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Stability of Sweet Potato Cultivars to Alternaria Leaf and Stem Blight Disease
Author(s) -
Osiru Moses Omongin,
Olanya Modesto Ocen,
Adipala Ekwamu,
Lemaga Berga,
Kapinga Regina
Publication year - 2009
Publication title -
journal of phytopathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.53
H-Index - 60
eISSN - 1439-0434
pISSN - 0931-1785
DOI - 10.1111/j.1439-0434.2008.01457.x
Subject(s) - cultivar , biology , alternaria , blight , ammi , horticulture , agronomy , plant disease resistance , veterinary medicine , gene–environment interaction , genotype , medicine , biochemistry , gene
Abstract Alternaria leaf petiole and stem blight is an economically important disease of sweet potato ( Ipomoea batatus L.) in tropical and sub‐tropical environments. Published research on cultivar resistance to the sweet potato disease is limited. To evaluate cultivar reaction and stability to the disease, multi‐location and replicated experiments were established in 12 environments in Uganda. Disease severity (area under disease progress curves – AUDPC), and cultivar root yield were also assessed. Significant differences (P < 0.001) in AUDPC were detected among cultivars. Mean AUDPC ranged from 46.3 (Araka Red) to 78.4 (New Kawogo) across locations and seasons and the genotypes Araka Red and Tanzania had the lowest disease values. The location and season effects accounted for 67.1% and 7.5% of the total variance of AUDPC recorded among cultivars. The ranking of cultivars based on predicted AUDPC from Additive Main Effect and Multiplicative Interactive model (AMMI) showed that the NASPOT 1, the susceptible check, and New Kawogo were most susceptible to the disease in 11 of the 12 environments. Low and stable disease was consistently recorded and predicted on NASPOT 3 and the landrace cultivars Tanzania, Dimbuca, and Araka Red across environments. These results suggest that landrace cultivars had relative stability to the disease and wide adaptation across environments. These results suggest that AMMI statistical model and other multivariate techniques can be utilized for prediction of Alternaria disease stability in these locations.