scholarly journals Banana Moko disease management with resistance inducers and chlorine dioxide

2015 ◽  
Vol 33 (2) ◽  
pp. 194-202
Author(s):  
Joaquín Guillermo Ramírez G. ◽  
Melissa Muñoz A. ◽  
Luis Fernando Patiño H. ◽  
Juan Gonzalo Morales O.
Keyword(s):  
Methyl Jasmonate ◽  
Chlorine Dioxide ◽  
Plant Disease ◽  
Sodium Salicylate ◽  
Bacterial Disease ◽  
Disease Development ◽  
Musa Sp ◽  
Progress Curve ◽  
Potassium Phosphite ◽  
Resistance Inducers

The plant disease Moko, caused by Ralstonia solanacearum, is the most important bacterial disease in banana and plantain crops worldwide. In the present study, chlorine dioxide and seven resistance inducers in banana plants (Musa sp.) infected with this bacterium were evaluated under greenhouse conditions. For the evaluation of chlorine dioxide, three doses were used (10, 30 and 50 mg L-1). The evaluation of the resistance inducers included the following: sodium salicylate 0.4 g L-1; hydrogen peroxide 1 mM; potassium phosphite 1.5 mL L-1; 3-aminobutanoic acid 1.0 g L-1; methyl jasmonate 0.2 g L-1; acibenzolar-s-methyl 0.3 mL L-1 and chitosan 3.0 mg mL-1. The results showed a significant reduction of 74% in the area under the disease progress curve (AUDPC) value, which was calculated for the disease development when the injected chlorine dioxide dose was 50 mg L-1. The AUDPC value for the resistance inducers was reduced by 45.4% for chitosan, 75.5% for methyl jasmonate and 65.5% for 3-aminobutanoic acid. Therefore, the results indicated that these molecules have the potential to be used for control of the Moko disease.

scholarly journals Estimation of a Time-varying Apparent Infection Rate from Plant Disease Progress Curves: A Particle Filter Approach

2019 ◽  
Author(s):  
Kaique dos S Alves ◽  
Willian B Moraes ◽  
Wellington B da Silva ◽  
Emerson M Del Ponte
Keyword(s):  
Particle Filter ◽  
Infection Rate ◽  
Plant Disease ◽  
Host Susceptibility ◽  
Time Varying ◽  
Factors Affecting ◽  
Initial Inoculum ◽  
Disease Progress ◽  
Progress Curve ◽  
Apparent Infection Rate

AbstractThe parameters of the simplest (two-parameter) epidemiological models that best fit plant disease progress curve (DPC) data are the surrogate for initial inoculum (y0) and the (constant) apparent infection rate (r), both being useful for understanding, predicting and comparing epidemics. The assumption thatris constant is not reasonable and fluctuations are expected due to systematic changes in factors affecting infection (e.g. weather favorability, host susceptibility, etc.), thus leading to a time-varyingr, orr(t). An arrangement of these models (e.g. logistic, monomolecular, etc.) can be used to obtainrbetween two time points, given the disease (y) data are available. We evaluated a data assimilation technique, Particle Filter (PF), as an alternative method for estimatingr(t). Synthetic DPC data for a hypothetical polycyclic epidemics were simulated using the logistic differential equation for scenarios that combined five patterns ofr(t) (constant, increasing, decreasing, random or sinusoidal); five increasing time assessment interval (Δt= 1, 3, 5, 7 or 9 time units - t.u.); and two levels of noise (α = 0.1 or 0.25) assigned toy(t). The analyses of 50 simulated 60-t.u. DPCs showed that the errors of PF-derivedwere lower (RMSE < 0.05) for Δt< 5 t.u. and least affected by the presence of noise in the measure compared with the logit-derivedr(t). The ability to more accurately estimater(t) using the novel method may be useful to increase knowledge of field epidemics and identify within-season drivers that may explainr(t) behaviour.

scholarly journals Draft Genome of the Macadamia Husk Spot Pathogen, Pseudocercospora macadamiae

2020 ◽  
Vol 110 (9) ◽  
pp. 1503-1506
Author(s):  
Olufemi A. Akinsanmi ◽  
Lilia C. Carvalhais
Keyword(s):  
Plant Disease Resistance ◽  
Plant Disease ◽  
De Novo ◽  
Draft Genome ◽  
Gc Content ◽  
Disease Development ◽  
Closely Related Species ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
The Family

Pseudocercospora macadamiae causes husk spot in macadamia in Australia. Lack of genomic resources for this pathogen has restricted acquiring knowledge on the mechanism of disease development, spread, and its role in fruit abscission. To address this gap, we sequenced the genome of P. macadamiae. The sequence was de novo assembled into a draft genome of 40 Mb, which is comparable to closely related species in the family Mycosphaerellaceae. The draft genome comprises 212 scaffolds, of which 99 scaffolds are over 50 kb. The genome has a 49% GC content and is predicted to contain 15,430 protein-coding genes. This draft genome sequence is the first for P. macadamiae and represents a valuable resource for understanding genome evolution and plant disease resistance.

scholarly journals Effects of Temperature, Relative Humidity, and Plant Age on Bacterial Disease of Onion Plants

2019 ◽  
Vol 20 (4) ◽  
pp. 200-206
Author(s):  
Kim E. Tho ◽  
Elizabeth Brisco-McCann ◽  
Prissana Wiriyajitsomboon ◽  
Mary K. Hausbeck
Keyword(s):  
Relative Humidity ◽  
Management Strategies ◽  
Bacterial Pathogen ◽  
Plant Age ◽  
Effect Of Temperature ◽  
Bacterial Disease ◽  
Disease Symptoms ◽  
Foliar Disease ◽  
Progress Curve ◽  
Effects Of Temperature

Foliar disease of onion in Michigan, caused by Pantoea agglomerans, Pantoea ananatis, or Enterobacter cowanii, has recently become a concern to producers. The objective of this study was to determine the effect of temperature, relative humidity (RH), and plant age in growth chamber and greenhouse experiments on onion plants inoculated with each pathogen. A significant level of disease resulted from each pathogen at 25 to 30°C, with strong positive associations detected using regression analysis between the area under the disease progress curve (AUDPC) and temperature. RH also significantly influenced symptom development. Foliar disease symptoms developed sooner and were more severe when RH was high (80 to 100%) but was limited at RH < 60%. Significant positive associations between RH and AUDPC, as described by linear regression, were also detected. When 6- to 14-week-old plants were inoculated with each bacterial pathogen, susceptibility increased significantly with age. These results provide insight into the epidemiology of P. agglomerans, P. ananatis, and E. cowanii bacterial pathogens of onions in Michigan and can assist in the development and timing of management strategies.

scholarly journals Comparative Analysis of Flexible Two-Parameter Models of Plant Disease Epidemics

2007 ◽  
Vol 97 (10) ◽  
pp. 1231-1244 ◽  
Author(s):  
B. Hau ◽  
E. Kosman
Keyword(s):  
Differential Equations ◽  
Goodness Of Fit ◽  
Inflection Point ◽  
Plant Disease ◽  
Shape Parameter ◽  
Relative Rate ◽  
Data Sets ◽  
Temporal Position ◽  
Progress Curve ◽  
Disease Epidemics

Eleven previously published models of plant disease epidemics, given as differential equations with a rate and a shape parameter, are compared using general model characteristics as well as their usefulness in fitting observed data. Six out of the eleven models can be solved analytically resulting in epidemic growth functions, while the others can be solved only numerically. When all 11 differential equations were fitted to two data sets, all models showed a similar goodness of fit, although the shape parameter in some models could not be estimated very precisely. With respect to useful characteristics (exponential population growth at the beginning, ability to generate monomolecular disease progression, and flexibility of the inflection point), the models of Fleming, Kosman-Levy, Birch, Richards and Waggoner, and Rich are recommended. Formulas were established to calculate the point of inflection as well as the weighted absolute and relative rate, respectively, depending on the shape and rate parameter. These formulas allow transformation of the parameter values of one model into those of another model in many cases. If the two models are required to have the same temporal position of the disease progress curve, then the initial disease level at the start of the epidemic or the time when the inflection point is reached have to be transformed.

scholarly journals Using Pseudomonas spp. for Integrated Biological Control

2007 ◽  
Vol 97 (2) ◽  
pp. 244-249 ◽  
Author(s):  
Virginia O. Stockwell ◽  
James P. Stack
Keyword(s):  
Biological Control ◽  
Pseudomonas Syringae ◽  
Environmental Protection Agency ◽  
Plant Disease ◽  
Fire Blight ◽  
Disease Suppression ◽  
Model Organisms ◽  
Bacterial Disease ◽  
Multiple Production ◽  
Pseudomonas Spp

Pseudomonas spp. have been studied for decades as model organisms for biological control of plant disease. Currently, there are three commercial formulations of pseudomonads registered with the U.S. Environmental Protection Agency for plant disease suppression, Bio-Save 10 LP, Bio-Save 11 LP, and BlightBan A506. Bio-Save 10 LP and Bio-Save 11 LP, products of Jet Harvest Solutions, Longwood, FL, contain Pseudomonas syringae strains ESC-10 and ESC-11, respectively. These products are applied in packinghouses to prevent postharvest fungal diseases during storage of citrus, pome, stone fruits, and potatoes. BlightBan A506, produced by NuFarm Americas, Burr Ridge, IL, contains P. fluorescens strain A506. BlightBan A506 is applied primarily to pear and apple trees during bloom to suppress the bacterial disease fire blight. Combining BlightBan A506 with the antibiotic streptomycin improves control of fire blight, even in areas with streptomycin-resistant populations of the pathogen. BlightBan A506 also may reduce fruit russet and mild frost injury. These biocontrol products consisting of Pseudomonas spp. provide moderate to excellent efficacy against multiple production constraints, are relatively easy to apply, and they can be integrated with conventional products for disease control. These characteristics will contribute to the adoption of these products by growers and packinghouses.

Using Electrophysiology to Study Plant Disease Development

BioScience ◽  
1976 ◽  
Vol 26 (8) ◽  
pp. 499-504 ◽  
Author(s):  
Anton Novacky ◽  
Arthur L. Karr ◽  
Jerome W. van Sambeek
Keyword(s):  
Plant Disease ◽  
Disease Development
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