Risk algorithms for soilborne diseases

Research is continuing to develop risk algorithms to reduce the frequency of false positive decisions in nematicide application using potato 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 is also developed to provide a rapid way of extracting DNA from soil and enumerating nematode populations using quantitative PCR. The sensitivity and precision of this technique will be compared to manual counting which is limited by low extraction efficiencies and the ability to often only provide genus-specific information. Prediction of damage from soilborne diseases 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. In this project, we are using root-knot nematode (Meloidogyne spp.) and potato as a model system.

This project involves a combination of on-farm trials and replicated trials conducting at Cornell University. 

Meloidogyne hapla

M. hapla


 

 

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