Pathogen Dispersal and Glomerella Leaf Spot Progress Within Apple Canopy in Brazil

Hamada, Natasha Akemi; Moreira, Rafaele Regina; Nesi, Cristiano Nunes; Mio, Louise Larissa May De

doi:10.1094/pdis-08-18-1375-re

Cited by 17 publications

(8 citation statements)

References 25 publications

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“…Ratio scales Many diseases lend themselves to severity estimation by ratio scales. The percentage scale is a widely applied scale to visually estimate severity (recent examples include Gent et al 2018;Bock and Chiang 2019;Hamada et al 2019;Xu et al 2019). The percentage scale ranges from zero to 100% and a rater gauges the proportion of the organ showing symptoms and estimates the severity accordingly.…”

Section: Methods Of Visual Estimation and Nature Of The Datamentioning

confidence: 99%

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

et al. 2020

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The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.

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Section: Methods Of Visual Estimation and Nature Of The Datamentioning

confidence: 99%

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

et al. 2020

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“…Rajasab and Chawda [62] demonstrated that single drops (a drop was 3 mm in diameter and carried till 2000 conidia) were able to wash conidia from acervuli of C. gloeosporioides onto onion leaves. The washing-off of Colletotrichum conidia within an apple tree canopy was also demonstrated by Hamada et al [64], and Guyot et al [65] found that a light rain (a "drizzle") can disperse Colletotrichum conidia within rubber trees. Daykin and Milholland [18] reported that the numerous insects attracted to rotting fruits may spread the secondary inoculum of C. gloeosporioides.…”

Section: Discussionmentioning

confidence: 56%

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

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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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“…Pathogenic fungi are involved in several crop diseases and cause enormous economic loss each year. The GLS pathogen mainly damages apple leaves causing dark spots followed by their dryness resulting in severe defoliation within 5-7 days of development ( Hamada et al, 2019 ; Sutton and Sanhueza, 1998 ; Wang et al, 2012 ). In apple leaves and fruits, C. gloeosporioides causes slightly small lesions, which develop into bitter rot ( Velho et al, 2015 ).…”

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confidence: 99%

Carbamoyl Phosphate Synthase Subunit CgCPS1 Is Necessary for Virulence and to Regulate Stress Tolerance in Colletotrichum gloeosporioides

et al. 2021

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Glomerella leaf spot (GLS) is a severe infectious disease of apple whose infective area is growing gradually and thus poses a huge economic threat to the world. Different species of Colletotrichum including Colletotrichum gloeosporioides are responsible for GLS. For efficient GLS control, it is important to understand the mechanism by which the cruciferous crops and C. gloeosporioides interact. Arginine is among one of the several types of amino acids, which plays crucial role in biochemical and physiological functions of fungi. The arginine biosynthesis pathway involved in virulence among plant pathogenic fungi is poorly understood. In this study, CgCPS1 gene encoding carbamoyl phosphate synthase involved in arginine biosynthesis has been identified and inactivated experimentally. To assess the effects of CgCPS1 , we knocked out CgCPS1 in C. gloeosporioides and evaluated its effects on virulence and stress tolerance. The results showed that deletion of CgCPS1 resulted in loss of pathogenicity. The Δcgcps1 mutants showed slow growth rate, defects in appressorium formation and failed to develop lesions on apple leaves and fruits leading to loss of virulence while complementation strain (CgCPS1-C) fully restored its pathogenicity. Furthermore, mutant strains showed extreme sensitivity to high osmotic stress displaying that CgCPS1 plays a vital role in stress response. These findings suggest that CgCPS1 is major factor that mediates pathogenicity in C. gloeosporioides by encoding carbamoyl phosphate that is involved in arginine biosynthesis and conferring virulence in C. gloeosporioides .

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Pathogen Dispersal and Glomerella Leaf Spot Progress Within Apple Canopy in Brazil

Cited by 17 publications

References 25 publications

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

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

Carbamoyl Phosphate Synthase Subunit CgCPS1 Is Necessary for Virulence and to Regulate Stress Tolerance in Colletotrichum gloeosporioides

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