Data and code from: Application variables that affect efficacy of disinfestants sprayed on different substrate materials to control colletotrichum siamense

Authors: Copes, Warren; Read, Quentin; Smith, Barbara

Submitted to: Ag Data Commons
Publication Type: Database / Dataset
Publication Acceptance Date: 6/25/2025
Publication Date: 7/28/2025
Citation: Copes, W.E., Read, Q.D., Smith, B.J. 2025. Data and code from: Application variables that affect efficacy of disinfestants sprayed on different substrate materials to control colletotrichum siamense. Ag Data Commons. 10:18.
DOI: https://doi.org/10.15482/USDA.ADC/27919398.v1

Interpretive Summary: Disinfestants are critically important for eliminating plant pathogens, but it is unknown how effective they are even when applied following recommended practices. In this study, we evaluated how well different commonly used disinfestants perform at reducing the growth of a fungus that is a major nuisance to the horticultural trade. We sprayed disinfestant on different surfaces and using different timing, and measured how much fungal growth we observed. This dataset, which is associated with a published manuscript, has all the data and software code needed to reproduce all the statistical models, figures, and tables presented in the paper. This includes processing the data, fitting the models, and estimating the means and the uncertainty around them. The estimates of the means and their uncertainty are presented in the figures and tables.

Technical Abstract: Disinfestants are an important sanitation tool used to eliminate plant pathogens. Product labels and related literature provide guidance for their proper usage, but their effectiveness may vary even when applied following recommended practices. The efficacy of several hypochlorite, isopropyl alcohol, quaternary ammonium and peroxygen compounds against Colletotrichum siamense was evaluated relative to substrate porosity, contact time and disinfestant wettability properties in seven studies where disinfestants were sprayed on six substrate materials (concrete, galvanized metal, polypropylene ground fabric, polyethylene plastic sheet, pressure-treated pine, and twin-wall clear polycarbonate) commonly found in ornamental plant production surfaces. This dataset includes the raw data and R statistical software code required to reproduce the data processing, statistical model fitting, and production of figures and tables in the associated manuscript. For each of the seven studies, we fit a Bayesian generalized linear mixed model to the data. We graphically assessed model fit, then used posterior distributions of the expected values of the posterior predictive to make predictions. From each of these distributions, we used the median to represent the point estimate and 95% equal-tailed credible intervals to represent uncertainty about the means.