Accurate prediction of black rot epidemics in vineyards using a weather-driven disease model

Onesti, Giovanni; González‐Domínguez, Elisa; Rossi, Vittorio

doi:10.1002/ps.4277

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…Rossi et al (2014) and Onesti et al (2016) indicated that the weather conditions were less conducive for the disease in 2012 and 2013 than in 2014. Although the amount of mummies used each year was the same (20 g per spore sampler), the mummies may have differed in their level of G. bidwellii colonisation and in the stromata-bearing pycnidia and ascomata, depending on bunch disease severity and weather conditions in the vineyards where the mummies were collected.…”

Section: Discussionmentioning

confidence: 99%

“…The mummies used as the inoculum source each year were obtained from one composite batch of mummies that was collected that year from different vineyards and areas affected by black rot; precise information about black rot severity on bunches in those vineyards was not available. Rossi et al (2014) and Onesti et al (2016) indicated that the weather conditions were less conducive for the disease in 2012 and 2013 than in 2014. Therefore, mummies used in 2015 (which would have been infected in 2014) may have produced more ascomata and pycnidia than those used in 2013 and 2014.…”

Section: Discussionmentioning

confidence: 99%

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Release ofGuignardia bidwelliiascospores and conidia from overwintered grape berry mummies in the vineyard

Onesti

González‐Domínguez

Manstretta

et al. 2017

Australian Journal of Grape and Wine Research

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Background and Aims Black rot, caused by Guignardia bidwellii, is a polycyclic grape disease with repeated primary and secondary infections. In this work, the release of primary inoculum (both ascospores and conidia) from overwintered grape mummies was studied over a 3‐year period. Methods and Results A spore sampler was designed consisting of: (i) a funnel for collecting spores washed off from grape mummies by rain; and (ii) microscope slides for spores being airborne in the proximity of mummies. Ascospores and conidia were frequently found in the runoff water; Gompertz equations were developed describing their seasonal cumulative numbers as a function of degree‐days, with R2 ≥ 0.92. The number of both spore types in the runoff water significantly increased as the amount of rain and its duration and intensity increased; >1 and >3 mm of rain were the best cut‐off points for predicting the release of conidia and ascospores, respectively. No ascospores were found on the microscope slides, but conidia were found on rainy days, clustered in circular areas, probably due to droplets splashing from mummies to slides during rainfall. Conclusions This work provides new information on the inoculum dynamics of G. bidwellii in vineyards that can be used in refining disease prediction models for improved disease control. Significance of the Study New equations were developed to describe the dynamics of G. bidwellii spore release as a function of degree‐days, and 1 and 3 mm of rain have been proposed as cut‐off values to predict the release of conidia and ascospores, respectively. This information could be incorporated into the mathematical models supporting the decision‐making for disease control.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Release ofGuignardia bidwelliiascospores and conidia from overwintered grape berry mummies in the vineyard

Onesti

González‐Domínguez

Manstretta

et al. 2017

Australian Journal of Grape and Wine Research

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“…The disease severities observed in the four epidemics mentioned in the previous section (AK-96, CL-12, CL-13, and CL-14; Table 2) were compared to the accumulated infection severities predicted by the model by using linear regression analysis. To make the data comparable [75,76], we rescaled (from 0 to 1) each infection severity value to the disease severity observed in each epidemic at harvest. The goodness-of-fit of observed vs. predicted data was estimated by determining the root mean square error (RMSE), the coefficient of residual mass (CRM), and the concordance correlation coefficient (CCC) [77,78].…”

Section: Prediction Of Disease Progressmentioning

confidence: 99%

Modeling the Effects of the Environment and the Host Plant on the Ripe Rot of Grapes, Caused by the Colletotrichum Species

Salotti

Dong

et al. 2021

Plants

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Ripe rot caused by Colletotrichum spp. is a serious threat in many vineyards, and its control relies mainly on the repeated use of fungicides. A mechanistic, dynamic model for the prediction of grape ripe rot epidemics was developed by using information and data from a systematic literature review. The model accounts for i) the production and maturation of the primary inoculum; ii) the infection caused by the primary inoculum; iii) the production of a secondary inoculum; and iv) the infection caused by the secondary inoculum. The model was validated in 19 epidemics (vineyard × year combinations) between 1980 and 2014 in China, Japan, and the USA. The observed disease incidence was correlated with the number of infection events predicted by the model and their severity (ρ = 0.878 and 0.533, respectively, n=37, P≤ 0.001). The model also accurately predicted the disease severity progress during the season, with a concordance correlation coefficient of 0.975 between the observed and predicted data. Overall, the model provided an accurate description of the grape ripe rot system, as well as reliable predictions of infection events and of disease progress during the season. The model increases our understanding of ripe rot epidemics in vineyards and will help guide disease control. By using the model, growers can schedule fungicides based on the risk of infection rather than on a seasonal spray calendar.

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“…In a chickpea field at Poggiorsini (40 • 54 N 16 The model was operated starting from the day of the last assessment in which no disease was observed. Both predicted infection severity and observed disease severity were accumulated during each disease assessment period and were rescaled to their final value; values were rescaled from 0 to 1 to make the data collected in different experiments comparable [76,77]. For the evaluation of model performance, the root mean square error (RMSE), the coefficient of residual mass (CRM), and the concordance correlation coefficient (CCC) were calculated [78,79].…”

Section: Disease Progressmentioning

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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Accurate prediction of black rot epidemics in vineyards using a weather-driven disease model

Abstract: The model was highly accurate and robust in predicting the infection periods and dynamics of black rot epidemics. The model can be used for scheduling fungicide sprays in vineyards. © 2016 Society of Chemical Industry.

Cited by 9 publications

References 24 publications

Release ofGuignardia bidwelliiascospores and conidia from overwintered grape berry mummies in the vineyard

Release ofGuignardia bidwelliiascospores and conidia from overwintered grape berry mummies in the vineyard

Modeling the Effects of the Environment and the Host Plant on the Ripe Rot of Grapes, Caused by the Colletotrichum Species

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

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