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ARS Home » Southeast Area » Tifton, Georgia » Crop Genetics and Breeding Research » Research » Research Project #436373

Research Project: Development and Evaluation of Cultivars with Improved Disease Resistances to Increase On-farm Profitability

Location: Crop Genetics and Breeding Research

Project Number: 6048-21000-029-09-R
Project Type: Reimbursable Cooperative Agreement

Start Date: Jul 1, 2019
End Date: Dec 31, 2020

Objective:
1) Continue hybridization program to combine resistance to the peanut root-knot nematode (PRN) with important characteristics such as high yield, high oleic acid and resistance to other diseases (Tomato Spotted Wilt Virus (TSWV), leaf spot, white mold). 2) Use recently developed molecular markers for disease resistance to combine resistance to leaf spot with high yield and high oleic acid. 3) Continue selection and evaluation program to identify progeny that have the desired combination of resistances and agronomic performance. 4) Evaluate late generation breeding lines under variable seeding rates and fungicide spray schedules to identify combinations that will maximize farm net profits.

Approach:
Continue crossing program to combine resistance to diseases (peanut root-knot nematode (PRN), Tomato Spotted Wilt Virus (TSWV), leaf spot, and white mold) with other desirable characteristics (eg, high yield and grades, and high oleic acid). Single seed descent is then used to rapidly advance this material to the F4 generation. F4:5 progeny are evaluated using genetic markers to improve our selection efficiency for resistance to nematodes and leaf spot, and for high oleic acid. Selections are then evaluated in field plots for resistance to TSWV, yield, grade, and other agronomic characteristics. Continue our accelerated backcrossing program using MAS to introgress resistance to leaf spot into high yielding genetic backgrounds adapted to Georgia. Evaluate breeding lines with high levels of resistance to leaf spot, nematodes, and TSWV in field trials. Treatments will include variable seeding rates and variable fungicide spray schedules. Data will be analyzed to identify those combinations that produce maximum net returns.