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Doidge C, Ånestad LM, Burrell A, Frössling J, Palczynski L, Pardon B, Veldhuis A, Bokma J, Carmo LP, Hopp P, Guelbenzu-Gonzalo M, Meunier NV, Ordell A, Santman-Berends I, van Schaik G, Kaler J. A Living Lab approach to understanding dairy farmers' technology and data needs to improve herd health: Focus groups from 6 European countries. J Dairy Sci 2024; 107:5754-5778. [PMID: 38490555 DOI: 10.3168/jds.2024-24155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/18/2024] [Indexed: 03/17/2024]
Abstract
For successful development and adoption of technology on dairy farms, farmers need to be included in the innovation process. However, the design of agricultural technologies usually takes a top-down approach with little involvement of end-users at the early stages. Living Labs offer a methodology that involve end-users throughout the development process and emphasize the importance of understanding users' needs. Currently, exploration of dairy farmers' technology needs has been limited to specific types of technology (e.g., smartphone apps) and adult cattle. The aim of this study was to use a Living Lab approach to identify dairy farmers' data and technology needs to improve herd health and inform innovation development. We conducted 18 focus groups with a total of 80 dairy farmers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the United Kingdom. Data were analyzed using Template Analysis, and 6 themes were generated representing the fundamental needs of autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers favored technologies that provided them with convenience, facilitated their knowledge and understanding of problems on farm, and allowed them to be self-reliant. Issues with data sharing and accessibility and usability of software were barriers to technology use. Furthermore, farmers were facing problems around recruitment and management of labor and needed ways to reduce stress. Controlling aspects of the barn environment, such as air quality, hygiene, and stocking density, were particular concerns in relation to youngstock management. Overall, the findings suggest that developers of farm technologies may want to include farmers in the design process to ensure a positive user experience and improve accessibility. The needs identified in this study can be used as a framework when designing farm technologies to strengthen need satisfaction and reduce any potential harm toward needs.
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Affiliation(s)
- C Doidge
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom.
| | - L M Ånestad
- Norwegian Veterinary Institute, 1431 Ås, Norway
| | - A Burrell
- Animal Health Ireland, Carrick-on-Shannon, Co. Leitrim N41 WN27, Ireland
| | - J Frössling
- Department of Epidemiology, Surveillance and Risk Assessment, Swedish Veterinary Agency (SVA), 751 89 Uppsala, Sweden; Department of Applied Animal Science and Welfare, Swedish University of Agricultural Sciences (SLU), 532 23 Skara, Sweden
| | - L Palczynski
- Innovation for Agriculture, Stoneleigh Park, Warwickshire CV8 2LZ, United Kingdom
| | - B Pardon
- Department of Internal Medicine, Reproduction, and Population Medicine, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium
| | - A Veldhuis
- Royal GD, 7400 AA Deventer, the Netherlands
| | - J Bokma
- Department of Internal Medicine, Reproduction, and Population Medicine, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium
| | - L P Carmo
- Norwegian Veterinary Institute, 1431 Ås, Norway
| | - P Hopp
- Norwegian Veterinary Institute, 1431 Ås, Norway
| | | | - N V Meunier
- Animal Health Ireland, Carrick-on-Shannon, Co. Leitrim N41 WN27, Ireland
| | - A Ordell
- Department of Epidemiology, Surveillance and Risk Assessment, Swedish Veterinary Agency (SVA), 751 89 Uppsala, Sweden
| | | | - G van Schaik
- Royal GD, 7400 AA Deventer, the Netherlands; Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, 3584 CL Utrecht, the Netherlands
| | - J Kaler
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom
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Dórea FC, Vergne T, Correia-Gomes C, Carmo LP, Fischer EAJ, Messam LLM, Brodbelt DC, Robinson PA. SVEPM 2023, the annual conference of the Society for Veterinary Epidemiology and Preventive Medicine: Toulouse finally welcomes us in person. Prev Vet Med 2024; 226:106174. [PMID: 38480088 DOI: 10.1016/j.prevetmed.2024.106174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Affiliation(s)
- Fernanda C Dórea
- The Society for Veterinary Epidemiology and Preventive Medicine, United Kingdom; Department of Disease Control and Epidemiology, National Veterinary Institute, Uppsala SE 75 189, Sweden.
| | - Timothée Vergne
- The Society for Veterinary Epidemiology and Preventive Medicine, United Kingdom; UMR IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | - Carla Correia-Gomes
- The Society for Veterinary Epidemiology and Preventive Medicine, United Kingdom; Animal Health Ireland, Carrick-on-Shannon N41 WN27, Ireland
| | - Luís Pedro Carmo
- The Society for Veterinary Epidemiology and Preventive Medicine, United Kingdom; Norwegian Veterinary Institute, Ås, Norway
| | - Egil A J Fischer
- The Society for Veterinary Epidemiology and Preventive Medicine, United Kingdom; Department of Population Health Sciences, Unit Farm Animal Health, Utrecht University, Utrecht, the Netherlands
| | - Locksley L McV Messam
- The Society for Veterinary Epidemiology and Preventive Medicine, United Kingdom; Section: Herd Health and Animal Husbandry, School of Veterinary Medicine, University College Dublin, Ireland
| | - Dave C Brodbelt
- The Society for Veterinary Epidemiology and Preventive Medicine, United Kingdom; Veterinary Epidemiology, Economics and Public Health Group, Royal Veterinary College, London, United Kingdom
| | - Philip A Robinson
- The Society for Veterinary Epidemiology and Preventive Medicine, United Kingdom; Harper & Keele Veterinary School, Harper Adams University Campus, Newport, Shropshire TF10 8NB, United Kingdom
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Doidge C, Burrell A, van Schaik G, Kaler J. A qualitative survey approach to investigating beef and dairy veterinarians' needs in relation to technologies on farms. Animal 2024; 18:101124. [PMID: 38547554 DOI: 10.1016/j.animal.2024.101124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 04/20/2024] Open
Abstract
Globally, farmers are being increasingly encouraged to use technologies. Consequently, veterinarians often use farm data and technologies to provide farmers with advice. Yet very few studies have sought to understand veterinarians' perceptions of data and technologies on farms. The aim of this study was to understand veterinarians' experiences and opinions on data and technology on beef and dairy farms. An online qualitative survey was conducted with a convenience sample of 36 and 24 veterinarians from the United Kingdom and Ireland, respectively. The data were analysed using reflexive thematic analysis to generate four themes: (1) Improving veterinary advice through data; (2) Ensuring stock person skills are retained; (3) Longevity of technology; and (4) Solving social problems on farms. We show that technologies and data can make veterinarians feel more confident in the advice they give to farmers. However, the quality and quantity of data collected on cattle farms were highly variable. Furthermore, veterinarians were concerned that farmers can become over-reliant on technologies by not using their stockperson skills. As herd sizes increase, technologies can help to improve working conditions on farms with multiple employees of various skillsets. Veterinarians would like innovations that can help them to demonstrate their competence, influence farmers' behaviour, and ensure sustainability of the beef and dairy industries.
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Affiliation(s)
- C Doidge
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, UK.
| | - A Burrell
- Animal Health Ireland, 2 - 5 The Archways, Carrick-on-Shannon, Co. Leitrim N41 WN27, Ireland
| | - G van Schaik
- Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands; Royal GD, Deventer, the Netherlands
| | - J Kaler
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, UK
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Thompson JS, Green MJ, Hyde R, Bradley AJ, O’Grady L. The use of machine learning to predict somatic cell count status in dairy cows post-calving. Front Vet Sci 2023; 10:1297750. [PMID: 38144465 PMCID: PMC10748400 DOI: 10.3389/fvets.2023.1297750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/23/2023] [Indexed: 12/26/2023] Open
Abstract
Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.
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Affiliation(s)
- Jake S. Thompson
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom
| | - Martin J. Green
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom
| | - Robert Hyde
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom
| | - Andrew J. Bradley
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom
- Quality Milk Management Services Ltd., Easton Hill, United Kingdom
| | - Luke O’Grady
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom
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