The aerial spray prediction model AgDRIFT embodies the computational engine found in the near-wake Lagrangian model AGricultural DISPersal (AGDISP) but with several important features added that improve the speed and accuracy of its predictions. This article summarizes those changes, describes the overall analytical approach to the model, and details model implementation, application, limits, and computational utilities.

A systematic evaluation of the AgDISP algorithms, which simulate off-site drift and deposition of aerially applied pesticides, contained in the AgDRIFT model was performed by comparing model simulations to field-trial data collected by the Spray Drift Task Force. Field-trial data used for model evaluation included 161 separate trials of typical agriculture aerial applications under a wide range of application and meteorological conditions. Input for model simulations included information on the aircraft and spray equipment, spray material, meteorology, and site geometry. The model input datasets were generated independently of the field deposition results, i.e., model inputs were in no way altered or selected to improve the fit of model output to field results. AgDRIFT shows a response similar to that of the field observations for many application variables (e.g., droplet size, application height, wind speed). However, AgDRIFT is sensitive to evaporative effects, and modeled deposition in the far-field responds to wet bulb depression whereas the field observations did not. The model tended to overpredict deposition rates relative to the field data for far-field distances, particularly under evaporative conditions. AgDRIFT was in good agreement with field results for estimating near-field buffer zones needed to manage human, crop, livestock, and ecological exposure.

A systematic evaluation of the AgDISP algorithms, which simulate off-site drift and deposition of aerially applied pesticides, contained in the AgDRIFT model was performed by comparing model simulations to field-trial data collected by the Spray Drift Task Force. Field-trial data used for model evaluation included 161 separate trials of typical agriculture aerial applications under a wide range of application and meteorological conditions. Input for model simulations included information on the aircraft and spray equipment, spray material, meteorology, and site geometry. The model input datasets were generated independently of the field deposition results, i.e., model inputs were in no way altered or selected to improve the fit of model output to field results. AgDRIFT shows a response similar to that of the field observations for many application variables (e.g., droplet size, application height, wind speed). However, AgDRIFT is sensitive to evaporative effects, and modeled deposition in the far-field responds to wet bulb depression whereas the field observations did not. The model tended to overpredict deposition rates relative to the field data for far-field distances, particularly under evaporative conditions. AgDRIFT was in good agreement with field results for estimating near-field buffer zones needed to manage human, crop, livestock, and ecological exposure.

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