Search

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]

Greenhouse gas (GHG) emissions and livestock production are topics that are increasingly intertwined. While livestock production is frequently presented as harmful to the environment, this perception fails to consider the carbon sequestration that takes place in livestock grazing systems and the role that grazing ruminants have in maintaining healthy grassland ecosystems. Grazing lands, which are often unsuited to row crop agriculture, are estimated to account for 25% of the global soil sequestration potential for soil carbon storage. Grazing livestock producers are often overlooked in the GHG reduction conversation, despite over 70% of beef GHG emissions being attributed to the cow-calf sector, which is primarily grazing. Furthermore, there is a relative lack of data surrounding the implementation of climatesmart agriculture practices on grazing operations and a lack of cost-effective methods to measure carbon/ GHG sinks and sources on these landscapes. The potential climate benefits are considerable, with nearly 940 million acres of rangelands in the U.S. supporting forage-based livestock production. South Dakota State University recently received funding to support a large-scale climate-smart project focused on grazing beef and bison. For this project, we are utilizing the Cottonwood Field Station that has over 80 yr of historical grazing data, along with six grazing beef and bison ranching operations in the Northern Great Plains. The work on these ranches will focus on measuring and monitoring the impacts of climate-smart NRCS land practices, including cover crops, improved pasture and range seedings, prescribed grazing, and prescribed burning. A significant focus will be extensive, annual soil sampling across these operations to continuously monitor changes in soil organic carbon, bulk density, texture, pH, and soil microbiome community profiles. Each operation will have GreenFeed methane pasture units deployed for 5 yr to monitor changes in beef cattle and bison methane emissions in response to climate-smart practice adoption. We are also assessing biodiversity pre and post climate-smart practice adoption, as biodiversity improvements may be one of the most overlooked and yet most important responses to climate-smart land practice implementation. Over the duration of this project, a large-scale, comprehensive data collection will be achieved and used to refine GHG and carbon sequestration estimates associated with range livestock systems. Data resulting from this project will be used to inform research protocols and prediction models, fill knowledge gaps, and shape science-based discussions and marketing strategies for climate-smart agricultural commodities. [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]

Advancements in technology have ushered in a new era of sensor-based measurement and management of livestock production systems. These sensor-based technologies have the ability to automatically monitor feeding, growth, and enteric emissions for individual animals across confined and extensive production systems. One challenge with sensor-based technologies is the large amount of data generated, which can be difficult to access, process, visualize, and monitor information in real time to ensure equipment is working properly and animals are utilizing it correctly. A solution to this problem is the development of application programming interfaces (APIs) to automate downloading, visualizing, and summarizing datasets generated from precision livestock technology. For this methods paper, we develop three APIs and accompanying processes for rapid data acquisition, visualization, systems tracking, and summary statistics for three technologies (SmartScale, SmartFeed, and GreenFeed) manufactured by C-Lock Inc (Rapid City, SD). Program R markdown documents and example datasets are provided to facilitate greater adoption of these techniques and to further advance precision livestock technology. The methodology presented successfully downloaded data from the cloud and generated a series of visualizations to conduct systems checks, animal usage rates, and calculate summary statistics. These tools will be essential for further adoption of precision technology. There is huge potential to further leverage APIs to incorporate a wide range of datasets such as weather data, animal locations, and sensor data to facilitate decision-making on times scales relevant to researchers and livestock managers. [ABSTRACT FROM AUTHOR]

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSM s). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals. [ABSTRACT FROM AUTHOR]

Approximately 40% of the land use within the Northern Great Plains is dedicated to livestock production, with much of the 89.9 million head of cattle and calves in the U.S. concentrated in this area. Precision livestock management has ushered in a new era of sensors and technology to monitor individual animal's health, reproductive, and nutritional status in real-time to improve efficiency. Despite these advances, most of the research has been conducted on dairy operations or within feedlot settings. Incorporation on extensive rangeland production systems remains relatively absent (Brennan et al., 2021). This is primarily due to difficulties in studying animals on rangelands caused by heterogeneity of forage resources, variable environmental conditions, and challenges associated with accessing information across vast distances, often without cellular or internet connection. Advances in communication technology are increasingly connecting remote areas, creating new opportunities to improve livestock production efficiency on extensive rangelands using precision technology. Numerous challenges still exist, including applying and integrating multiple technologies across platforms, effectiveness in a real-world setting, technical skills, and knowledge to utilize realtime data, and achieving economic return for livestock producers. Specifically, we discuss the application of precision technologies and mathematical models for improving ruminant nutrition in rangeland systems (Menendez and Tedeschi, 2020). Opportunities exist to refine or develop the next generation of equations/models that more adequately represent nutrient dynamics such as diet selection, supplementation, movement, behavior, water intake, feed conversion efficiency, heat/cold stress, and gain on an individual animal basis. However, effective adoption and adaptability of new technologies/data analytics merit the consideration of potential intended-and-unintended consequences, such as producer dependency on complex hardware and software systems. Hence, precision capabilities coupled with mathematical models are likely the next step to substantially enhance livestock performance in extensive systems when coupled with feasible and reliable long-term strategies. [ABSTRACT FROM AUTHOR]