Project Number: 2090-21000-037-013-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jul 1, 2023
End Date: May 7, 2024
This proposal focuses on screening USDA-NPGS germplasm for two important foliar diseases, Cercospora leaf spot (CLS) and Bacterial leaf spot (BLS), using procedures developed in our lab over the last several years for greenhouse screening (BLS) and field screening (CLS). CLS has always been the most prominent foliar disease for beet production, but few table beet cultivars possess resistance to this disease. As foliage crops have become more valuable in recent decades and top-pulling harvesters have become more common for root crops, greater resistance is needed. BLS is an emerging disease that can be damaging on both foliar and seed crops. Because BLS is seedborne, it may spread through the seed trade and cause problems in many regions where beet production takes place.
This work will characterize the reaction of plant introductions in the USDA-NPGS collection to two foliar diseases, Cercopsora leaf spot and Bacterial leaf spot, and assemble a pictorial dataset of this information for inclusion in GRIN. Check cultivars will be included so that performance of the NPGS accessions can be compared with commercially-available material. For BLS screening, accessions will be planted in replicates in the greenhouse in summer, 2023. This will include between 100 and 150 accessions of table beet, sugar beet, fodder beet, Swiss chard, Beta maritima, and leaf/bunching beets. At emergence of the first set of two true leaves, each pot will be inoculated with a Pseudomonas syringae pathovar aptata bacterial solution. At several intervals post inoculation, each pot will be rated using a scale based on percentage of diseased leaf area. These accessions will also be screened for CLS in the field in replicated trials. BLS disease screening will take place in the summer and fall of 2023, and CLS disease screening will take place in the summer of 2023. We will score their reaction to the pathogens, develop an image-based dataset for each disease and PI combination, and deposit this information into the GRIN system.