Economic Evaluation of Drone-Based Aerial Application Technologies in U.S. Agriculture

Drone‑based aerial application is a fast‑growing part of U.S. agriculture, but we still know little about how much farm income depends on these services in dollar terms (Agriculture Spraying Equipment Market (2025 - 2033) 2025). Farmers and custom applicators are interested in drones because they can spray quickly, reach hard‑to‑access areas, and apply pesticides more precisely than some traditional methods (Blake 2011). At the same time, drones are a new technology, and they can be costly to buy and operate compared to many ground sprayers (Talaeizadeh, et al. 2025). This study estimates the economic value of the agricultural drone application industry by asking a simple counterfactual question, what would happen to farm‑level revenue if drone‑based spraying were no longer an option (Hughes, Dharmasena and Martin 2022). We focus on Texas as a test case and examine five major Texas crops: corn, cotton, grain sorghum, soybeans, and wheat. Using existing data on crop acreage, yields, and prices, along with assumptions about how much acreage is treated with drones, we build a simulation model that compares current farm‑gate revenue with a scenario where drone spraying is removed and only partly replaced by other methods such as ground rigs. The model follows the basic structure used in earlier work (Hughes, Dharmasena and Martin 2024) on the value of the agricultural aerial application industry, but we simplify it by looking only at the producer side and only at the drone share of aerial applications. In the “no‑drones” 5 scenario, some of the acres that were previously sprayed by drones are assumed to be taken over by ground rigs, while other acres are left untreated or are treated too late, leading to yield losses and lower total revenue on those acres. A stochastic simulation is run to reflect uncertainty in the amount of acreage switched to ground rigs. By comparing simulated revenue with and without drones, the resulting difference in yield and revenue is the economic value attributable to drone use. The losses represent amounts occurring if drone applications were not available. The counterfactual result places a lower‑bound value on the contribution of the drone application industry to Texas for these five major crops and measures the avoided revenue loss associated with drone‑enabled access, timeliness, and treatment capability. The results do not account for economic value on the industry because costs, profits, capital investment, and operational efficiency are not included in this preliminary result; however, it does place an estimate of the minimum economic importance of drones, because it captures the yield losses that would occur if drones were unavailable.