Precision Livestock Research Articles

Human-computer interactions with farm animals—enhancing welfare through precision livestock farming and artificial intelligence

While user-centered design approaches stemming from the human-computer interaction (HCI) field have notably improved the welfare of companion, service, and zoo animals, their application in farm animal settings remains limited. This shortfall has catalyzed the emergence of animal-computer interaction (ACI), a discipline extending technology’s reach to a multispecies user base involving both animals and humans. Despite significant strides in other sectors, the adaptation of HCI and ACI (collectively HACI) to farm animal welfare—particularly for dairy cows, swine, and poultry—lags behind. Our paper explores the potential of HACI within precision livestock farming (PLF) and artificial intelligence (AI) to enhance individual animal welfare and address the unique challenges within these settings. It underscores the necessity of transitioning from productivity-focused to animal-centered farming methods, advocating for a paradigm shift that emphasizes welfare as integral to sustainable farming practices. Emphasizing the ‘One Welfare’ approach, this discussion highlights how integrating animal-centered technologies not only benefits farm animal health, productivity, and overall well-being but also aligns with broader societal, environmental, and economic benefits, considering the pressures farmers face. This perspective is based on insights from a one-day workshop held on June 24, 2024, which focused on advancing HACI technologies for farm animal welfare.

Agricultural Drones in Africa: Exploring Adoption, Applications, and Barriers

The adoption of drone technology in agriculture is transforming farming practices across Africa by enhancing resource efficiency, reducing operational costs, and promoting environmental sustainability. This paper explores the various applications of drones in African agriculture, including precision farming, crop monitoring, pest control, and livestock management. Drones enable farmers to optimize the use of water, fertilizers, and pesticides, leading to increased yields and improved profitability. Despite the potential benefits, the deployment of drones faces challenges such as high costs, lack of technical expertise, and regulatory barriers. This study highlights the importance of addressing these obstacles to facilitate the widespread adoption of drone technology among both small and large-scale farmers. By leveraging drones, African agriculture can improve food security, scale up operations, and contribute to the continent’s sustainable development goals. The findings of the review include case studies from Ghana, Kenya, and Nigeria which show that government intervention and private-sector partnerships are crucial for overcoming these barriers. This paper highlights the need for targeted training programs and policy reforms to enhance accessibility for smallholder farmers. By leveraging drones, African agriculture can improve food security, scale up operations, and contribute to the continent’s sustainable development goals.

Cattle identification based on multiple feature decision layer fusion

In farming scenarios, cattle identification has become a key issue for the development of precision farming. In precision livestock farming, single-feature recognition methods are prone to misjudgment in complex scenarios involving multiple cattle obscuring each other during drinking and feeding. This paper proposes a decision-level identification method based on the multi-feature fusion of cattle faces, muzzle patterns, and ear tags. The method utilizes the SOLO algorithm to segment images and employs the FaceNet and PP-OCRv4 networks to extract features for the cattle’s faces, muzzle patterns, and ear tags. These features are compared with the Ground truth, from which the Top 3 features are extracted. The corresponding cattle IDs of these features are then processed using One-Hot encoding to serve as the final input for the decision layer, and various ensemble strategies are used to optimize the model. The results show that using the multimodal decision fusion method makes the recognition accuracy reach 95.74%, 1.4% higher than the traditional optimal unimodal recognition accuracy. The verification rate reaches 94.72%, 10.65% higher than the traditional optimal unimodal recognition verification rate. The research results demonstrate that the multi-feature fusion recognition method has significant advantages in drinking and feeding farm environments, providing an efficient and reliable solution for precise identification and management of cattle in farms and significantly improving recognition accuracy and stability.

Dystocia in Dairy Cows and Heifers: A Review with a Focus on Future Perspectives

Dairy cow management has evolved tremendously in recent decades, particularly regarding reproductive techniques. The widespread adoption of synchronization protocols, sexed semen, beef semen in dairy cows, reproductive biotechnologies such as in vivo-derived (IVD) or in vitro-produced embryos (IVP), and precision livestock farming is transforming the daily practices of dairy farmers and veterinarians. These implementations are typically adapted in different ways when applied to the breeding of heifers or cows. Considering these developments, dystocia—a significant welfare and productivity concern—may warrant reevaluation. The aforementioned changes are likely to have a substantial impact on its prevalence, severity, and outcome. This review aims to address the main aspects of dystocia in dairy cows and heifers, with a particular focus on the potential impact of recent advances in reproductive and calving management.

Internet of Things (IoT): Sensors Application in Dairy Cattle Farming.

The expansion of dairy cattle farms and the increase in herd size have made the control and management of animals more complex, with potentially negative effects on animal welfare, health, productive/reproductive performance and consequently farm income. Precision Livestock Farming (PLF) is based on the use of sensors to monitor individual animals in real time, enabling farmers to manage their herds more efficiently and optimise their performance. The integration of sensors and devices used in PLF with the Internet of Things (IoT) technologies (edge computing, cloud computing, and machine learning) creates a network of connected objects that improve the management of individual animals through data-driven decision-making processes. This paper illustrates the main PLF technologies used in the dairy cattle sector, highlighting how the integration of sensors and devices with IoT addresses the challenges of modern dairy cattle farming, leading to improved farm management.

A review and future trends of precision livestock over dairy and beef cow cattle with artificial intelligence

Abstract The demand for meat and dairy products is expected to rise significantly in the current demographic and economic growth context. Concurrently, various factors, including financial crises and reduced profitability, have decreased the number of livestock farms. Consequently, the livestock industry is undergoing intensification, with an increased number of cows per farm. This shift makes the management of animals increasingly complex. Amid these challenges, the society’s growing concern for animal health and welfare and the quality of consumed products underscores the importance of systems that aid in the individual and herd-level monitoring and control within livestock operations. In this scenario, precision livestock farming has become essential with the emergence of new sensors and technologies and the evolution of artificial intelligence. Against this backdrop, this paper reviews systems and techniques used in precision livestock farming to detect anomalies caused by reproductive processes and diseases in dairy and beef cattle. The article analyzes sensors and technologies employed, presents key studies conducted, and discusses the most commonly used artificial intelligence techniques in detecting prevalent anomalies.

Precision livestock farming in the 21st century: Challenges and opportunities for sustainable agriculture

Abstract This study aimed to review the recent development of different technologies in precision livestock farming (PLF), along with their scopes and challenges. PLF is an innovative, contemporary, and fast-expanding approach to agriculture that aims to improve sustainable livestock farming. The growing global population has increased the demand for animal products. To meet this demand, farmers have to increase their production, so without the integration of precision technology, this cannot be achieved. PLF currently employs a variety of technologies. Some of these methods include vision-based solutions, load cells, accelerometers, microphones, thermal cameras, photoelectric sensors, and radio-frequency identification (RFID). Despite the availability of different PLF technologies, their adoption by farmers varies widely on the basis of the cost of investment, ease of operation, availability, and accessibility. These technologies are used to track different activities of livestock farming, such as feeding, drinking, physical behavior, temperature regulation, tracking and identification, estrus detection, disease detection, and milking. PLF contributes significantly to technological advancement, human-animal relationships, environmental sustainability, and increased productivity. It does, however, present a number of obstacles and eventual advantages.

Individual Buffalo Identification Through Muzzle Dermatoglyphics Images using Deep Learning Approaches

Animal identification is essential for routine farm operations, residue traceback, insurance, and ownership management. Owing to their uniqueness, incorrigible nature, tamperproof over time, environment-friendly, and pain-free, visual biometrics-based animal identification has recently gained momentum over traditional animal identification methods. Among visual biometrics-based cues, muzzle identification is a simple and relatively low-cost method. Therefore, to address the inherent significant limitations of conventional animal identification systems, we undertook this investigation to collect a database of digital images of muzzles that works as a benchmark, to apply deep learning frameworks to identify individual buffaloes from their muzzle images, and to compare their accuracy in terms of their identification capabilities. Muzzle images of 198 Surti buffaloes were subjected to transfer learning and fine-tuning processes in deep-learning neural networks. The performance was recorded for each pre-train model (ResNet50, InceptionV3, VGG16, AlexNet) with different hyperparameters of the epoch, batch size, and learning rate. A perusal of the data revealed that ResNet50 has the highest train accuracy (99.8%) and test accuracy (99.69%) among all four models used. AlexNet has the lowest train accuracy (90.8%) among the models. The findings concluded that all these four models could be applied to identify individual buffaloes; however, ResNet50 had the highest accuracy, and deep learning applications have great potential for individual buffalo identification and are promising tools for precision livestock farming

Automatic location and recognition of horse freezing brand using rotational YOLOv5 deep learning network

Individual livestock identification is of great importance to precision livestock farming. Liquid nitrogen freezing labeled horse brand is an effective way for livestock individual identification. Along with various technological developments, deep-learning-based methods have been applied in such individual marking recognition. In this research, a deep learning method for oriented horse brand location and recognition was proposed. Firstly, Rotational YOLOv5 (R-YOLOv5) was adopted to locate the oriented horse brand, then the cropped images of the brand area were trained by YOLOv5 for number recognition. In the first step, unlike classical detection methods, R-YOLOv5 introduced the orientation into the YOLO framework by integrating Circle Smooth Label (CSL). Besides, Coordinate Attention (CA) was added to raise the attention to positional information in the network. These improvements enhanced the accuracy of detecting oriented brands. In the second step, number recognition was considered as a target detection task because of the requirement of accurate recognition. Finally, the whole brand number was obtained according to the sequences of each detection box position. The experiment results showed that R-YOLOv5 outperformed other rotating target detection algorithms, and the AP (Average Accuracy) was 95.6 %, the FLOPs were 17.4 G, the detection speed was 14.3 fps. As for the results of number recognition, the mAP (mean Average Accuracy) was 95.77 %, the weight size was 13.71 MB, and the detection speed was 68.6 fps. The two-step method can accurately identify brand numbers with complex backgrounds. It also provides a stable and lightweight method for livestock individual identification.

Repeatability and predictability of lying and feeding behaviours in dairy cattle

Subtle changes in behaviour, which may be useful as early indicators of disease or stress in farm animals, can be detected using precision livestock technologies. However, as animals often display differences in their baseline behaviours from one another, individual variation needs to be measured and accounted for if we want to successfully detect abnormalities. In addition to consistent individual differences in their mean behaviour, which are called behavioural types and measured by repeatability, animals may differ in the amount of day-to-day variation around their own mean. This is known as predictability and it is very rarely measured in animals as it requires data with high granularity for extended periods of time, which is hard to obtain without precision technologies. Here we aim to quantify the repeatability and predictability of lying and feeding, two behaviours which have been investigated as potential indicators of disease with inconsistent results, in dairy cows. We used data on daily lying behaviours from leg-mounted sensors for 2183 cows and data on daily feeding behaviours from neck-mounted sensors for 2584 cows belonging to multiple herds to quantify the variation between individuals, the individual differences in predictability and any correlations between individual behaviour type and predictability of the same behaviour. Using multivariate double hierarchical generalised linear models, we were able to quantify individual variation in feeding and lying behaviours of adult cows for the first time. Consistent variation between individuals was present for all measures of lying and feeding with repeatability values of 0.38 for daily feeding time and 0.31 for daily lying time. Individuals also varied in their levels of predictability with coefficients of 0.46 and 0.29 for the daily lying and feeding time respectively. Lastly, there were significant positive correlations between several behaviours and the amount of residual intra-individual variation for the same behaviour (mean lying bout length: 0.93, mean feeding bout length: 0.88), indicating that cows with the longest bouts of the two behaviours were also more variable and unpredictable, perhaps due to an opportunistic strategy. These results highlight the importance of measuring individual variation in behaviour, supporting the evidence of personality traits in cattle, as well as differences in predictability, which must be taken into account in any use of behavioural indicators for early disease diagnosis.

Reducing life cycle environmental impacts of milk production through precision livestock farming

In recent decades, the livestock sector has significantly improved its efficiency, productivity, and environmental sustainability. Precision Livestock Farming (PLF) represents a driver in this direction, since it enables to monitor individual animals and herds, and supports the farmer in making better decisions. Although the benefits are clear on a livestock perspective, it is difficult to quantify the environmental benefit of having technology on farm, mostly due to the complexity of collecting data on the same farm before and after a certain solution.In this context, this paper focuses on the assessment of the environmental sustainability of a case-study Italian dairy cattle farm where different technologies were installed one by one: first a mechanical ventilation system (MV) and second an automatic milking system (AMS), without introducing other significant changes to the farm management and practices in the meantime. The environmental impact of milk production on the farm was quantified through the Life Cycle Assessment (LCA) method, and the initial farm configuration was compared with the two scenarios in which each technology was incorporated. Fat and protein corrected milk (FPCM) was used as Functional Unit, and a cradle to farm gate system boundary and biophysical allocation method were selected. This enabled to provide valuable insights for stakeholders about the effect on the environmental sustainability of the use of the two technologies. The results show that for all the evaluated impact categories there is an environmental benefit of the improved scenarios. The biggest benefit can be observed with the installation of mechanical ventilation, to which correspond benefits in terms of animal health, welfare and productivity. Then, also AMS entails sustainability improvements, mainly linked with increased efficiency and productivity. In conclusion, the use of technology on dairy farms improves not only the farm efficiency and the animal management, but also the environmental sustainability. Furthermore, the rapid technological advancements may further enhance this positive trend in reducing the contribution of livestock farming to the environmental impacts provided that farmers adopt them.

A computer vision approach for the automatic detection of social interactions of dairy cows in automatic milking systems

The integration of digital technologies and Artificial Intelligence (DT&AI) in veterinary practice is one of the key topics to improve Herd Health Management (HHM). The HHM includes the prevention of diseases, the assessment of the welfare, and the sustainability production of farm animals. In dairy cattle farming, particular attention is paid to automatic cow detection and tracking, as such information is closely related to animal welfare and thus to possible health issues. Cows are highly social animals; therefore, a better comprehension of social context can help improve their management and welfare. In the field of Precision Livestock Farming, computer vision represents a suitable and non-invasive method for automatic cow detection and tracking. In this study, we developed and tested the reliability of a deep learning-based computer vision system for the automatic recognition of dairy cows in a barn equipped with Automatic Milking System. We aimed to build the social network of 240 dairy cows (primiparous and multiparous) to understand how social interactions can influence their welfare and productivity.

PSIII-30 Development of an application programming interface (API) to automate downloading and processing of precision livestock data

Abstract 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.

456 Allocating distribution of pasture utilization across the grazing landscape in grazing steers equipped with virtual fencing collars

Abstract 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.

511 Assessing the carry-over effects of precision livestock technology on steer performance and carcass characteristics

Abstract 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

420 Automated identification and behavioral estrus detection in freestall-housed dairy cows using computer vision and machine learning: A two-layered algorithm approach

Abstract A critical aspect of dairy management involves maintaining accurate records of individual animals and promptly detecting estrus behavior, which is vital for successful breeding and reproductive outcomes. Recent advancements in precision livestock management, utilizing computer vision and machine learning technologies, have paved the way for automated and precise solutions in these areas. The objective of this study is to investigate the application of computer vision and machine learning for animal identification and behavioral estrus detection in dairy cows housed in freestall settings, employing a two-layered algorithmic approach. The research was conducted at the Joe Bearden Dairy Research Center, where a total of 10 lactating Holstein-Friesian cows were assessed across a 7-d trial period. For synchronization efforts, cows were provided a single injection of Prostaglandin F2α at trial initiation with expected estrus behaviors to be exhibited within the following 56 h. Continuous video was captured in 4k resolution using a five-camera closed caption media recording system strategically placed in the pen corners and over the feed bunk alleyway to capture multiple perspectives. Video data extrapolated into frames and manual annotation was utilized to generate the machine learning algorithm’s training data. Frames were randomly selected from a scattering of 56 h of video across all five camera viewpoints and down sampled to 384 x 640 p for both layers. A two-layer approach was employed utilizing an algorithm for each aspect of the study with the first model detecting the behavior, and the second model using the detected region of interest to identify specific cows. The first layer was trained on 682 frames and validated against 123 manually annotated frames. The second layer was trained on 102 frames and validated against 16 frames manually annotated for individual animals. Results demonstrated that the model used for mounting behavior identification has high precision (0.819) and recall (0.89), achieving a 0.926 mAP at an IoU of 0.5. This model achieved an F1 score of 0.86 at a confidence threshold of 0.268. The results demonstrated that the model used for individual animal identification across different classes achieved high precision (0.861) and recall (0.842), with a mAP of 0.897 at an IoU of 0.5. The F1 score for all classes was 0.89 at a confidence threshold of 0.788. In conclusion, these promising findings underscore the potential of computer vision and machine learning technologies to revolutionize dairy cow management practices. This innovative approach not only has potential to enhance operational efficiency, but also contributes significantly to improved reproductive management, thereby advancing precision and productivity in dairy operations.

463 Cyber-Physical Systems with robots and AI for precision dairy farming

Abstract The emergence of precision livestock farming (PLF) offers a transformative lens through which to prioritize animal welfare. At the heart of this transformation lies the rumen, a complex microbial ecosystem within dairy cows responsible for converting feed into nutrients and impacting cow health, productivity, and environmental footprint. Despite its pivotal role, our understanding of the inner workings of the rumen remains limited due to a lack of real-time monitoring tools. My research leverages AI and robotics in PLF to address this gap, focusing on developing a closed-loop system for continuous, in-vivo rumen monitoring. This system employs cyber-physical systems (CPS) embedded with modular sensors and adaptive algorithms. These sensor modules, powered by AI-based data tracking, autonomously monitor rumen temperature, humidity, pressure, and methane concentration, enabling early detection of imbalances and prompt interventions to maintain optimal rumen health. A key innovation is the self-powered in-vivo robot capable of traversing the rumen’s stratified layers. Guided by AI-powered localization, this robot unlocks unparalleled access to new data, aiding in identifying specific regions associated with conditions like subacute ruminal acidosis (SARA) and methane production. This non-invasive approach eliminates the need for traditional cannulation, minimizing stress on the animal and opening new avenues for animal scientists. The impact of this research extends beyond individual cow health. By optimizing rumen function, we can improve feed efficiency, reduce methane emissions, and promote overall sustainability in dairy farming. Additionally, real-time data insights pave the way for precision nutrition, tailored interventions, and improved disease prevention, directly enhancing animal welfare. This novel approach to rumen monitoring represents a significant step towards realizing the full potential of PLF. By integrating AI and robotics, we can usher in a future where animal welfare and environmental sustainability go hand-in-hand, ultimately contributing to a more responsible and ethical dairy industry.

3 Hands -on 1: Applying system dynamics to develop “Flight Simulators” for sustainable animal production

Abstract 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 user-friendliness. 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.

371 Applying precision rangeland grazing management systems in western South Dakota

Abstract 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 climate-smart 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).

Designing precision livestock farming system innovations: A farmer perspective

Precision livestock farming (PLF) is increasingly being suggested as a promising tool for contributing to the sustainable transition of livestock farming as a market system innovation for farm-animal value chains. The innovations of PLF systems are over and above applications aimed at achieving on-farm goals (e.g. improving efficiency and monitoring farm animals), which are well recognised in the existing literature. This paper specifies the new dimensions of PLF systems, including data-sharing with retailers in the chain, with certifiers/control organisations, with other farmers and with market innovators, as well as data governance. Because farmers approach PLF applications that extend beyond on-farm use with reluctance, it is crucial to take farmer preferences into account when designing multi-actor PLF-system innovations. Proceeding from a mixed-method design, this study begins with a literature review and expert interviews, followed by an examination of farmer preferences regarding the architectural-design attributes of a PLF-system innovation. The latter takes the form of a conjoint study based on data obtained from 367 pig and dairy farmers in Finland, the Netherlands and Spain. Results indicate that farmers attach the greatest importance to the governing structure that manages their data, followed by the opportunity to use an on-farm early-warning system to monitor farm animals, and the possibility of sharing the collected data with value-chain actors mainly for purposes of optimising business innovation and certification. A cluster analysis further highlights the importance of finding customised solutions, considering the heterogenous preferences of farmers for PLF-system innovations beyond European borders. These preferences should be considered in the design of multi-actor PLF-system innovations.