Predicting quantitative host plant resistance against phoma black stem in sunflower

Abstract

Phoma black stem is an important disease in sunflower, against which no specific management method is currently deployed in France. Relevant phenotyping methods for quantitative resistance are critical for integration of this trait into breeding programmes. Components of resistance associated with physiological resistance, and morphological traits associated with disease escape were measured on 21 sunflower genotypes under growth chamber (on seedlings), greenhouse (on adult plants), and field conditions, together with disease intensity in the field. Potential predictors were first selected for sensitivity and robustness from mixed model ANOVAs. Analyses involving ranking tests and logistic regressions were then performed to identify predictors for field resistance. The identification of predictors for resistance involved analyses conducted in two broad steps: process-oriented experiments, and epidemic-oriented experiments. This stepwise approach departs from many studies aimed at identifying predictors for field resistance, which rely mainly on the computation of correlation coefficients between predictors and measured field disease variables. Predictors for quantitative resistance were identified: (i) lesion length and lesion expansion on stems of plants before flowering stage, and (ii) lesion length on first leaf petioles of seedlings. A high number of leaves and tall plants were associated with disease escape. Control genotypes for susceptibility and quantitative resistance were identified, and implications for breeding and improvements were derived from this work.

Components of partial resistance to Plasmopara viticola enable complete phenotypic characterization of grapevine varieties

“…Similar results were found for yellow rust (Puccinia striiformis f. sp. hordei) on barley 33 , leaf rust (Puccinia triticina) on wheat 36 , and Phoma black stem of sunflower 38 , indicating that the mechanisms of resistance act on different stages of the infection process, as previously discussed. Surprisingly, sporulation was not correlated with the other RCs, and this requires further investigation.…”

“…Resistance components have been analyzed 31 for a number of pathosystems, including Cercospora leaf spot of sugar beet 29,32 , yellow rust (Puccinia striiformis f. sp. hordei) of barley 33 , rice blast (Pyricularia oryzae) 34 , rice sheath blight (Rhizoctonia solani) 35 , leaf rust (Puccinia triticina) of wheat 36 , Fusarium head blight of wheat 37 , and Phoma black stem of sunflower 38 . However, such analysis has not been conducted on the DM-grapevine pathosystem.…”

Modelling the effect of partial resistance on epidemics of downy mildew of grapevine

“…Components of resistance have been measured in many pathogens (Willocquet et al, 2017), mostly in the case of aerially dispersed fungal pathogens, such as the causal agents of barley leaf rust (Parlevliet & Van Ommeren, 1975), wheat leaf rust (Azzimonti et al, 2013;Zadoks, 1972), rice leaf blast (Yeh & Bonman, 1986;Villareal et al, 1981), groundnut rust (Savary & Zadoks, 1989a, 1989bSavary et al, 1988), Cercospora leaf spot in sugar beet (Rossi et al, 2000;Rossi et al, 1999b), and barley yellow rust (Sandoval-Islas et al, 2007). Components of resistance have also been measured in other pathosystems such as grey leaf spot on maize (Gordon et al, 2006), rice sheath blight (Willocquet et al, 2011), wheat Fusarium head blight (Burlakoti et al, 2010), and Phoma black stem of sunflower (Schwanck et al, 2016). The measurement of components of partial resistance for the grapevinedowny mildew pathosystem has been undertaken in a previously published work (Bove & Rossi, 2020).…”

Assessment of Resistance Components for Improved Phenotyping of Grapevine Varieties Resistant to Downy Mildew

“…Usually, the in vitro screening of partial resistance to DM is based on leaf disc bioassays (Bavaresco and Eibach, 1987; Staudt and Kassemeyer, 1995; Cadle-Davidson, 2008) and the assessment of the degree of resistance based on a visual score (from 1 = very low to 9 = very high), according to the 2nd Edition of the OIV Descriptor List for Grape Varieties and Vitis species (2009), i.e., the OIV 452-1 (hereafter referred to as OIV scale). In host-pathogen systems different from grape- P. viticola (Savary and Zadoks, 1989; Rossi et al, 1999; Rossi et al, 2000; Gordon et al, 2006; Burlakoti et al, 2010; Willocquet et al, 2011; Azzimonti et al, 2013; Schwanck et al, 2016), the measurement of resistance components (hereafter referred to as RCs) efficiently supports the evaluation of plant genotypes showing partial resistance, a type of resistance that affects several stages of the infection cycle, such as the resistance to P. viticola . Resistance components analysis is based on the phenotypic dissection of resistance into its components, which classically include: infection efficiency of spores, duration of latent period (i.e., the time from infection to the start of sporulation on lesions), lesion size, production of spores on lesions, and the duration of infectious period (i.e., the time a lesion continues producing spores) (Van der Plank, 1963; Zadoks, 1972; Parlevliet, 1979).…”