Cercospora leaf spot in Toona ciliata: Epidemiology and infection process of Cercospora cf. alchemillicola

Silva, Humberson Rocha; Pozza, Edson Ampélio; Souza, Paulo Estevão de; Ferreira, M. A.; Freitas, Aurivan Soares de; Moreira, Silvino Intra

doi:10.1111/efp.12451

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…After evaluating the three methodologies for the re-isolation of P. grandis-urophylla (S, WD and D), it was possible to determine that WD was superior to the other methodologies, since the area under the mycelial growth curve (AUMGC), which has been used to compare mycelial growth between different fungal isolates or treatments [31], showed higher values in comparison with the other two methods, with R showing intermediate results. This performance can be explained by the absence of disinfectants, which, besides removing any contaminants, can also unduly disrupt the target fungus [32].…”

Section: Resultsmentioning

confidence: 99%

Re-Isolation Methodologies for Recovering Sporulation of Eucalyptus Pestalotiopsis grandis-urophylla Isolates after 14 Months Storage

Silva

Marques

Muniz

et al. 2021

JSRR

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After long periods of storage, plant pathogen isolates lose their sporulation capacity. The objective of this study was to evaluate re-isolation methodologies for recovering sporulation of Pestalotiopsis grandis-urophylla isolates after subjection to a long period of storage. Isolates of P. grandis-urophylla were kept for 14 months on Petri dishes with PDA medium at 10°C. After this period, the isolate colonies showed reduced mycelial growth and no sporulation. The isolates were inoculated on healthy Eucalyptus grandis-urophylla leaves, and after ten days they were subjected to three re‑isolation methods: scraping of the lesions (S) removing of injured plant tissue fragments, followed by disinfestation (D) and without disinfestation (WD). Then, the purified isolates were evaluated for the recovery of its sporulation ability. The different methods for re-isolation resulted in the occurrence of differences among the isolates, showing that sporulation is an isolate-dependent feature. The three methods (S, WD and D) allowed the sporulation recovery of P. grandis-urophylla, even after these isolates have been subjected to 14 months.

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Section: Resultsmentioning

confidence: 99%

Re-Isolation Methodologies for Recovering Sporulation of Eucalyptus Pestalotiopsis grandis-urophylla Isolates after 14 Months Storage

Silva

Marques

Muniz

et al. 2021

JSRR

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“…The amount variability of cercosporin produced by C. coffeicola, C. piaropi, Cercospora cf. alchemillicola and Pseudocercosporella capsellae isolates has already been reported by other authors using the SPEC method (Gunasinghe et al 2016;Silva 2018;Vale et al 2019). Cercosporin biosynthesis is affected by external factors such as light, temperature, pH, nutrient supply and incubation time (Jenns et al 1989;Vale et al 2019;Zhou et al 2021).…”

Section: Resultsmentioning

confidence: 75%

Cercosporin production by Cercospora coffeicola isolates: spectrophotometry and HPLC quantification and image analysis

et al. 2021

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Cercosporin has excellent properties of photosensitization that have been widely used in organophotocatalyst and photodynamic therapy as well as an antimicrobial agent. Therefore, there is a need to quantify it accurately with accessible methods. A comparative analysis of cercosporin quantification obtained by spectrophotometry (SPEC) and high-performance liquid chromatography (HPLC) was performed for nineteen Cercospora coffeicola isolates from different coffee-producing municipalities in Brazil. Image analysis of cercosporin crystals was performed in isolates with either high or low production of the toxin. Our results show that SPEC and HPLC are equally valid for the cercosporin evaluation of C. coffeicola cultures grown in vitro. The isolates with high cercosporin production had a higher crystal number and size when compared to the one with low cercosporin production.

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“…14 The disease is favored by brightness, air temperature (17 to 22 °C), [15][16][17] relative humidity (>90%), 18 and free water time on plant tissue (>9 h day −1 ). 19 Factors such as nutritional imbalance, mainly nitrogen deficiency or excess potassium, water deficit (>150 mm), very clayey or sandy soils, and cold and excessive winds, can influence a Cercospora attack. 2,20,21 The fungus spreads mainly through water, rainfall, or sprinkler irrigation, and wind.…”

Section: Introductionmentioning

confidence: 99%

Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models

de Oliveira Aparecido,

de Lima,

Torsoni

et al. 2024

J Sci Food Agric

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BACKGROUNDClimate significantly influences the interaction between pathogens and their hosts, and this is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown eye spot, can drastically reduce yields. Our study focuses on forecasting coffee brown eye spot using various models that incorporate agrometeorological data, allowing for predictions at least one week prior to disease occurrence. Data was gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais State. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown eye spot, identifying one with potential for advanced decision‐making. The top‐performing models were then employed in the next stage to forecast and spatially project the severity of brown eye spot across 2,681 key Brazilian coffee‐producing municipalities. Meteorological data were sourced from NASA's POWER platform, and the Penman‐Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather (1955) water balance calculation. Six ML models — K‐Nearest Neighbors (KNN), Artificial Neural Network Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forests (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Regression (GradBOOSTING) — were employed, considering a 7‐day disease latency to define input variables.RESULTSThese models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high‐yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low‐yielding scenarios. The incidence of brown eye spot varied noticeably between high and low‐yield conditions, with significant regional differences observed. The accuracy of predicting brown eye spot severity in coffee plantations depended on the biennial production cycle. High‐yielding trees showed superior results with the XGBoost model (R2 = 0.77, RMSE = 10.53), while under low‐yielding conditions, the Support Vector Machine (SVM) model performed better (precision 0.76, RMSE = 12.82).CONCLUSIONThe study's application of agrometeorological variables and ML models successfully predicted the incidence of brown eye spot in coffee plantations with a seven‐day lead time, illustrating a valuable tool for managing this significant agricultural challenge.This article is protected by copyright. All rights reserved.

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Cercospora leaf spot in Toona ciliata: Epidemiology and infection process of Cercospora cf. alchemillicola

Cited by 7 publications

References 19 publications

Re-Isolation Methodologies for Recovering Sporulation of Eucalyptus Pestalotiopsis grandis-urophylla Isolates after 14 Months Storage

Re-Isolation Methodologies for Recovering Sporulation of Eucalyptus Pestalotiopsis grandis-urophylla Isolates after 14 Months Storage

Cercosporin production by Cercospora coffeicola isolates: spectrophotometry and HPLC quantification and image analysis

Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models

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