Project Number: 2092-21220-002-055-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jul 1, 2020
End Date: Jun 29, 2022
Our main objective is to develop tools to quantify and mitigate the risk of losses potato growers face due to soil-borne pathogens. One major objective is the mapping of quantitative trait loci (QTL) associated with disease resistance to Tobacco rattle virus (TRV) and selection of families that segregate for Columbia root knot nematode (CRKN) resistance for genetic mapping. Molecular markers derived from these linkage mapping studies will be used to accelerate the development of germplasm resistant to these diseases. Another major objective is the development of molecular assays to quantify pathogen abundance in soils or plant tissues and and computer vision techniques to monitor plant health/performance throughout the field season. Quantifying the abundance of potentially damaging pathogens in soil and measurement of plant health indices will help forecast risk of loss, whereas the deployment of disease resistant potato clones will dramatically reduce the risk of loss due to pathogen infection.
QTL mapping for TRV resistance is being performed by planting a bi-parental linkage mapping population that segregates for TRV resistance in a field containing viruliferous stubby root nematodes (TRV). We have genotyped this population and mapped a major resistance locus to the distal end of Chromosome IX. We are currently in the process of validating this discovery and identifying markers for other traits of agronomic importance. We are also performing hybridizations with novel pentaploid CRKN resistant germplasm with elite tetraploid clones and selecting resulting tetraploid offspring. To monitor soil health we will perform nucleic acid extraction from soils and develop RT-PCR assays to quantify the abundance of pathogens within soil samples collected from experimental fields. We are also utilizing computer vision techniques to quantify plant health/disease symptoms in both laboratory and field settings using consumer grade DLSR cameras, flatbed scanners, and multi-spectral imagery acquired using small unmanned aerial systems (sUAS).