Yellow Sigatoka Research Articles

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 comprises 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 since 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 50 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 suggests 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.

Using Mutation Breeding to Improve the Eating Characteristics of the Fusarium Wilt-Resistant Banana Variety, ‘Goldfinger’ (AAAB)

Banana production in Australia is under threat from Fusarium wilt Tropical Race 4 (TR4), and the option of growing a disease-resistant variety is sought after by many farmers. Goldfinger is one such alternative; however, it was previously rejected by Australian consumers. In Phase I of this investigation, mutagenesis was employed as a practical method to generate large numbers of novel variants. In Phase II, 631 variants were established in a field trial where agronomic and postharvest assessments, including taste-testing, identified 20 improved selections. Phase III involved a replicated field evaluation of the selections with favourable mutations, where further taste-testing revealed five selections with the most promising eating characteristics. In Phase IV, the five selections underwent large-scale consumer and sensory evaluations where four of them performed as well as the industry standard controls ‘Williams’ Cavendish and Lady Finger. The sensory analysis revealed differences in the fruit flavour profile and textural attributes, and these features will play a key role in bringing a new banana variety to market and for promotion to consumers. Screening of the variants’ resistance to yellow Sigatoka was integrated into the project, but further work is needed to ensure these four selections have retained their TR4 resistance.

Resistance to Site-Specific Succinate Dehydrogenase Inhibitor Fungicides Is Pervasive in Populations of Black and Yellow Sigatoka Pathogens in Banana Plantations from Southeastern Brazil

The Sigatoka disease complex (SDC), caused by Mycosphaerella fijiensis (Mf) and M. musicola (Mm), comprises the most destructive fungal leaf streak and spot diseases of commercial banana crops worldwide. In Brazil, the site-specific succinate dehydrogenase inhibitor (SDHI) fungicides labeled for SDC management since 2014 present a high risk for the emergence of resistance if deployed intensively and solo. Our study determined the levels of sensitivity to boscalid and fluxapyroxad in four populations of the SDC pathogens sampled in 2020 from three distinct geographical regions under contrasting fungicide programs. Resistance, defined as EC50 values exceeding 20 µg mL−1, was prevalent at 59.7% for fluxapyroxad and 94.0% for boscalid. Only 1.5% of isolates exhibited sensitivity to both fungicides. We also assessed the changes in the corresponding fungicide target protein-encoding genes (SdhB, C, and D). None of the target site alterations detected were associated with reduced sensitivity. A second SdhC paralog was also analyzed, but target alterations were not found. However, MDR (multidrug resistance) was detected in a selection of isolates. Further monitoring for Sdh target mutations will be important, but an important role for other resistance mechanisms such as the presence of additional Sdh paralogs and MDR cannot be ruled out. These results highlight the importance of implementing sound anti-resistance management strategies when SDHI fungicides are deployed for the management of SDC.

New treasures in Cordycipitaceae: Fungicolous fungi associated with Pseudocercospora fijiensis and P. musae in Brazil, including Matutinistella gen. nov.

Cordycipitaceae is a large family belonging to the order Hypocreales, including cosmopolitan species found on different substrates. This family includes species with different lifestyles such as entomopathogenic, endophytic, and mycoparasitic fungi. Some mycoparasitic species in this family attack phytopathogenic fungi. We identified species of Cordycipitaceae found during a survey of fungicolous fungi associated with Pseudocercospora fijiensis and P. musae in Brazil. Based on morphological characters combined with phylogenetic analysis of ITS, LSU, SSU, RPB1, RPB2, and TEF1-α, we propose a new genus in Cordycipitaceae, namely Matutinistella, with its type species M. brasiliensis, and a new species of the genus Simplicillium, namely S. pseudocercosporicola. Furthermore, we report P. fijiensis as a new host of the mycoparasitic fungus Simplicillium lanosoniveum. In this study we newly report fungicolous fungi of Cordycipitaceae associated with the causal agents of Black Sigatoka and Yellow Sigatoka on banana crops. We provide relevant information for future work involving control measures for these diseases that cause major losses in banana crops.

Evaluation of different management practices against yellow Sigatoka disease of banana (Musa spp.) caused by Mycosphaerella musicola Leach

Leaf spot or yellow sigatoka disease (caused by Mycosphaerella musicola) of banana causes significant yield loss as well as in quality of fruits in every year and reported up to 65% or even more under favorable epidemic conditions. In this perspective, an experiment was conducted at ZBNF project Research field, college of horticulture, Sirsi (Uttara Kannada district of Karnataka) for consecutive two seasons (2020-21 & 2021-22). Experiment accompanied with RBD statistical design with five replications and four treatments. Four different management practices involving viz. Propiconazole 25EC @ 0.1% (Recommended package of practices-UHS, Bagalkot), Trichoderma harzianum 10g/lit. (Organic farming), sour butter milk 5 lit. per 200 liter of water (Natural farming) and Tebuconazole 50% + Trifloxystrobin 25% WG @0.5gm/lit. (Chemical farming) were evaluated against sigatoka leaf spot disease. Among the management practices, chemical farming comprises tebuconazole 50%+ trifloxystrobin 25%WG@1gm/lit was found effective in managing the disease (12.38% PDI) followed by recommended package of practice comprises propiconazole 25%EC @1ml/l (16.33% PDI), organic farming comprises of talk-based Trichoderma harzianum 10g/lit (17.33% PDI). Natural farming showed least effective to combat disease recorded maximum disease severity (19.66% PDI) after 210 days after planting. Although chemical farming can effectively control the disease but results in the serious risk on human health and environmental hazards. Therefore, organic and natural farming are an alternative approach that are eco-friendly and economically viable against sigatoka leaf spot disease management.

BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases

All over the world, bananas are one of the most common fruits. It accounts for nearly 16% of global fruit production. However, every year, a large amount of banana yield losses occur due to different diseases of the banana leaf. It is essential to identify these diseases at an early stage in order to increase banana production. A visual inspection is the most common method of identifying banana leaf diseases. With a visual inspection, errors are common, time is a factor, and expertise is required. This study shows how deep learning and Bayesian optimization can be used to effectively diagnose banana leaf diseases from images without any human intervention. We collected the Banana Leaf Spot Diseases (BananaLSD) dataset from various locations in Bangladesh. The dataset consists of images of three banana leaf diseases: Pestalotiopsis, Sigatoka, and Cordana. Our proposed BananaSqueezeNet model performed exceptionally well in diagnosing banana leaf diseases from the images with an overall accuracy of 96.25%, precision of 96.53%, recall of 96.25%, specificity of 98.75%, F1-score of 96.17%, and MCC of 95.13%. The BananaSqueezeNet model outperforms some state-of-the-art convolutional neural networks that include EfficientNetB0, MobileNetV3, ResNet-101, ResNet-50, InceptionNet-V3, and VGG16. The BananaSqueezeNet model also detected seven other diseases that affect banana leaves, fruits, and stems, including banana fruit scarring beetle, black sigatoka, bacterial soft rot, pseudo stem weevil, yellow sigatoka, banana aphids, and panama disease, with an accuracy of 95.13%. BananaSqueezeNet will enable banana growers to detect banana diseases early, and we hope that it will ultimately lead to an increase in banana production in Bangladesh and around the world.

Symptomatology of Sigatoka leaf spot disease in banana landraces and identification of its pathogen as Mycosphaerella eumusae

Mycosphaerella musicola, M. fijiensis and M. eumusae are known to cause banana yellow Sigatoka, black Sigatoka and Eumusae leaf spot diseases respectively. However, differentiating the causative species has been challenging and the responses of banana landraces to this pathogen were little known. Purposive sampling surveys were conducted in different agro-climatic zones of Kerala state, India, to collect the leaf samples with Sigatoka leaf spot-like symptoms, to document the symptom expression on different landraces and to confirm the identity of the pathogen at species level. Fields surveyed during the post-monsoon period had 100% disease incidence with highest per cent disease severity of 43.90% in nine-month old cv. Palayankodan, while lowest PDS of 3.33% was observed in three-month old cv. Njalipoovan, during a pre-monsoon survey. Initial symptoms were small brown streaks, later developing into oval or elliptical brown spots with greyish centre and subsequently coalescing for leaf necrosis. Symptoms in different landraces varied across the zones. Sequencing and analysis of the ITS region from 50 isolates collected during the survey showed 99–100% homology with M. eumusae . Phylogenetic analyses further proved the species identity of the local isolates. Comparing the ITS barcodes of the three species of Mycosphaerella along with the local isolates confirmed that the pathogen inciting Sigatoka leaf spot in Kerala state, India, is Mycosphaerella eumusae .

Comparison of color-based feature extraction methods in banana leaf diseases classification using SVM and K-NN

In Malaysia, banana is a top fruit production which contribute to the economy growth in agriculture field. Hence, it is significant to have a quality production of banana and important to detect the plant diseases at the early stage. There are many types of banana leaf diseases such as Banana Mosaic, Black Sigatoka and Yellow Sigatoka. These three diseases are related to color changes at banana. This research paper is an experiment based and need to identify the best color feature extraction method to classify banana leaf diseases. Total of 48 banana leaf images that are used in this research paper. Four types of color feature extraction methods which are color histogram, color moment, hue, saturation, and value (HSV) histogram and color auto correlogram are experimented to determine the best method for banana leaf diseases classification. While for the classifiers, support vector machine (SVM) and k-Nearest neighbors (k-NN) are used to evaluate the performance and accuracy of each color feature extraction methods. There are also preliminary experiments to identify accurate parameters to use during classification for both classifiers. Our experimental result express that HSV histogram is the best method to classify banana leaf diseases with 83.33% of accuracy and SVM classifier perform better compared to k-NN.

The Vulnerability of Bananas to Globally Emerging Disease Threats.

Banana cultivation has increased significantly over the last century to meet the growing demand for this popular fruit. Originating in Southeast Asia, bananas are now produced in >135 different countries in tropical and subtropical regions of the world. Most of this expansion of production is based on a single banana variety, Cavendish, which makes up almost all the export trade grown in large-scale monocultures and a large part of the local trade and represents >40% of all bananas grown globally. Over the last century several major diseases of the banana have emerged and widely expanded their geographic ranges. Cultivars within the Cavendish variety are highly susceptible to these diseases, including yellow Sigatoka, black leaf streak, Eumusae leaf spot, freckle, Fusarium wilt tropical race 4, banana bunchy top, and the bacterial wilts Moko, Xanthomonas wilt, and banana Blood disease. This review graphically illustrates the emergence and rapid intercontinental spread of these diseases and discusses several major disease epidemics in bananas. Evidently, the large-scale monoculture based on the single variety Cavendish has resulted in an extreme level of genetic vulnerability. The resistance to diversification in the Cavendish production chain and the lack of investment in genetics and plant breeding in the recent past means that currently limited genetic solutions are available to replace the Cavendish banana with a set of market acceptable resistant varieties from a range of different genetic backgrounds.

Biocontrol of <i>Mycosphaerella fijiensis</i> Morelet, the Causal Agent of Black Sigatoka of Banana Tree (<i>Musa</i> spp.) Using Biopesticides in Côte d’Ivoire

Black Leaf Streak Disease (BLSD) is the most restricting leaf disease to banana tree cultivation around the world. In order to control this disease, synthetic fungicides are extensively used. However, these products pose a real danger to environmental pollution and the health of applicators and consumers. Faced with this situation, alternative solutions must be considered to overcome their systematic use. This study was initiated in this context so as to assess the effectiveness of 20 biopesticide formulations on <i>Mycosphaerella fijiensis</i> conidia stemming from banana tree leaf samples originating from village plantations and showing the typical symptoms of stage 2 or 3 black Sigatoka. The assessment method used was that of dispersion in solid medium. Observations were made under an optical microscope equipped with a camera and consisted in determining the inhibition rates of conidia germ tube growth. A pathogenicity test was performed with 8 <i>Mycosphaerella</i> spp. isolates according to an inoculation technique under controlled conditions on whole plants of 5 banana tree cultivar vivoplants. The assessment of biopesticide protection effectiveness against BLSD was conducted on cultivar "Orishele" (very susceptible) with the most aggressive and virulent strain selected during isolate pathogenicity test. The results obtained show that all biopesticide formulations have significant antifungal activity on <i>M. fijiensis</i> conidia germ tube elongation. The average inhibition rate ranged from 83.31 to 99.89% for all biopesticides. The 8 <i>M. fijiensis</i> isolates used have all raised symptoms characteristic of black leaf streak disease regardless of the cultivar. In contrast, no isolate caused symptoms characteristic of Sigatoka disease (Yellow Sigatoka). Biopesticides and synthetic fungicides significantly reduced disease development rate compared to inoculated and untreated controls, but at varying degrees. Preventive treatment of seedlings is found to be much more effective than curative treatment. However, in order to protect banana and plantain tree varieties against <i>M. fijiensis</i>, both types of treatments are necessary.

Pseudocercospora and allied genera associated with leaf spots of banana (Musa spp.).

The Sigatoka leaf spot complex on Musa spp. includes three major pathogens: Pseudocercospora, namely P. musae (Sigatoka leaf spot or yellow Sigatoka), P. eumusae (eumusae leaf spot disease), and P. fijiensis (black leaf streak disease or black Sigatoka). However, more than 30 species of Mycosphaerellaceae have been associated with Sigatoka leaf spots of banana, and previous reports of P. musae and P. eumusae need to be re-evaluated in light of recently described species. The aim of the present study was thus to investigate a global set of 228 isolates of P. musae, P. eumusae and close relatives on banana using multigene DNA sequence data [internal transcribed spacer regions with intervening 5.8S nrRNA gene (ITS), RNA polymerase II second largest subunit gene (rpb2), translation elongation factor 1-alpha gene (tef1), beta-tubulin gene (tub2), and the actin gene (act)] to confirm if these isolates represent P. musae, or a closely allied species. Based on these data one new species is described, namely P. pseudomusae, which is associated with leaf spot symptoms resembling those of P. musae on Musa in Indonesia. Furthermore, P. eumusae, P. musae and P. fijiensis are shown to be well defined taxa, with some isolates also representing P. longispora. Other genera encountered in the dataset are species of Zasmidium (Taiwan leaf speckle), Metulocladosporiella (Cladosporium leaf speckle) and Scolecobasidium leaf speckle. Citation: Crous P, Carlier J, Roussel V, Groenewald JZ (2020). Pseudocercospora and allied genera associated with leaf spots of banana (Musa spp.). Fungal Systematics and Evolution 7: 1-19. doi: 10.3114/fuse.2021.07.01.

Performance of Plantain (Musa Paradisiaca L) Fruit Yield and Quality under Different Planting Densities and Water Application

The performance of plantain fruit yield and quality under different planting densities under an influence of irrigation system was investigated at International Institute of Tropical Agriculture, Ibadan, Oyo State, Nigeria. Experiments were set up as a split plot fitted into randomized complete block design (RCBD) with three replications. Main plots are irrigation system while sub-plots are plant densities. Treatments included four plant densities (1 x 2, 2 x 2, 2 x 3, and 3 x 3 m), two irrigation systems and their combination where irrigation system was the main plot and plant density the sub-plot. Over 2,000 agbagba suckers were pared and hot–water treated, and planted during the first week of the experiment. Manure was applied at the rate of three spades full per plant for nutrient supplement; and mulching was done. Weeds were manually controlled as at when due. Plantain diseases like yellow sigatoka and black sigatoka were also controlled manually by de-leafing infected leaves to prevent disease spread. Data were collected and analyzed using ANOVA. Duncan multiple range test (DMRT) was used for mean separation at 5% level of significance. The results of ANOVA showed that there were significant effects (P &lt; 0.05) due to irrigation application for all measured parameters whereas effects due to plant densities were significant for only these parameters; number of suckers (NOS), sucker height (SUHT), number of leaves on sucker (NLSC), bunch weight (BWT), number of hand (NHND), total number of finger (TFNG) and number of sucker at flowering (NSCK). Therefore, it can be concluded that plant spacing, and regular water supply plays a significant role in obtaining good yield in plantain production

Detection of foliar diseases using image processing techniques

ABSTRACT This paper presents the development of a methodology to detect the percentage of affected area of Phytophthora infestans disease in tomato plants, using digital image processing techniques to extract the regions of interest with color analysis, where the YIQ and TSL models for the detection of the disease. The method consists of solving one of the most common problems in images that is segmentation, in this case the background and the disease with the Plant Village database, which was captured under uncontrolled lighting conditions. In the experiments conducted, it is observed that our method achieved a performance of 98.60% for the detection of healthy pixels and 98.17% in detection of sick pixels. This process was subjected to comparison against other alternatives of the state of the art like K-means with HSV and LAB, showing a referred error regarding the leaf size of 4.32 ± 5.44% in the detection of the disease and a computational time of 0.03 ± 0.01 [s] in comparison with the other procedures, in addition, this methodology was implemented to detect the foliar diseases black Sigatoka and yellow Sigatoka in banana leaves obtaining satisfactory results.

The use of UAVs in monitoring yellow sigatoka in banana

Monitoring pests and diseases is an extremely important activity for increasing productivity in agriculture. In this scenario, remote sensing, coupled with techniques of machine learning, offer new prospects for monitoring and identifying characteristic specific patterns, such as manifestations of diseases, pests, and water and nutritional stress. The aim was to use high spatial resolution aerial images to monitor the extent of an attack of yellow sigatoka in a banana crop, following the basic assumptions of identification, classification, quantification and prediction of phenotypic factors. Monthly flights were carried out on a commercial banana plantation using an unmanned aerial vehicle, equipped with a 16-megapixel RGB camera (GSD of 0.016781 m pixel−1). Five classification algorithms were used to identify and quantify the disease while field evaluations were also made following traditional methodology. The results showed that, for September 2017, the Support Vector Machine algorithm achieved the best performance (99.28% overall accuracy and 97.13 Kappa Index), followed by the Artificial Neural Network and Minimum Distance algorithms. In quantifying the disease, the SVM algorithm was more effective than other algorithms compared to the conventional methodology used to estimate the extent of yellow sigatoka, demonstrating that the tools used for monitoring leaf spots can be handled by remote sensing, machine learning and high spatial-resolution RGB images.

Genetic Diversity and Azole Fungicide Sensitivity in Pseudocercospora musae Field Populations in Brazil.

Pseudocercospora musae, causal agent of Sigatoka leaf spot, or yellow Sigatoka disease, is considered a major pathogen of banana (Musa spp.). Widely disseminated in Brazil, this study explored the genetic diversity in field populations of the pathogen from production areas in the Distrito Federal and the States of Bahia, Minas Gerais, and Rio Grande do Norte. Resistance to demethylation inhibitor (DMI) fungicides was also examined. For 162 isolates from 10 banana growing regions, analysis of mating type idiomorph frequency was conducted, together with estimation of genetic diversity at 15 microsatellite loci. A total of 149 haplotypes were identified across the examined populations, with an average genetic diversity of 4.06. In general, populations displayed 1:1 proportions of idiomorphs MAT1-1 and MAT1-2, providing evidence for sexual recombination. Multilocus linkage disequilibrium also indicated asexual reproduction contributing to the genetic structure of certain populations. AMOVA revealed that 86.3% of the genetic differentiation of the pathogen occurred among isolates within populations. Discriminant Analysis of Principal Components (DAPC) identified six most probable genetic groups, with no population structure associated with geographic origin or collection site. Although genetic similarity was observed among certain populations from different states, data revealed increasing genetic differentiation with increasing geographic distance, as validated by Mantel’s test (r = 0.19, P < 0.001). On the basis of DMI fungicide sensitivity testing and CYP51 gene sequence polymorphism, isolates from the Distrito Federal separated into two main groups, one with generally higher EC50 values against eight DMI fungicides. A clear phenotype-to-genotype relationship was observed for isolates carrying the CYP51 alteration Y461N. Conventionally adopted fungicides for control of Sigatoka leaf spot are likely to be overcome by combined sexual and asexual reproduction mechanisms in P. musae driving genetic variability. Continued analysis of pathogen genetic diversity and monitoring of DMI sensitivity profiles of Brazilian field populations is essential for the development of integrated control strategies based on host resistance breeding and rational design of fungicide regimes.

Reaction of Banana (Musa spp. L.) genotypes to yellow Sigatoka (Pseudocercospora musae (Zimm.) on Brazil’s central plateau and an epidemiological study of the disease Progress curves comparing four non-flexible models

Banana (Musa spp.) is cultivated throughout Brazil, with genotypes varying from region to region. Yellow Sigatoka disease (Pseudocercospora musae) is one of the most common foliar diseases of banana, causing severe losses when it is not properly managed. The objectives of this study were to evaluate banana genotypes for their reaction to yellow Sigatoka infection, and to perform an epidemiological study of this disease to have a better understanding of how it progresses under the environmental conditions of the Brazilian Cerrado. The reaction of 21 genotypes of banana to the infection by P. musae was evaluated in an experiment at Embrapa Cerrados (Planaltina, DF, Brazil), between August 2009 and January 2011, using a diagrammatic scale adapted from Stover, which ranged from 1 ( 51% leaf area showing symptoms). Assessments occurred every 15 days. At the end of the evaluation period the mean area under the disease progress curve (AUDPC) for each rated material was calculated. Genotypes were selected by Embrapa Mandioca e Fruticultura Tropical and separated into four sets: Cavendish/Gros Michel, Pome-Pacovan, Pome-Prata-ana and Silk/Mysore. An epidemiological study of the disease progress curve for each genotype was carried out to evaluate which non-flexible model (Monomolecular, Logistic, Exponential or Gompertz) would have the best fit to the disease severity progress curve data. Each set revealed genotypes with significant resistance to Yellow-Sigatoka. In the Pome-Pacovan set, resistance was observed in genotypes ‘Vitoria’, ‘PV 94–01’, ‘Pacovan Ken’, ‘Japira ‘and ‘PV 79–34′; in the Silk/Mysore genotypes ‘Caipira’, ‘YB 42–03′, ‘YB 42–07′ and ‘BRS Tropical’; in the Pome-Prata-ana set, genotypes ‘PA 94–01′, ‘BRS Platina’ and ‘BRS Garantida’; and in the Cavendish/Gros Michel set the genotype ‘Bucaneiro’. The monomolecular model displayed the best fit, with consistently low mean square error (MSE) values for all banana genotypes presented in this study.

Geostatistical stationary space-time covariance functions modeling of Yellow Sigatoka progress in banana crop

Banana production is affected by Yellow Sigatoka, one of the causes of leaf lesions, which causes the reduction of the photosynthetic area of the plant and, consequently, the quality of the fruit and the production. The objective of this study was to analyze using geostatistics and comparing separable and non-separable spatio-temporal covariance models with different adjustment methods. The experiment was carried out in a banana plantation of the Prata-Ana variety, which presented high severity of the disease, without any control measures, which allowed the study of behavior under natural conditions. The Separable Doubly Exponential and the non-separable model of Gneiting were tested with the Weight Least Squares (WLS), Restricted Maximum Likelihood (REML) and Likelihood Pairwise estimation methods. The Gneiting model, WLS curve-fitting methods for estimatives space-time covariance structure, allowed to reduce the uncertainties of the spatial and temporal prediction of the disease, as well as to characterize the spatio-temporal pattern of the disease.

Field evaluation of six Gros Michel banana accessions (Musa spp., AAA group) for agronomic performance, resistance to Fusarium wilt race 1 and yellow Sigatoka

Three Gros Michel mutants (‘IBP 5-B’, ‘IBP 5–61’ and ‘IBP 12’) from the Cuban Instituto de Biotecnología de las Plantas, two semi-dwarf Gros Michel varieties (‘Highgate’ and ‘Cocos’) and a Thai accession (‘Hom Thong Mokho’) were evaluated in Australia over a five year period. They were screened for their resistance to Fusarium wilt Race 1 (FocR1) caused by the pathogen Fusarium oxysporum f.sp. cubense, as well as resistance to yellow Sigatoka (Pseudocercospora musae Zimm [teleomorph Mycosphaerella musicola Leach]). They were also grown for a plant and ratoon crop in the tropics (17°S) and a plant crop in the subtropics (28°S) with no disease pressure to record their agronomic characteristics. They were compared with Australian industry standards, ‘Williams’ (AAA, Cavendish subgroup) and ‘Lady Finger’ (AAB, Pome subgroup). In the subtropics the Gros Michel mutants and semi-dwarf accessions were sensitive to cold and very susceptible to FocR1 and yellow Sigatoka while their agronomic performance in the tropics was good, with ‘Highgate’ having the best bunch weight on a shorter, more manageable plant. Of the six accessions evaluated, ‘Hom Thong Mokho’ showed the highest level of resistance to FocR1 although it had poor cold tolerance, as did the other Gros Michel selections, and consequently had low productivity compared to ‘Williams’ and even ‘Lady Finger’. However in the warmer, more humid tropics ‘Hom Thong Mokho's performance was much better and it was less susceptible to yellow Sigatoka than the other Gros Michel selections. Subsequent genetic analysis by Christelová et al. (2011) has revealed that although ‘Hom Thong Mokho’ is marketed as a Gros Michel variety in Asia, it is closer to the Rio subgroup (AAA) of dessert bananas.

Thap Maeo bananas: Fast ripening and full ethylene perception at low doses

Brazil is a major producer and consumer of various banana types. Thap Maeo is a promising cultivar for the market due to its resistance to Black and Yellow Sigatoka disease. However, a lack of information and postharvest technologies concerning Thap Maeo physiology seems to be a significant problem limiting its expansion in the market. Thus, this study aims to establish Thap Maeo fruit's physical, biochemical, and physiological aspects, defining the best ethylene dosage for treatment considering fruit ripening parameters. Bananas were harvested and monitored during both natural and ethylene-induced ripening processes. Assessments of pulp firmness, peel color and endogenous ethylene production showed different profiles between ethylene-treated and non-treated fruits, whereas the ethylene responses concerning the carbohydrates and hormones profiles, as well as the ethylene receptors expression, were observed in all ethylene-treated fruits, even applying low concentrations of the hormone. It thus indicated the high ethylene-sensitivity of Thap Maeo cultivar. Such postharvest behaviors reverberate in lower ethylene requirements for treatment, which was established at 10μLL−1. Ethylene-inducible changes in fruit volatile compounds throughout ripening are also discussed.

Severity of yellow Sigatoka in banana cultivated in silicon nutrient solution

Silicon (Si) is known to reduce the severity of pathogens on many plants. This study aimed to determine the effect of silicon (Si) in reducing the severity of yellow Sigatoka (Mycosphaerella musicola) on banana plants cultivated in nutrient solution. The treatments included five concentrations of silicic acid (H4SiO4): 0, 0.5, 1.0, 1.8 and 3.6 mmol L−1. The area under the disease severity progress curve (AUDSPC) was calculated for each treatment, and the nutrient content of the leaves determined. AUDSPC for plants receiving 3.05 mmol L−1 H4SiO4 was 49.27% lower compared to plants receiving no supplemental H4SiO4. In contrast, plants grown in a solution of 3.6 mmol L−1 H4SiO4 had 23.53% more Si content in the leaves compared to plants cultivated without supplemental H4SiO4. These results suggest Si could be used as a component in the integrated management of yellow Sigatoka.