Identification and molecular characterisation of cercospora leaf spot disease pathogen on cowpea (<i>Vigna unguiculata</i>L. Walp)

Edet, Iwebaffa Amos; Afolabi, C. G.; Popoola, A. R.; Arogundade, Olawale; Akinbode, Olufolake Adenike

doi:10.1080/03235408.2021.2000782

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…2). Temperatures (minT, avgT, and maxT) and dewT averaged 19.98 (±2.82), 22.06 (±2.62), 28.27 (±2.69), and 15.97 (±3.18) °C, respectively (Fig. 2(a)-(d), respectively).…”

Section: Resultsmentioning

confidence: 94%

“…2,20,21 The fungus spreads mainly through water, rainfall, or sprinkler irrigation, and wind. 22 To avoid the occurrence and proliferation of disease, computational tools and mathematical models have been used to forecast incidence in a practical, accurate, and fast way. 23,24 Climate forecasts have been improved and incorporated as input data for plant pathology forecast models.…”

Section: Introductionmentioning

confidence: 99%

“…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 22 …”

Section: Introductionmentioning

confidence: 99%

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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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“…2). Temperatures (minT, avgT, and maxT) and dewT averaged 19.98 (±2.82), 22.06 (±2.62), 28.27 (±2.69), and 15.97 (±3.18) °C, respectively (Fig. 2(a)-(d), respectively).…”

Section: Resultsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Jost et al [56] indicated that as there are several complexities of genetic and molecular processes in the resistance mechanisms of host plants against pathogens, there is a need to conduct continuous and extensive research to save resistance genes from rapid breakdown due to constant pathogen evolution. The cultivation of potentially disease-resistant crop varieties could be a sustainable approach in integrated disease management, and host plant resistance can serve as a key strategy to control diseases such as CLS in mungbean [2,26,57]. Despite the lack of information and omics-based studies on the interaction between Cercospora canescens and the crop, it is essential to thoroughly review and consolidate the current research findings.…”

Section: Fundamental Overview Of Omics Approachesmentioning

confidence: 99%

Progress in the Use of Combined Omics for Mungbean Breeding Improvement and Its Potential in Promoting Resistance against Cercospora Leaf Spot

Gudeta,

Keneni,

Figlan

2024

IJPB

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Cercospora leaf spot (CLS) is the most destructive fungal disease, deteriorating the production and productivity of mungbean (Vigna radiata (L.) Wilczek). Mungbean is one of the most nutritionally and environmentally important legumes, with popularity currently increasing as a ‘future smart food crop’ due to its several health benefits. In recent years, there has been considerable research progress in improving disease resistance in legumes. However, only a limited number of studies have pinpointed potential genes and candidate genes associated with resistance traits to CLS in mungbeans. Identifying the potential resistant resources through combined omics approaches is an efficient strategy to screen the best Cercospora-resistant mungbean varieties for further molecular breeding and improvement. Potential omics approaches are important tools to predict disease management strategies, alleviate chemical overuse, and mitigate problems due to malnutrition. Sustainable breeding research efforts using potential combined omics, including automated phenotyping, to promote important resistant traits associated with CLS in mungbeans are still unexplored and a key issue that needs to be addressed. Omics-technology-based research findings on resistance genes, proteins, and metabolites against CLS in mungbean are recognised in this review. Due to a limitation of research findings specifically underscoring the use of omics tools for screening resistant mungbean against CLS, best related research outcomes on other crops are included in this review.

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“…Fungi disease mainly Cercospora Leaf Spot (CLS) caused by Cercospora canescens (Ellis and Martins) is one of the principal infections that causes more destructive damage on cowpea crops with symptoms of severe defoliation at the reproductive stages of growth 4,5 . This disease is of great importance to farmers and breeders as it poses a serious threat to cowpea production [6][7][8] . It spreads rapidly in susceptible varieties causing premature defoliation and reduction in the size of pods and grains.…”

Section: Introductionmentioning

confidence: 99%

Cowpea (Vigna unguiculata (L.) Walp) Accessions Evaluation for Cercospora Leaf Spot Resistance Under Changing Weather

Edet¹,

Afolabi²,

Akinbode³

2023

Trends Agric. Sci

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Background and Objective: Cowpea (Vigna unguiculata L. Walp) is considered to be an important food crop in Nigeria. However, its production in commercial quantity is constrained majorly by 64% fungal diseases especially Cercospora Leaf Spot Disease (CLSD). The objective of this research was to evaluate sixty-two cowpea accessions for resistance to Cercospora Leaf Spot Disease (CLSD) and determine their agronomic parameters to identify resistant accessions that could be used as breeding materials for improved cowpea germplasm. The study also aimed to assess the impact of weather conditions on disease incidence and severity. Materials and Methods: In line with this, a study was conducted to evaluate sixtytwo cowpea accessions for resistance to CLSD at the Research Farm, Institute of Agricultural Research and Training, Obafemi Awolowo University, Ibadan, the site is located at (Latitude 07°23'N, Longitude 03°51'E).The trial took place during the dry season (August to November) 2017 and the wet season (May to August) 2018. The cowpea accessions were laid-out in Randomized Complete Block Design with three replications. Data were collected on disease incidence and severity as well as agronomic parameters. Data collected were subjected to Analysis of Variance and means of significant treatments were separated using Duncan's Multiple Range Test at p<0.05. Results: The results indicated that due to emerging weather conditions, the disease incidence and severity were significantly (p<0.05) different for the two seasons and also for the accessions, the disease reaction in the dry season showed that 2 accessions (TVu-9202 and TVU-9276) were highly resistant, 31 were resistant, 23 were moderately resistant and 6 were moderately susceptible. However, in the wet season, 29 accessions were moderately resistant, 32 moderately susceptible and one (IFE BPC) was susceptible based on 0-5 adopted severity scale. Conclusion:Cercospora Leaf Spot (CLS) disease has serious implications on cowpea production and yield based on the prevalent weather condition and the inherent genetic trait. Hence, the observed resistant cowpea accessions found in this study can be further evaluated in different agroecological zones.

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Identification and molecular characterisation of cercospora leaf spot disease pathogen on cowpea (Vigna unguiculataL. Walp)

Cited by 5 publications

References 26 publications

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

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

Progress in the Use of Combined Omics for Mungbean Breeding Improvement and Its Potential in Promoting Resistance against Cercospora Leaf Spot

Cowpea (Vigna unguiculata (L.) Walp) Accessions Evaluation for Cercospora Leaf Spot Resistance Under Changing Weather

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