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The lack of real-life experiences, such as handling livestock at a production facility (e.g., ranch), exists for a variety of reasons such as availability, liability, time, and cost, amongst others. As more students enter undergraduate animal science programs without prior exposure to animal handling, the necessity for more hands-on, real-life experiences has increased dramatically. Complementary, educational simulation games (edugames) might provide means to overcome the lack of "hands-on" experiential learning by providing similar interactions in a virtual context. The primary goal of this study was to document the design and construction phase of a virtual cattle-handling simulation (CowSim) edugame, and to analyse preliminary survey data. An association exists between students' notion of cattle being mishandled (or not) depending on students' previous opportunity to work with animals (X[superscript 2]P value = 0.0017). Furthermore, students with previous experience handling cattle did not feel more prepared to handle cattle after playing CowSim, but students with previous experience handling cattle indicated they learned more about cattle handling after playing CowSim. After playing the CowSim game, students were, in general, optimistic about their playing experience. They perceived the CowSim game was realistic enough to increase their preparedness towards handling cattle. Our findings suggested there is heightened interest was for the use of an edugame to help visualize difficult concepts. Virtual learning tools such as the CowSim edugame are essential for advancing animal science education through the integration of virtual technology. However, improvements are warranted in the CowSim to capture more realistic scenarios given the complexity of the simulation game.

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]

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]

The environmental impact of the livestock industry, particularly concerning greenhouse gas (GHG) emissions, has become a growing concern. Addressing this challenge requires accurate and efficient measurement methods for monitoring and mitigating gas emissions. One noteworthy method is the GreenFeed (GF), developed by C-Lock Inc., designed to measure methane (CH4 ) emissions and carbon dioxide (CO2 ) concentrations in ruminants through a real-time non-invasive monitoring device with reduced labor intensity and a high level of precision. The GF is the most used equipment to measure GHG in cattle; however, there is still considerable variability that is not explained due to the variation among animals. Thus, the objective of this work is to analyze other environmental factors influencing GF measurements in cattle to explain the variability observed. For this, a total of 127 yearling Angus steers were evaluated for GHG emissions through 6 GF units placed in 6 different pastures (n = 21 to 22 animals/pasture). Animals were evaluated from June to August (2023) at the South Dakota State University Cottonwood Field Station (Cottonwood, SD). The mean values of GHG evaluated were 174g CH4 /d and 5,885 g CO2 /d, showing a coefficient of variation (CV%) of 41% and 27%, respectively. To evaluate GHG variation, we used ANOVA type III (package car in R) for the following variables measured by the GF: temperature, wind speed, wind direction, variance influenced by the GF unit by itself, and animal factors as the number of visits and the duration per visit. Table 1 shows the percentage of variance explained by all these factors on the variation of CH4 and CO2 ; betweenanimal variation (13 and 15.5%), the GF unit (7.2 and 6.3%), duration of visits (2.7 and 2.8%), wind speed (1.7 and 1.3%), temperature (1.3 and 0.0%), wind direction (0.1 and 0.1%) and number of visits (0.3 and 0.0%). The residual error of the model represented 73.7% for CH4 and 73.9% for CO2, indicating the vast percentage of non-explained variance in these two gases, which, in this case, could be associated with different grazing systems or stocking density of animals in the different pastures. Results of this study demonstrate how other factors, non-related to the animal or the type of feed, may contribute to explaining the variation observed in CH4 and CO2 emissions. In future experiments, these factors should be considered to improve the accuracy of measurements and establish smaller differences between groups/individuals. [ABSTRACT FROM AUTHOR]

Objectives were to evaluate the effects of winter-feeding forage (2.02 Mcal/kg dietary ME) vs. concentrate (2.84 Mcal/kg of dietary ME) diets preand post- calving on enteric methane emissions, dry matter intake (DMI), and performance of beef cows. Forty-six pregnant (210 ± 10 d of gestation) Angus and Simmental-Angus cows [n = 46; body weight (BW) = 630 ± 12.0 kg) were blocked by breed, age, and BW and assigned to one of two treatments: 1) Ad-libitum feeding of forage-based diet (HFOR; n = 23); or 2) a concentrate corn-based diet with a restricted intake of 1.2% BW (HCON; n = 23). Cows were housed in a group-pen at the South Dakota State University CowCalf Education and Research Facility (Brookings, SD). Feed consumption was tracked using an electronic feeder (Insentec, The Hague, Netherlands) and enteric methane emissions were monitored using the GreenFeed trailer system (C-Lock Inc., Rapid City, SD). Body weight measurements were taken bi-weekly. Treatments started being applied on d 50 (± 10) precalving and will continue to be applied up to d 84 post-calving. Preliminary data reported herein was collected from d 42 pre-calving up to d 42 post-calving. Performance and dry matter intake data were analyzed as repeated measures using the MIXED procedure of SAS with treatment, period, and their interaction as fixed effects and animal as a random effect. Enteric emissions were analyzed using a linear mixed effects model with animal as a random effect in R, with a posthoc contrast among treatments. Pre-calving, HFOR had greater (P < 0.01) CH4 emissions than HCON (264 ± 9.19 g/d and 246 ± 9 g/d for HFOR and HCON, respectively). Both groups presented dynamic changes in CH4 emissions over time (P < 0.01). Post-calving, no differences were observed between treatments (P = 0.55); however, temporal changes (P < 0.01) and treatment × d interaction (P < 0.01) persisted, indicating ongoing diet-temporal interplay. No treatment × period interactions (P ≥ 0.16) were observed for DMI and performance. As designed, dry matter intake was less (P < 0.01) for HCON (8.66 ± 0.45 kg) than HFOR (17.3 ± 0.45 kg) cows. Further, dry matter intake was less (P < 0.01) pre-calving (11.11 ± 0.28 kg) than postcalving (14.85 ± 0.48 kg). Cows in the HFOR group were heavier (P = 0.01) than HCON (646.22 ± 14 kg vs. 593.43 ± 14 kg, respectively) likely due to ruminal fill; and as expected cows were heavier (P < 0.01) prethan post-calving, with the greatest BW observed on d 14 pre-calving (639.52 ± 9.92 kg). These data indicate that high concentrate corn-based diets may be used in dry-lot settings to optimize nutritional management and reduce methane emissions when compared with conventional high forage winter-feeding strategies. [ABSTRACT FROM AUTHOR]

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]

It is widely accepted that accumulation of greenhouse gases in the atmosphere, including methane (CH4), is promoting climate change. Cattle contribute significantly to CH4 emissions, mainly generated during ruminal fermentation. Measuring CH4 emissions is expensive and time-consuming, hampering individual, constant, and scalable monitoring at the farm level. Thus, developing innovative methodologies to measure CH4 emissions from cattle is crucial. These methodologies should enable the identification of lower-emitting animals and to assess the effectiveness of various mitigation strategies. This study aimed to develop a low-cost microcontroller-based device to record variations in CH4 concentrations in the air exhaled by animals during indoor intake periods. The device was assembled into a plastic box with an internal compartment for an MQ-4 sensor that measures CH4 concentration (ppm). The device is equipped with an air pump and a plastic tubing with a 4 mm diameter for air flow generation (2 L/min) from the feeder area to the sensor, ensuring individualized measurements. An ESP8266 module collects the sensor signal and sends it to a laptop through a Wi-Fi network. To test the device, emissions from 16 dairy cows were monitored at milking (0500 and 1600 h) during three consecutive days. Variations in CH4 concentration were recorded three times per second in a text file for each animal at milking times. The mean CH4 concentration among the cows was 666 ppm (20.4% CV). Figure 1 compares emissions between two animals: a lowemitter (458 ppm, 4.1% CV) and high-emitter cow (853 ppm, 9.2% CV). The findings reveal the capability of the device for monitoring CH4 emissions on an individual basis, underscoring its value in promoting sustainability at the farm level. Future iterations could benefit from integrating sensors for temperature, humidity, and atmospheric pressure to refine CH4 measurements. Additionally, distance measurement technologies could standardize the proximity of the air sampler to the animals, thereby enhancing the accuracy of the emissions captured. Implementing a CH4 background reader could allow for continuous monitoring of ambient conditions, adjusting the CH4 readings to account for fluctuations that might affect concentrations. Finally, incorporating animal identification tools, such as RFID tags or computer vision identification systems, could automate the process of associating CH4 emission data with specific animals, ensuring consistent data collection. These advancements would not only improve the precision of the device but also offer a more comprehensive understanding of individual enteric CH4 emissions. [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]

Time series analysis is a traditional approach to analyzing a sequence of data. This approach allows us to study the trend over time, discover the temporal dependencies, and analyze the fluctuations within the data. An understanding of the underlying data generative process can lead to better forecast and decision-making. The time series application can be found across diverse domains including animal sciences, economics, and environmental sciences. In this talk, we will present the fundamental concepts of time series and traditional and state-of-the-art approaches for analyzing such data. By providing a comprehensive overview of time series analysis, this talk aims to equip the audience with a foundational understanding and practical insights into harnessing the power of temporal data and the use of mathematical models that inform and improve decision-making in animal production settings. [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]

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]

Virtual fencing (VF) has the capacity to transform the landscape of livestock management within extensive rangeland systems. The objective of this study was to evaluate the effect of Vence (Merck & Co., Inc., Rahway, NJ) VF technology on yearling steer behavior and performance. Steers were allocated into one of two grazing systems [continuous grazing (CG) or rotational grazing (RG) managed using VF] across three different stocking rates (light, moderate, and heavy, 0.32, 0.40, and 0.72 animal unit months, respectively) for three grazing seasons. Based on initial body weight, steers were randomly blocked into six pastures (346 ± 39, 273 ± 34, and 319 ± 29 kg, respectively) at the South Dakota State University Cottonwood Field Station (n = 127, 135, and 127, in 2021, 2022 and 2023, respectively). Steers assigned to the CG treatment had free access to the entire pasture for the duration of the grazing season. Steers assigned to the RG treatment were rotated among virtual ‘paddocks’ for the duration of the grazing season. The days spent grazing in each paddock for the RG treatment were determined based on bi-weekly clip plots for biomass estimation. The VF collars were used to track steer location at 5-min intervals; only steers assigned to the RG treatment were managed with VF boundaries and exposed to auditory and electrical cues from the collars. Raw data was downloaded from Vence Herd Manager through an automatic programming interface and cleaned by removing messages that failed to transmit correctly or were outside the bounds of study site pastures. Animal behavior metrics of daily distance traveled (DDT), daily grazing time, daily resting time, and daily walking time were calculated for each individual animal and averaged by pasture using Program R. Steer weights were collected using SmartScales (C-Lock Inc, Rapid City, SD). Average daily gain (ADG) was calculated using a linear model to develop a regression equation for each individual animal using weight as the dependent variable and day of trial as the independent variable. The slope of the regression line was used to estimate ADG. Individual DDT, behavior, and performance metrics were analyzed using a mixed model analysis of variance (ANOVA) to compare the impact of grazing treatment and stocking rate (fixed effects) with year as the random effect. Treatment did not influence (P > 0.05) grazing, walking, or resting behavior. Similarly, no differences were detected in DDT between treatment groups or across different stocking rates. Furthermore, there were no differences (P > 0.05) in ADG across treatment groups. Implementation of VF had no impact on animal behavior or performance, justifying its use in grazing settings; however, financial and cattle finishing aspects still need to be evaluated to determine its overall benefits and costs. [ABSTRACT FROM AUTHOR]

Today's livestock producers face increasing public scrutiny because animal agriculture is often cited as a major contributor to greenhouse gas (GHG) emissions. However, this negative perception does not take into account the carbon (C) sequestration benefits of grazing livestock nor that grazing lands account for 25% of the global soil sequestration potential of soil C storage. Grazing beef cattle and bison are key to healthy grassland ecosystems and provide the most nutrient dense source of protein available for human consumption. Beef and bison producers who graze their livestock are widely recognized as environmental stewards because they use sustainable grazing practices that maintain grassland health. These practices are considered climate-smart agriculture (CSA) because they promote soil C sequestration and resilience to increased climate variability. Yet, these same producers are generally overlooked in GHG reduction and C sequestration incentives because of a relative lack of cost-effective methods to measure C and GHG sinks and sources. Emerging technologies are beginning to bridge that gap by paving the way to develop, implement, and monitor CSA practices. Furthermore, rangelands in the U.S. dedicated to supporting forage-based livestock production amount to nearly 940 million acres. This is more than double the acres dedicated to row crops and represents enormous potential to contribute to U.S. climate change commitments. Successful implementation and monitoring of CSA land management practices creates potential to develop climate-smart livestock commodities that sustainably provide economic incentives to producers operating resilient livestock grazing systems. Recently, South Dakota State University, along with ten external partners, was awarded a CSA commodity development grant by the USDA and NRCS focused on CSA-raised grazing beef cattle and bison in the Northern Great Plains. Specific objectives include: 1) developing and implementing existing and novel CSA practices alongside custom grazing plans; 2) measuring, monitoring, reporting, and verifying CSA practice impacts on GHG emissions, C sequestration, and ecosystem biodiversity of plants, arthropods, and wildlife; and 3) creating sustainable, value-added market opportunities for producers using CSA practices. To achieve these objectives, the project team is partnering with producers throughout the Northern Great Plains and beyond and will provide incentives for CSA practice implementation. Additionally, emerging and novel technologies will be used to collect large-scale data across producer operations and test sites to determine and refine GHG and C sequestration estimates associated with range beef cattle and bison systems. The long-term goal of this project is to provide CSA beef and bison producers with sustainable, value-added market opportunities that align with consumer preferences and demand. [ABSTRACT FROM AUTHOR]

Predicting dry matter intake (DMI) for beef cattle on extensive rangelands presents a significant challenge to determining stocking rates. Traditionally, DMI is estimated by taking full body weight (BW) multiplied by a percentage selected based on animal class, production phase, and forage quality, which introduces tremendous levels of accumulated error at the herd level. Animal Unit Months (AUMs) are utilized to simplify the determination of stocking rate (animal units per area per a specific period of time) of pastures. This challenge represents a tremendous opportunity to leverage precision technology to account for individual animal variation in BW and growth, with subsequent impacts on herd-level decisions. Therefore, the objective of this study was to utilize precision livestock technology (PLT) collected data to build a precision system model (PSM) to evaluate the differences in predicted DMI using either initial BW, expected midseason BW, or PLT measured BW. The PSM model was built utilizing BW data measured using SmartScale (C-Lock Inc.) for 60 days during the summers of 2021 and 2022 on Angus yearling steers (average initial BW 393.71 ± 39.01 and 315.23 ± 53.91 kg, n = 130 and 124, for year 2021 and 2022, respectively) on native pastures at the South Dakota State University Cottonwood Field Station. The PSM evaluated total forage consumption and deterministically estimated hectares of pasture required to meet the herd forage demands relative to available biomass (kg/ha). the PSM estimated 4.49% and 6.94% more DMI at the herd level compared with using initial BW for years 2021 and 22, respectively, and 1.64% less DMI than mid-season BW in 2021. This resulted in an additional 14.03 and 17.95 ha required for years 2021 and 22, respectively, according to PSM estimates compared with initial BW, while animals were understocked by 5 and 0.3 ha for 2021 and 2022, respectively, using mid-season BW. Individuals expressed a divergent growth rate, resulting in greater misalignment between static and PSM predicted DMI as the grazing period progressed (Menendez et al., 2023), indicating greater opportunities for targeted grazing management in long grazing periods. Therefore, applying precision data provides more precise DMI estimates and demonstrates the advantages and disadvantages of herd-level estimates. The use of PSM helps to identify high-leverage precision tools to minimize a performance gap like overgrazing extensive rangeland systems and demonstrates the critical need to develop robust data-collection and processing steps to leverage continuously collected PLT data. [ABSTRACT FROM AUTHOR]

Attaining sustainability of livestock production and understanding environmental synergies requires in-depth knowledge of grazing animal growth and behavior. A pilot study (2021-22) was conducted at the South Dakota State University Cottonwood Field Station as part of a broader project to evaluate precision livestock technology and environmental synergies on native rangelands. Yearling Angus steers (n = 262) were fitted with virtual fencing collars (Vence), weighed on a traditional chute scale, and allocated to one of six native grass pastures equipped with individual weighing (SmartScale) scales at the water source. Each pasture was assigned either Rotational (RG) or Continuous (CG) grazing strategy and one of three stocking rates (Low, Medium, and High, 0.3, 0.42, and 0.7 AUMs, respectively) in a 2x3 factorial design. The data were downloaded, (Rcore Team, 2023), and spurious weights identified and removed using Robust Regression (hwts > 0.99, Parsons et al. 2023). Effects of stocking rate and grazing strategy were analyzed on water visits, time spent drinking, and growth using a linear mixed effects model to calculate effect sizes. Differences between BW and ADG calculated using the smart-scale vs. traditional chute weights were evaluated using a paired t-test. Steers visited the smart-scale 3.44 ± 2.79 visits per day, with significant effect of RG vs. CG strategies (3.65 ± 0.09 vs. 2.89 ± 0.10 visits per day respectively, P < 0.05). Total water visit duration averaged 5.04 ± 7.58 minutes per day, with most visits occurring between 0600 and 2000 hours and no observations occurring between 2000 and 0000 hours. RG managed steers exhibited significantly higher water visit duration (5.28 ± 0.308 vs 4.16 ± 0.317 minutes/day, P < 0.05) compared with CG managed steers. Smartscale measured weights were significantly heavier than chute weights at the beginning and end of the study period, (7.47 ± 24.54 and 9.77 ± 46.25 kg respectively, P < 0.05). No differences in overall ADG were found between smartscale and traditional chute weights(P > 0.58), however, significant temporal interactions occurred (P < 0.05), which demonstrates that ADG varied over the trial duration. This created significant discrepancies in predicted vs. actual stocking rates in heavy stocked pastures (0.84 AUM, P < 0.05), indicating pastures exceeded their target stocking rate. Overall, we found steers readily acclimated to smart-scale weighing systems, and creates a viable technology to monitor watering behavior, real-time body weight, and ADG in extensively managed cattle. Further, we showed RG vs CG grazing strategies significantly affect animal drinking behavior, while stocking rate resulted in inequalities between expected vs. actual assigned animal unit months. Precision livestock technologies offer a vital solution towards enhancing sustainable livestock management practices and improving nutrition and modeling in extensive rangeland systems. [ABSTRACT FROM AUTHOR]

Cattle dry matter intake (DMI) is an essential component of calculating cattle stocking rates, determining nutrient requirements, and evaluating grazing efficiency. Cattle DMI and digestion of forages impact enteric greenhouse gas (CO2e) emissions. Enteric emissions include methane (CH4) and carbon dioxide (CO2), that are eructated by ruminants. The amount of methane produced is affected by consumption, quality, and type of feedstuffs. Intake of grazing animals varies on environmental factors and physiological stage. Additionally, increased GHG levels indicate energy loss during the rumen fermentation process. However, there may be a silver lining to enteric GHG emissions to predict DMI of grazing animals since they are highly correlated with DMI and forage composition. There is limited data on the relationship of DMI and GHG on extensive rangeland systems. Obtaining data for beef cattle DMI and enteric emissions on forage-based diets similar to extensive rangelands is needed to develop an equation capable of predicting DMI for grazing cattle. Therefore, our objective was to determine the relationship between CH4, CO2, oxygen (O2), and hydrogen (H2) emissions and DMI of dry beef cows to develop a mathematical model that predicts grazing DMI from enteric emissions. The predictive equation or precision system model (PSM; Menendez et al., 2022) was developed using data from two feeding trials that were conducted using GreenFeed, SmartFeed Pro, and SmartScale (C-Lock Inc. Rapid City, SD). This study was conducted at the SDSU Cottonwood Field Station (Cottonwood, SD). The two feeding trials consisted of dry beef cows (n=10) receiving low (8% CP) or high (15% CP) quality grass hay using a 14-day adaptation period and a 14-day period of data collection. Regression, artificial neural network, and dynamicmechanistic models were developed using these data and assessed to identify a model that accurately and precisely predicts forage DMI for dry beef cows on pasture. Model evaluation of the machine learning algorithms used a training, testing, and cross-validation scheme to determine model accuracy. Evaluation of mechanistic models used the Model Evaluation System (MES; Tedeschi, 2006) to measure accuracy (mean bias, Cb, RMSEP), precision (R2, MEF, CCC), and screening for systematic errors. This study successfully integrated three precision technologies which improve research capabilities on extensive rangeland systems through precision enhanced data collection. Deploying a precision-based DMI algorithm enhances researchbased capabilities to manage range beef cattle on an individual level by more precisely setting stocking rates, providing supplementation, and evaluating individual animal efficiency; ultimately leading to lower cost, optimized resources, and enhanced environmental sustainability. Further, the enteric emissions data collected fills a gap for missing GHG data of dry beef cows in maintenance phase in semi-arid western South Dakota rangelands. [ABSTRACT FROM AUTHOR]

The power and utility of modeling approaches are only as good as the animal scientist's ability to apply them. Critical to this ability is knowledge of what tools are available, how they can be applied, and where to find resources to turn ideas into action. After experiencing a hands-on workshop on Agent-Based Modeling in Agriculture and Making Sense of Big Data, Machine Learning, and Modeling, the National Animal Nutrition Program-Pre-Conference Symposia attendees will have a chance to debrief. This roundtable provides a time to ask critical questions and to raise ideas regarding challenges and opportunities regarding modeling training and implementation to benefit animal science research and student training. Finally, updates will be given to provide a further vision of the next steps regarding the National Animal Nutrition Program's efforts in mathematical modeling. [ABSTRACT FROM AUTHOR]

Advancements in precision livestock technology have resulted in an unprecedented amount of data being collected on individual animals. Often processing of these datasets can be time consuming, tedious, and prone to human-error if processed with conventional software. Open-source statistical software (e.g., R of Python) can provide users with tools to automate many data processing steps for compiling and aggregating data. However, the steps from data collection to processing and training machine learning (ML) models can be time intensive for those new to statistical programming, with few examples pertaining to livestock. The objectives of this hands-on training are: 1) introduce workshop participants to methods for streamlining data processing tasks in Python and R, 2) demonstrate and provide examples of compiling large accelerometer datasets for determining daily livestock behavior; 3) introduce a suite of classification algorithms and validation testing approaches for classifying accelerometer training datasets, and 4) utilize model predictions to estimate and analyze daily behavior for beef cattle. Real life example datasets and code will be provided to workshop attendees to demonstrate how to take raw accelerometer datasets through a finished machine learning analysis. An example of estimating daily energy expenditure for individual animals using behavior data will be provided to highlight linkages to potential rangeland nutrition modeling applications. To obtain maximum benefit from this workshop, participants should bring a portable laptop computer to the workshop and will be encouraged to load software and preview content from a shared cloud directory prior to this training. [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]

Conversion of grassland to cropland has been linked to many complex environmental challenges in natural resource systems. South Dakota is a mosaic of grasslands, wetlands, and cropland that has experienced tremendous land use change over the past 10 years and is expected to continue for the next 50 years. The rate of future conversion may vary greatly depending on economic, policy, and social factors. Land conversion influences cumulative erosion from arable soils which could impact hydrologic flow and water quality. Quantifying future changes for these three externalities is important to understand the possible long‐term consequences of grassland conversion. A system dynamics model was developed to address the dynamic complexity of these natural resource systems by capturing its structure and behavior and was able to adequately replicate historical changes in erosion, discharge, and total suspended solids from 1947 to 2012. Recommendations for resource managersResource managers should apply this tool for problems that require quantitative assessment of environmental consequences to be coupled with economic, policy, and social factors that influence long‐term land‐use change decisions.The model can be used to evaluate alternative policies and indicate the magnitude of change for three critical environmental factors using different long‐term grassland conversion, climate, and tillage (conservation and conventional) patterns.Model output of four spatially explicit water‐catchments that span South Dakota from east to west: Big Sioux, James, Bad, and Belle Fourche rivers can be used to quantify differences between unique natural resource systems.The model is adequate for the purpose of generating forecast for future annual erosion (t·ha−1·year−1), discharge (million cubic meters), and total suspended solids (mg/L) under different potential future grassland conversion rates and should be leveraged by managers to gain insight into future landscape scale consequences of grassland conversion in South Dakota.Potential for additional natural resource applications. [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 ( PLT ). 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 PLT. 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 time scales relevant to researchers and livestock managers., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)