Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/341032 
Year of Publication: 
2026
Citation: 
[Journal:] Journal of Integrative Agriculture [ISSN:] 2352-3425 [Volume:] 25 [Issue:] 6 [Publisher:] Elsevier [Place:] Amsterdam [u.a.] [Year:] 2026 [Pages:] 2188-2200
Publisher: 
Elsevier, Amsterdam [u.a.]
Abstract: 
Agricultural drones can improve productivity, save labor, and reduce environmental impacts by offering digital multifunctionality in agricultural production. Yet, the lagging adoption among smallholders is still prevalent. Existing literature commonly explains it by the lack of capital, land fragmentation, and digital illiteracy, with little delving into specific adoption modes under uncertainty. In this study, we demonstrate the potential of hiring agricultural drone services and investigate the role of supplier uncertainty in the adoption decision. We conduct a discrete choice experiment among 338 farmers in Hubei Province, China. Mixed logit models are used to analyze farmers' preferences for the agricultural drone service (ADS) and its attributes. The results show that the large majority of sampled farmers are willing to adopt ADS. Besides low prices, farmers prefer services with local suppliers and contracts. Potential adopters in this choice experiment are characterized by youth, higher education, owning poor-topography farms, drone learning via word-of-mouth, and adoption experience. The willingness to pay analysis indicates that farmers would like to spend 25 CNY per mu (53 USD per ha) on average for ADS. Notably, farmers value the localness of suppliers more than the form of agreements when choosing a particular drone service. These findings suggest that the mode of hiring ADS can effectively motivate farmers' adoption intention, thereby requiring supply-side incentives and uncertainty-reducing promotion strategies to enhance smallholders' access to and adoption of agricultural drones.
Subjects: 
UAV
digital agriculture
technology adoption
mixed logit model
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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