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. Advances in sensor technology have ushered in the ability to collect vast amounts of data in real-time for animal production systems. Though there is much promise in improving animal production and research utilizing precision systems, for this technology to be truly impactful, data generated needs to be accessed and processed in near real time for relevant decision-making. In this paper, three application programming interfaces were developed to facilitate near real-time data access. In addition, open-source tutorials were developed to generate summary statistics of three precision livestock technologies and systems checks to ensure equipment is working properly and animal adoption rates of technology are adequate for data collection. The development of data processing outlined in this paper will be essential for the successful implementation of precision technology and integration of data in real time with animal nutrition or decision-making algorithms.

An essential component required for calculating stocking rates for livestock grazing extensive rangeland is dry matter intake (DMI). Animal unit months are used to simplify this calculation for rangeland systems to determine the rate of forage consumption and the cattle grazing duration. However, there is an opportunity to leverage precision technology deployed on rangeland systems to account for the individual animal variation of DMI and subsequent impacts on herd-level decisions regarding stocking rate. Therefore, the objectives of this study were, first, to build a precision system model (PSM) to predict total DMI (kg) and required pasture area (ha) using precision body weight (BW), and second, to evaluate differences in PSM-predicted stocking rates compared to the traditional herd-level method using initial or estimated mid-season BW. A deterministic model was constructed in both Vensim (version 10.1.2) and Program R (version 4.2.3) to incorporate individual precision BW data into a commonly used rangeland equation using %BW to estimate individual DMI, daily herd DMI, and area (ha) required to meet animal DMI requirements throughout specific grazing periods. Using the PSM, differences in outputs were evaluated using three scenarios: (1) initial BW (business as usual); (2) average mid-season BW; and (3) individual precision BW using data from two precision rangeland experiments conducted at the South Dakota State University Cottonwood Field Station. The data from the two experiments were used to develop PSM case studies. The trial data were collected using precision weight data (SmartScale™) collected from replacement heifers (Case study 1, n = 60) and steers (Case study 2, n = 254) grazing native rangeland. In Case study 1 (heifers), Scenario 1 versus Scenario 3 resulted in an additional 73.41 ha required. Results from Case study 2 indicated an average additional 4.4 ha required per pasture when comparing Scenario 3 versus Scenario 1. Sensitivity analyses resulted in a difference between maximum and minimum simulated values of 27,995 and 4265 kg forage consumed, and 122 and 8.9 pasture ha required for Case studies 1 and 2, respectively. Thus, results from the scenarios indicate an opportunity to identify both under- and over-stocking situations using precision DMI estimates, which helps to identify high-leverage precision tools that have practical applications for enhancing animal and plant productivity and environmental sustainability on extensive rangelands.

Complex livestock production problems are barriers to achieving long-term sustainability and profitability. Mathematical nutrition models are an integral component of daily livestock feeding systems, as they help improve animal performance, productivity, and environmental sustainability while maintaining or reducing costs. Different actors, such as research institutions, single-owner farmers or ranchers, integrated livestock producers, or technology providers, have different needs for mathematical models. Many actors use spreadsheets and existing empirical models for nutrition and management, while others use mechanistic, dynamic, or artificial intelligence models. Regardless of model type, understanding the complexity of livestock systems is important, especially as many modeling projects often fail to give new insights to experienced actors within these systems. Flight simulators, also known as decision support tools, help identify high-leverage solutions while causing learning that leads to insight. However, understanding interrelationships between the parts of a system is difficult because intended and unintended consequences are often delayed. Our article reviews the pipeline of developing a flight simulator and contextualizes it within relevant technological-economic-agriculture issues, such as the growing use of precision livestock farming and associated cyberinfrastructure concerns. Fully documented and easy-to-use flight simulators provide a means to introduce stakeholders (actors) to a methodology for generating insights, as users design and run model scenarios for themselves, thereby challenging their mental models. This experiential learning is often more impactful (compared with someone telling them) because insight is gained as the actors challenge their own mental models through inductively or deductively assessing equations, parameters, and outcomes. As precision livestock technology increases on-farm, so do the intended and unintended consequences of data collection, maintenance, model development, deployment, and scale for enhancing animal production. Flight simulators help actors anticipate desired reinforcing actions such as increasing efficiencies and balancing actions within systems that slow the growth of efficiencies such as new training, labor, or infrastructure to manage large datasets or cyberinfrastructure concerns.

Global concern about the environmental impact of livestock production has been increasing over the past decade; consequently, research has focused on mitigating these emissions. Among all devices deployed to access greenhouse gases (GHG), the GreenFeedTM (GF) system is the most used for grazing systems. Nonetheless, the primary issue about GF use is its higher variability and the low repeatability (R) of GHG measurements in grazing individuals compared to other methods. Thus, this work aimed to assess both environmental and animal factors, as well as the variability of the GF itself, to explain GHG variability in cattle. These evaluations are usually conducted under experimental conditions, but in this work, both the variability and R values of CH4 were analyzed under commercial settings. Three and two GF units were deployed simultaneously in a dry lot and then in a grazing pasture, respectively, on a commercial cattle ranch. To evaluate the variability and R values of GHG, the GF collected enteric emissions, wind speed, wind direction, temperature, and animal data from 175 and 169 Angus heifers in dry lot and pasture settings, respectively. Variance component analyses were used to evaluate the influence of these factors on the GHG measurements. Results showed a coefficient of variation (CV) of CH4 and CO2 measured through the GF of 32% and 20%, respectively. Of the total CH4 variation in grazing conditions, 2.73% was explained by environmental factors and 38.3% was explained by animal factors, including feeding behavior (4.58%), between-animal variation (16.4%), date of the visit (12.5%), hour of the visits (3.05%), visits’ duration (1.78%), and number of visits/day (1.25%). In contrast, in the dry lot, environmental and animal factors explained 7.49% and 8.58% of the CH4 variation, respectively. Finally, the GF equipment itself explained 0.15% and 2.06% of the variation of the CH4 in the grazing and dry lot conditions, respectively. The R values, measured as the animal variance divided by the sum of animal and error variances, increased linearly (0.094 to 0.23) in the dry lot, while in the grazing settings, it decreased linearly (0.098 to 0.056) from days 1 to 21. This study identified the most influential factors contributing to the variability of GHG emissions and the low R values of CH4 in commercial grazing cattle production, paving the way for enhanced future use of GF devices for reporting GHG emissions in grazing systems.

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 mid-season 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.