Policies aimed at reducing plant protection products (PPPs) are part of the UN’s 2030 Agenda for Sustainable Development. Sustainable management of PPPs is crucial for soil health, biodiversity, and ecosystem services, including food provision. While PPPs can control pests and enhance agricultural yields, they also pose environmental and health risks by contaminating water, soil, and non-target organisms through airborne drift. Investigating innovative and more sustainable distribution methods can support sustainability goals. This study aimed to evaluate the potential impact of the pesticide Spintor^® Fly on non-target soil organisms in olive orchards comparing two spraying methods: a traditional Casotti^® pump mounted on a tractor and an innovative Unmanned Aerial Vehicle (UAV) developed for the project. The study was conducted in 2021 in an organic olive orchard, which was divided into two plots: a Casotti-treated plot (CAS) and a drone-treated plot (DRO). A strip of uncultivated land at the edge of the orchard was used as a (non-treated) control plot (CAP). The impact on native soil microarthropod communities was assessed using the arthropod-based Soil Biological Quality Index (QBS-ar) and Bait Lamina Test (BLT). Soil samples were collected for earthworm avoidance tests and soil chemical–physical analysis. The results obtained with QBS-ar and BLT indicated no significant differences between DRO and CAS, in both sampling periods (pre- and post-treatment). However, DRO generally exhibited slightly better performance than CAS. The avoidance behaviour was confirmed for both CAS and DRO, although it was lower for the latter. Overall, drone aerial spray performed slightly better, suggesting a potentially lower impact on soil communities. Our results provide initial clues for the sustainable use of drones in agriculture with no increased risks for soil health compared to traditional methods. Further long-term studies should be conducted to validate these findings and possibly confirm the long-term benefits of drone applications compared to traditional methods.

The need for reliable biological indicators to support soil biomonitoring is increasing, particularly in agricultural systems where management practices and environmental pressures interact to influence soil ecological integrity. Among soil fauna-based indicators that measure soil health, the Soil Biological Quality indices based on microarthropods (QBS-ar) and earthworms (QBS-e) provide functional measures of soil condition, however their combined application remains largely unexplored. In this study, for the first time both indices were applied simultaneously to assess soil biological quality in the same agroecosystem. The context was that of contrasting agricultural systems (strip cropping vs. pure stands) in a real-farm experimental set-up. Additional biological variables and soil physico-chemical parameters were also considered. Statistical analyses included Spearman correlations, linear mixed-effect models (LMMs), and redundancy analysis (RDA). QBS-ar and QBS-e showed contrasting responses to management systems, with higher QBS-ar values under strip cropping and higher QBS-e values in pure stands. No significant relationship was observed between the two indices. Multivariate analyses indicated that both indices were significantly influenced by management and environmental variables, although with different patterns of association. QBS-ar appeared more responsive to variation in topsoil conditions, whereas QBS-e was associated with broader soil properties and seasonal community dynamics. These results indicate that QBS-ar and QBS-e capture complementary aspects of soil biological quality and should not be used interchangeably. Their combined use improves the interpretation of soil biological responses to agricultural management and associated environmental pressures. Overall, this study highlights the potential of soil fauna-based indices as practical tools for biomonitoring in agroecosystems and supports further exploration on the comparative responses of biological indexes.