Validation of CFD-SAAS, a modeling tool for predicting pesticide spray deposition and drift from air-assisted sprayers in apple orchards

A computational fluid dynamics (CFD) based computer program, Simulation of Air-Assisted Sprayers (SAAS), was developed to predict pesticide spray deposition and drift risk for conventional and intelligent orchard sprayers under various weather and field conditions. However, the predictive accuracy of SAAS must be validated to ensure reliability. The CFD simulations used to generate the SAAS data (CFD-SAAS) and the direct output of the SAAS were both analyzed. In-field measurements of airborne drift and spray deposition on the canopy and ground from a conventional and intelligent sprayer in an experimental apple orchard were used for the validations. Mass balances of total spray deposition and airborne drift were included in the validation analyses. CFD-SAAS was valid for predicting the deposition on the canopy and ground at specific locations, with average relative errors of 36.4% and 34.4%, respectively. In addition, the model successfully predicted spatial distributions of airborne drift at distances from 3 m to 12 m downwind and heights up to 2 m above the ground with average relative errors of 16.8% and 20.4% for a conventional and intelligent sprayer, respectively. Furthermore, the CFD-SAAS was able to accurately predict the droplet mass balance for the ground deposition at downwind distances up to 48 m for the conventional sprayer and up to 12 m for the intelligent sprayer with an overall relative error of 37.5%.