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

Abstract

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.

Improving welfare assessment in aquaculture

“…PLF involves the use of digital technologies to monitor animals and potentially alert farmers of any issues. Although there is a growing literature on the causes for caution with these methods (81)(82)(83)(84), used well they could represent a promising method for monitoring in aquaculture, as "precision fish farming" (85). It is often difficult to monitor groups of fish, as the groups are large and many are underwater where they cannot be directly observed by humans.…”

Social and ethical implications of data and technology use on farms: a qualitative study of Swedish dairy and pig farmers

“…Dominant discourses around livestock technologies relate to animal health, welfare, and productivity ( 23 , 103 ). However, the welfare discourses depicted by farmers in our study and others tend to prioritise the physical aspects over the social aspects ( 104 ).…”

“…Most literature related to livestock technologies do not consider their social and ethical contexts ( 23 ). However, agricultural social scientists have started to approach digital farm technologies through a more critical perspective.…”

“…A user-centred design approach ensures that the needs and decision-making practices of farmers are considered, as well as the ethical, legal, and social acceptability of an innovation ( 25 , 27 ). These considerations are often missed in the development of digital farm technologies ( 23 ).…”

Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach

“…With the development of visual sensors and image analysis technologies, vision-based cattle identification as a non-contact approach is becoming feasible (Van Der Zande et al, 2021). For example, biometric and visual features, extracted from images of cattle muzzle, face, coat, torso, retinas and irises, have been shown to be helpful for identifying cattle (Jiang et al, 2019;Guzhva et al, 2021). Cai and Li (2013) and Kumar et al (2017) presented a facial representation model of cattle based on extracted facial features, while Gaber et al (2016) used the Weber Local Descriptor to extract robust features from cattle muzzle print images for cattle identification.…”