Principles and Practices of Plant Disease Management

Chaube, H. S.; Singh, U. S.

doi:10.1201/9781351075725-5

Cited by 9 publications

(11 citation statements)

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“…Disease symptoms were scored as follows: 0, no symptoms; 1, a few small clubs on the lateral roots; 2, larger clubs on the lateral roots; 3, swelling of the main roots; 4, severe galling of tissues of both lateral and main roots. The DI was calculated according to the formula DI = [nw] × 100/4T, where n is the number of plants in each class, w is disease symptoms (0–4), and T is the total number of plants tested ( Chaube and Singh, 1991 ; Siemens et al, 2010 ). Data on the phenotype of disease index of the tested lines were analyzed for statistical significance using analysis of variance (ANOVA) conducted using SPSS 17.0.…”

Section: Methodsmentioning

confidence: 99%

Genome Wide Identification and Expression Profiling of SWEET Genes Family Reveals Its Role During Plasmodiophora brassicae-Induced Formation of Clubroot in Brassica rapa

Xuan

et al. 2018

Front. Plant Sci.

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Plasmodiophora brassicae is a soil borne pathogen and the causal agent of clubroot, a devastating disease of Brassica crops. The pathogen lives inside roots, and hijacks nutrients from the host plants. It is suggested that clubroot galls created an additional nutrient sink in infected roots. However, the molecular mechanism underlying P. brassicae infection and sugar transport is unclear. Here, we analyzed sugar contents in leaves and roots before and after P. brassicae infection using a pair of Chinese cabbage near-isogenic lines (NILs), carrying either a clubroot resistant (CR) or susceptible (CS) allele at the CRb locus. P. brassicae infection caused significant increase of glucose and fructose contents in the root of CS-NIL compared to CR-NIL, suggesting that sugar translocation and P. brassicae growth are closely related. Among 32 B. rapa SWEET homologs, several BrSWEETs belonging to Clade I and III were significantly up-regulated, especially in CS-NIL upon P. brassicae infection. Moreover, Arabidopsis sweet11 mutant exhibited slower gall formation compared to the wild-type plants. Our studies suggest that P. brassicae infection probably triggers active sugar translocation between the sugar producing tissues and the clubbed tissues, and the SWEET family genes are involved in this process.

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Section: Methodsmentioning

confidence: 99%

Genome Wide Identification and Expression Profiling of SWEET Genes Family Reveals Its Role During Plasmodiophora brassicae-Induced Formation of Clubroot in Brassica rapa

Xuan

et al. 2018

Front. Plant Sci.

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“…After the roots were thoroughly washed, disease symptoms were scored as follows: 0, no symptoms; 1, a few small clubs on the lateral roots; 2, larger clubs on the lateral roots or small clubs on the main roots; and 3, large galls both on the lateral and main roots. Disease index (DI) was further calculated according to the formula DI = Σ[nw] × 100/3T, where n is the number of plants in each score class, w is the disease score (0 to 3), and T is the total number of plants tested ( Chaube and Singh, 1990 ). If the disease incidence and DI of “BJN3-1” was lower than 90% and 80, respectively, or the disease incidence of CR hosts was intermediate, the resistance tests were repeated until the susceptible controls were fully diseased.…”

Section: Methodsmentioning

confidence: 99%

Development of a Sinitic Clubroot Differential Set for the Pathotype Classification of Plasmodiophora brassicae

et al. 2020

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Plasmodiophora brassicae , which is known for its broad genetic diversity for virulence, is the causal agent of clubroot disease of Brassica crops worldwide. Studies on pathotype characterization with four differential hosts according to Williams’ classification system showed the predominance of pathotype 4 in China. However, the genetic variability within pathotype 4 complicates the breeding of durable clubroot-resistant (CR) cultivars. Herein, a Sinitic clubroot differential (SCD) set was developed using a set of eight differential inbred lines of Chinese cabbage with known or novel CR genes. The presence of immense diversity within pathotype 4 of Williams’ system was verified, and 11 pathotypes were characterized using the developed SCD system. The scalability and practicability of the system was further confirmed with a subset of 95 field isolates from different Brassica crops and different regions of China and Korea. Sixteen pathotypes were detected from 132 field isolates, named Pb1 to Pb16, respectively. Among them, Pb1 and Pb4 were prevalent in diverse Brassica crops in the southern and northern regions of China. Pb12, Pb13, Pb14, and Pb16 showed area-specific distribution. The SCD set developed herein will provide important genetic resources for pathogenicity studies of P. brassicae and for CR breeding in Chinese cabbage and other Brassica crops.

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“…A special application of quantitative ordinal scales when estimating plant disease severity is the calculation of a disease severity index (DSI). The DSI is a single index number for summarizing a large amount of information on disease severity (Chester 1950 ; Chaube and Singh 1991 ). The disease severity is estimated by a rater as a value on the scale and has been used to determine a DSI on a percentage basis.…”

Section: An Overview Of Quantitative Ordinal Scalesmentioning

confidence: 99%

“…Besides the midpoint of the interval being used, slightly different values may be selected based on the HB scale (Horsfall and Barratt 1945 ) by using the Elanco tables (Redman et al 1969 ; Campbell and Madden 1990 ), but this approach has not been widely adopted. The geometric mean of the percentage assessment could be another choice as it is less distorted by an extreme individual score (Chaube and Singh 1991 ; Hartung and Piepho 2007 ). But Chiang et al ( 2017a ) showed that using the geometric mean in place of the midpoint value for each class did not improve the accuracy of estimates.…”

Section: An Overview Of Quantitative Ordinal Scalesmentioning

confidence: 99%

“…Although the use of quantitative ordinal scales has been described and discussed as a component of broader reviews on plant disease assessment (Chester 1950 ; Chaube and Singh 1991 ; Madden et al 2007 ; Bock et al 2010b ), there have been some different terms used to discuss quantitative or qualitative ordinal scale data in the plant pathology literature. For example, Fu et al ( 2012 ) used the term “ordinal qualitative data” collected for phenotypical measurements in plant pathology and other biological sciences.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

Chiang

Bock

2021

Trop. plant pathol.

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The severity of plant diseases, traditionally defined as the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases but is prone to error. Plant pathologists face many situations in which the measurement by nearest percent estimates (NPEs) of disease severity is time-consuming or impractical. Moreover, rater NPEs of disease severity are notoriously variable. Therefore, NPEs of disease may be of questionable value if severity cannot be determined accurately and reliably. In such situations, researchers have often used a quantitative ordinal scale of measurement—often alleging the time saved, and the ease with which the scale can be learned. Because quantitative ordinal disease scales lack the resolution of the 0 to 100% scale, they are inherently less accurate. We contend that scale design and structure have ramifications for the resulting analysis of data from the ordinal scale data. To minimize inaccuracy and ensure that there is equivalent statistical power when using quantitative ordinal scale data, design of the scales can be optimized for use in the discipline of plant pathology. In this review, we focus on the nature of quantitative ordinal scales used in plant disease assessment. Subsequently, their application and effects will be discussed. Finally, we will review how to optimize quantitative ordinal scales design to allow sufficient accuracy of estimation while maximizing power for hypothesis testing.

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Principles and Practices of Plant Disease Management

Cited by 9 publications

References 0 publications

Genome Wide Identification and Expression Profiling of SWEET Genes Family Reveals Its Role During Plasmodiophora brassicae-Induced Formation of Clubroot in Brassica rapa

Genome Wide Identification and Expression Profiling of SWEET Genes Family Reveals Its Role During Plasmodiophora brassicae-Induced Formation of Clubroot in Brassica rapa

Development of a Sinitic Clubroot Differential Set for the Pathotype Classification of Plasmodiophora brassicae

Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

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