Precision Livestock Farming Technologies Research Articles

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.

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.

Adoption of precision livestock farming technologies has the potential to mitigate greenhouse gas emissions from beef production

To meet the objectives of the Paris Agreement, which aims to limit the increase in global temperature to 1.5°C, significant greenhouse gas (GHG) emission reductions will be needed across all sectors. This includes agriculture which accounts for a significant proportion of global GHG emissions. There is therefore a pressing need for the uptake of new technologies on farms to reduce GHG emissions and move towards current policy targets. Recently, precision livestock farming (PLF) technologies have been highlighted as a promising GHG mitigation strategy to indirectly reduce GHG emissions through increasing production efficiencies. Using Scotland as a case study, average data from the Scottish Cattle Tracing System (CTS) was used to create two baseline beef production scenarios (one grazing and one housed system) and emission estimates were calculated using the Agrecalc carbon footprinting tool. The effects of adopting various PLF technologies on whole farm and product emissions were then modelled. Scenarios included adoption of automatic weigh platforms, accelerometer-based sensors for oestrus detection (fertility sensors) and accelerometer-based sensors for early disease detection (health sensors). Model assumptions were based on validated technologies, direct experience from farms and expert opinion. Adoption of all three PLF technologies reduced total emissions (kg CO2e) and product emissions (kg CO2e/kg deadweight) in both the grazing and housed systems. In general, adoption of PLF technologies had a larger impact in the housed system than in the grazing system. For example, while health sensors reduced total emissions by 6.1% in the housed system, their impact was slightly lower in the grazing system at 4.4%. The largest reduction in total emissions was seen following the adoption of an automatic weight platform which reduced the age at slaughter by 3 months in the grazing system (6.8%) and sensors for health monitoring in the housed system (6.1%). Health sensors also resulted in the largest reduction in product emissions for both the housed (12.0%) and grazing systems (10.5%). These findings suggest PLF could be an effective GHG mitigation strategy for beef systems in Scotland. Although this study utilised data from beef farms in Scotland, comparable emission reductions are likely attainable in other European countries with similar farming systems.

39 Using genetics and technology to improve swine robustness

Abstract Abstract Pig robustness has been an increasing concern in the global swine industry over the last 10 yr. This trend of increased mortality across all phases of production continues to worsen over time despite greater focus on research, management, and other production practices to control it. During this time, dramatic improvements in genetic progress have been realized through the selection of economically important traits. Historically, swine genetic companies have relied on data from their purebred animals reared in high-health and well-managed environments which has proven difficult to provide meaningful insight on variation in robustness traits. More recently, leading genetic companies have invested in large commercial test herds where crossbred pigs and breeding animals are individually identified with pedigree and genomic information. These pigs are reared under challenging and varying commercial conditions and mortality, removals, and reproductive efficiency data are gathered and used to generate crossbred breeding values. This type of data increases the accuracy of genetic estimates as well as creates new traits aimed at improving robustness and ease of management. Further investments have been made in precision livestock farming technologies such as computer vision which is quickly being developed due to the low cost of cameras and the ability to use edge computing to process images in real-time or to transfer images and data to the cloud. These developments are particularly compelling in the genetic improvement space when they can be paired with individual animal identification enabling novel traits to be measured in large populations more consistently and at a reduced cost compared with previous labor-intensive or cost-prohibitive methods. Early examples of this are the use of cameras to score feet and leg soundness in replacement gilts as lameness and associated feet and leg injuries are still one of the largest contributors to sow culls and mortality. This new digital feet and leg soundness scoring method has initially resulted in dramatically greater accuracy and heritability compared with conventional subjective human scoring methods. Other camera-based algorithms have been developed to track individual pigs and assess their daily behaviors and interactions. These developments are key to collecting new phenotypes for the selection of positive behavior traits for genetic selection compared with what was previously possible with human observer or scan sampling methods. Gene editing is another promising technology that has been used in human health and plant breeding but is now being widely researched in livestock. This technology has been used to create the first PRRS-resistant pigs to address one of the most costly diseases in the industry. New sources of data and technologies are key to creating and capturing existing and novel phenotypes on large populations of pigs in commercial environments to aid in the genetic improvement of non-conventional traits.

Precision Livestock Farming Technology: Applications and Challenges of Animal Welfare and Climate Change

This study aimed to review recent developments in the agri-food industry, focusing on the integration of innovative digital systems into the livestock industry. Over the last 50 years, the production of animal-based foods has increased significantly due to the rising demand for meat. As a result, farms have increased their livestock numbers to meet consumer demand, which has exacerbated challenges related to environmental sustainability, human health, and animal welfare. In response to these challenges, precision livestock farming (PLF) technologies have emerged as a promising solution for sustainable livestock production. PLF technologies offer farmers the opportunity to increase efficiency while mitigating environmental impact, securing livelihoods, and promoting animal health and welfare. However, the adoption of PLF technologies poses several challenges for farmers and raises animal welfare concerns. Additionally, the existing legal framework for the use of PLF technologies is discussed. In summary, further research is needed to advance the scientific understanding of PLF technologies, and stakeholders, including researchers, policymakers, and funders, need to prioritize ethical considerations related to their implementation.

Genomic selection in dairy cattle: impact and contribution to the improvement of bovine fertility

Genomic selection has revolutionized the dairy cattle breeding industry, with ripple effects that have greatly impacted dairy herd management. Rate of genetic progress has increased markedly, especially in Holstein and Jersey breeds, for production, health, and fertility traits. Genomic testing of young bulls and heifers provides greater accuracy of selection decisions involving traditional fertility traits, such as daughter pregnancy rate, while creating the opportunity to improve novel traits, such as fetal loss. Cameras, wearable sensors, and other precision livestock farming technologies will allow selection for traits such as estrus duration and intensity that require high frequency phenotyping. At the same time, synergies between genomic testing and advanced reproductive technologies have led to rapid and widespread adoption of sexed semen, coupled with mating of females whose offspring are not needed as herd replacements to beef sires. This strategy produces added-value crossbred calves for the beef supply chain, while allowing genetically inferior mature cows that are still producing at a high level to remain in the herd for additional lactations.

Heat stress measuring methods in dairy cows

The most widely used predictor to assess the incidence of thermal stress in livestock is THI, the temperature humidity index. However, it is an indicator that disregards the individual animal and the specific farm conditions. This review aims to list and summarize other thermal stress predictor factors, by using non-invasive and cost-effective strategies, in particular with the aid of Precision Livestock Farming technologies. When it comes to dairy animals the metabolic load is already increased by milk production, so the effect of heat stress can exacerbate the overall welfare of the cow. Therefore, the animals enact copying mechanisms that may result in physiological, behavioral and productive alterations. Those animal-based parameters can be used as early predictors of heat stress, allowing the farmer to collect real time data and address the condition operating management strategies in order to prevent further detrimental effect on the performance and consequent economic losses.

Influence of risk and trust on beef producers’ use of precision livestock farming

Precision livestock farming (PLF), which utilizes digital technologies for real-time data collection to improve various farming operations, is an emerging interdisciplinary field of study that could aid US and global livestock production. Currently, dairy, hog, and poultry producers are utilizing PLF technologies for real-time decision making, however, use by beef cattle producers has been less widespread. Using data collected from an online survey of beef cattle producers in Tennessee, we examined factors associated with the use of various PLF technologies. Logistic regression models revealed beef cattle producers’ decisions regarding technology use were influenced by their individual risk preferences and attitudes towards farm data privacy. Producers with greater trust in farm data privacy were more likely to use software management systems and drones while those more willing to take risks were more likely to use drones. Overall, results suggest widespread use of these technologies will require that they be affordable, relevant to production, and capable of improving on-farm profits. Findings from this study can inform the development, deployment, and marketing of PLF technologies related to beef cattle production.

Experience and prospects of the use of precision livestock farming in the Russian Federation

The purpose of the work was to analyze the use of precision livestock farming technologies in the Russian Federation. A number of highly promising directions in the development of agriculture in general and animal husbandry in particular in the agro-industrial complex of the Russia Federation was described in the article. The main attention was paid to the development of precision livestock farming technologies as the most important component in the digital transformation of agricultural production with its most developed livestock segment and especially dairy cattle breeding. An overview of some important software products being developed, implemented and already widely used, information systems, and other digital solutions are extremely necessary for the effective functioning of the livestock industry of Russia. Some landmark developments, examples of the introduction of precision livestock farming elements, automation systems, information and analytical systems in agriculture, which are actively engaged in a number of organizations of the Russian Academy of Sciences and especially scientific and practical centers, scientific and research institutes as well as other domestic organizations of various departmental affiliation and form of ownership, have been considered. The new opportunities and advantages received by the state and farmers from the use of various digital solutions in the field of animal husbandry have been discussed.

On the use of 3D camera to accurately measure volume and weight of dairy cow feed

The paper discusses the challenges facing the dairy industry due to increased farm sizes and reduced staff-to-animal ratios, which are impacting animal welfare. The development of precision livestock farming (PLF) technologies has gained momentum to address these challenges. PLF technologies can assess animal welfare and health status by monitoring animal behavior and biological changes, and alerting farmers of any issues. However, the applicability of PLF tools in other productive phases of the dairy cattle is still limited. The article focuses on the challenges of managing unweaned dairy calves, particularly the variability in relation to when calves start consuming solid feed, and how PLF technologies can be used to monitor individual calf intake and manage weaning at the individual level. The attention is mainly focused on the advantages of using automated feeders for unweaned dairy calves, including labor savings, greater precision in measurement and control of individual intake of liquid and solid feed, and higher preweaning growth rates. In particular, a method is proposed, involving a 3D depth camera and a proper algorithm to measure the volume and weight of eaten feed. The method is preliminarily assessed in tests conducted in laboratory, which highlight a remarkable concurrence (differences as low as 2 %) with respect to nominal values.

38 Application of Real-Time Location Systems for Automated Behavioral Monitoring in Sheep

Abstract Evaluating how, when, and where an animal moves in their environment can provide insights into their health and welfare states. Spatial tracking systems for monitoring sheep are less well developed than accelerometer-based systems, but have the potential to add important behavioral information to Precision Livestock Farming (PLF) applications. Global Positioning Systems (GPS) can be used to track pasture-based animals, but are not suitable to establish indoor locations. Real-Time Location Systems (RTLS) are a recent development that use radio-frequency signals emitted by tags to track the indoor location of an animal online and continuously. Raw RTLS data in the form of x, y coordinates can be used to calculate multiple spatial metrics, including velocity, acceleration, distance moved, the proportion of time an animal spends in different functional areas, social network attributes, and range size. RTLS can also be integrated with accelerometers to potentially improve predictive accuracy of behavioral algorithms. RTLS have been primarily tested and validated for indoor tracking of dairy cattle and have been used to assess behavioral time budgets and to detect space-use changes related to estrus or health status. For on-farm applications, these data must be transformed into threshold-based algorithms to generate notifications on changes in the health or welfare status of an animal. Algorithm development needs to account for confounding factors such as breed, sex, age, parity, and stage of lactation, as well as environmental variability that can influence the performance of decision-making tools under real-life conditions. In addition, independent validation studies of commercial systems are lacking, and many calculate positioning error with stationary objects rather than moving animals, potentially inflating accuracy estimates. In conclusion, there is growing empirical support for the use of RTLS as a PLF technology for on-farm animal health and welfare assessment. However, research is needed to validate these systems in sheep and identify spatial metrics that are accurate and robust predictors of health and welfare states for commercial applications.

Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves

Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of -0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.

Real-Time Monitoring of Animals and Environment in Broiler Precision Farming—How Robust Is the Data Quality?

Increasing digitalization in animal farming, commonly addressed as Precision Livestock Farming (PLF), offers benefits in terms of productivity, sustainability, reduced labor and improved monitoring of animal welfare. However, the large amounts of collected data must be stored, processed and evaluated in a proper way. In practice, challenges of continuous and exact data collection can arise, e.g., from air pollutants like dust occluding cameras and sensors, degrading material, the ever-present commotion caused by animals, workers and machines, regularly required maintenance or weak signal transmission. In this study, we analyzed the quality of multi-source spatio-temporal data from a broiler house with 8100 birds over a period of 31 months collected by the Farmer Assistant System (FAS). This is a ceiling-suspended robot equipped with several sensors and cameras that continuously collect data while moving through the barn. The data analysis revealed numerous irregularities: missing values, outliers, repetitive measurements, systematic errors, and temporal and spatial inconsistencies. About 40–50% of all records collected with the early version of the FAS had to be sorted out. The newer version of FAS provided cleaner data, although still about 10–20% of the data had to be removed. Our study has shown that where sophisticated technological systems meet a challenging environment, a thorough and critical review of data completeness and quality is crucial to avoid misinterpretation. The pipeline developed here is designed to help developers and farmers detect failures in signal processing and localize the problem in the hardware components. Scientists, industrial developers and farmers should work more closely together to develop new PLF technologies to more easily advance digitization in agriculture.

Unsupervised and supervised machine learning approach to assess user readiness levels for precision livestock farming technology adoption in the pig and poultry industries

This study used machine learning, particularly k-means clustering, to identify distinct clusters of users and their technological readiness to adopt various precision livestock farming (PLF) technologies based on their responses to a carefully designed questionnaire. The analysis revealed initially two as well as three distinct clusters representing different levels of technological readiness among farmers considering the adoption of various PLF technologies. In addition to the validation of the cluster results by internal metrics, a related principal component analysis, and a focus group evaluation, this paper describes the application of a Decision Tree as an explainable supervised machine learning approach to investigate the predictive power of specific survey questions. In combination, this research aims to provide valuable insights for understanding farmers’ technological readiness, to enhance further survey designs, and to support the development of targeted strategies to promote the successful adoption of PLF technologies in the agricultural sector generally.

US Swine Industry Stakeholder Perceptions of Precision Livestock Farming Technology: A Q-Methodology Study.

This study used the Q-methodology approach to analyze perceptions of precision livestock farming (PLF) technology held by stakeholders directly or indirectly involved in the US swine industry. To see if stakeholders' perceptions of PLF changed over time as PLF is a rapidly evolving field, we deliberately followed up with stakeholders we had interviewed 6 months earlier. We identified three distinct points of view: PLF improves farm management, animal welfare, and laborer work conditions; PLF does not solve swine industry problems; PLF has limitations and could lead to data ownership conflict. Stakeholders with in-depth knowledge of PLF technology demonstrated elevated levels of optimism about it, whereas those with a basic understanding were skeptical of PLF claims. Despite holding different PLF views, all stakeholders agreed on the significance of training to enhance PLF usefulness and its eventual adoption. In conclusion, we believe this study's results hold promise for helping US swine industry stakeholders make better-informed decisions about PLF technology implementation.

Diffusion of innovation, internet access, and adoption barriers for precision livestock farming among beef producers

This study examined the relationship between internet access type and perceptions of Precision Livestock Farming (PLF) Technologies among beef producers in a specific state. Using data collected from an internet-based survey of beef producers (n = 137), this study conducted an exploratory factor analysis to construct variables corresponding to Diffusion of Innovation (DOI) attributes that influence innovation adoption. Findings indicate producers with cable, cellular, and broadband internet access had more favorable perceptions of PLF technologies in terms of barriers to adoption, while those with no internet access or satellite connections reported higher perceived complexity with the use of PLF technologies. Trialability and observability varied across internet types, suggesting hands-on experience and practical demonstrations might be more impactful for certain groups. Beef producers with satellite internet connections were more likely to perceive the need to trial PLF technologies before adoption. This study highlights the importance of internet access in rural areas and its potential impact on the adoption of PLF technologies, offering valuable insights for industry stakeholders and policymakers to promote the adoption of PLF technologies.

The quest to develop automated systems for monitoring animal behavior

Automated behavior analysis (ABA) strategies are being researched at a rapid rate to detect an array of behaviors across a range of species. There is growing optimism that soon ethologists will not have to manually decode hours (and hours) of animal behavior videos, but that instead computers will process them for us. However, before we assume ABA is ready for practical use, it is important to take a realistic look at exactly what ABA is being developed, the expertise being used to develop it, and the context in which these studies occur. Once we understand common pitfalls occurring during ABA development and identify limitations, we can construct robust ABA tools to achieve automated (ultimately even continuous and real time) analysis of behavioral data, allowing for more detailed or longer-term studies of behavior on larger numbers of animals than ever before. ABA is only as good as it is trained to be. A key starting point is having manually annotated data for model training and assessment. However, most ABA developers are not trained in ethology. Often no formal ethogram is developed and descriptions of target behaviors in ABA publications are limited or inaccurate. In addition, ABA is also frequently developed using small datasets, which lack sufficient variability in animal morphometrics, activities, camera viewpoints, and environmental features to be generalizable. Thus, ABA often needs to be further validated before being used satisfactorily on different populations or under other conditions, even for research purposes. Multidisciplinary teams of researchers including ethologists and ethicists as well as computer scientists, data scientists, and engineers are needed to help address problems when applying computer vision ABA to measure behavior. Reference datasets that can be used for behavior detection should be generated and shared that include image data, annotations, and baseline analyses for benchmarking. Also critical is the development of standards for creating such reference datasets and descriptions of best practices for methods for validating results from detection tools to ensure they are robust and generalizable. At present, only a handful of publicly available datasets exist that can be used for development of ABA tools. As we work to realize the promise of ABA (and subsequent precision livestock farming technologies) to detect animal behavior, a clear understanding of best practices, access to accurately annotated datasets, and networking among ethologists and ABA developers will increase our chances for rapid and robust successes.

As if you were hiring a new employee: on pig veterinarians’ perceptions of professional roles and relationships in the context of smart sensing technologies in pig husbandry in the Netherlands and Germany

Veterinarians are increasingly confronted with new technologies, such as Precision Livestock Farming (PLF), which allows for automated animal monitoring on commercial farms. At the same time, we lack information on how veterinarians, as stakeholders who may play a mediating role in the public debate on livestock farming, perceive the use and the impact of such technologies. This study explores the meaning veterinarians attribute to the application of PLF in the context of public concerns related to pig production. Semi-structured interviews were carried out with pig veterinarians located in the Netherlands and Germany. By using an inductive and semantic approach to reflexive thematic analysis, we developed four main themes from the interview data: (1) the advisory role of the veterinarian, which is characterized by a diverse scope, including advice on PLF, generally positive evaluations and financial dependencies; (2) the delineation of PLF technologies as supporting tools, which are seen as an addition to human animal care; (3) the relationship between veterinarian and farmer, which is context-related, ranging from taking sides with to distancing oneself from farmers; and (4) the distance between agriculture and society, in the context of which PLF has both a mitigating and reinforcing potential. The present findings indicate that veterinarians play an active role in the emerging field of PLF in livestock farming. They are aware of and reflect on competing interests of different groups in society and share positions with different stakeholders. However, the extent to which they are able to mediate between stakeholder groups in practice seems to be constrained by external factors, such as financial dependencies.

Precision Livestock Farming Technologies for thermal stress management in Farm animals

Precision Livestock Farming Technologies for thermal stress management in Farm animals

Precision Livestock Farming Technologies for thermal stress management in Farm animals

Precision Livestock Farming Technologies for thermal stress management in Farm animals