189-3 Quantifying the Effects of Pyrethroid Use On Miticide Applications On California Walnuts.
Poster Number 1014
See more from this Division: ASA Section: Biometry and Statistical Computing
See more from this Session: General Biometry and Statistical Computing
Tuesday, November 5, 2013
Tampa Convention Center, East Exhibit Hall
Abstract:
The published correlations between pyrethroid use and subsequent mite outbreaks are largely based on laboratory and field experimental studies, and sometimes yield conflicting results. This study used actual reported pyrethroid and miticide application data from 1995 to 2009 on California walnuts to quantify the relationship between the two pesticide groups. To further delineate this relationship, a nonlinear regression model of the form MI=1.48-0.86E{-77.37PI} (RMSE=0.099; R2=0.80; p<0.01) was fitted to the (PI, MI) centroids at small equal intervals of PI, where PI and MI are pyrethroid and miticide use intensity, respectively. The results confirm that more miticide is used to prevent or control mite resurgence when pyrethroids are applied, a practice that is not only costly but might be expected to aggravate mite resistance to miticides and increase risk associated with these chemicals to the environment and human health. A three-range scheme is proposed in this study to quantify pesticide applications based on the change rate of MI to PI. The PI range of 0-0.03 kg/ha is identified as the rapidly-increasing range, where MI increases vastly when PI increases. Since 91% of annual average miticide use on California walnuts during 1995-2009 (18.45 ton/year) is in this range, the result is of high statistical significance and can strengthen the existing integrated pest management (IPM) in reducing miticide applications by controlling pyrethroid use strictly in the rapidly-increasing range. The results of our study are particularly interesting as they do not rely on predictions of miticide use based on laboratory or experimental field studies, but rather actual pesticide use data that account for additional effects such as environmental conditions, grower behaviors, and pesticide regulations.
See more from this Division: ASA Section: Biometry and Statistical Computing
See more from this Session: General Biometry and Statistical Computing
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