The Experts below are selected from a list of 30 Experts worldwide ranked by ideXlab platform
Thomas Miedaner - One of the best experts on this subject based on the ideXlab platform.
-
Segregation for aggressiveness and deoxynivalenol production of a population of Gibberella zeae causing head blight of wheat
European Journal of Plant Pathology, 2004Co-Authors: Christian Joseph R. Cumagun, Thomas MiedanerAbstract:Gibberella zeae is a pathogen of wheat and other small-grain cereals, causing yield losses and reducing grain quality by producing the trichothecene deoxynivalenol (DON) which is harmful to animals and humans. One hundred and fifty three progeny from a cross between two European DON-producing isolates of G. zeae were analyzed for aggressiveness and DON production in three environments (location–year combinations) in Germany. Aggressiveness, measured as head blight rating and Relative Plot yield, and DON production showed continuous distribution for each environment and across environments. There was significant ( P =0.01) genotypic variation for all three traits. Transgressive segregants occurred for all three traits. Both repeatability estimates within an environment and heritability estimates on an entry-mean basis for head blight rating and DON production were medium to high (0.5–0.7). Progeny–environment interaction accounted for about 29% of the total variance for the two aggressiveness traits and 19% for DON production. The large genetic variation derived from a cross between two rather similar European parents indicates a potential for increasing fungal aggressiveness in the G. zeae population.
-
Genetic variation and covariation for aggressiveness, deoxynivalenol production and fungal colonization among progeny of Gibberella zeae in wheat
Plant Pathology, 2004Co-Authors: Christian Joseph R. Cumagun, F. Rabenstein, Thomas MiedanerAbstract:Gibberella zeae (anamorph Fusarium graminearum) causes head blight of cereals and contaminates grains with mycotoxins such as deoxynivalenol (DON). To determine the correlations among aggressiveness traits, fungal colonization and DON production, 50 progeny from a segregating population of G. zeae were inoculated onto a susceptible winter wheat cultivar in three field environments (year–location combinations). Aggressiveness traits were measured as head-blight rating and Plot yield Relative to noninoculated Plots. Fungal colonization, measured as Fusarium exoantigen (ExAg) content, and DON production were analysed with two ELISA formats. Disease severity was moderate to high based on head-blight rating and Relative Plot yield. Fusarium ExAg content and DON production ranged from 0·26–1·41 units and from 4·18–43·70 mg kg−1, respectively. Significant (P = 0·01) genotypic variation was found for all traits. Heritability for Fusarium ExAg content was rather low because of high progeny–environment interaction and error. DON/Fusarium ExAg ratio did not vary significantly (P > 0·1) among progeny. Correlation between DON production and Fusarium ExAg content across environments was high (r = 0·8, P = 0·01), but no covariation existed between aggressiveness traits and DON/Fusarium ExAg content ratio.
-
Effects of genotype and genotype—environment interaction on deoxynivalenol accumulation and resistance to Fusarium head blight in rye, triticale, and wheat
Plant Breeding, 2001Co-Authors: Thomas Miedaner, C. Reinbrecht, U. Lauber, Margit Schollenberger, Hartwig H. GeigerAbstract:Fusarium culmorum is one of the most important Fusarium species causing head blight infections in wheat, rye, and triticale. It is known as a potent mycotoxin producer with deoxynivalenol (DON), 3-acetyl deoxynivalenol (3-ADON), and nivalenol (NIV) being the most prevalent toxins. In this study, the effect of winter cereal species, host genotype, and environment on DON accumulation and Fusarium head blight (FHB) was analysed by inoculating 12 rye, eight wheat, and six triticale genotypes of different resistance levels with a DON-producing isolate at three locations in 2 years (six environments). Seven resistance traits were assessed, including head blight rating and Relative Plot yield. In addition, ergosterol, DON and 3-ADON contents in the grain were determined. A growth-chamber experiment with an artificially synchronized flowering date was also conducted with a subset of two rye, wheat and triticale genotypes. Although rye genotypes were, on average, affected by Fusarium infections much the same as wheat genotypes, wheat accumulated twice as much DON as rye. Triticale was least affected and the grain contained slightly more DON than rye. In the growth-chamber experiment, wheat and rye again showed similar head blight ratings, but rye had a somewhat lower Relative head weight and a DON content nine times lower than wheat (3.9 vs. 35.3 mg/kg). Triticale was least susceptible with a five times lower DON content than wheat. Significant (P = 0.01) genotypic variation for DON accumulation existed in wheat and rye. The differences between and within cereal species in the field experiments were highly influenced by environment for resistance traits and mycotoxin contents. Nevertheless, mean mycotoxin content of the grain could not be associated with general weather conditions in the individual environments. Strong genotype-environment interactions were found for all cereal species. This was mainly due to three wheat varieties and one rye genotype being environmentally extremely unstable. The more resistant entries, however, showed a higher environmental stability of FHB resistance and tolerance to DON accumulation. Correlations between resistance traits and DON content were high in wheat (P = 0.01), with the most resistant varieties also accumulating less DON, but with variability in rye. In conclusion, the medium to large genotypic variation in wheat and rye offers good possibilities for reducing DON content in the grains by resistance selection. Large confounding effects caused by the environment will require multiple locations and/or years to evaluate FHB resistance and mycotoxin accumulation.
Xiaobing Li - One of the best experts on this subject based on the ideXlab platform.
-
Analysis of urban heat island using a remote sensing model
Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium 2005. IGARSS '05., 2005Co-Authors: Yunhao Chen, Xiaobing LiAbstract:The difference in temperature between an urban area and its surrounding rural is known as the urban heat island (UHI) phenomenon. Study of the patterns structure and change process of urban thermal environment is very important, while construction of the model for analysis and simulation of urban thermal environment is the kernel for research of quantitative thermal environment study. Supported by GIS and RS, we have developed a GABP-based urban heat island model that lays a foundation for systematic analysis of urban thermal environment in Shanghai City. Describe the correlations between all thermal environment factors by introducing the concept of influence degree and correlation degree, determine the influence weight of the factors to the thermal environment, and identify the energy consumption density and Relative Plot ratio being the predominant factors to influence urban thermal environment.
-
IGARSS - Analysis of urban heat island using a remote sensing model
Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium 2005. IGARSS '05., 2005Co-Authors: Yunhao Chen, Xiaobing LiAbstract:The difference in temperature between an urban area and its surrounding rural is known as the urban heat island (UHI) phenomenon. Study of the patterns structure and change process of urban thermal environment is very important, while construction of the model for analysis and simulation of urban thermal environment is the kernel for research of quantitative thermal environment study. Supported by GIS and RS, we have developed a GABP-based urban heat island model that lays a foundation for systematic analysis of urban thermal environment in Shanghai City. Describe the correlations between all thermal environment factors by introducing the concept of influence degree and correlation degree, determine the influence weight of the factors to the thermal environment, and identify the energy consumption density and Relative Plot ratio being the predominant factors to influence urban thermal environment.
Yunhao Chen - One of the best experts on this subject based on the ideXlab platform.
-
Analysis of urban heat island using a remote sensing model
Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium 2005. IGARSS '05., 2005Co-Authors: Yunhao Chen, Xiaobing LiAbstract:The difference in temperature between an urban area and its surrounding rural is known as the urban heat island (UHI) phenomenon. Study of the patterns structure and change process of urban thermal environment is very important, while construction of the model for analysis and simulation of urban thermal environment is the kernel for research of quantitative thermal environment study. Supported by GIS and RS, we have developed a GABP-based urban heat island model that lays a foundation for systematic analysis of urban thermal environment in Shanghai City. Describe the correlations between all thermal environment factors by introducing the concept of influence degree and correlation degree, determine the influence weight of the factors to the thermal environment, and identify the energy consumption density and Relative Plot ratio being the predominant factors to influence urban thermal environment.
-
IGARSS - Analysis of urban heat island using a remote sensing model
Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium 2005. IGARSS '05., 2005Co-Authors: Yunhao Chen, Xiaobing LiAbstract:The difference in temperature between an urban area and its surrounding rural is known as the urban heat island (UHI) phenomenon. Study of the patterns structure and change process of urban thermal environment is very important, while construction of the model for analysis and simulation of urban thermal environment is the kernel for research of quantitative thermal environment study. Supported by GIS and RS, we have developed a GABP-based urban heat island model that lays a foundation for systematic analysis of urban thermal environment in Shanghai City. Describe the correlations between all thermal environment factors by introducing the concept of influence degree and correlation degree, determine the influence weight of the factors to the thermal environment, and identify the energy consumption density and Relative Plot ratio being the predominant factors to influence urban thermal environment.
Christian Joseph R. Cumagun - One of the best experts on this subject based on the ideXlab platform.
-
Segregation for aggressiveness and deoxynivalenol production of a population of Gibberella zeae causing head blight of wheat
European Journal of Plant Pathology, 2004Co-Authors: Christian Joseph R. Cumagun, Thomas MiedanerAbstract:Gibberella zeae is a pathogen of wheat and other small-grain cereals, causing yield losses and reducing grain quality by producing the trichothecene deoxynivalenol (DON) which is harmful to animals and humans. One hundred and fifty three progeny from a cross between two European DON-producing isolates of G. zeae were analyzed for aggressiveness and DON production in three environments (location–year combinations) in Germany. Aggressiveness, measured as head blight rating and Relative Plot yield, and DON production showed continuous distribution for each environment and across environments. There was significant ( P =0.01) genotypic variation for all three traits. Transgressive segregants occurred for all three traits. Both repeatability estimates within an environment and heritability estimates on an entry-mean basis for head blight rating and DON production were medium to high (0.5–0.7). Progeny–environment interaction accounted for about 29% of the total variance for the two aggressiveness traits and 19% for DON production. The large genetic variation derived from a cross between two rather similar European parents indicates a potential for increasing fungal aggressiveness in the G. zeae population.
-
Genetic variation and covariation for aggressiveness, deoxynivalenol production and fungal colonization among progeny of Gibberella zeae in wheat
Plant Pathology, 2004Co-Authors: Christian Joseph R. Cumagun, F. Rabenstein, Thomas MiedanerAbstract:Gibberella zeae (anamorph Fusarium graminearum) causes head blight of cereals and contaminates grains with mycotoxins such as deoxynivalenol (DON). To determine the correlations among aggressiveness traits, fungal colonization and DON production, 50 progeny from a segregating population of G. zeae were inoculated onto a susceptible winter wheat cultivar in three field environments (year–location combinations). Aggressiveness traits were measured as head-blight rating and Plot yield Relative to noninoculated Plots. Fungal colonization, measured as Fusarium exoantigen (ExAg) content, and DON production were analysed with two ELISA formats. Disease severity was moderate to high based on head-blight rating and Relative Plot yield. Fusarium ExAg content and DON production ranged from 0·26–1·41 units and from 4·18–43·70 mg kg−1, respectively. Significant (P = 0·01) genotypic variation was found for all traits. Heritability for Fusarium ExAg content was rather low because of high progeny–environment interaction and error. DON/Fusarium ExAg ratio did not vary significantly (P > 0·1) among progeny. Correlation between DON production and Fusarium ExAg content across environments was high (r = 0·8, P = 0·01), but no covariation existed between aggressiveness traits and DON/Fusarium ExAg content ratio.
F. J. Wylie - One of the best experts on this subject based on the ideXlab platform.
-
II—The Relative Plot
Journal of Navigation, 2020Co-Authors: F. J. WylieAbstract:The mariner is, on the whole, a cautious creature. When he takes action, he likes to know within fairly narrow limits how much is necessary and where that much will take him in relation to all potentially dangerous objects. He has as much respect for the fire as for the frying pan.