Fusarium crown rot (FCR), caused by Fusarium pseudograminearum and F. culmorum, reduces wheat (Triticum aestivum L.) yields in the Pacific Northwest (PNW) of the US by as much as 35%. Resistance to FCR has not yet been discovered in currently grown PNW wheat cultivars. Several significant quantitative trait loci (QTL) for FCR resistance have been documented on chromosomes 1A, 1D, 2B, 3B, and 4B in resistant Australian cultivars. Our objective was to identify QTL and tightly linked SSR markers for FCR resistance in the partially resistant Australian spring wheat cultivar Sunco using PNW isolates of F. pseudograminerarum in greenhouse and field based screening nurseries. A second objective was to compare heritabilities of FCR resistance in multiple types of disease assaying environments (seedling, terrace, and field) using multiple disease rating methods. Two recombinant inbred line (RIL) mapping populations were derived from crosses between Sunco and PNW spring wheat cultivars Macon and Otis. The Sunco/Macon population comprised 219 F6:F7 lines and the Sunco/Otis population comprised 151 F5:F6 lines. Plants were inoculated with a single PNW F. pseudograminearum isolate (006-13) in growth room (seedling), outdoor terrace (adult) and field (adult) assays conducted from 2008 through 2010. Crown and lower stem tissues of seedling and adult plants were rated for disease severity on several different scales, but mainly on a numeric scale from 0 to 10 where 0 = no discoloration and 10 = severe disease. Significant QTL were identified on chromosomes 2B, 3B, 4B, 4D, and 7A with LOD scores ranging from 3 to 22. The most significant and consistent QTL across screening environments was located on chromosome 3BL, inherited from the PNW cultivars Macon and Otis, with maximum LOD scores of 22 and 9 explaining 36 and 23% of the variation, respectively for the Sunco/Macon and Sunco/Otis populations. The SSR markers Xgwm247 and Xgwm299 flank these QTL and are being validated for use in marker-assisted selection for FCR resistance. This is the first report of QTL associated with FCR resistance in the USElectronic supplementary materialThe online version of this article (doi:10.1007/s00122-012-1818-6) contains supplementary material, which is available to authorized users.

Fusarium crown rot (FCR) is one of the most widespread root and crown diseases of wheat in the Pacific Northwest (PNW) of the United States. Our objectives were to characterize crown rot severity and distribution throughout the PNW by conducting a survey of 210 fields covering the diverse dryland wheat-producing areas of Washington and Oregon and to utilize a factor analysis statistical approach to determine the effects of climate and geography on species distribution and disease severity. Climatic variables were based on 30-year averages and 2008 and 2009 separately (the 2 years of the survey). Mean annual temperature, mean temperature in the coldest month, mean temperature in the warmest month, mean annual precipitation, snowfall, elevation, soil type, and cropping intensity were highly intercorrelated. The factor analysis of the climate variables resulted in the development of two latent factors that could be used as predictor variables in logistic regression models for the presence or absence of Fusarium spp. and of FCR disease scores. Isolates of Fusarium spp. were recovered from 99% of 105 fields sampled in 2008 and 97% of fields in 2009. There were differences between years for responses of FCR and nodes scores, and isolations of Fusarium pseudograminearum with more significant results in 2008, due to warmer drier weather. Results of the factor analysis showed that the distribution of F. pseudograminearum occurred in a greater frequency in areas of the PNW at lower elevations with lower moisture and higher temperatures in 2008, whereas F. culmorum occurred in greater frequency in areas at higher elevations with moderate to high moisture and cooler temperatures consistently across both years. Disease scores increased with increasing levels of factors 1 (primarily temperature) and 2 (primarily precipitation). Both the frequency of pathogen species and disease scores were influenced by the year, indicating that soilborne pathogens are responsive to short-term changes in environment. This factor analysis approach can be utilized in studies to determine the effects of climate and other environmental (soil, cropping system, and so on) factors on the distribution and severity of root diseases.

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Root diseases have long been prevalent in Australian grain-growing regions, and most management decisions to reduce the risk of yield loss need to be implemented before the crop is sown. The levels of pathogens that cause the major root diseases can be measured using DNA-based services such as PreDicta B. Although these pathogens are often studied individually, in the field they often occur as mixed populations and their combined effect on crop production is likely to vary across diverse cropping environments. A 3-year survey was conducted covering most cropping regions in Western Australia, utilizing PreDicta B to determine soilborne pathogen levels and visual assessments to score root health and incidence of individual crop root diseases caused by the major root pathogens, including Rhizoctonia solani (anastomosis group [AG]-8), Gaeumannomyces graminis var. tritici (take-all), Fusarium pseudograminearum, and Pratylenchus spp. (root-lesion nematodes) on wheat roots for 115, 50, and 94 fields during 2010, 2011, and 2012, respectively. A predictive model was developed for root health utilizing autumn and summer rainfall and soil temperature parameters. The model showed that pathogen DNA explained 16, 5, and 2% of the variation in root health whereas environmental parameters explained 22, 11, and 1% of the variation in 2010, 2011, and 2012, respectively. Results showed that R. solani AG-8 soil pathogen DNA, environmental soil temperature, and rainfall parameters explained most of the variation in the root health. This research shows that interactions between environment and pathogen levels before seeding can be utilized in predictive models to improve assessment of risk from root diseases to assist growers to plan more profitable cropping programs.