A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare

Several precision livestock farming (PLF) technologies, conceived for optimizing farming processes, are developed to detect the physical and behavioral changes of animals continuously and in real-time. The aim of this review was to explore the capacity of existing PLF technologies to contribute to the assessment of pig welfare. In a web search for commercially available PLF for pigs, 83 technologies were identified. A literature search was conducted, following systematic review guidelines (PRISMA), to identify studies on the validation of sensor technologies for assessing animal-based welfare indicators. Two validation levels were defined: internal (evaluation during system building within the same population that were used for system building) and external (evaluation on a different population than during system building). From 2,463 articles found, 111 were selected, which validated some PLF that could be applied to the assessment of animal-based welfare indicators of pigs (7% classified as external, and 93% as internal validation). From our list of commercially available PLF technologies, only 5% had been externally validated. The more often validated technologies were vision-based solutions (n = 45), followed by load-cells (n = 28; feeders and drinkers, force plates and scales), accelerometers (n = 14) and microphones (n = 14), thermal cameras (n = 10), photoelectric sensors (n = 5), radio-frequency identification (RFID) for tracking (n = 2), infrared thermometers (n = 1), and pyrometer (n = 1). Externally validated technologies were photoelectric sensors (n = 2), thermal cameras (n = 2), microphone (n = 1), load-cells (n = 1), RFID (n = 1), and pyrometer (n = 1). Measured traits included activity and posture-related behavior, feeding and drinking, other behavior, physical condition, and health. In conclusion, existing PLF technologies are potential tools for on-farm animal welfare assessment in pig production. However, validation studies are lacking for an important percentage of market available tools, and in particular research and development need to focus on identifying the feature candidates of the measures (e.g., deviations from diurnal pattern, threshold levels) that are valid signals of either negative or positive animal welfare. An important gap identified are the lack of technologies to assess affective states (both positive and negative states).

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Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

Frontiers in Animal Science ◽

10.3389/fanim.2021.639678 ◽

2021 ◽

Vol 2 ◽

Author(s):

Juliette Schillings ◽

Richard Bennett ◽

David Christian Rose

Keyword(s):

Animal Welfare ◽

Radio Frequency Identification ◽

Management Practices ◽

Early Stage ◽

Animal Health ◽

Welfare States ◽

Negative Consequences ◽

Livestock Farming ◽

Precision Livestock Farming ◽

Welfare Benefits

The rise in the demand for animal products due to demographic and dietary changes has exacerbated difficulties in addressing societal concerns related to the environment, human health, and animal welfare. As a response to this challenge, Precision Livestock Farming (PLF) technologies are being developed to monitor animal health and welfare parameters in a continuous and automated way, offering the opportunity to improve productivity and detect health issues at an early stage. However, ethical concerns have been raised regarding their potential to facilitate the management of production systems that are potentially harmful to animal welfare, or to impact the human-animal relationship and farmers' duty of care. Using the Five Domains Model (FDM) as a framework, the aim is to explore the potential of PLF to help address animal welfare and to discuss potential welfare benefits and risks of using such technology. A variety of technologies are identified and classified according to their type [sensors, bolus, image or sound based, Radio Frequency Identification (RFID)], their development stage, the species they apply to, and their potential impact on welfare. While PLF technologies have promising potential to reduce the occurrence of diseases and injuries in livestock farming systems, their current ability to help promote positive welfare states remains limited, as technologies with such potential generally remain at earlier development stages. This is likely due to the lack of evidence related to the validity of positive welfare indicators as well as challenges in technology adoption and development. Finally, the extent to which welfare can be improved will also strongly depend on whether management practices will be adapted to minimize negative consequences and maximize benefits to welfare.

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Towards Facial Expression Recognition for On-Farm Welfare Assessment in Pigs

Agriculture ◽

10.3390/agriculture11090847 ◽

2021 ◽

Vol 11 (9) ◽

pp. 847

Author(s):

Mark F. Hansen ◽

Emma M. Baxter ◽

Kenneth M. D. Rutherford ◽

Agnieszka Futro ◽

Melvyn L. Smith ◽

...

Keyword(s):

Animal Welfare ◽

Animal Husbandry ◽

Welfare States ◽

Automated System ◽

Expression Recognition ◽

Livestock Farming ◽

Precision Livestock Farming ◽

Innovative Work ◽

Leave One Out ◽

On Farm

Animal welfare is not only an ethically important consideration in good animal husbandry but can also have a significant effect on an animal’s productivity. The aim of this paper was to show that a reduction in animal welfare, in the form of increased stress, can be identified in pigs from frontal images of the animals. We trained a convolutional neural network (CNN) using a leave-one-out design and showed that it is able to discriminate between stressed and unstressed pigs with an accuracy of >90% in unseen animals. Grad-CAM was used to identify the animal regions used, and these supported those used in manual assessments such as the Pig Grimace Scale. This innovative work paves the way for further work examining both positive and negative welfare states with the aim of developing an automated system that can be used in precision livestock farming to improve animal welfare.

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Practical considerations for the use of precision livestock farming to improve animal welfare.

10.1079/9781789247893.0015 ◽

2021 ◽

pp. 241-265

Author(s):

Janice M. Siegford ◽

Kaitlin Wurtz

Keyword(s):

Animal Welfare ◽

Livestock Farming ◽

Precision Livestock Farming

Abstract This chapter provides general concepts and definitions important for an understanding of precision livestock farming (PLF) and how PLF can improve animal welfare.

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Dairy Cows Activity under Heat Stress: A Case Study in Spain

Animals ◽

10.3390/ani11082305 ◽

2021 ◽

Vol 11 (8) ◽

pp. 2305

Author(s):

Adrián Ramón-Moragues ◽

Patricia Carulla ◽

Carlos Mínguez ◽

Arantxa Villagrá ◽

Fernando Estellés

Keyword(s):

Heat Stress ◽

Dairy Cattle ◽

Dairy Cows ◽

Animal Welfare ◽

Animal Behavior ◽

Environmental Conditions ◽

Stress Conditions ◽

Livestock Farming ◽

Precision Livestock Farming ◽

Activity Sensors

Heat stress plays a role in livestock production in warm climates. Heat stress conditions impair animal welfare and compromise the productive and reproductive performance of dairy cattle. Under heat stress conditions, dairy cattle modify their behavior. Thus, the assessment of behavior alterations can be an indicator of environmental or physiological anomalies. Moreover, precision livestock farming allows for the individual and constant monitoring of animal behavior, arising as a tool to assess animal welfare. The purpose of this study was to evaluate the effect of heat stress on the behavior of dairy cows using activity sensors. The study was carried out in Tinajeros (Albacete, Spain) during the summer of 2020. Activity sensors were installed in 40 cows registering 6 different behaviors. Environmental conditions (temperature and humidity) were also monitored. Hourly data was calculated for both animal behavior and environmental conditions. Temperature and Heat Index (THI) was calculated for each hour. The accumulated THI during the previous 24 h period was determined for each hour, and the hours were statistically classified in quartiles according to the accumulated THI. Two groups were defined as Q4 for no stress and Q1 for heat stress. The results showed that animal behavior was altered under heat stress conditions. Increasing THI produces an increase in general activity, changes in feeding patterns and a decrease in rumination and resting behaviors, which is detrimental to animal welfare. Daily behavioral patterns were also affected. Under heat stress conditions, a reduction in resting behavior during the warmest hours and in rumination during the night was observed. In conclusion, heat stress affected all behaviors recorded as well as the daily patterns of the cows. Precision livestock farming sensors and the modelling of daily patterns were useful tools for monitoring animal behavior and detecting changes due to heat stress.

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Animal welfare and other ethical implications of Precision Livestock Farming technology

CABI Agriculture and Bioscience ◽

10.1186/s43170-021-00037-8 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Juliette Schillings ◽

Richard Bennett ◽

David Christian Rose

Keyword(s):

Animal Welfare ◽

Livestock Farming ◽

Policy Makers ◽

Precision Livestock Farming ◽

Ethical Implications

AbstractIn this commentary, we explore the risks and challenges associated with Precision Livestock Farming technologies based on an online workshop with over 70 international animal welfare experts, policy-makers, NGO, students, farmers and industry staff.

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Towards Facial Expression Recognition for On-farm Welfare Assessment in Pigs

10.20944/preprints202108.0405.v1 ◽

2021 ◽

Author(s):

Mark F. Hansen ◽

Emma M. Baxter ◽

Kenny M.D. Rutherford ◽

Agnieszka Futro ◽

Melvyn L. Smith ◽

...

Keyword(s):

Animal Welfare ◽

Animal Husbandry ◽

Welfare States ◽

Automated System ◽

Expression Recognition ◽

Livestock Farming ◽

Precision Livestock Farming ◽

Innovative Work ◽

Leave One Out ◽

On Farm

Animal welfare is not only an ethically important consideration in good animal husbandry, but can also have a significant effect on an animal’s productivity. The aim of this paper is to show that a reduction in animal welfare, in the form of increased stress, can be identified in pigs from frontal images of the animals. We train a Convolutional Neural Network (CNN) using a leave-one-out design and show that it is able to discriminate between stressed and unstressed pigs with an accuracy of >90% in unseen animals. Grad-CAM is used to identify the animal regions used, and these support those used in manual assessments such as the Pig Grimace Scale. This innovative work paves the way for further work examining both positive and negative welfare states with a view to the development of an automated system that can be used in precision livestock farming to improve animal welfare.

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The Hitchhiker's Guide to Integration of Social and Ethical Awareness in Precision Livestock Farming Research

Frontiers in Animal Science ◽

10.3389/fanim.2021.725710 ◽

2021 ◽

Vol 2 ◽

Author(s):

Oleksiy Guzhva ◽

Janice M. Siegford ◽

Christina Lunner Kolstrup

Keyword(s):

Animal Welfare ◽

Language Processing ◽

Latent Dirichlet Allocation ◽

Probabilistic Models ◽

Research Field ◽

Ethical Awareness ◽

Livestock Farming ◽

Precision Livestock Farming ◽

Human Animal ◽

The Impact

While fully automated livestock production may be considered the ultimate goal for optimising productivity at the farm level, the benefits and costs of such a development at the scale at which it needs to be implemented must also be considered from social and ethical perspectives. Automation resulting from Precision Livestock Farming (PLF) could alter fundamental views of human-animal interactions on farm and, even further, potentially compromise human and animal welfare and health if PLF development does not include a flexible, holistic strategy for integration. To investigate topic segregation, inclusion of socio-ethical aspects, and consideration of human-animal interactions within the PLF research field, the abstracts from 644 peer-reviewed publications were analysed using the recent advances in the Natural Language Processing (NLP). Two Latent Dirichlet Allocation (LDA) probabilistic models with varying number of topics (13 and 3 for Model 1 and Model 2, respectively) were implemented to create a generalised research topic overview. The visual representation of topics produced by LDA Model 1 and Model 2 revealed prominent similarities in the terms contributing to each topic, with only weight for each term being different. The majority of terms for both models were process-oriented, obscuring the inclusion of social and ethical angles in PLF publications. A subset of articles (5%, n = 32) was randomly selected for manual examination of the full text to evaluate whether abstract text and focus reflected that of the article as a whole. Few of these articles (12.5%, n = 4) focused specifically on broader ethical or societal considerations of PLF or (9.4%, n = 3) discussed PLF with respect to human-animal interactions. While there was consideration of the impact of PLF on animal welfare and farmers in nearly half of the full texts examined (46.9%, n = 15), this was often limited to a few statements in passing. Further, these statements were typically general rather than specific and presented PLF as beneficial to human users and animal recipients. To develop PLF that is in keeping with the ethical values and societal concerns of the public and consumers, projects, and publications that deliberately combine social context with technological processes and results are needed.

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Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Animals ◽

10.3390/ani11082253 ◽

2021 ◽

Vol 11 (8) ◽

pp. 2253

Author(s):

Severiano R. Silva ◽

José P. Araujo ◽

Cristina Guedes ◽

Flávio Silva ◽

Mariana Almeida ◽

...

Keyword(s):

Animal Welfare ◽

Body Condition ◽

Farm Animals ◽

Economic Sustainability ◽

Farm Animal Welfare ◽

Livestock Farming ◽

Technological Support ◽

Technological Tools ◽

Cow Welfare ◽

Time Assessment

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.

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Evaluation of Inter-Observer Reliability of Animal Welfare Indicators: Which Is the Best Index to Use?

Animals ◽

10.3390/ani11051445 ◽

2021 ◽

Vol 11 (5) ◽

pp. 1445

Author(s):

Mauro Giammarino ◽

Silvana Mattiello ◽

Monica Battini ◽

Piero Quatto ◽

Luca Maria Battaglini ◽

...

Keyword(s):

Animal Welfare ◽

Confidence Intervals ◽

Bootstrap Method ◽

Dairy Goat ◽

Microsoft Excel ◽

Bootstrap Methods ◽

Welfare Indicators ◽

Observer Reliability ◽

High Concordance ◽

Variance Estimates

This study focuses on the problem of assessing inter-observer reliability (IOR) in the case of dichotomous categorical animal-based welfare indicators and the presence of two observers. Based on observations obtained from Animal Welfare Indicators (AWIN) project surveys conducted on nine dairy goat farms, and using udder asymmetry as an indicator, we compared the performance of the most popular agreement indexes available in the literature: Scott’s π, Cohen’s k, kPABAK, Holsti’s H, Krippendorff’s α, Hubert’s Γ, Janson and Vegelius’ J, Bangdiwala’s B, Andrés and Marzo’s ∆, and Gwet’s γ(AC1). Confidence intervals were calculated using closed formulas of variance estimates for π, k, kPABAK, H, α, Γ, J, ∆, and γ(AC1), while the bootstrap and exact bootstrap methods were used for all the indexes. All the indexes and closed formulas of variance estimates were calculated using Microsoft Excel. The bootstrap method was performed with R software, while the exact bootstrap method was performed with SAS software. k, π, and α exhibited a paradoxical behavior, showing unacceptably low values even in the presence of very high concordance rates. B and γ(AC1) showed values very close to the concordance rate, independently of its value. Both bootstrap and exact bootstrap methods turned out to be simpler compared to the implementation of closed variance formulas and provided effective confidence intervals for all the considered indexes. The best approach for measuring IOR in these cases is the use of B or γ(AC1), with bootstrap or exact bootstrap methods for confidence interval calculation.

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