Consumer Perceptions of Precision Livestock Farming—A Qualitative Study in Three European Countries
Abstract
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Focus Group Procedure
2.3. Topics and Questions Addressed
2.4. Data Analysis
3. Results
3.1. Consumer Perceptions of PLF: Benefits and Fears
- ‘[PLF data makes the] lives of pigs transparent’.
- ‘[PLF technologies provide] faster and detailed information about total effects in the big picture [of livestock farming]’.
3.2. Consumer Perceptions of PLF: Transformational Impact on Livestock Farming
3.3. Consumer Perceptions of PLF: Is It Purely a Matter of Communication?
4. Discussion and Implications
4.1. The Fear of Industrialisation, Robotisation and Digitalisation
4.2. The Concerns Related to Cyber-Crime and Data Misuse
4.3. The Concerns Relating to Inadequate Communication Systems
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Acknowledgments
Conflicts of Interest
References
- Berckmans, D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev. Sci. Technol. 2014, 33, 189–196. [Google Scholar] [CrossRef] [PubMed]
- Buller, H.; Blokhuis, H.; Lokhorst, K.; Silberberg, M.; Veissier, I. Animal welfare management in a digital world. Animals 2020, 10, 1779. [Google Scholar] [CrossRef] [PubMed]
- Rowe, E.; Dawkins, M.S.; Gebhardt-Henrich, S.G. A systematic review of precision livestock farming in the poultry sector: Is technology focused on improving bird welfare? Animals 2019, 9, 614. [Google Scholar] [CrossRef] [PubMed]
- Benjamin, M.; Yik, S. Precision livestock farming in swine welfare: A review for swine practitioners. Animals 2020, 9, 133. [Google Scholar] [CrossRef]
- Caria, M.; Sara, G.; Todde, G.; Polese, M.; Pazzona, A. Exploring smart glasses for augmented reality: A valuable and integrative tool in precision livestock farming. Animals 2020, 9, 903. [Google Scholar] [CrossRef]
- Patelli, N.; Mandrioli, M. Blockchain technology and traceability in the agrifood industry. J. Food Sci. 2020, 85, 3670–3678. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Lee, S.; van de Ligt, J.L.G. Blockchain Technology: What Is It? 2019. Available online: https://vetmed.umn.edu/sites/vetmed.umn.edu/files/shmp_2018l19.47_blockchain_technology_part_2-sciencepage.pdf (accessed on 19 February 2021).
- Ingram, J.; Damian, M. What are the implications of digitalisation for agricultural knowledge? Front. Sustain. Food Syst. 2020, 4, 66. [Google Scholar] [CrossRef]
- Rotz, S.; Duncan, E.; Small, M.; Botschner, J.; Dara, R.; Mosby, I.; Fraser, E.D. The politics of digital agricultural technologies: A preliminary review. Sociol. Rural. 2019, 59, 203–229. [Google Scholar] [CrossRef]
- Abeni, F.; Petrera, F.; Galli, A. A survey of Italian dairy farmers’ propensity for precision livestock farming tools. Animals 2019, 9, 202. [Google Scholar] [CrossRef]
- Aune, J.B.; Coulibaly, A.; Giller, K.E. Precision farming for increased land and labour productivity in semi-arid West Africa. A review. Agron. Sustain. Dev. 2017, 37, 16. [Google Scholar] [CrossRef]
- Klerkx, L.; Jakku, E.; Labarthe, P. A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wagening. J. Life Sci. 2019, 90, 100315. [Google Scholar] [CrossRef]
- Broom, D.M. Indicators of poor welfare. Br. Vet. J. 1986, 142, 524–526. [Google Scholar] [CrossRef]
- Siegrist, M.; Hartmann, C. Consumer acceptance of novel food technologies. Nat. Food 2020, 1, 343–350. [Google Scholar] [CrossRef]
- Cardello, A.V.; Schutz, H.G.; Lesher, L.L. Consumer perceptions of foods processed by innovative and emerging technologies: A conjoint analytic study. Innov. Food Sci. Emerg. Technol. 2007, 8, 73–83. [Google Scholar] [CrossRef]
- Bruhn, C.M. Enhancing consumer acceptance of new processing technologies. Innov. Food Sci. Emerg. Technol. 2007, 8, 555–558. [Google Scholar] [CrossRef]
- Rollin, F.; Kennedy, J.; Wills, J. Consumers and new food technologies. Trends Food Sci. Technol. 2011, 22, 99–111. [Google Scholar] [CrossRef]
- Frewer, L.J.; Bergmann, K.; Brennan, M.; Lion, R.; Meertens, R.; Rowe, G.; Siegrsist, M.; Vereijken, C.M.J.L. Consumer response to novel agri-food technologies: Implications for predicting consumer acceptance of emerging food technologies. Trends Food Sci. Technol. 2011, 22, 442–456. [Google Scholar] [CrossRef]
- Short, S.E. Focus groups: Focus group interviews. In A Handbook for Social Science Field Research: Essays & Bibliographic Sources on Research Design and Methods; SAGE Publications, Inc.: New York, NY, USA, 2006; pp. 104–117. [Google Scholar]
- Lunt, P.; Livingstone, S. Rethinking the focus group in media and communications research. J. Commun. 1996, 46, 79–98. [Google Scholar] [CrossRef]
- Lune, H.; Berg, B.L. Qualitative Research Methods for the Social Sciences; Pearson: London, UK, 2017. [Google Scholar]
- Nyumba, T.; Wilson, K.; Derrick, C.J.; Mukherjee, N. The use of focus group discussion methodology: Insights from two decades of application in conservation. Methods Ecol. Evol. 2018, 9, 20–32. [Google Scholar] [CrossRef]
- Cornwall, A.; Jewkes, R. What is participatory research? Soc. Sci. Med. 1995, 41, 1667–1676. [Google Scholar] [CrossRef]
- Hayward, C.; Simpson, L.; Wood, L. Still left out in the cold: Problematising participatory research and development. Sociol. Rural. 2004, 44, 95–108. [Google Scholar] [CrossRef]
- Israel, B.A.; Schulz, A.J.; Parker, E.A.; Becker, A.B. Review of community-based research: Assessing partnership approaches to improve public health. Annu. Rev. Public Health 1998, 19, 173–202. [Google Scholar] [CrossRef]
- Miltgen, C.L.; Peyrat-Guillard, D. Cultural and generational influences on privacy concerns: A qualitative study in seven European countries. Eur. J. Inf. Syst. 2014, 23, 103–125. [Google Scholar] [CrossRef]
- Kitzinger, J. The methodology of focus groups: The importance of interaction between research participants. Sociol. Health Illn. 1994, 16, 103–121. [Google Scholar] [CrossRef]
- Eurobarometer, S. Attitudes of EU Citizens towards Animal Welfare; European Commission: Brussels, Belgium, 2007; Available online: https://ec.europa.eu/commfrontoffice/publicopinion/archives/ebs/ebs_270_en.pdf (accessed on 19 February 2021).
- Probst, L.; Pedersen, B.; Lonkeu, O.K.; Martinez-Diaz, C.; Araujo, L.N.; Klitou, D.; Rasmussen, M. Digital Transformation Scoreboard 2017: Evidence of Positive Outcomes and Current Opportunities for EU Businesses; The European Commission: Brussels, Belgium, 2017; Available online: http://ec.europa.eu/DocsRoom/documents/21124 (accessed on 10 March 2021).
- European Commission. Eurobarometer, Special, Future of Europe: Reflections and Scenarios for the EU27 by 2025. 2017. Available online: https://ec.europa.eu/info/sites/info/files/white_paper_on_the_future_of_europe_en.pdf (accessed on 24 February 2021).
- Stremersch, S.; Tellis, G.J. Understanding and managing international growth of new products. Int. J. Res. Mark. 2004, 21, 421–438. [Google Scholar] [CrossRef]
- Alonso, M.E.; González-Montaña, J.R.; Lomillos, J.M. Consumers’ concerns and perceptions of farm animal welfare. Animals 2020, 10, 385. [Google Scholar] [CrossRef]
- Bruner, G.C.; Kumar, A.; Heppner, C. Predicting innovativeness: Development of the technology adoption scale. In Progress in Wireless Communications Research; Nova Science Publishers, Inc.: Hauppauge, NY, USA, 2007; pp. 1–20. Available online: https://www.researchgate.net/publication/277020646 (accessed on 24 February 2021).
- Herzog, H.; Grayson, S.; McCord, D. Brief measures of the animal attitude scale. Anthrozoös 2015, 28, 145–152. [Google Scholar] [CrossRef]
- Cacioppo, J.T.; Petty, R.E. The need for cognition. J. Personal. Soc. Psychol. 1982, 42, 116. [Google Scholar] [CrossRef]
- Donthu, N.; Garcia, A. The internet shopper. J. Advert. Res. 1999, 39, 52. [Google Scholar] [CrossRef]
- Morgan, D.L. Focus Groups as Qualitative Research; Sage Publications: New York, NY, USA, 1996; Volume 16. [Google Scholar]
- Van Riemsdijk, L.; Ingenbleek, P.T.M.; Van Der Veen, G.; Van Trijp, H.C.M. Positioning Strategies for Animal-Friendly Products: A Social Dilemma Approach. J. Consum. Aff. 2020, 54, 100–129. [Google Scholar] [CrossRef]
- Cox, D.N.; Evans, G. Construction and validation of a psychometric scale to measure consumers’ fears of novel food technologies: The food technology neophobia scale. Food Qual. Prefer. 2008, 19, 704–710. [Google Scholar] [CrossRef]
- Levitt, T. Communications and industrial selling. J. Mark. 1967, 31, 15–21. [Google Scholar] [CrossRef]
- Wicker, A.W. Attitudes versus actions: The relationship of verbal and overt behavioral responses to attitude objects. J. Soc. Issues 1969, 25, 41–78. [Google Scholar] [CrossRef]
- Moser, A.K. Thinking green, buying green? Drivers of pro-environmental purchasing behavior. J. Consum. Mark. 2015, 32, 167–175. [Google Scholar] [CrossRef]
- Yoo, C.W.; Parameswaran, S.; Kishore, R. Knowing about your food from the farm to the table: Using information systems that reduce information asymmetry and health risks in retail contexts. Inf. Manag. 2015, 52, 692–709. [Google Scholar] [CrossRef]
- Suchman, M.C. Managing legitimacy: Strategic and institutional approaches. Acad. Manag. Rev. 1995, 20, 571–610. [Google Scholar] [CrossRef]
- Pinillos, R.G.; Appleby, M.C.; Manteca, X.; Scott-Park, F.; Smith, C.; Velarde, A. One Welfare: A platform for improving human and animal welfare. Vet. Rec. 2016, 179, 412–413. [Google Scholar] [CrossRef]
- Clark, B.; Panzone, L.A.; Stewart, G.B.; Kyriazakis, I.; Niemi, J.K.; Latvala, T.; Tranter, R.; Jones, P.; Frewer, L.J. Consumer attitudes towards production diseases in intensive production systems. PLoS ONE 2019, 14. [Google Scholar] [CrossRef]
- Lubell, M.; Hillis, V.; Hoffman, M. Innovation, cooperation, and the perceived benefits and costs of sustainable agriculture practices. Ecol. Soc. 2011, 16, 23. [Google Scholar] [CrossRef]
- Fraune, M.R.; Sherrin, S.; Šabanović, S.; Smith, E.R. Is human-robot interaction more competitive between groups than between individuals? In Proceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction, Daegu, Korea, 11–14 March 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 104–113. [Google Scholar]
- Złotowski, J.; Yogeeswaran, K.; Bartneck, C. Can we control it? Autonomous robots threaten human identity, uniqueness, safety, and resources. Int. J. Hum. Comput. Stud. 2017, 100, 48–54. [Google Scholar] [CrossRef]
- Astill, J.; Dara, R.A.; Campbell, M.; Farber, J.M.; Fraser, E.D.; Sharif, S.; Yada, R.Y. Transparency in food supply chains: A review of enabling technology solutions. Trends Food Sci. Technol. 2019, 91, 240–247. [Google Scholar] [CrossRef]
- Kamrath, C.; Wesana, J.; Bröring, S.; De Steur, H. What do we know about chain actors’ evaluation of new food technologies? A systematic review of consumer and farmer studies. Compr. Rev. Food Sci. Food Saf. 2019, 18, 798–816. [Google Scholar] [CrossRef] [PubMed]
- Frewer, L.J. Consumer acceptance and rejection of emerging agrifood technologies and their applications. Eur. Rev. Agric. Econ. 2017, 44, 683–704. [Google Scholar] [CrossRef]
- Wiseman, L.; Sanderson, J.; Zhang, A.; Jakku, E. Farmers and their data: An examination of farmers’ reluctance to share their data through the lens of the laws impacting smart farming. NJAS Wagening. J. Life Sci. 2019, 90, 100301. [Google Scholar] [CrossRef]
- Jakku, E.; Taylor, B.; Fleming, A.; Mason, C.; Fielke, S.; Sounness, C.; Thorburn, P. “If they don’t tell us what they do with it, why would we trust them?” Trust, transparency and benefit-sharing in Smart Farming. NJAS Wagening. J. Life Sci. 2019, 90, 100285. [Google Scholar] [CrossRef]
- Lusk, J.L.; Roosen, J.; Bieberstein, A. Consumer acceptance of new food technologies: Causes and roots of controversies. Annu. Rev. Resour. Econ. 2014, 6, 381–405. [Google Scholar] [CrossRef]
- Anagnostou, A.; Ingenbleek, P.T.; van Trijp, H.C. Sustainability labelling as a challenge to legitimacy: Spillover effects of organic Fairtrade coffee on consumer perceptions of mainstream products and retailers. J. Consum. Mark. 2015, 32, 422–431. [Google Scholar] [CrossRef]
- van der Burg, S.; Wiseman, L.; Krkeljas, J. Trust in farm data sharing: Reflections on the EU code of conduct for agricultural data sharing. Ethics Inf. Technol. 2020, 1–14. [Google Scholar] [CrossRef]
- Grewal, D.; Gauri, D.K.; Roggeveen, A.L.; Sethuraman, R. Strategizing Retailing in the New Technology Era. J. Retail. 2021, 97, 6–12. [Google Scholar] [CrossRef]
- Laurent, G.; Kapferer, J.N. Measuring consumer involvement profiles. J. Mark. Res. 1985, 22, 41–53. [Google Scholar] [CrossRef]
- De Jonge, J.; van Trijp, H.C. Meeting heterogeneity in consumer demand for animal welfare: A reflection on existing knowledge and implications for the meat sector. J. Agric. Environ. Ethics 2013, 26, 629–661. [Google Scholar] [CrossRef]
Country/Criteria | Spain | Netherlands | Finland | Total |
---|---|---|---|---|
Number participants | N = 20 (Dairy n = 10; pork n = 10) | N = 16 (Dairy n = 8; pork n = 8) | N = 20 (Dairy n = 10; pork n = 10) | N = 56 (Dairy n = 28; pork n = 28) |
Mean Age of Participants | Dairy focus group Mage = 30.40; SD = 7.93 Pork focus group Mage = 32.70; SD = 8.73 | Dairy focus group Mage = 28.25; SD = 7.94 Pork focus group Mage = 34.13; SD = 6.64 | Dairy focus group Mage = 39.60; SD = 9.35 Pork focus group Mage = 39.60; SD = 7.31 | Dairy focus group Mage = 33.07, SD = 8.41 Pork focus group Mage = 35.57, SD = 7.56 |
Innovative Attitude Scale | Dairy focus group Minnovativeness = 17.90; SD = 2.28 Pork focus group Minnovativeness = 18.40; SD = 3.27 | Dairy focus group Minnovativeness = 21.88; SD = 2.58 Pork focus group Minnovativeness = 21.88; SD = 2.58 | Dairy focus group Minnovativeness = 17.1; SD = 2.85 Pork group Minnovativeness = 17.5; SD = 3.03 | Dairy focus group Minnovativeness = 18.7; SD= 2.57 Pork focus group Minnovativeness = 19.07; SD = 2.96 |
Benefits | Risks |
---|---|
Increased transparency in the value chain and its processes | Increased digitalisation and robotisation in animal farming |
Improved health and welfare of farm animals | Decreased ‘human attention’ to farm animals→decreased animal welfare |
Environmental improvements: less emissions | Vulnerability of PLF technologies and data leaks |
Improved productivity and control of production processes | Highly dependent on the digital and energy infrastructure and supply |
Improved food safety | Lack of trust in the management of PLF data |
Pain-free slaughtering | More administrative work for stakeholders in the short term |
More freedom for farmers | More technological waste |
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Krampe, C.; Serratosa, J.; Niemi, J.K.; Ingenbleek, P.T.M. Consumer Perceptions of Precision Livestock Farming—A Qualitative Study in Three European Countries. Animals 2021, 11, 1221. https://doi.org/10.3390/ani11051221
Krampe C, Serratosa J, Niemi JK, Ingenbleek PTM. Consumer Perceptions of Precision Livestock Farming—A Qualitative Study in Three European Countries. Animals. 2021; 11(5):1221. https://doi.org/10.3390/ani11051221
Chicago/Turabian StyleKrampe, Caspar, Jordi Serratosa, Jarkko K. Niemi, and Paul T. M. Ingenbleek. 2021. "Consumer Perceptions of Precision Livestock Farming—A Qualitative Study in Three European Countries" Animals 11, no. 5: 1221. https://doi.org/10.3390/ani11051221
APA StyleKrampe, C., Serratosa, J., Niemi, J. K., & Ingenbleek, P. T. M. (2021). Consumer Perceptions of Precision Livestock Farming—A Qualitative Study in Three European Countries. Animals, 11(5), 1221. https://doi.org/10.3390/ani11051221