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]

Our objective was to evaluate how virtual fencing grazing management during the stocker phase influences steer growth performance, carcass traits, and enteric emissions during the finishing phase. Angus steers [n = 96; initial body weight (BW) = 375 ± 7.2 kg) grazed native rangeland from 07 June 2023 to 31 August 2023 in Cottonwood, SD at South Dakota State University’s Cottonwood Field Station. Following summer grazing, steers were transported to Brookings, SD and fed for 130-d at the South Dakota State University Cow-Calf Education and Research Facility. During the summer grazing season, steers were assigned to different stocking rates (Light, Moderate, and Heavy, at 0.32, 0.42, and 0.72 animal unit months, respectively), and grazing strategies [continuous (CG) or virtual fencing rotational (RG)] in a 2 × 3 factorial design. Steers were transitioned from a 30% roughage to a 10% roughage diet over 21-d. The finishing diet (13.7% CP, 17.2% NDF, 1.36 Mcal/kg NEg, and 30 g/907-kg monensin sodium) was manufactured twice daily and provided ad libitum. Feed consumption was tracked using precision monitoring equipment (Insentec, The Hague, Netherlands). Steers were individually weighed on d 1 (entry BW), 21, 63, 90, and 130 and administered a steroidal implant (200 mg trenbolone acetate and 20 mg estradiol; Revalor-200, Merck Animal Health, DeSoto, KS) on d 21. Enteric emissions, including methane and carbon dioxide, were monitored using the GreenFeed trailer system (C-Lock Inc., Rapid City, SD), growth performance, carcass traits, and categorical carcass traits were analyzed in a factorial design using a randomized complete block design with individual steer considered the experimental unit. Final BW was determined from hot carcass weight divided by 0.625. Steers from heavy-CG had greater feedlot entry BW (P ≤ 0.05) compared with all treatments except light-CG and light-RG. Light-CG and light-RG had greater entry BW compared with heavy-RG (P ≤ 0.05). Gain efficiency was greater for light-CG compared with light-RG, moderate-CG, and heavy-CG (P ≤ 0.05), while moderate-RG and heavy-RG were intermediate (P ≤ 0.05). Steers from moderate-RG had greater marbling scores compared with heavy-RG (P = 0.05), and all other treatments were intermediate. Neither stocking density (P ≥ 0.15) nor grazing management strategy (P ≥ 0.13) had any influence on other measures of growth performance, emissions (CH4 and CO2 ) production rate, or carcass traits. These findings suggest that stocking density and grazing-management strategy (CG vs. virtual fencing-RG) influence gains on pasture and alter feedlot entry BW, highlighting the need for further investigation into their implications for greenhouse gas emissions, as well as for determining optimal stocking densities and management strategies for ranchers [ABSTRACT FROM AUTHOR]

Western rangelands represent approximately 58% of the total arable land in the U.S. and are used primarily for cow-calf production, which has the largest greenhouse gas (GHG) emission footprint of all beef production phases. Further, beef production sustainability concerns involve climate mitigation (reducing GHG output) and adaptation (climate-resilient soil-plant-animals). Precision livestock farming (PLF) may help address sustainability concerns by providing innovative solutions and new opportunities for extensive rangeland production. The use of precision measurement and management tools with precision system models can connect measurement data to inform management capabilities. For example, real-time individual weighing and remote sensing (precision measurement tools) can be used to inform and implement dynamic rotational grazing management using virtual fencing technology (precision management; Menendez et al., 2022, 2023). Recent USDA investment in climatesmart agriculture (CSA) commodities has provided over $3 billion in funding to implement practices to reduce GHG emissions in agriculture production, for which PLF may be one approach to accomplish these goals. The overall purpose is to develop commodities produced using NRCS climate-smart practices (e.g., prescribed grazing, 528) and document reductions to provide market opportunities associated with inset GHG. Currently, South Dakota State University is leading two simultaneous programs, which include precision ranching (virtual fencing, precision weighing, and GHG evaluation) and a beef and bison CSA program (GHG evaluation; 7 producer-owned research ranches, representing more than 1.09 million ha). The scale of the CSA program has revealed that current PLF research has only been a prelude to providing the precision tools necessary to successfully implement CSA practices and document their impact through monitoring, measuring, reporting, and verification (MMRV). This presentation will include rangeland grazing case studies that cover the application of virtual fencing, animal location, behavior tracking, remote sensing, precision weighing, feeding, supplementation, and enteric emissions and soil moisture monitoring on extensive rangelands. Case studies will include an overview of big data processing and precision system model development methods. Increasing awareness of available PLF tools for optimizing grazing management, animal performance, productivity, and associated challenges (maintenance, costs) is essential for meeting livestock and sustainability goals. PLF and data-driven approaches aid in the creation of scalable, cost-effective MMRV protocols and models (soil carbon and enteric GHG) for extensive rangelands (20 to 70,000 ha areas) that allow producers to realize potential CSA market incentives. Further, MMRV will likely help guide management decisions by identifying CSA strategies that build climate-resilient landscapes for sustainable livestock production and other environmental synergies (soil microbiome, bird and insect habit, water retention). [ABSTRACT FROM AUTHOR]

Spatial utilization of pasture landscapes by grazing animals is often heterogeneous and driven by complex environmental and physiological interactions between plants and animals. Synergistic energy pathways can be modeled using mechanistic relationships describing resource distribution, animal behavior, metabolic energy rate, and pasture utilization using biometric sensors. The objective of this study was to demonstrate the ability to measure the energy landscape by mapping fine-scale contributions to animal energy expenditure, nutrient acquisition, growth, and overall economic profitability. We utilized animal trajectories and location fixes recorded by virtual fencing collars (Vence) daily body weight (BW) using precision livestock scales (Smartscale, C-Lock Inc.) and oxygen consumption (GreenFeeds, C-Lock Inc.) on steers (n = 127), managed as part of a broader project to evaluate environmental synergies in extensive grazing systems. Steers were allocated to one of six native grass pastures assigned to either rotational or continuous grazing strategies, and one of three stocking rates (low, moderate, high) in a 2 x 3 factorial arrangement. Space utilization was quantified as the frequency of fixes located within each cell of a 5 x 5-meter Grid and the distance between grid cells and water was calculated using the Pythagorean theorem. Movement behavior was quantified using Program R to calculate step length, the distance between two temporally continuous fix points utilizing the Pythagorean Theorem, and turn angles calculated as the cosine of the pre- and pro-ceeding step lengths where larger values indicated greater deviation from a straight trajectory. A mixed linear regression model was fitted using the lmer function in the lme4 package. Space utilization decreased with increased distance from water (P < 0.05), with rotational grazing strategies increasing concentration around water sources (P < 0.05). This indicates an opportunity to utilize precision livestock technology to facilitate pasture management and increase space utilization in grazing cattle. [ABSTRACT FROM AUTHOR]

The methodology of analyzing methane emissions in cattle grazing native rangeland is imperative for producing reliable and repeatable estimations of total enteric emissions on an individual and herd basis. However, due to seasonal differences in climate from year to year, there are distinct differences in forage biomass availability and nutrient composition on extensive rangelands. Thus, our objective was to evaluate the differences in methane emissions using phenological forage phases compared with specific time periods. Yearling Angus steers (n = 127) were allocated to one of six pastures grazed from June to September (2023) at the South Dakota State University Cottonwood Field Station (Cottonwood, SD). Each pasture was equipped with a GreenFeed (C-Lock Inc., Rapid City, SD) pasture system to measure individual animal CH4 emissions. A subset of CH4 data from two cool-seasondominated pastures were aggregated from the overall study. We used two methods to segment CH4 by either 1) month (June, July, August) or 2) phenology (vegetative, reproductive, and senescence) to derive average CH4 (g animal -1 d-1) for each period. Western wheatgrass (WWG), a cool-season native, was used as a proxy for cool season grass development. On May 28th, 2023, the growing degree days (GDD) reached 632, representing the 3.5 leaf stage. WWG has been documented to reach maturity at approximately 1,100 GDD, which occurred on June 20th, 2023. Nutrient content of the forage (i.e., lignin) was used to approximate the start of senescence on August 11th. These developmental markers created estimates of the vegetative, reproductive, and senescence periods within our emissions data. Differences in enteric emissions for each approach were analyzed using a linear mixed effects model in R, and posthoc contrast between methods was assessed using the lsmeans package. There were no statistical differences in CH4 by month (P > 0.05), and the means for June, July, and August were 217, 214, and 211 ± 4, respectively By phenological phases, there was a difference (P < 0.05) between maturity and senescence, with means for vegetative, reproductive, and senescence as 214, 219, and 206 ± 4, respectively. Methane intensity (g animal-1 d-1) per period was then multiplied by the number of days in each month or phase to estimate total emissions for the entire grazing season (86 d). The monthly approach totaled 18,383 g CH4 per animal, while phenology totaled 18,504 g for the grazing season. This is a difference of 0.654% between the same data segmented by month or by phenology. Therefore, because of the dynamic nature of forage and cattle production, it is likely that accounting for phenology will provide greater clarity for GHG on rangeland systems rather than arbitrary periods of time and will require the use of dynamic models to handle the complexity of enteric emissions of grazing animals. [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]