Resistance assessment and biochemical responses of sugar beet lines against Pythium aphanidermatum, causing root rot

Kakueinezhad, Mozhdeh; Taheri, Parissa; Mahmoudi, S B; Tarighi, Saeed

doi:10.1007/s10658-017-1373-8

Cited by 6 publications

(4 citation statements)

References 57 publications

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“…P. aphanidermatum is also one of the most critical sugar beet diseases in temperate areas with high soil moisture levels. In addition to direct damage to plants in the fields, this pathogen also causes root rot in storage [231]. Several sugar beet genotypes have been found to be partially resistant to P. aphanidermatum root rot (Table 4) [216,231].…”

Section: Oomycetesmentioning

confidence: 99%

“…In addition to direct damage to plants in the fields, this pathogen also causes root rot in storage [231]. Several sugar beet genotypes have been found to be partially resistant to P. aphanidermatum root rot (Table 4) [216,231]. Other economically important plant species affected by Pythium spp are parsnip and parsley [232], wheat [233], and sugarcane [234].…”

Section: Oomycetesmentioning

confidence: 99%

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Understanding Root Rot Disease in Agricultural Crops

Williamson-Benavides¹,

Dhingra²

2020

Preprint

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Root rot diseases remain a major global threat to the productivity of agricultural crops. They are usually caused by more than one type of pathogen and are thus often referred to as a root rot complex. Fungal and oomycete species are the predominant participants in the complex, while bacteria and viruses are also known to cause root rot. Incorporating genetic resistance in cultivated crops is considered as the most efficient and sustainable solution to counter root rot; however, resistance is often quantitative in nature. Several genetics studies in various crops have identified quantitative trait loci associated with resistance. With access to whole genome sequences, the identity of the genes within the reported loci is becoming available. Several of the identified genes have been implicated in pathogen response. However, it is becoming apparent that at the molecular level, each pathogen engages a unique set of proteins to either infest the host successfully or be defeated or contained in doing so. In this review, a comprehensive summary of the genes and potential mechanisms underlying resistance or susceptibility against the most investigated root rots of important agricultural crops is presented.

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Section: Oomycetesmentioning

confidence: 99%

Section: Oomycetesmentioning

confidence: 99%

Understanding Root Rot Disease in Agricultural Crops

Williamson-Benavides¹,

Dhingra²

2020

Preprint

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“…; Wann et al, 2017); and disease severity in wheat (Iqbal et al, 2016;Saintenac et al, 2018), sugar beet (Beta vulgaris L.; Kakueinezhad et al, 2018;Vogel et al, 2018), common bean (Amaro et al, 2007), and coffee (Coffea arabica L.; Botelho et al, 2017). ; Wann et al, 2017); and disease severity in wheat (Iqbal et al, 2016;Saintenac et al, 2018), sugar beet (Beta vulgaris L.; Kakueinezhad et al, 2018;Vogel et al, 2018), common bean (Amaro et al, 2007), and coffee (Coffea arabica L.; Botelho et al, 2017).…”

Section: New Proposals To Estimate Unbiased Selection Gain and Coeffimentioning

confidence: 99%

“…L. ; Wann et al, 2017); and disease severity in wheat (Iqbal et al, 2016;Saintenac et al, 2018), sugar beet (Beta vulgaris L.; Kakueinezhad et al, 2018;Vogel et al, 2018), common bean (Amaro et al, 2007), and coffee (Coffea arabica L.; Botelho et al, 2017). Usually, these score scales are treated as interval scales.…”

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confidence: 99%

New Proposals to Estimate Unbiased Selection Gain and Coefficient of Variation in Traits Evaluated Using Score Scales

et al. 2019

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937 RESEARCHR andom variables can be classified as qualitative, discrete quantitative, or continuous quantitative, whose scales of measurement can be nominal, ordinal, interval, or ratio (Resende et al., 2014). The nominal scale classifies the data into distinct categories, assigning names and arbitrary numerical correspondence to the variable's categories (e.g., gender: 1 = female and 2 = male). The ordinal scale classifies data into distinct categories, and in this case, the order or position is assigned to the categories (e.g., water quality level: 1 = good, 2 = intermediate, and 3 = bad). Either nominal or ordinal scales show no relation of difference or ratio between the numerical values of the scale (Levine et al., 2014;Resende et al., 2014). The interval scale is an ordered scale in which the difference between any two numbers in the scale is known; however, 0 is included as an arbitrary point (e.g., temperature in °C). Finally, a ratio scale is an ordered scale in which the distances between any two numbers in the scale are known, and their measurements include the true zero as the point of origin (e.g., height in centimeters) (Levine et al., 2014;Resende et al., 2014).In plant evaluations, several multicategorical variables are visually evaluated by score scales, such as plant architecture and grain appearance in common bean (Phaseolus vulgaris L.; Batista et al., 2017); lodging in common bean (Soltani et al., 2016), soybean [Glycine max (L.) Merr.; Akpertey et al., 2018], and wheat (Triticum aestivum L.; Iqbal et al., 2016); vigor in chickpea (Cicer arietinum L.; Sivasakthi et al., 2018); leaf senescence and leaf rolling in maize (Zea mays L.; Soni et al., 2018); herbicide tolerance in lentils (Lens culinaris Medik.; Sharma et al., 2018); salt tolerance in soybean (Do et al., 2018); injury caused by pests in cotton (Gossypium hirsutum ABSTRACTIn plant breeding, several multicategorical variables are evaluated using score scales that are treated as interval scales. However, statistics that use ratio operations, such as the experimental CV and the selection gain, are only appropriate for ratio scales data. Thus, this work aimed to propose strategies to mitigate the inconveniences faced in the data analysis of score scales, especially those involving CV and selection gain obtained from different scales. This work proposes the following strategies: (i) the use of a standard score scale with the properties of a ratio scale (ascending scale with 0 as the point of origin); (ii) the conversion of the scores from different scales into a common ratio scale; (iii) the adjustment of the data from an interval scale to a ratio scale using the Delta Scale (Scale d ui ), and (iv) the use of unbiased estimator for CV and selection gain in score scales data. The proposed strategies resulted in unbiased estimates of CV and selection gain from data of different score scales. These strategies have the potential to be used in the meta-analysis of data within and between plant breeding programs.

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Soil-Borne Pathogen-Mediated Root Rot Diseases of Sugar Beet and Their Management

Das

Pattanayak

2022

Sugar Beet Cultivation, Management and Processing

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Resistance assessment and biochemical responses of sugar beet lines against Pythium aphanidermatum, causing root rot

Cited by 6 publications

References 57 publications

Understanding Root Rot Disease in Agricultural Crops

Understanding Root Rot Disease in Agricultural Crops

New Proposals to Estimate Unbiased Selection Gain and Coefficient of Variation in Traits Evaluated Using Score Scales

Soil-Borne Pathogen-Mediated Root Rot Diseases of Sugar Beet and Their Management

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