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Impedance of the grape berry cuticle as a novel phenotypic trait to estimate resistance to Botrytis cinerea.

Herzog K, Wind R, Töpfer R - Sensors (Basel) (2015)

Bottom Line: Warm and moist weather conditions during berry ripening provoke Botrytis cinerea (B. cinerea) causing notable bunch rot on susceptible grapevines with the effect of reduced yield and wine quality.Statistical experiments revealed highly significant correlations between relative impedance of CW and the resistance of grapevines to B. cinerea.Applying the I-sensor, a preliminary QTL region was identified indicating that the novel phenotypic trait is as well a valuable tool for genetic analyses.

View Article: PubMed Central - PubMed

Affiliation: Julius Kühn-Institut-Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen 76833, Germany. Katja.herzog@jki.bund.de.

ABSTRACT
Warm and moist weather conditions during berry ripening provoke Botrytis cinerea (B. cinerea) causing notable bunch rot on susceptible grapevines with the effect of reduced yield and wine quality. Resistance donors of genetic loci to increase B. cinerea resistance are widely unknown. Promising traits of resistance are represented by physical features like the thickness and permeability of the grape berry cuticle. Sensor-based phenotyping methods or genetic markers are rare for such traits. In the present study, the simple-to-handle I-sensor was developed. The sensor enables the fast and reliable measurement of electrical impedance of the grape berry cuticles and its epicuticular waxes (CW). Statistical experiments revealed highly significant correlations between relative impedance of CW and the resistance of grapevines to B. cinerea. Thus, the relative impedance Zrel of CW was identified as the most important phenotypic factor with regard to the prediction of grapevine resistance to B. cinerea. An ordinal logistic regression analysis revealed a R2McFadden of 0.37 and confirmed the application of Zrel of CW for the prediction of bunch infection and in this way as novel phenotyping trait. Applying the I-sensor, a preliminary QTL region was identified indicating that the novel phenotypic trait is as well a valuable tool for genetic analyses.

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Exemplary image-based determination of B. cinerea infection on grapevine cultivars to improve reference evaluations of bunch rot in the field. The background of the field image was manually removed. Image segmentation into two phenotypic classes “healthy” (green) and “disease” (red) was performed by using Matlab®. The percentage amount of B. cinerea infection is quoted in the classified image.
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sensors-15-12498-f008: Exemplary image-based determination of B. cinerea infection on grapevine cultivars to improve reference evaluations of bunch rot in the field. The background of the field image was manually removed. Image segmentation into two phenotypic classes “healthy” (green) and “disease” (red) was performed by using Matlab®. The percentage amount of B. cinerea infection is quoted in the classified image.

Mentions: For the generation of an improved regression model the set of investigated plants and further parameters should be included in the model, which may influence the susceptibility of grapevines to B. cinerea, e.g., the time of ripening or weather data (rainfall and temperature). In order to increase the objectivity of the model in the future, the B. cinerea infection of grapevine bunches could be determined with much more accuracy, e.g., by the application of objective, image-based phenotyping methods (an example is shown in Figure 8).


Impedance of the grape berry cuticle as a novel phenotypic trait to estimate resistance to Botrytis cinerea.

Herzog K, Wind R, Töpfer R - Sensors (Basel) (2015)

Exemplary image-based determination of B. cinerea infection on grapevine cultivars to improve reference evaluations of bunch rot in the field. The background of the field image was manually removed. Image segmentation into two phenotypic classes “healthy” (green) and “disease” (red) was performed by using Matlab®. The percentage amount of B. cinerea infection is quoted in the classified image.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507599&req=5

sensors-15-12498-f008: Exemplary image-based determination of B. cinerea infection on grapevine cultivars to improve reference evaluations of bunch rot in the field. The background of the field image was manually removed. Image segmentation into two phenotypic classes “healthy” (green) and “disease” (red) was performed by using Matlab®. The percentage amount of B. cinerea infection is quoted in the classified image.
Mentions: For the generation of an improved regression model the set of investigated plants and further parameters should be included in the model, which may influence the susceptibility of grapevines to B. cinerea, e.g., the time of ripening or weather data (rainfall and temperature). In order to increase the objectivity of the model in the future, the B. cinerea infection of grapevine bunches could be determined with much more accuracy, e.g., by the application of objective, image-based phenotyping methods (an example is shown in Figure 8).

Bottom Line: Warm and moist weather conditions during berry ripening provoke Botrytis cinerea (B. cinerea) causing notable bunch rot on susceptible grapevines with the effect of reduced yield and wine quality.Statistical experiments revealed highly significant correlations between relative impedance of CW and the resistance of grapevines to B. cinerea.Applying the I-sensor, a preliminary QTL region was identified indicating that the novel phenotypic trait is as well a valuable tool for genetic analyses.

View Article: PubMed Central - PubMed

Affiliation: Julius Kühn-Institut-Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen 76833, Germany. Katja.herzog@jki.bund.de.

ABSTRACT
Warm and moist weather conditions during berry ripening provoke Botrytis cinerea (B. cinerea) causing notable bunch rot on susceptible grapevines with the effect of reduced yield and wine quality. Resistance donors of genetic loci to increase B. cinerea resistance are widely unknown. Promising traits of resistance are represented by physical features like the thickness and permeability of the grape berry cuticle. Sensor-based phenotyping methods or genetic markers are rare for such traits. In the present study, the simple-to-handle I-sensor was developed. The sensor enables the fast and reliable measurement of electrical impedance of the grape berry cuticles and its epicuticular waxes (CW). Statistical experiments revealed highly significant correlations between relative impedance of CW and the resistance of grapevines to B. cinerea. Thus, the relative impedance Zrel of CW was identified as the most important phenotypic factor with regard to the prediction of grapevine resistance to B. cinerea. An ordinal logistic regression analysis revealed a R2McFadden of 0.37 and confirmed the application of Zrel of CW for the prediction of bunch infection and in this way as novel phenotyping trait. Applying the I-sensor, a preliminary QTL region was identified indicating that the novel phenotypic trait is as well a valuable tool for genetic analyses.

Show MeSH
Related in: MedlinePlus