Predicting and Measuring Drift of Bacillus Thuringiensis Sprays

Barry, John W.; Skyler, Patricia J.; Teske, Milton E.; Rafferty, James A.; Grim, Bruce S.

doi:10.1897/1552-8618(1993)12[1977:pamdob]2.0.co;2

Cited by 3 publications

(2 citation statements)

References 2 publications

(2 reference statements)

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“…The three trials were conducted on May 28, June 4, and June 10, 1993, with spraying operations starting/stopping at 0548/ 0819, 0547/0814, and 0533/0848 Mountain Standard Time (MST), respectively. Whaley et al [7] provides more details regarding the 1993 Mill Creek Canyon spray trials, and Barry et al [11] further explains the Rotorod and Mylar sheet sampling techniques.…”

Section: Field Drift Datamentioning

confidence: 99%

The agricultural dispersal‐valley drift spray drift modeling system compared with pesticide drift data

Allwine

Thistle

Teske

et al. 2002

Enviro Toxic and Chemistry

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The coupling of the valley drift (VALDRIFT) atmospheric dispersion/deposition model with the agricultural dispersal (AGDISP) aircraft wake model generates a modeling system for predicting the off-target drift of pesticides sprayed in a mountain valley. The approach uses the AGDISP near-field spray model to estimate the mass fraction of pesticide remaining airborne after initial application, then the VALDRIFT complex terrain model to estimate the drift of pesticide from the target area. The modeling system inputs include detailed spray information, a measure (or estimate) of winds in the valley, and the valley topographic characteristics; the results are pesticide concentrations throughout the valley atmosphere and pesticide deposition to the valley surface. The AGDISP and VALDRIFT models are operated independently, with the results from AGDISP being used as input to VALDRIFT through user-created data files. The modeling system was evaluated using pesticide drift data from spray trials conducted in the Mill Creek Canyon of Utah's Wasatch Mountains, USA, during the late spring of 1993. The predicted deposition compared within a factor of three of the observations (70% of the time) at all sampling locations extending several kilometers down-valley from the spray treatment block. The overall average ratio of predicted-to-observed deposition was 0.9.

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Section: Field Drift Datamentioning

confidence: 99%

The agricultural dispersal‐valley drift spray drift modeling system compared with pesticide drift data

Allwine

Thistle

Teske

et al. 2002

Enviro Toxic and Chemistry

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“…Forest Service Cramer-Barry-Grim (FSCBG), which was developed by the United States Department of Agriculture (USDA) Forest Service in cooperation with the United States Army, is the first model for droplet deposition and drift prediction in aerial spraying [13,14] . Agriculture Dispersal (AGDISP) was developed by Spray Drift Task Force (SDTF) based on FSCBG and identified as a valuable software for substantiating drift data which is collected in the extensive aerial field studies.…”

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Development of droplet characteristics prediction models for air induction nozzles based on wind tunnel tests

Liao

Hewitt

Wang

et al. 2019

International Journal of Agricultural and Biological Engineering

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Nozzle flowrate and spray pressure are two of the most important factors influencing on droplet characteristics. With the aim to develop prediction models for air-induction nozzles (AINs), a series of Billericay Farm Services (BFS) AINs with different orifice diameters in combination with tap water were tested. 0.2 MPa, 0.3 MPa, 0.4 MPa, 0.5 MPa, 0.6 MPa and 0.7 MPa of spray pressures and 2 m/s, 3 m/s, 4 m/s and 5 m/s of air speeds were setup. Based on the wind tunnel tests data, prediction models with input variables of nozzle flowrate and spray pressure and output variables of D v0.1 , D v0.5 , D v0.9 , %<150 µm (proportion of spray volume contained in droplets with diameter below 150 µm), relative span (RS) and coefficient of variation (CV) of D v0.5 were developed. The developed models were validated based on wind tunnel experimental data. Results showed that: for D v0.1 , D v0.5 , D v0.9 and %<150 µm, R 2 were equal to 0.768, 0.823, 0.868 and 0.811, indicating that the predictive ability for these four parameters is strong. For RS and CV, R 2 were equal to 0.100 and 0.113, respectively, indicating that the predictive ability for these two parameters is poor. The models developed in the present study are helpful for facilitating the use of AIN in agricultural spray application. et al. Development of droplet characteristics prediction models for air induction nozzles based on wind tunnel tests. Int J Agric & Biol Eng, 2019; 12(6): 1-6.

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Control of Gypsy Moth, Lymantria dispar, in North America since 1878

Solter

Hajek²

2009

Use of Microbes for Control and Eradication of Invasive Arthropods

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Predicting and Measuring Drift of Bacillus Thuringiensis Sprays

Cited by 3 publications

References 2 publications

The agricultural dispersal‐valley drift spray drift modeling system compared with pesticide drift data

The agricultural dispersal‐valley drift spray drift modeling system compared with pesticide drift data

Development of droplet characteristics prediction models for air induction nozzles based on wind tunnel tests

Control of Gypsy Moth, Lymantria dispar, in North America since 1878

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