Screening of triploid banana population under natural and controlled black sigatoka disease for genomic selection.

The black sigatoka disease (BSD) is the most important foliar threat in banana production and breeding efforts against it should take advantage of genomic selection (GS) that has become one of the most explored tools to increase genetic gain, save time and reduce selection costs. In order to evaluate the potential of GS in banana for BSD, 210 triploid accessions were obtained from the African Banana and Plantain Research Center (CARBAP) to constitute a training population (TP). The variability in the population was assessed at the phenotypic level using BSD- and agronomic-related traits and at the molecular level using Single Nucleotide Polymorphism (SNPs). The analysis of variance showed a significant difference between accessions for almost all traits measured, while at the genomic group level; there was no significant difference for BSD-related traits. The Index of Non spotted leave among accessions ranged from 0.11 to 0.8. The accessions screening in controlled conditions confirmed the susceptibility of all genomic groups to BSD. The principal components analysis with phenotypic data revealed no clear diversity partition of the population. However, the structure analysis and the hierarchical clustering analysis with SNPs grouped the population into four (4) clusters and two (2) sub-populations respectively. The field and laboratory screening of the banana genomic selection TP confirmed that all genomic groups are susceptible to BSD but did not reveal any genetic structure while SNPs markers exhibited clear genetic structure and provided useful information in the perspective of applying genomic selection.

The Sigatoka Disease Complex Caused by Pseudocercospora spp. and Other Fungal Pathogens Associated with Musa spp. in Puerto Rico.

Bananas (Musa spp.) are among the world's most economically important staple food crops. The most important fungal leaf diseases of Musa spp. worldwide are caused by the Sigatoka disease complex, which comprise black Sigatoka (Pseudocercospora fijiensis), yellow Sigatoka (P. musae), and Eumusae leaf spot (P. eumusae). Considering the rapid spreading rate of black Sigatoka in Puerto Rico after its first observation in 2004, a disease survey was conducted from 2018 to 2020 to evaluate the Sigatoka disease complex on the island. Sixty-one leaf samples showing Sigatoka-like symptoms were collected throughout the island for diagnosis by molecular approaches and fungal isolation. Molecular analysis using species-specific primers for P. fijiensis, P. musae and P. eumusae detected the presence of P. fijiensis in fifty leaf samples. Thirty-eight fungal isolates were collected and identified by morphology and genomic sequencing from various nuclear genes. The analysis identified 24 isolates as P. fijiensis, while the rest of the isolates belonged to the genus Cladosporium spp. and Cladosporium-like spp. (n=5), Neocordana musae (n=2), Zasmidium spp. (n=6), and Z. musigenum (n=1). The high frequency of P. fijiensis found in leaf samples and collected isolates suggest that black Sigatoka has displaced the yellow Sigatoka (P. musae) in Puerto Rico. Accurate identification of fungal species causing foliar diseases in Musa spp. will allow the establishment of quarantine regulations and specific management approaches in Puerto Rico.

Pseudocercospora fijiensis Conidial Germination Is Dominated by Pathogenicity Factors and Effectors.

Conidia play a vital role in the survival and rapid spread of fungi. Many biological processes of conidia, such as adhesion, signal transduction, the regulation of oxidative stress, and autophagy, have been well studied. In contrast, the contribution of pathogenicity factors during the development of conidia in fungal phytopathogens has been poorly investigated. To date, few reports have centered on the pathogenicity functions of fungal phytopathogen conidia. Pseudocercospora fijiensis is a hemibiotrophic fungus and the causal agent of the black Sigatoka disease in bananas and plantains. Here, a conidial transcriptome of P. fijiensis was characterized computationally. Carbohydrates, amino acids, and lipid metabolisms presented the highest number of annotations in Gene Ontology. Common conidial functions were found, but interestingly, pathogenicity factors and effectors were also identified. Upon analysis of the resulting proteins against the Pathogen-Host Interaction (PHI) database, 754 hits were identified. WideEffHunter and EffHunter effector predictors identified 618 effectors, 265 of them were shared with the PHI database. A total of 1107 conidial functions devoted to pathogenesis were found after our analysis. Regarding the conidial effectorome, it was found to comprise 40 canonical and 578 non-canonical effectors. Effectorome characterization revealed that RXLR, LysM, and Y/F/WxC are the largest effector families in the P. fijiensis conidial effectorome. Gene Ontology classification suggests that they are involved in many biological processes and metabolisms, expanding our current knowledge of fungal effectors.

Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania.

Banana is among major crops cultivated by most smallholder farmers in Tanzania and other parts of Africa. This crop is very important in the household economy as well as food security since it serves as both food and cash crops. Despite these benefits, the majority of smallholder farmers are experiencing low yields which are attributed to diseases. The most problematic diseases are Black Sigatoka and Fusarium Wilt Race 1. Black Sigatoka is a disease that produces spots on the leaves of bananas and is caused by an air-borne fungus called Pseudocercospora fijiensis, formerly known as Mycosphaerella fijiensis. Fusarium Wilt Race 1 disease is one of the most destructive banana diseases that is caused by a soil-borne fungus called Fusarium oxysporum f.sp. Cubense (Foc). The dataset of curated banana crop image is presented in this article. Images of both healthy and diseased banana leaves and stems were taken in Tanzania and are included in the dataset. Smartphone cameras were used to take pictures of the banana leaves and stems. The dataset is the largest publicly accessible dataset for banana leaves and stems and includes 16,092 images. The dataset is significant and can be used to develop machine learning models for early detection of diseases affecting bananas. This dataset can be used for a number of computer vision applications, including object detection, classification, and image segmentation. The motivation for generating this dataset is to contribute to developing machine learning tools and spur innovations that will help to address the issue of crop diseases and help to eradicate the problem of food security in Africa.

Disentangling the Factors Affecting the Dynamic of Pseudocercospora fijiensis: Quantification of Weather, Fungicide, and Landscape Effects.

Quantifying the effect of landscape composition on disease dynamics remains challenging because it depends on many factors. In this study, we used a hybrid process-based/statistical modeling approach to separate the effect of the landscape composition on the epidemiology of banana leaf streak disease (BLSD) from weather and fungicide effects. We parameterized our model with a 5-year dataset, including weekly measures of BLSD on 83 plots in Martinique. After estimating the intrinsic growth parameters of the stage evolution of the disease (SED), we evaluated the dynamic effect of five fungicides. Then, we added the intra- and inter-annual effect on disease dynamics using a generalized linear model. Finally, the whole model was used to assess the annual effect of the landscape on the SED for 11 plots. We evaluated the significance of the landscape composition (proportions of landscape elements in 200-, 500-, 800-, 1,000-m-radius buffer zones) on the landscape effect evaluated with the model. The percentage of hedgerows in a 200-m-radius buffer zone was negatively correlated to the landscape effect, i.e., it acted as a constraint against BLSD spreading and development. The proportion of managed-banana-plants in a 1,000-m-radius buffer zone was negatively correlated to the landscape effect, probably due to a mass effect of fungicide treatments. Inversely, the proportions of forest and the proportion of unmanaged-banana-plants, both in 1,000-m-radius buffer zones, were positively correlated with the landscape effect. Our study provides a holistic approach of the role biotic and abiotic factors play on the dynamics of BLSD.

Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species.

Genomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas (Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought-two major threats to banana production-used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome.

Effective Methods Based on Distinct Learning Principles for the Analysis of Hyperspectral Images to Detect Black Sigatoka Disease.

Current chemical methods used to control plant diseases cause a negative impact on the environment and increase production costs. Accurate and early detection is vital for designing effective protection strategies for crops. We evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging. We discuss the learning features of the techniques used, which will help researchers improve their understanding of the required data conditions and identify a method suitable for their research needs. A set of hyperspectral images of banana leaves inoculated with a conidial suspension of black sigatoka fungus (Pseudocercospora fijiensis) was used to train and validate machine learning models. Support vector machine (SVM), multilayer perceptron (MLP), neural networks, N-way partial least square-discriminant analysis (NPLS-DA), and partial least square-penalized logistic regression (PLS-PLR) were selected due to their high predictive power. The metrics of AUC, precision, sensitivity, prediction, and F1 were used for the models' evaluation. The experimental results show that the PLS-PLR, SVM, and MLP models allow for the successful detection of black sigatoka disease with high accuracy, which positions them as robust and highly reliable HSI classification methods for the early detection of plant disease and can be used to assess chemical and biological control of phytopathogens.

Gene Expression, Histology and Histochemistry in the Interaction between Musa sp. and Pseudocercospora fijiensis.

Bananas are the main fruits responsible for feeding more than 500 million people in tropical and subtropical countries. Black Sigatoka, caused by the fungus Pseudocercospora fijiensis, is one of the most destructive disease for the crop. This fungus is mainly controlled with the use of fungicides; however, in addition to being harmful to human health, they are associated with a high cost. The development of resistant cultivars through crosses of susceptible commercial cultivars is one of the main focuses of banana breeding programs worldwide. Thus, the objective of the present study was to investigate the interaction between Musa sp. and P. fijiensis through the relative expression of candidate genes involved in the defence response to black Sigatoka in four contrasting genotypes (resistant: Calcutta 4 and Krasan Saichon; susceptible: Grand Naine and Akondro Mainty) using quantitative real-time PCR (RT-qPCR) in addition to histological and histochemical analyses to verify the defence mechanisms activated during the interaction. Differentially expressed genes (DEGs) related to the jasmonic acid and ethylene signalling pathway, GDSL-like lipases and pathogenesis-related proteins (PR-4), were identified. The number and distance between stomata were directly related to the resistance/susceptibility of each genotype. Histochemical tests showed the production of phenolic compounds and callosis as defence mechanisms activated by the resistant genotypes during the interaction process. Scanning electron microscopy (SEM) showed pathogenic structures on the leaf surface in addition to calcium oxalate crystals. The resistant genotype Krasan Saichon stood out in the analyses and has potential for use in breeding programs for resistance to black Sigatoka in banana and plantains.

Genetic Characteristics and Metabolic Interactions between Pseudocercospora fijiensis and Banana: Progress toward Controlling Black Sigatoka.

The international importance of banana and severity of black Sigatoka disease have led to extensive investigations into the genetic characteristics and metabolic interactions between the Dothideomycete Pseudocercospora fijiensis and its banana host. P. fijiensis was shown to have a greatly expanded genome compared to other Dothideomycetes, due to the proliferation of retrotransposons. Genome analysis suggests the presence of dispensable chromosomes that may aid in fungal adaptation as well as pathogenicity. Genomic research has led to the characterization of genes and metabolic pathways involved in pathogenicity, including: secondary metabolism genes such as PKS10-2, genes for mitogen-activated protein kinases such as Fus3 and Slt2, and genes for cell wall proteins such as glucosyl phosphatidylinositol (GPI) and glycophospholipid surface (Gas) proteins. Studies conducted on resistance mechanisms in banana have documented the role of jasmonic acid and ethylene pathways. With the development of banana transformation protocols, strategies for engineering resistance include transgenes expressing antimicrobial peptides or hydrolytic enzymes as well as host-induced gene silencing (HIGS) targeting pathogenicity genes. Pseudocercospora fijiensis has been identified as having high evolutionary potential, given its large genome size, ability to reproduce both sexually and asexually, and long-distance spore dispersal. Thus, multiple control measures are needed for the sustainable control of black Sigatoka disease.

Sources of resistance to Pseudocercospora fijiensis, the cause of black Sigatoka in banana.

Black Sigatoka, caused by Pseudocercospora fijiensis, is one of the most devastating diseases of banana. In commercial banana-growing systems, black Sigatoka is primarily managed by fungicides. This mode of disease management is not feasible for resource-limited smallholder farmers. Therefore, bananas resistant to P. fijiensis provide a practical solution for managing the disease, especially under smallholder farming systems. Most banana and plantain hybrids with resistance to P. fijiensis were developed using few sources of resistance, which include Calcutta 4 and Pisang Lilin. To broaden the pool of resistance sources to P. fijiensis, 95 banana accessions were evaluated under field conditions in Sendusu, Uganda. Eleven accessions were resistant to P. fijiensis. Black Sigatoka symptoms did not progress past Stage 2 (narrow brown streaks) in the diploid accessions Pahang (AA), Pisang KRA (AA), Malaccensis 0074 (AA), Long Tavoy (AA), M.A. Truncata (AA), Tani (BB), and Balbisiana (BB), a response similar to the resistant control Calcutta 4. These accessions are potential sources of P. fijiensis resistance and banana breeding programmes can use them to broaden the genetic base for resistance to P. fijiensis.

Genetic Improvement for Resistance to Black Sigatoka in Bananas: A Systematic Review.

Bananas are an important staple food crop in tropical and subtropical regions in Asia, sub-Saharan Africa, and Central and South America. The plant is affected by numerous diseases, with the fungal leaf disease black Sigatoka, caused by Mycosphaerella fijiensis Morelet [anamorph: Pseudocercospora fijiensis (Morelet) Deighton], considered one of the most economically important phytosanitary problem. Although the development of resistant cultivars is recognized as most effective method for long term control of the disease, the majority of today's cultivars are susceptible. In order to gain insights into this pathosystem, this first systematic literature review on the topic is presented. Utilizing six databases (PubMed Central, Web of Science, Google Academic, Springer, CAPES and Scopus Journals) searches were performed using pre-established inclusion and exclusion criteria. From a total of 3,070 published studies examined, 24 were relevant with regard to the Musa-P. fijiensis pathosystem. Relevant papers highlighted that resistant and susceptible cultivars clearly respond differently to infection by this pathogen. M. acuminata wild diploids such as Calcutta 4 and other diploid cultivars can harbor sources of resistance genes, serving as parentals for the generation of improved diploids and subsequent gene introgression in new cultivars. From the sequenced reference genome of Musa acuminata, although the function of many genes in the genome still require validation, on the basis of transcriptome, proteome and biochemical data, numerous candidate genes and molecules have been identified for further evaluation through genetic transformation and gene editing approaches. Genes identified in the resistance response have included those associated with jasmonic acid and ethylene signaling, transcription factors, phenylpropanoid pathways, antioxidants and pathogenesis-related proteins. Papers in this study also revealed gene-derived markers in Musa applicable for downstream application in marker assisted selection. The information gathered in this review furthers understanding of the immune response in Musa to the pathogen P. fijiensis and is relevant for genetic improvement programs for bananas and plantains for control of black Sigatoka.

A world-wide analysis of reduced sensitivity to DMI fungicides in the banana pathogen Pseudocercospora fijiensis.

BACKGROUND: Pseudocercospora fijiensis is the causal agent of the black leaf streak disease (BLSD) of banana. Bananas are important global export commodities and a major staple food. Their susceptibility to BLSD pushes disease management towards excessive fungicide use, largely relying on multisite inhibitors and sterol demethylation inhibitors (DMIs). These fungicides are ubiquitous in plant disease control, targeting the CYP51 enzyme. We examined sensitivity to DMIs in P. fijiensis field isolates collected from various major banana production zones in Colombia, Costa Rica, Dominican Republic, Ecuador, the Philippines, Guadalupe, Martinique and Cameroon and determined the underlying genetic reasons for the observed phenotypes. RESULTS: We observed a continuous range of sensitivity towards the DMI fungicides difenoconazole, epoxiconazole and propiconazole with clear cross-sensitivity. Sequence analyses of PfCYP51 in 266 isolates showed 28 independent amino acid substitutions, nine of which correlated with reduced sensitivity to DMIs. In addition to the mutations, we observed up to six insertions in the Pfcyp51 promoter. Such promoter insertions contain repeated elements with a palindromic core and correlate with the enhanced expression of Pfcyp51 and hence with reduced DMI sensitivity. Wild-type isolates from unsprayed bananas fields did not contain any promoter insertions. CONCLUSION: The presented data significantly contribute to understanding of the evolution and global distribution of DMI resistance mechanisms in P. fijiensis field populations and facilitate the prediction of different DMI efficacy. The overall reduced DMI sensitivity calls for the deployment of a wider range of solutions for sustainable control of this major banana disease. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Genetic Diversity and Mating Type Distribution of Pseudocercospora fijiensis on Banana in Uganda and Tanzania.

Black Sigatoka, caused by Pseudocercospora fijiensis, is a major foliar disease of banana and plantain worldwide. There are few available data regarding the genetic diversity and population structure of the pathogen in East Africa, which are needed to design effective and durable disease management strategies. We genotyped 319 single-spore isolates of P. fijiensis collected from seven regions in Uganda and Tanzania and five isolates from Nigeria using 16 simple sequence repeat markers and mating type-specific primers. Isolates from each country and region within the country were treated as populations and subpopulations, respectively. A total of 296 multilocus genotypes (MLGs) were recovered, representing a clonal fraction of 7%. Subpopulations had a moderate level of genetic diversity (Hexp = 0.12 to 0.31; mean, 0.29). Mating type distribution did not deviate from equilibrium (MAT1-1: MAT1-2, 1:1 ratio) in Uganda; however, in Tanzania the mating types were not in equilibrium (4:1 ratio). The index of association tests (IA and r̄d) showed that all populations were at linkage equilibrium (P > 0.05), thus supporting the hypothesis of random association of alleles. These findings are consistent with a pathogen that reproduces both clonally and sexually. Low and insignificant levels of population differentiation were detected, with 90% of the variation occurring among isolates within subpopulations. The high intrapopulation variation has implications in breeding for resistance to P. fijiensis because isolates differing in aggressiveness and virulence are likely to exist over small spatial scales. Diverse isolates will be required for resistance screening to ensure selection of banana cultivars with durable resistance to Sigatoka in East Africa.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Early detection of black Sigatoka in banana leaves using hyperspectral images.

PREMISE: Black Sigatoka is one of the most severe banana (Musa spp.) diseases worldwide, but no methods for the rapid early detection of this disease have been reported. This paper assesses the use of hyperspectral images for the development of a partial-least-squares penalized-logistic-regression (PLS-PLR) model and a hyperspectral biplot (HS biplot) as a visual tool for detecting the early stages of black Sigatoka disease. METHODS: Young (three-month-old) banana plants were inoculated with a conidia suspension of the black Sigatoka fungus (Pseudocercospora fijiensis). Selected infected and control plants were evaluated using a hyperspectral imaging system at wavelengths in the range of 386-1019 nm. PLS-PLR models were run on the hyperspectral data set. The prediction power was assessed using leave-one-out cross-validation as well as external validation. RESULTS: The PLS-PLR model was able to predict the presence of the disease with a 98% accuracy. The wavelengths with the highest contribution to the classification ranged from 577 to 651 nm and from 700 to 1019 nm. DISCUSSION: PLS-PLR and HS biplot effectively estimated the presence of black Sigatoka disease at the early stages and can be used to graphically represent the relationship between groups of leaves and both visible and near-infrared wavelengths.

Distribution of Pseudocercospora species causing Sigatoka leaf diseases of banana in Uganda and Tanzania.

Sigatoka leaf diseases are a major constraint to banana production. A survey was conducted in Tanzania and Uganda to assess the distribution of Pseudocercospora species and severity of Sigatoka leaf diseases. Pseudocercospora species were identified using species-specific primers. Sigatoka-like leaf diseases were observed in all farms and on all cultivars, but disease severity varied significantly (P < 0.001) between countries, districts/regions within countries, altitudinal ranges and banana cultivars. In all regions except Kilimanjaro, P. fijiensis, the causal agent of black Sigatoka, was the only pathogen associated with Sigatoka disease. Mycosphaerella musae was associated with Sigatoka-like symptoms in Kilimanjaro region. Black Sigatoka disease was more severe in Uganda, with a mean disease severity index (DSI) of 37.5%, than in Tanzania (DSI = 19.9%). In Uganda, black Sigatoka disease was equally severe in Luwero district (mean DSI = 40.4%) and Mbarara district (mean DSI = 37.9%). In Tanzania, black Sigatoka was most severe in Kagera region (mean DSI = 29.2%) and least in Mbeya region (mean DSI = 11.5%). Pseudocercospora fijiensis, the most devastating sigatoka pathogen, was detected at altitudes of up to 1877 m a.s.l. This range expansion of P. fijiensis, previously confined to altitudes lower than 1350 m a.s.l. in East Africa, is of concern, especially for smallholder banana farmers growing the susceptible East African Highland bananas (EAHB). Among the banana varieties sampled, the EAHB, FHIA hybrids and Mchare were the most susceptible. Here, the loss of resistance in Yangambi KM5, a banana variety previously resistant to P. fijiensis, is reported for the first time.

Protein compounds of Bacillus subtilis with in vitro antifungal activity against Pseudocercospora fijiensis (Morelet).

The metabolites of Bacillus subtilis CCIBP-M27 were evaluated as an antagonist of Pseudocercospora fijiensis. The culture filtrate did not inhibit ascospore germination but significantly reduced conidial germination and mycelial growth. Through microscopic analysis, deformations were observed as vacuolization and swelling in P. fijiensis mycelia when exposed to culture filtrate during 48 h. A similar response was induced by peptide-type compounds found on Bacillus subtilis CCIBP-M27 culture filtrate. The results obtained suggest that the in vitro antifungal effect of the strain CCIBP-M27 against P. fijiensis is related to the action of diffused metabolites such as proteins or peptide substances.

Early Detection of the Fungal Banana Black Sigatoka Pathogen Pseudocercospora fijiensis by an SPR Immunosensor Method.

Black Sigatoka is a disease that occurs in banana plantations worldwide. This disease is caused by the hemibiotrophic fungus Pseudocercospora fijiensis, whose infection results in a significant reduction in both product quality and yield. Therefore, detection and identification in the early stages of this pathogen in plants could help minimize losses, as well as prevent the spread of the disease to neighboring cultures. To achieve this, a highly sensitive SPR immunosensor was developed to detect P. fijiensis in real samples of leaf extracts in early stages of the disease. A polyclonal antibody (anti-HF1), produced against HF1 (cell wall protein of P. fijiensis) was covalently immobilized on a gold-coated chip via a mixed self-assembled monolayer (SAM) of alkanethiols using the EDC/NHS method. The analytical parameters of the biosensor were established, obtaining a limit of detection of 11.7 µg mL-1, a sensitivity of 0.0021 units of reflectance per ng mL-1 and a linear response range for the antigen from 39.1 to 122 µg mL-1. No matrix effects were observed during the measurements of real leaf banana extracts by the immunosensor. To the best of our knowledge, this is the first research into the development of an SPR biosensor for the detection of P. fijiensis, which demonstrates its potential as an alternative analytical tool for in-field monitoring of black Sigatoka disease.

Development of a rapid methodology for biological efficacy assessment in banana plantations: application to reduced dosages of contact fungicide for Black Leaf Streak Disease (BLSD) control.

BACKGROUND: Black sigatoka is the main disease of banana crop production and is controlled by using either systemic or contact fungicides through spray applications. Biological efficacy is typically assessed on a whole cropping cycle with a natural infestation and periodic spray applications. Developing a faster methodology for assessment of the biological efficacy of a contact fungicide offers promising perspectives for testing current and new fungicides or application techniques. RESULTS: The methodology is based on the time of occurrence of the first BLSD symptoms. An artificial infestation protocol was optimized by multiplying the infestation spots and by covering the infested plants. Biological efficacy tests were based on a single spray application after infestation combining three mancozeb dose reductions and two nozzle types. Results demonstrated that a 50% reduction in the mancozeb rated dosage gave significant efficacy independently of the nozzle type, with a reduction of the number of lesions of up to 55% compared with control plants. CONCLUSIONS: The described method provides rapid and significant infestation. Further comparison of spray settings and fungicide doses was possible. This methodology will be tested at the plantation scale over a longer period covering the whole crop cycle. © 2018 Society of Chemical Industry.

Effect of Bacillus pumilus CCIBP-C5 on Musa-Pseudocercospora fijiensis interaction.

The effect of antifungal activity of culture filtrate (CF) of Bacillus pumilus strain CCIBP-C5, an isolate from a phyllosphere of banana (Musa) leaves, was determined on Pseudocercospora fijiensis challenged banana plants. The CF was shown to decrease the fungal biomass and induce changes in banana plant. In this sense, at 70 days post inoculation (dpi), a lower infection index as well as a decrease in fungal biomass after 6 dpi was obtained in treated plants with respect to control ones. At the same time, changes in the activities of several enzymes related to plant defense responses, such as phenylalanine ammonia lyase, chitinases, ß-1,3-glucanases and peroxidases were observed. These results indicate that B. pumilus CCIBP-C5 has a potential role for biological control of P. fijiensis possibly due to the production of antifungal metabolites.

Increased Silicon Acquisition in Bananas Colonized by Rhizophagus irregularis MUCL 41833 Reduces the Incidence of Pseudocercospora fijiensis.

This work aimed to test the hypothesis that the combination of arbuscular mycorrhizal fungi (AMF) and accumulation of silicon (Si) in banana plants via its uptake and transport by the fungus reduces the incidence of Black Leaf Steak Disease (BLSD) caused by Pseudocercospora fijiensis. Methods: A pot experiment was conducted to compare BLSD symptoms on leaves of banana plants colonized or not by the AMF Rhizophagus irregularis MUCL 41833 and exposed or not to Si added to the growth substrate. Results: A marked increase in plant growth parameters (i.e., pseudostem diameter and height, leaf surface area, shoot, root and total dry weight) as well as accumulation of Si, P, and Ca were noticed in the AMF-colonized banana plants in presence as well as in absence of Si added to the growth substrate. Similarly Si addition to the substrate increased plant growth parameters. Leave symptoms caused by the pathogen were observed in all the treatments but were reduced in presence of AMF as well as in presence of Si added to the growth substrate. The more drastic reduction was noticed in the AMF-colonized plants with Si added to the growth substrate. The Severity Index as well as Area Under Disease Progress Curve were considerably decreased both at 21 (∼48% and 48%, respectively) and 35 days (∼21% and ∼32%, respectively) after inoculation of the pathogen as compared with non-AMF-colonized plants in absence of Si added to the substrate. Conclusion: Our findings revealed that AMF-colonized banana plants grown in a subs-trate supplemented with Si were less impacted by P. fijiensis than non-colonized plants grown without Si added to the growth substrate. The combination of AMF-colonized banana plants (during the weaning phase or in vitro) with the application of Si to soil seems thus a thoughtful option to mitigate the impact of BLSD in bananas, although such strategy needs first to be evaluated under field conditions to appraise its real potential.