Abstract
Off-target drift of crop protection materials from aerial spraying can be detrimental to sensitive crops, beneficial insects, and the environment. So, it is very important to accurately characterize weather effects for accurate recommendations on drift mitigation. Wind is the single-most important weather factor influencing localized off-target drift of crop protection materials. In drift sampling experiments, it is difficult to accurately characterize wind speed and direction at a drift sampling location, owing to the natural variability of spray movement on the way to the sampling target. Although it is difficult or impossible to exactly track wind movement to a target, much information can be gained by altering the way wind speed and tracking is characterized from field experiments and analyzed using statistical models of spray drift. In this study two methods of characterizing weather were compared to see how they affect results from a statistical model of downwind spray drift using field data. Use of a method that implemented weather averages over the length of a spray run resulted in model-based estimates for spray tracer concentration that compared well with field data averages. Model results using this method showed only a slight sensitivity to changes in wind speed, and this difference was more pronounced further downwind. The degree of this effect was consistent with field results. Another method that used single weather values obtained at the beginning of each run resulted in an unexpected inverse relationship of residue concentration with respect to increases in wind speed by sensitivity analysis and would thus not be recommended for use in a statistical model of downwind spray drift. This study could provide a guideline for general agricultural aviation analysis and unmanned aerial vehicle spray application studies.
Subject
Agronomy and Crop Science
Reference50 articles.
1. Huang, Y., and Zhang, Q. (2021). Agricultural Cybernetics, Springer Nature.
2. Ganesh, A.S. (2014). The Hindu, ISSN International Centre. Retrieved 2022-03-11.
3. Johnson, M.A. (2002). McCook Field 1917–1927, Landfall Press.
4. Agricultural aviation perspective on precision agriculture in the Mississippi Delta;Huang;Smart Agric.,2019
5. Improving Flow Response of a Variable-rate Aerial Application System by Interactive Refinement;Thomson;Comput. Electron. Agric.,2010
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