Risk algorithms for soilborne diseases

Research is continuing to develop risk algorithms to reduce the frequency of false positive decisions for soil-applied fungicides using soilborne diseases of table beet as a model system.  In contrast to foliar diseases, management decisions for soilborne diseases often need to be made prior to planting. Unfortunately, these decisions are usually made in the absence of quantitative information on inoculum densities.  These decisions are therefore often risk averse and necessitate the application of costly preventative treatments before or at planting, with high environmental impact quotients, multiplying the effect of a false positive decision.  A method using ferromagnetic nanoparticles has also been adapted to provide a rapid way of extracting DNA from soil and enumerating soilborne fungal populations using quantitative PCR. The sensitivity and precision of this technique is being compared to traditional enumeration techniques such as selective media and dilutions and bait plants.  Prediction of damage from soilborne pathogens such as Rhizoctonia solani and Pythium spp. may be substantially improved by the provision and adoption of soil tests that are highly sensitive and specific for the pathogens (and often races) of interest, and the determination of their type I and II error rates in coupling pathogen levels with damage thresholds at harvest.  This project involves a combination of on-farm trials and replicated trials conducted on the field research facilities at Cornell University. 



Comments are closed