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German sugar beet farmers' intention to use autonomous field robots for seeding and weeding.
Uehleke, Reinhard; von Plettenberg, Lousia; Leyer, Michael; Hüttel, Silke.
  • Uehleke R; Department of Agricultural Economics and Rural Development, University of Göttingen, Platz der Göttinger Sieben 5, 37073, Göttingen, Germany. Electronic address: reinhard.uehleke@uni-goettingen.de.
  • von Plettenberg L; Department of Agricultural Economics and Rural Development, University of Göttingen, Platz der Göttinger Sieben 5, 37073, Göttingen, Germany. Electronic address: louisa.plettenberg@uni-goettingen.de.
  • Leyer M; School of Business and Economics, University of Marburg, Barfüßertor 2, 35037, Marburg, Germany; School of Management, Queensland University of Technology, 2 George Street, Brisbane, 4000, Australia. Electronic address: michael.leyer@wiwi.uni-marburg.de.
  • Hüttel S; Department of Agricultural Economics and Rural Development, University of Göttingen, Platz der Göttinger Sieben 5, 37073, Göttingen, Germany. Electronic address: silke.huettel@uni-goettingen.de.
J Environ Manage ; 370: 122472, 2024 Sep 13.
Article en En | MEDLINE | ID: mdl-39276655
ABSTRACT
Robotic weed control is not yet widely adopted, despite its technological availability and proven economics and sustainability in crop cultivation by replacing seasonal labor and synthetic pesticides. This impedes technologically enabled changes toward more sustainable agricultural systems. Given that adopting robotics for the weeding process requires changing existing systems, farmers' appraisals for the new and the current weeding technology may constitute barriers. However, this dualism has been largely ignored by previous studies. Based on a duality approach, we investigate farmers' beliefs, and adaptive and maladaptive appraisals of current and new robotic weeding in sugar beets. The main variable of interest is their behavioral intention to adopt weeding robots. For our sample of German farmers, we identify the main enablers perceived efficacy of the robots and social norms. The main barrier are maladaptive rewards from traditional weeding. We recommend policy incentives to promote large-scale uptake of new and more sustainable robotic technologies. To improve efficacy perceptions of such robotic systems public demonstrations/talks are mostly relevant. Maladaptive rewards can be reduced, for instance, by notifying about the dependency of the current practices on future availability of synthetic inputs or seasonal workers.
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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2024 Tipo del documento: Article