Comparison of Weather Acquisition Periods Influencing a Statistical Model of Aerial Pesticide Drift

Author:

Thomson Steven J.,Huang YanboORCID

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.

Publisher

MDPI AG

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

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3