Positive public attitudes towards agricultural robots.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
06 Jul 2024
Historique:
received: 08 02 2024
accepted: 28 06 2024
medline: 7 7 2024
pubmed: 7 7 2024
entrez: 6 7 2024
Statut: epublish

Résumé

Robot technologies could lead to radical changes in farming. But what does the public know and think about agricultural robots? Recent experience with other agricultural technologies-such as plant genetic engineering-shows that public perceptions can influence the pace and direction of innovation, so understanding perceptions and how they are formed is important. Here, we use representative data from an online survey (n = 2269) to analyze public attitudes towards crop farming robots in Germany-a country where new farming technologies are sometimes seen with skepticism. While less than half of the survey participants are aware of the use of robots in agriculture, general attitudes are mostly positive and the level of interest is high. A framing experiment suggests that the type of information provided influences attitudes. Information about possible environmental benefits increases positive perceptions more than information about possible food security and labor market effects. These insights can help design communication strategies to promote technology acceptance and sustainable innovation in agriculture.

Identifiants

pubmed: 38971894
doi: 10.1038/s41598-024-66198-4
pii: 10.1038/s41598-024-66198-4
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

15607

Subventions

Organisme : Deutsche Forschungsgemeinschaft
ID : EXC 2070 - 390732324

Informations de copyright

© 2024. The Author(s).

Références

Tang, F. H. M., Lenzen, M., McBratney, A. & Maggi, F. Risk of pesticide pollution at the global scale. Nat. Geosci. 14, 206–210 (2021).
doi: 10.1038/s41561-021-00712-5
Johnson, J.M.-F., Franzluebbers, A. J., Weyers, S. L. & Reicosky, D. C. Agricultural opportunities to mitigate greenhouse gas emissions. Environ. Pollut. 150, 107–124 (2007).
pubmed: 17706849 doi: 10.1016/j.envpol.2007.06.030
Le Mouël, C. & Forslund, A. How can we feed the world in 2050? A review of the responses from global scenario studies. Eur. Rev. of Agric. Econ. 44, 541–591 (2017).
doi: 10.1093/erae/jbx006
Alexander, P. et al. Losses, inefficiencies and waste in the global food system. Agric. Syst. 153, 190–200 (2017).
pubmed: 28579671 pmcid: 5437836 doi: 10.1016/j.agsy.2017.01.014
Ogunyiola, A. The changing face of Agrarian labor in the age of artificial intelligence and machine learning: balancing benefits and risks. AI Soc. 28, 1–2 (2024).
Rotz, S. et al. Automated pastures and the digital divide: How agricultural technologies are shaping labour and rural communities. J. Rural Stud. 68, 112–122 (2019).
doi: 10.1016/j.jrurstud.2019.01.023
Ogunyiola, A. & Gardezi, M. Restoring sense out of disorder? Farmers’ changing social identities under big data and algorithms. Agric. Hum. Values 39, 1451–1464 (2022).
doi: 10.1007/s10460-022-10334-1
Sparrow, R. & Howard, M. Robots in agriculture: prospects, impacts, ethics, and policy. Precis. Agric. 22, 818–833 (2021).
doi: 10.1007/s11119-020-09757-9
Ditzler, L. & Driessen, C. Automating agroecology: How to design a farming robot without a monocultural mindset?. J. Agric. Environ. Ethics 35, 1–31 (2022).
doi: 10.1007/s10806-021-09876-x
Walter, A., Finger, R., Huber, R. & Buchmann, N. Opinion: Smart farming is key to developing sustainable agriculture. Proc. Natl Acad. Sci. USA. 114, 6148–6150 (2017).
pubmed: 28611194 pmcid: 5474773 doi: 10.1073/pnas.1707462114
Daum, T. Farm robots: ecological utopia or dystopia?. Trends Ecol. Evol. 36, 774–777 (2021).
pubmed: 34272072 doi: 10.1016/j.tree.2021.06.002
Rübcke von Veltheim, F., Theuvsen, L. & Heise, H. German farmers’ intention to use autonomous field robots: A PLS-analysis. Precis. Agric. 23, 670–697 (2021).
doi: 10.1007/s11119-021-09854-3
Gil, G., Casagrande, D., Cortés, L. P. & Verschae, R. Why the low adoption of robotics in the farms? Challenges for the establishment of commercial agricultural robots. Smart Agric. Technol. 3, 100069 (2023).
doi: 10.1016/j.atech.2022.100069
Gerhards, R. et al. Advances in site-specific weed management in agriculture—a review. Weed Res. 62, 123–133 (2022).
doi: 10.1111/wre.12526
Ju, C., Kim, J., Seol, J. & Son, H. I. A review on multirobot systems in agriculture. Comput. Electron. Agric. 202, 107336 (2022).
doi: 10.1016/j.compag.2022.107336
Roser, M. Employment in Agriculture. Our world in data; https://ourworldindata.org/employment-in-agriculture (2013).
Lusk, J. L., Roosen, J. & Bieberstein, A. Consumer acceptance of new food technologies: Causes and roots of controversies. Annu. Rev. Resour. Econ. 6, 381–405 (2014).
doi: 10.1146/annurev-resource-100913-012735
Grunert, K. G. Current issues in the understanding of consumer food choice. Trends Food Sci. Technol. 13, 275–285 (2002).
doi: 10.1016/S0924-2244(02)00137-1
Qaim, M. Role of new plant breeding technologies for food security and sustainable agricultural development. Appl. Econ. Perspect. Policy 2, 129–150 (2020).
doi: 10.1002/aepp.13044
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. 23, 185–198 (2021).
doi: 10.1007/s10676-020-09543-1
Boogaard, B. K., Bock, B. B., Oosting, S. J., Wiskerke, J. S. C. & van der Zijpp, A. J. Social acceptance of dairy farming: The ambivalence between the two faces of modernity. J. Agric. Environ. Ethics 24, 259–282 (2011).
doi: 10.1007/s10806-010-9256-4
Millar, K. M., Tomkins, S. M., White, R. P. & Mepham, T. B. Consumer attitudes to the use of two dairy technologies. Br. Food. J. 104, 31–44 (2002).
doi: 10.1108/00070700210418721
Pfeiffer, J., Gabriel, A. & Gandorfer, M. Understanding the public attitudinal acceptance of digital farming technologies: A nationwide survey in Germany. Agric. Hum. Values 38, 107–128 (2021).
doi: 10.1007/s10460-020-10145-2
Spykman, O., Emberger-Klein, A., Gabriel, A. & Gandorfer, M. Autonomous agriculture in public perception—German consumer segments’ view of crop robots. Comput. Electron. Agric. 202, 107385 (2022).
doi: 10.1016/j.compag.2022.107385
Wilmes, R., Waldhof, G. & Breunig, P. Can digital farming technologies enhance the willingness to buy products from current farming systems?. PloS One 17, e0277731 (2022).
pubmed: 36374858 pmcid: 9662715 doi: 10.1371/journal.pone.0277731
Luhmann, N. Öffentliche Meinung. Politische Vierteljahresschrift 11, 2–28 (1970).
Downs, A. Up and down with ecology—the “issue-attention cycle”. Public Interest 28, 38–50 (1972).
Busch, G., Ryan, E., von Keyserlingk, M. A. G. & Weary, D. M. Citizen views on genome editing: Effects of species and purpose. Agric. Hum. Values 39, 151–164 (2022).
doi: 10.1007/s10460-021-10235-9
Busch, G., Weary, D. M., Spiller, A. & von Keyserlingk, M. A. G. American and German attitudes towards cow-calf separation on dairy farms. PloS One 12, e0174013 (2017).
pubmed: 28301604 pmcid: 5354428 doi: 10.1371/journal.pone.0174013
Themen Einkommen, Konsum und Lebensbedingungen—IT Nutzung. (Statistisches Bundesamt, 2023); https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Einkommen-Konsum-Lebensbedingungen/IT-Nutzung/_inhalt.html .
Rothschild, M. L. Perspectives on involvement: Current problems and future directions. Adv. Con. Res. 1, 216–217 (1984).
Amsalem, E. & Zoizner, A. Real, but limited: A meta-analytic assessment of framing Effects in the political domain. Brit. J. Polit. Sci. 52, 221–237 (2022).
doi: 10.1017/S0007123420000253
Diamond, E., Bernauer, T. & Mayer, F. Does providing scientific information affect climate change and GMO policy preferences of the mass public? Insights from survey experiments in Germany and the United States. Environ. Polit. 29, 1199–1218 (2020).
doi: 10.1080/09644016.2020.1740547
King, A. Technology: The future of agriculture. Nature 544, 21–23 (2017).
doi: 10.1038/544S21a
Prause, L. Digital agriculture and labor: A few challenges for social sustainability. Sustainability 13, 59–80. https://doi.org/10.3390/su13115980 (2021).
doi: 10.3390/su13115980
Mohr, S. & Höhler, J. Media coverage of digitalization in agriculture—an analysis of media content. Technol. Forecast Soc. Change 187, 122238 (2023).
doi: 10.1016/j.techfore.2022.122238
Alba, J. W. & Hutchinson, J. W. Knowledge calibration: what consumers know and what they think they know. J. Consum. Res. 27, 123–156 (2000).
doi: 10.1086/314317
Rosenberg, M. J. & Hovland, C. I. Cognitive, affective, and behavioral components of attitude. In Attitude Organization and Change. 1–14 (Yale University Press, New Haven, 1960).
Ostrom, T. M. The relationship between the affective, behavioral, and cognitive components of attitude. J. Exp. Psych. 5, 12–30 (1969).
Solomon, M.R. Attitudes. In Consumer behaviour. A European perspective. 3rd ed. 139–167 (Financial Times/Prentice Hall, Harlow England, New York, 2006).
Mathew, P. M. Attitude segmentation of Indian online buyers. J. Enterp. Inf. Manag. 29, 359–373 (2016).
doi: 10.1108/JEIM-08-2014-0078
Makhtuna, W. Non-English students’ attitudes towards learning speaking. J. Basis 2, 281–290. https://doi.org/10.33884/basisupb.v8i2.3709 (2021).
doi: 10.33884/basisupb.v8i2.3709
Wisniewska, M. & Czernyszewicz, E. Survey of young consumer’s attitudes using food sharing attitudes and behaviors model. Br. Food. J. 125, 242–261 (2023).
doi: 10.1108/BFJ-09-2021-1025
Ostertagová, E., Ostertag, O. & Kováč, J. Methodology and application of the Kruskal–Wallis test. Appl. Mech. Mater. 611, 115–120. https://doi.org/10.4028/www.scientific.net/AMM.611.115 (2014).
doi: 10.4028/www.scientific.net/AMM.611.115
Brunsø, K. et al. Core dimensions of food-related lifestyle: A new instrument for measuring food involvement, innovativeness and responsibility. Food Qual. Prefer. 91, 104192 (2021).
doi: 10.1016/j.foodqual.2021.104192
DiStefano, C., Zhu, M. & Mîndrilã, D. Understanding and using factor scores: Considerations for the applied researcher. Pract. Assess. Res. Evaluation 14, 1–11 (2009).
Williams, R. Generalized ordered logit/partial proportional odds models for ordinal dependent variables. Stata J. 6, 58–82 (2006).
doi: 10.1177/1536867X0600600104
Larrabee, B., Scott, H. M. & Bello, N. M. Ordinary least squares regression of ordered categorical data: Inferential implications for practice. J. Agric. Biol. Environ. Stat. 19, 373–386 (2014).
doi: 10.1007/s13253-014-0176-z
Busch, G., Kassas, B., Palma, M. A. & Risius, A. Perceptions of antibiotic use in livestock farming in Germany, Italy and the United States. Livest. Sci. 241, 104251 (2020).
doi: 10.1016/j.livsci.2020.104251
Heinze, R. G., Bieckmann, R., Kurtenbach, S. & Küchler, A. Bauernproteste in Deutschland. Forschungsjournal Soziale Bewegungen 34, 360–379 (2021).
doi: 10.1515/fjsb-2021-0035
Wheeler, R., Lobley, M., McCann, J. & Phillimore, A. ‘It’s a lonely old world’: Developing a multidimensional understanding of loneliness in farming. Soc. Rural. 63, 11–36 (2023).
doi: 10.1111/soru.12399
Wolz, A. & Weiß, W. Demographic change, provision of public services and the role of agriculture in remote rural areas: findings from East Germany; https://doi.org/10.22004/ag.econ.182960 (2014).
Damelang, A. & Otto, M. Who is replaced by robots? robotization and the risk of unemployment for different types of workers. Work Occup.51, 81–206 (2023).
Gabriel, A. & Gandorfer, M. Adoption of digital technologies in agriculture—an inventory in a European small-scale farming region. Precis. Agric. 24, 1–24 (2022).
Wie viele Menschen leben von der Landwirtschaft? (Bundesministerium für Ernährung und Landwirtschaft, 2023); https://www.bmel-statistik.de/landwirtschaft/landwirtschaftliche-arbeitskraefte .
Lusk, J. L., Schroeder, T. C. & Tonsor, G. T. Distinguishing beliefs from preferences in food choice. Eur. Rev. Agric. Econ. 41, 627–655 (2014).
doi: 10.1093/erae/jbt035
House, L. et al. Objective and subjective knowledge: impacts on consumer demand for genetically modified foods in the United States and the European Union. AgBioForum 7, 113–123 (2004).
Langer, G. & Kühl, S. Perception and acceptance of robots in dairy farming—a cluster analysis of German citizens. Agric. Hum. Values 41, 249–267 (2024).
doi: 10.1007/s10460-023-10483-x
Kuntke, F., Linsner, S., Steinbrink, E., Franken, J. & Reuter, C. Resilience in agriculture: communication and energy infrastructure dependencies of German farmers. Int. J. Disaster Risk. Sci. 13, 214–229 (2022).
doi: 10.1007/s13753-022-00404-7
Bossi Fedrigotti, V. & Fischer, C. Welche landwirtschaft hätten Sie gerne? Zahlungsbereitschaften der Südtiroler Bevölkerung für produktions und absatzstrukturmerkmale. J. Austrian Soc. Agric. Econ. 29, 141–148 (2020).

Auteurs

Hendrik Hilmar Zeddies (HH)

Center for Development Research (ZEF), University of Bonn, Bonn, Germany. hzeddies@uni-bonn.de.

Gesa Busch (G)

Food Consumption and Wellbeing, Department of Sustainable Agriculture and Energy Systems, University of Applied Sciences Weihenstephan-Triesdorf, Freising, Germany.

Matin Qaim (M)

Center for Development Research (ZEF), University of Bonn, Bonn, Germany.
Institute for Food and Resource Economics, University of Bonn, Bonn, Germany.

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