Browsing by Author "Schillings, J."
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Item Open Access Managing end-user participation for the adoption of digital livestock technologies: expectations, performance, relationships, and support(Taylor & Francis, 2023-03-29) Schillings, J.; Bennett, R.; Rose, David ChristianPurpose: End-user participation is often encouraged to promote the uptake of Digital Livestock Technologies (DLTs). However, managing participation during DLT development can be challenging. We explore how participation decisions can impact end-users’ engagement and attitudes towards the process, before suggesting strategies for improved management of the participation process. Methodology: We explored the experiences of end-users (e.g. farmers and farm assessors) and other stakeholders (e.g. developers, researchers, industry) involved in the development and testing of DLTs on UK farms, using semi-structured, in-depth interviews (N= 31). Findings: Participation can help develop technologies that better align with users’ needs, promote learning, and encourage feelings of ownership. However, participation can be a double-edged sword. Inadequate levels of involvement, management of stakeholder relationships and expectations, and available support can negatively impact end-users’ engagement and attitudes. Practical implications: Our study highlights the importance of understanding how management decisions during the participatory development of DLTs can influence the engagement and attitudes of end-users towards the process. Theoretical implications: The study contributes to the participation literature in agriculture and demonstrates the importance of using a critical lens to avoid making normative assumptions that participation necessarily promotes uptake in a linear, uncomplicated fashion. Originality/Value: Participation is seen as key for technology adoption. However, the potential downsides of participation have received less attention in relation to the engagement of end-users in the process.Item Open Access Perceptions of farming stakeholders towards automating dairy cattle mobility and body condition scoring in farm assurance schemes(Elsevier, 2023-04-17) Schillings, J.; Bennett, R.; Rose, DavidAnimal welfare standards are used within the food industry to demonstrate efforts in reaching higher welfare on farms. To verify compliance with those standards, inspectors conduct regular on-farm animal welfare assessments. Conducting these welfare assessments can, however, be time-consuming and prone to human bias. The emergence of Digital Livestock Technologies (DLTs) offers new ways of monitoring farm animal welfare and can alleviate some of the challenges related to animal welfare assessments by collecting data automatically and more frequently. Whilst automating welfare assessments with DLTs may be promising, little attention has been paid to farmers’ perceptions of the challenges that could prevent successful implementation. This study aims to address this gap by focusing on the trial of a DLT (a 3D machine learning camera) to automate mobility and body condition scoring on 11 dairy cattle farms. Semi-structured, in-depth interviews were conducted with farmers, technology developers and a stakeholder involved in a farm assurance scheme (N=14). Findings suggest that stakeholders perceived important benefits to the use of the camera in this context, from building consumer trust by increasing transparency to improved management efficiency. There was also a potential for greater consistency in data collection and thus for enhanced fairness across the UK dairy sector, particularly on the issue of lameness prevalence. However, stakeholders also raised important concerns, such as a lack of clarity around data ownership, reliability, and use, and the possibility of some farmers being penalised (e.g., if the technology failed to work). Better clarity should thus be given to farmers in relation to data governance and evidence provided in terms of technical performance and accuracy. The findings of this study highlighted the need for more inclusive approaches to ensure farmers’ concerns are adequately identified and addressed. These approaches can help minimise negative consequences to farmers and animal welfare, whilst maximising the potential benefits of automating welfare-related data collection.