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Sources of Resistance to Stem Rust (Ug99) in Bread Wheat and Durum Wheat Identified Using Focused Identification of Germplasm Strategy
Author(s) -
Endresen Dag Terje Filip,
Street Kenneth,
Mackay Michael,
Bari Abdallah,
Amri Ahmed,
De Pauw Eddy,
Nazari Kumarse,
Yahyaoui Amor
Publication year - 2012
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2011.08.0427
Subject(s) - stem rust , germplasm , biology , puccinia , identification (biology) , trait , crop , rust (programming language) , breeding program , agronomy , microbiology and biotechnology , resistance (ecology) , botany , cultivar , computer science , mildew , programming language
The focused identification of germplasm strategy (FIGS) has been validated using predictive computer models in simulation studies to predict a priori known trait scores. This study was designed as a “blind” study where the person calculating the computer model did not know the actual trait scores. This study design provides a more realistic test of the predictive capacity of the FIGS approach compared to previous studies. Furthermore this study also explored the suitability of FIGS for the identification of resistance in bread wheat ( Triticum aestivum L. subsp. aestivum ) and durum wheat [ Triticum turgidum L. subsp. durum (Desf.) Husn.] to Ug99—a strain of stem rust ( Puccinia graminis Pers. f. sp. tritici Eriks. & Henn.) and typified to race TTKSK. The predictions were validated against a dataset with the screening of wheat accessions conducted in Yemen in 2008. Only a small training set representing 20% of the trait screening results was disclosed to the person conducting the data analysis for the calibration of the prediction model. The hit rate for identification of Ug99‐resistant accessions was more than two times higher when using the FIGS approach compared to a random selection of accessions. These results suggested that FIGS was well suited for the identification of samples with resistance to fungal pathogens. It is therefore recommended that FIGS approach be used as a complement to expert knowledge and experience when selecting accessions for plant breeding and crop research activities.