DOI: 10.15482/usda.adc/25673073
title: Data and code from: Environmental influences on drying rate of spray applied disinfestants from horticultural production services

description: This dataset includes all the data and R code needed to reproduce the analyses in a forthcoming manuscript:Copes, W. E., <b>Q. D. Read</b>, and B. J. Smith. Environmental influences on drying rate of spray applied disinfestants from horticultural production services. <i>PhytoFrontiers</i>, DOI pending.<b>Study description: </b>Instructions for disinfestants typically specify a dose and a contact time to kill plant pathogens on production surfaces. A problem occurs when disinfestants are applied to large production areas where the evaporation rate is affected by weather conditions. The common contact time recommendation of 10 min may not be achieved under hot, sunny conditions that promote fast drying. This study is an investigation into how the evaporation rates of six commercial disinfestants vary when applied to six types of substrate materials under cool to hot and cloudy to sunny weather conditions. Initially, disinfestants with low surface tension spread out to provide 100% coverage and disinfestants with high surface tension beaded up to provide about 60% coverage when applied to hard smooth surfaces. Disinfestants applied to porous materials were quickly absorbed into the body of the material, such as wood and concrete. Even though disinfestants evaporated faster under hot sunny conditions than under cool cloudy conditions, coverage was reduced considerably in the first 2.5 min under most weather conditions and reduced to less than or equal to 50% coverage by 5 min. <b>Dataset contents: </b>This dataset includes R code to import the data and fit Bayesian statistical models using the model fitting software CmdStan, interfaced with R using the packages brms and cmdstanr. The models (one for 2022 and one for 2023) compare how quickly different spray-applied disinfestants dry, depending on what chemical was sprayed, what surface material it was sprayed onto, and what the weather conditions were at the time. Next, the statistical models are used to generate predictions and compare mean drying rates between the disinfestants, surface materials, and weather conditions. Finally, tables and figures are created. These files are included:<b>Drying2022.csv</b>: drying rate data for the 2022 experimental run<b>Weather2022.csv</b>: weather data for the 2022 experimental run<b>Drying2023.csv</b>: drying rate data for the 2023 experimental run<b>Weather2023.csv</b>: weather data for the 2023 experimental run<b>disinfestant_drying_analysis.Rmd</b>: RMarkdown notebook with all data processing, analysis, and table creation code<b>disinfestant_drying_analysis.html</b>: rendered output of notebook<b>MS_figures.R</b>: additional R code to create figures formatted for journal requirements<b>fit2022_discretetime_weather_solar.rds</b>: fitted brms model object for 2022. This will allow users to reproduce the model prediction results without having to refit the model, which was originally fit on a high-performance computing cluster<b>fit2023_discretetime_weather_solar.rds</b>: fitted brms model object for 2023<b>data_dictionary.xlsx</b>: descriptions of each column in the CSV data files<br>