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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]