Meta-approaches for analyzing large-scale data networks to improve plant disease management (PDM)

Abstract

Improvement in Plant Disease Management (PDM) can be obtained with research on new or improved control methods and enhanced epidemiological knowledge to aid decision making. Most commonly, control methods are developed and need to be evaluated for their efficacy as well as the economic aspects. Experimental research is the mainstream in this regard and is usually based on the treatment effects assessed across environments. The larger the sample size, or the number of experiments, ideally at various environmental conditions, the more reliable are the estimates and the certainty on the results. In Brazil, research networks for evaluating fungicide and biocontrol treatments have been established for many plant diseases including soybean rust, soybean white mold,d Fusarium head blight of wheat and wheat blast. Both disease and yield data are obtained and the relatively large (big) data can be used for various purposes such as modeling disease-yield relationships, development/testing of prediction models and covariate (moderator) analysis, besides summarizing treatment effects. In this seminar I will highlight the value of the research networks and the analytical approaches that have been used to make sense of the data.

Date
Location
Ithaca, NY