Drone adoption and farm performance in smallholder rice farming: Evidence of adoption without adaptation from Vietnam’s Mekong Delta

While agricultural drones have demonstrated technical benefits in controlled experiments, their real-world adoption patterns and performance among smallholders remain underexplored. This study examines drone uptake and its economic impacts on rice farmers in Vietnam’s Mekong Delta, where commercial drone services have expanded rapidly in recent years. Using panel data from 756 seasonal observations across 387 households over six rice cropping seasons from 2022 to 2024, we employ generalised linear models and generalised linear mixed models to examine adoption determinants and linear mixed-effects models to assess farm performance outcomes. Results show that drone adoption is primarily determined by structural factors, farm size and local service availability, rather than farmer characteristics, such as education or training, whose effects are conditional on operational scale and disappear once household-level heterogeneity is accounted for. Drone adopters achieved modest yield (1.9%) and revenue (3.3%) gains, especially on smaller farms; however, higher spraying costs entirely offset these gains, resulting in no net improvement in profitability. Drone use did not reduce pesticide or fertiliser expenditures, revealing an “adoption without adaptation” pattern where technology uptake occurs largely without corresponding adjustments to input management practices. Additionally, larger farms adopt drones more readily, yet productivity and revenue benefits are concentrated on smaller farms, a structural paradox in technology diffusion. Since adoption alone does not ensure economic benefits, policies should bundle drone services with agronomic guidance and invest in cooperative-based delivery models to improve accessibility. Grounded in actual farming systems, this study advances evidence-based understanding of digital agricultural technology adoption under real-world conditions.