Influence of Temperature, Wetness Period, Plant Age, and Inoculum Concentration on Infection and Development of Ascochyta Blight of Chickpea

Trapero-Casas, A.; Kaiser, W. J.

doi:10.1094/phyto-82-589

Cited by 85 publications

(14 citation statements)

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“…For each study, the duration of wetness required to achieve either 5% disease severity or 20% disease incidence (W min ) was recorded by reading values from the original graphs or tables. A severity value of 25% was used for Ascochyta rabiei because the slope of the response at 5% was zero (84). Studies that used lesion counts were difficult to compare because the counts could be made per square centimeter, per leaf, or per plant.…”

Section: Theorymentioning

confidence: 99%

See 1 more Smart Citation

A Simple Generic Infection Model for Foliar Fungal Plant Pathogens

Magarey¹,

Sutton²,

Thayer³

2005

Phytopathology®

207

231

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In this study, a simple generic infection model was developed for predicting infection periods by fungal foliar pathogens. The model is designed primarily for use in forecasting pathogens that do not have extensive epidemiological data. Most existing infection models require a background epidemiological data set, usually including laboratory estimates of infection at multiple temperature and wetness combinations. The model developed in this study can use inputs based on subjective estimates of the cardinal temperatures and the wetness duration requirement. These inputs are available for many pathogens or may be estimated from related pathogens. The model uses a temperature response function which is scaled to the minimum and optimum values of the surface wetness duration requirement. The minimum wetness duration requirement (W(min)) is the number of hours required to produce 20% disease incidence or 5% disease severity on inoculated plant parts at a given temperature. The model was validated with published data from 53 controlled laboratory studies, each with at least four combinations of temperature and wetness. Validation yielded an average correlation coefficient of 0.83 and a root mean square error of 4.9 h, but there was uncertainty about the value of the input parameters for some pathogens. The value of W(min) varied from 1 to 48 h and was relatively uniform for species in the genera Cercospora, Alternaria, and Puccinia but less so for species of Phytophthora, Venturia, and Colletotrichum. Operationally, infection models may use hourly or daily weather inputs. In the case of the former, information also is required to estimate the critical dry-period interruption value, defined as the duration of a dry period at relative humidities <95% that will result in a 50% reduction in disease compared with a continuous wetness period. Pathogens were classified into three groups based on their critical dry-period interruption value. The infection model is being used to create risk maps of exotic pests for the U.S. Department of Agriculture's Animal Plant Health and Inspection Service.

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Section: Theorymentioning

confidence: 99%

“…Many infection models use regression equations, such as those based on polynomials (13,31,39,43,68,84), logistic equations (15,47,68), and complex three-dimensional response surfaces (14,20,26,32,36,49,67,72,93). Other infection models have been constructed using variations of the Analytis Beta function, including infection (5,30,50).…”

mentioning

confidence: 99%

A Simple Generic Infection Model for Foliar Fungal Plant Pathogens

Magarey¹,

Sutton²,

Thayer³

2005

Phytopathology®

207

231

View full text Add to dashboard Cite

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“…Flipper and Genesis 114 also displayed greater resistance at maturity than other cultivars that are rated as more resistant. Since resistance is conditioned by multiple quantitative genetic effects (Gowen, Orton, Thurley, & White, 1989;Vail & Banniza, 2008;Flandez-Galves et al, 2003a) with a relatively large environmental influence (Trapero-Casas & Kaiser, 1992), different genes and possibly pathways may be in operation at different growth stages. This has previously been shown for Ascochyta blight of pea (Prioul, Frankewitz, Deniot, Morin, & Baranger, 2004).…”

Section: Discussionmentioning

confidence: 99%

“…It is likely that expression of individual resistance genes is triggered by external (environmental/pathogen) and internal (plant age/pathogen) stimuli. Expression of each of these genes can differ at different times in the plants life (Trapero-Casas & Kaiser, 1992;Chongo & Gossen, 2001;Sharma, Pande, & Rathore, 2010), and hence cultivars which show good seedling resistance may not have the same highly effective resistance at maturity. The resistance level to A. rabiei has previously been correlated with chickpea plant age (Nene & Reddy, 1987;Chongo & Gossen, 2001;Basandrai et al, 2007;Sharma et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Changes in Foliar Host Reaction to Ascochyta Rabiei With Plant Maturity

Elliott¹,

Taylor²,

Ford³

2013

JAS

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Ascochyta rabiei is the most significant foliar disease of chickpea (Cicer arietinum) worldwide, and can cause both yield loss and reduced seed quality. Significant mean disease score differences were observed from the reactions among a collection of Ascochyta rabiei isolates and chickpea hosts at both seedling and maturity growth stages (P<0.000). Although isolates ranked similarly within their range of pathogenicity at either growth stage, the ranking of host disease reaction was different. This suggests that genotype-specific expression of resistance genes to A. rabiei may be related to growth stage. Hence, durable selection methods for resistance will require screening of isolates representative of the pathogenicity within the population on target genotypes at growth stages representative of the type of resistance being sought, for example seedling resistance or pod resistance.

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“…Data for fitting Equation (6) were derived from Basandrai et al [22], Trapero-Casas and Kaiser [49], and Chauhan and Sinha [50], and the fit had R 2 = 0.887; for equation (7), data were derived from Trapero-Casas and Kaiser [49], with R 2 = 0.945 (see Supplementary material).…”

Section: Lesion Appearance and Production Of Secondary Inoculummentioning

confidence: 99%

A Mechanistic Weather-Driven Model for Ascochyta rabiei Infection and Disease Development in Chickpea

Salotti

Rossi

2021

Plants

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Ascochyta blight caused by Ascochyta rabiei is an important disease of chickpea. By using systems analysis, we retrieved and analyzed the published information on A. rabiei to develop a mechanistic, weather-driven model for the prediction of Ascochyta blight epidemics. The ability of the model to predict primary infections was evaluated using published data obtained from trials conducted in Washington (USA) in 2004 and 2005, Israel in 1996 and 1998, and Spain from 1988 to 1992. The model showed good accuracy and specificity in predicting primary infections. The probability of correctly predicting infections was 0.838 and the probability that there was no infection when not predicted was 0.776. The model’s ability to predict disease progress during the growing season was also evaluated by using data collected in Australia from 1996 to 1998 and in Southern Italy in 2019; a high concordance correlation coefficient (CCC = 0.947) between predicted and observed data was obtained, with an average distance between real and fitted data of root mean square error (RMSE) = 0.103, indicating that the model was reliable, accurate, and robust in predicting seasonal dynamics of Ascochyta blight epidemics. The model could help growers schedule fungicide treatments to control Ascochyta blight on chickpea.

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Influence of Temperature, Wetness Period, Plant Age, and Inoculum Concentration on Infection and Development of Ascochyta Blight of Chickpea

Cited by 85 publications

References 0 publications

A Simple Generic Infection Model for Foliar Fungal Plant Pathogens

A Simple Generic Infection Model for Foliar Fungal Plant Pathogens

Changes in Foliar Host Reaction to Ascochyta Rabiei With Plant Maturity

A Mechanistic Weather-Driven Model for Ascochyta rabiei Infection and Disease Development in Chickpea

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