to poor userfriendliness. A systems understanding goes beyond simply obtaining a desired output, such as optimizing a total mixed ration, but instead leads to identifying high-leverage solutions and gaining insight. Further, parameterizing and calibrating variables and equations and testing management scenarios is straightforward. However, developing causal feedback linkages (A to B and B to A) and identifying time delays is less intuitive and more challenging for novices. Model flight simulators grounded in fully documented, calibrated models provide a means to introduce practitioners to a methodology of insight generation because the user designs and runs the model scenarios for themselves, challenging their mental models. Such approaches are generally more impactful (compared with someone telling them) because they have gained insight into the system themselves, and, in the case of open source (white box models), they can explore equations and parameters. Therefore, understanding how to utilize dynamic models in scenario-based simulations is critical in training current and future modelers in animal science. This hands-on model training will cover the basics of System Dynamics modeling and allow participants to run real-time animal production simulations. Finally, participants will be “debriefed” to unpack “ah ha” moments that were unexpected. The debrief will include using models to develop accurate guidelines and recommendations for those who cannot use computer models and developing experimental designs to test hypotheses and “validate” model recommendations (proof in the pudding). Participants will gain knowledge of System Dynamics applications for animal production systems, experience using flight simulators, and their utility in teaching, informing, and guiding their livestock production or that of a client. The “flight simulator” will focus on production and nutrition with species-agnostic principles. Participants will also have a new tool to identify areas for improvement in model development (i.e., what is missing?), research questions, or industry needs. The need for modelers who can turn big data into insight, knowledge, and wisdom using a systems approach is becoming even more critical due to the increasing use of precision livestock farming. Thus, providing the livestock industry with trained systems modelers will help achieve current and future sustainability challenges. [ABSTRACT FROM AUTHOR]