Analysis of the relationship between parameters of resistance to Fusarium head blight and in vitro tolerance to deoxynivalenol of the winter wheat cultivar WEK0609 �

2005 ◽  
Vol 111 (1) ◽  
pp. 57-66 ◽  
Author(s):  
N. Gosman ◽  
E. Chandler ◽  
M. Thomsett ◽  
R. Draeger ◽  
P. Nicholson
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Wheat Cultivar ◽  
Head Blight ◽  
Winter Wheat Cultivar ◽  
The Relationship

Quantitative Trait Loci for Fusarium Head Blight Resistance in U.S. Hard Winter Wheat Cultivar Heyne

Crop Science ◽  
2012 ◽  
Vol 52 (3) ◽  
pp. 1187-1194 ◽  
Author(s):  
Xianghui Zhang ◽  
Guihua Bai ◽  
Willium Bockus ◽  
Xiaojia Ji ◽  
Hongyu Pan
Keyword(s):  
Quantitative Trait Loci ◽  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Quantitative Trait ◽  
Wheat Cultivar ◽  
Fusarium Head Blight Resistance ◽  
Blight Resistance ◽  
Head Blight ◽  
Winter Wheat Cultivar ◽  
Trait Loci

Mapping of Fusarium Head Blight Resistance Quantitative Trait Loci in Winter Wheat Cultivar NC-Neuse

Crop Science ◽  
2016 ◽  
Vol 56 (4) ◽  
pp. 1473-1483 ◽  
Author(s):  
Stine Petersen ◽  
Jeanette H. Lyerly ◽  
Peter V. Maloney ◽  
Gina Brown-Guedira ◽  
Christina Cowger ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Quantitative Trait ◽  
Wheat Cultivar ◽  
Fusarium Head Blight Resistance ◽  
Blight Resistance ◽  
Head Blight ◽  
Winter Wheat Cultivar ◽  
Trait Loci

Identification and validation of Fusarium head blight resistance QTL in the U.S. soft red winter wheat cultivar ‘Jamestown’

Crop Science ◽  
2020 ◽  
Vol 60 (6) ◽  
pp. 2919-2930 ◽  
Author(s):  
Neal R. Carpenter ◽  
Emily Wright ◽  
Subas Malla ◽  
Lovepreet Singh ◽  
David Van Sanford ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Wheat Cultivar ◽  
Fusarium Head Blight Resistance ◽  
Blight Resistance ◽  
Head Blight ◽  
Winter Wheat Cultivar ◽  
Soft Red Winter Wheat ◽  
Red Winter Wheat ◽  
The U.S

Quantification of the relationship between the environment and Fusarium head blight, Fusarium pathogen density, and mycotoxins in winter wheat in Europe

2012 ◽  
Vol 133 (4) ◽  
pp. 975-993 ◽  
Author(s):  
Alissa B. Kriss ◽  
Pierce A. Paul ◽  
Xiangming Xu ◽  
Paul Nicholson ◽  
Fiona M. Doohan ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Head Blight ◽  
The Relationship

The relationship between competitive ability and yield stability in an old and a modern winter wheat cultivar

2011 ◽  
Vol 347 (1-2) ◽  
pp. 7-23 ◽  
Author(s):  
Yan Fang ◽  
Lin Liu ◽  
Bing-Cheng Xu ◽  
Feng-Min Li
Keyword(s):  
Winter Wheat ◽  
Competitive Ability ◽  
Wheat Cultivar ◽  
Yield Stability ◽  
Winter Wheat Cultivar ◽  
The Relationship

scholarly journals Meta-Analysis of Regression Coefficients for the Relationship Between Fusarium Head Blight and Deoxynivalenol Content of Wheat

2006 ◽  
Vol 96 (9) ◽  
pp. 951-961 ◽  
Author(s):  
P. A. Paul ◽  
P. E. Lipps ◽  
L. V. Madden
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Spring Wheat ◽  
Meta Analysis ◽  
Regression Coefficients ◽  
Wheat Cultivars ◽  
Head Blight ◽  
Regression Slope ◽  
Winter Wheat Cultivars ◽  
The Relationship

A total of 126 field studies reporting deoxynivalenol (DON; ppm) content of harvested wheat grain and Fusarium head blight index (IND; field or plot-level disease severity) were analyzed to determine the overall mean regression slope and intercept for the relationship between DON and IND, and the influence of study-specific variables on the slope and intercept. A separate linear regression analysis was performed to determine the slope and intercept for each study followed by a meta-analysis of the regression coefficients from all studies. Between-study variances were significantly (P < 0.05) greater than 0, indicating substantial variation in the relationship between the variables. Regression slopes and intercepts were between -0.27 and 1.48 ppm per unit IND and -10.55 to 32.75 ppm, respectively. The overall mean regression slope and intercept, 0.22 ppm per unit IND and 2.94 ppm, respectively, were significantly different from zero (P < 0.001), and the width of the 95% confidence interval was 0.07 ppm per unit IND for slope and 1.44 ppm for intercept. Both slope and intercept were significantly affected by wheat type (P < 0.05); the overall mean intercept was significantly higher in studies conducted using winter wheat cultivars than in studies conducted using spring wheat cultivars, whereas the overall mean slope was significantly higher in studies conducted using spring wheat cultivars than in winter wheat cultivars. Study location had a significant effect on the intercept (P < 0.05), with studies from U.S. winter wheat-growing region having the highest overall mean intercept followed by studies from Canadian wheat-growing regions and U.S. spring wheat-growing regions. The study-wide magnitude of DON and IND had significant effects on one or both of the regression coefficients, resulting in considerable reduction in between-study variances. This indicates that, at least indirectly, environment affected the relationship between DON and IND.

scholarly journals The relationship between Fusarium head blight traits, thousand-kernel weight, and yield in winter wheat

2022 ◽  
Vol 79 (3) ◽  
Author(s):  
Radivoje Jevtić ◽  
Nina Skenderović ◽  
Vesna Župunski ◽  
Mirjana Lalošević ◽  
Branka Orbović ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Kernel Weight ◽  
Head Blight ◽  
Thousand Kernel Weight ◽  
The Relationship

scholarly journals Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

2021 ◽  
Vol 13 (15) ◽  
pp. 3024
Author(s):  
Huiqin Ma ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Linyi Liu ◽  
Anting Guo
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Hyperspectral Imagery ◽  
Spectral Feature ◽  
Support Vector ◽  
Detection Accuracy ◽  
Feature Combination ◽  
Field Scale ◽  
Head Blight ◽  
Detection Model

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.

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