Wheat stripe rust epidemics in interaction with climate, genotype and planting date

Naseri, Bita; Marefat, Alireza

doi:10.1007/s10658-019-01729-8

Cited by 20 publications

(24 citation statements)

References 19 publications

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“…Under prevailing environmental conditions in Eslamabad-e Gharb district of Kermanshah, only the early stripe rust spread before the middle of spring in coincidence with appropriate climate can result in severe epidemics in wheat crops ( Naseri and Sharifi 2020 ). Disease incidence and severity were recorded at a 7–10 days interval as reported previously ( Naseri and Marefat 2019 ). In brief, the incidence of stripe rust was measured as the percentage of diseased plants having long stripes of yellow pustules on the leaves for each of three random observations (100 plants per observation) per plot.…”

Section: Methodsmentioning

confidence: 99%

“…All the statistical methods were performed using GENSTAT (Payne et al, 2009), which fits standard (nonlinear) curves by maximum likelihood. The co-efficient of determination (R 2 ) and Fisher's (F) test were applied to testing goodness-of-fit (Brusco and Stahl 2005;Naseri and Marefat 2019). In the next step, predictions of stripe rust progression over time based on the above-mentioned standard curves were examined according to simple regression analysis of fitted and observed values (Berger 1981).…”

Section: Curve Elements and Statistical Analysismentioning

confidence: 99%

“…For instance, according to the large-scale study performed from 2009 to 2018 in Kermanshah province, Iran ( Naseri and Sharifi 2020 ), the occurrence and intensity of stripe rust epidemics corresponded with the mean maximum relative humidity for Aban (from October 23 to November 21), mean minimum temperature for Esfand (from February 20 to March 20), number of icy days, number of rainy days, number of days with minimum temperatures within the range of 7–8 °C and relative humidity above 60%, and number of periods involving consecutive days with minimum temperature within the range of 6–9 °C and RH% > 60% during a 240-day period, from September 23 to May 21. At plot scale, Naseri and Marefat (2019) identified the disease-onset and maturity date, number of days with minimum temperatures within the range of 5–12 °C and RH above 60% during the growing season, resistance index, and planting date as the best agro-ecological indicators of wheat stripe rust development. Although comparative epidemiology of plant diseases has been known as a powerful tool to develop more effective disease management strategies ( Jeger 2004 ), the characterization of wheat stripe rust progress curves has received little consideration.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, Kranz (2003) attributed the strength of association between a curve element and the disease progress to the degree of heterogeneity within curve variables. This finding suggests that a better understanding of wheat rust dynamics depends on greater disease variability following the inclusion of influential agro-ecological parameters in epidemiological experiments (Naseri and Marefat 2019). Therefore, the identification of specific disease progress curve elements and the evaluation of relationships among disease growth-rate indicators for more accurate measurement of temporal dynamics of wheat stripe rust are still lacking.…”

Section: Introductionmentioning

confidence: 99%

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Structural characterization of stripe rust progress in wheat crops sown at different planting dates

Naseri

Kazemi

2020

Heliyon

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An advanced insight into characterizing stripe rust progress curves is required to improve accuracy and efficiency of future research for disease measurement and estimation purposes. The rate of stripe rust increase in wheat crops is highly variable, resulting in variations and uncertainties in evaluating disease progress over time. This variability was described by fitting standard curves to disease severity data collected over a four-season experiment to identify effective disease curve elements in Iranian wheat cultivars planted at different dates. Gaussian curves appeared to be the best fitted models for all four growing seasons. Three Gaussian parameters in combination with the area under the disease progress curve (AUDPC), disease onset time, maximum disease incidence and severity were then considered to describe the rate of disease increase. Based on H-tests of Kruskal-Wallis oneway analysis of variance, cultivar and planting date significantly affected AUDPC, maximum disease incidence and severity. There were significant correlations between continuous disease descriptors. Then, significant associations were determined between AUDPC, disease onset time, Gaussian parameters, maximum disease incidence and severity according to factor analysis. With these novel findings, we should be aware of descriptive value of wheat-stripe-rust progress variables. Such information will assist with stripe rust measurements for wheat breeding programs, yield loss estimation, development of disease control strategy, and epidemiological studies.

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Section: Methodsmentioning

confidence: 99%

Section: Curve Elements and Statistical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structural characterization of stripe rust progress in wheat crops sown at different planting dates

Naseri

Kazemi

2020

Heliyon

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“…In Kermanshah Province, where is a major wheat producer in Iran, the occurrence of severe powdery mildew epidemics corresponded with further rainy days and speedy winds in spring, later sowings, late‐maturing wheat crops, less resistant cultivars and warmer growing seasons, suggesting these agro‐ecological variables as potential predictive tools (Naseri & Sheikholeslami, 2021). Further to powdery mildew, noticeable associations of climate, cultivar resistance, sowing and maturity dates with wheat rust development have been found as potential disease prediction and management tools (Naseri & Marefat, 2019; Naseri & Sabeti, 2020; Naseri & Sasani, 2020). An advanced understanding of the fitting model parameters of temporal dynamics of wheat powdery mildew is still required to develop more influential and sustainable disease management programmes.…”

Section: Introductionmentioning

confidence: 99%

Progression of powdery mildew in susceptible‐resistant wheat (Triticum aestivum) cultivars sown at different dates

Naseri

Sheikholeslami

2021

Journal of Phytopathology

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A better understanding of non‐climatic predictors of temporal dynamics of wheat powdery mildew is needed to improve accuracy of disease measurement for breeding and control programmes. The present study examined fourteen types of standard curves to model powdery mildew progress data collected from 282 plots of commercial wheat cultivars sown at different dates over four growing seasons (2013–2017). An exponential model was fitted to disease progress data, and then, relationships between commonly used disease descriptors and exponential parameters were examined. From exponential function, parameters b and r defined disease increase factor and rate of disease increase, respectively. This model described a slow increase of wheat powdery mildew at first, followed by a more rapid increase lacking bound. According to Kruskal–Wallis one‐way ANOVA results, wheat resistance and sowing date were significantly effective on area under disease progress curve (AUDPC), maximum disease severity and exponential parameter b. Greatest mean values for AUDPC, maximum severity and exponential parameter b were detected in later sowings of susceptible cultivars. There were significant correlations between powdery mildew curve elements. Based on principal component analysis, AUDPC, disease onset time, maximum severity and exponential parameters b and r accounted for 77% of total data variance. This information on specific predictors of wheat powdery mildew progress may improve accuracy of future disease estimates in breeding and control programmes.

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Prediction of Severe Epidemics of Chickpea Ascochyta Blight Using Weather Variables

Naseri,

Mahmodi

2024

Legume Science

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Chickpea production is threatened by severe epidemics of Ascochyta blight occurring in main chickpea growing lands under appropriate weather conditions worldwide. In this 4‐year research, occurrence of Ascochyta blight was monitored across nine main chickpea growing areas of Kermanshah province, western part of Iran. Each year, commercial chickpea fields were studied on a weekly basis from March to June. Disease data were collected as disease incidence (percentage of infected plants) and severity (percentage of infected tissues) and occurrence of epidemics. Weather data were collected as air temperature, rainfall, and relative humidity (RH) on a daily basis. According to a factor analysis, which explained 83% of data variance, 13 weather predictors were selected to estimate disease epidemics developed across different areas. Before modeling, a principal component analysis determined predictive values for these selected weather variables. Then, eight predictors of rainy days in March and April, mean RH in February, mean minimum temperature in January–March–April, and rainfalls in May and June were involved in model based on their predictive values. Current findings advanced our knowledge on the best weather predictors of severe epidemics of Ascochyta blight in chickpea crops at large scale.

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Wheat stripe rust epidemics in interaction with climate, genotype and planting date

Cited by 20 publications

References 19 publications

Structural characterization of stripe rust progress in wheat crops sown at different planting dates

Structural characterization of stripe rust progress in wheat crops sown at different planting dates

Progression of powdery mildew in susceptible‐resistant wheat (Triticum aestivum) cultivars sown at different dates

Prediction of Severe Epidemics of Chickpea Ascochyta Blight Using Weather Variables

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