A LITERATURE REVIEW OF DOWNWIND DRIFT FROM AIRBLAST SPRAYERS: DEVELOPMENT OF STANDARD METHODOLOGIES AND A DRIFT DATABASE

摘要

Empirical data available from studies of downwind drift of agrochemicals associated with horticultural broadcast air-assisted spraying within the U.S. are limited compared to the data available for aerial or ground applications to arable crops. There is interest in evaluating the extent to which drift studies conducted in the European Union and elsewhere are applicable to U.S. agricultural practices due to the lack of data currently available. To expand the database available for drift assessment, a review of the literature was undertaken. The original intent of the work presented here was to conduct a meta-analysis of the data collected in order to assess the feasibility of developing standard curves for applications made under a standard set of conditions. It is seldom possible to pool data from different geographic regions for row crop applications due to large differences in the scale of agriculture, leading to significant disparities in agronomic practices. These differences make it impossible to use studies generated in one geographic region to make inferences elsewhere. It is possible, however, that there is enough geographic similarity in the agronomic practices used in bush and tree crops to allow the pooling of data. Although there was nothing about the scale of agriculture that prevented direct comparison, a meta-analysis of the data was not conducted due to the variations in crop type, application techniques, and sampling methods in the collected studies. In response, this article discusses the global issues in methodology and reporting that precluded the meta-analysis of data and describes which parameters require standardization to enable comparative assessment. The product of this effort is the collation of large quantities of data into an open-source database. The individual datasets could be used to make inferences regarding drift potential of specific analogous applications, or the data may be used by researchers for the design and validation of models.