Evaluation of Methods for Measuring <i>Fusarium</i>-Damaged Kernels of Wheat
<i>Fusarium</i> head blight (FHB) is one of the most economically destructive diseases of wheat (<i>Triticum aestivum</i> L.), causing substantial yield and quality loss worldwide. <i>Fusarium graminearum</i> is the predominant causal pathogen of FHB in the U.S.,...
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| Format: | Article |
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MDPI AG
2022-02-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/2/532 |
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| author | Arlyn J. Ackerman Ryan Holmes Ezekiel Gaskins Kathleen E. Jordan Dawn S. Hicks Joshua Fitzgerald Carl A. Griffey Richard Esten Mason Stephen A. Harrison Joseph Paul Murphy Christina Cowger Richard E. Boyles |
| author_facet | Arlyn J. Ackerman Ryan Holmes Ezekiel Gaskins Kathleen E. Jordan Dawn S. Hicks Joshua Fitzgerald Carl A. Griffey Richard Esten Mason Stephen A. Harrison Joseph Paul Murphy Christina Cowger Richard E. Boyles |
| author_sort | Arlyn J. Ackerman |
| collection | DOAJ |
| description | <i>Fusarium</i> head blight (FHB) is one of the most economically destructive diseases of wheat (<i>Triticum aestivum</i> L.), causing substantial yield and quality loss worldwide. <i>Fusarium graminearum</i> is the predominant causal pathogen of FHB in the U.S., and produces deoxynivalenol (DON), a mycotoxin that accumulates in the grain throughout infection. FHB results in kernel damage, a visual symptom that is quantified by a human observer enumerating or estimating the percentage of <i>Fusarium</i>-damaged kernels (FDK) in a sample of grain. To date, FDK estimation is the most efficient and accurate method of predicting DON content without measuring presence in a laboratory. For this experiment, 1266 entries collectively representing elite varieties and SunGrains advanced breeding lines encompassing four inoculated FHB nurseries were represented in the analysis. All plots were subjected to a manual FDK count, both exact and estimated, near-infrared spectroscopy (NIR) analysis, DON laboratory analysis, and digital imaging seed phenotyping using the Vibe QM3 instrument developed by Vibe imaging analytics. Among the FDK analytical platforms used to establish percentage FDK within grain samples, Vibe QM3 showed the strongest prediction capabilities of DON content in experimental samples, <i>R</i><sup>2</sup> = 0.63, and higher yet when deployed as FDK GEBVs, <i>R</i><sup>2</sup> = 0.76. Additionally, Vibe QM3 was shown to detect a significant SNP association at locus S3B_9439629 within major FHB resistance quantitative trait locus (QTL) <i>Fhb1</i>. Visual estimates of FDK showed higher prediction capabilities of DON content in grain subsamples than previously expected when deployed as genomic estimated breeding values (GEBVs) (<i>R</i><sup>2</sup> = 0.71), and the highest accuracy in genomic prediction, followed by Vibe QM3 digital imaging, with average Pearson’s correlations of <i>r =</i> 0.594 and <i>r =</i> 0.588 between observed and predicted values, respectively. These results demonstrate that seed phenotyping using traditional or automated platforms to determine FDK boast various throughput and efficacy that must be weighed appropriately when determining application in breeding programs to screen for and develop resistance to FHB and DON accumulation in wheat germplasms. |
| first_indexed | 2024-03-09T22:49:43Z |
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| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T22:49:43Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-be35832af7864f29bf96e63e3269f9ae2023-11-23T18:23:38ZengMDPI AGAgronomy2073-43952022-02-0112253210.3390/agronomy12020532Evaluation of Methods for Measuring <i>Fusarium</i>-Damaged Kernels of WheatArlyn J. Ackerman0Ryan Holmes1Ezekiel Gaskins2Kathleen E. Jordan3Dawn S. Hicks4Joshua Fitzgerald5Carl A. Griffey6Richard Esten Mason7Stephen A. Harrison8Joseph Paul Murphy9Christina Cowger10Richard E. Boyles11Cereal Grains Breeding and Genetics, Pee Dee Research and Education Center, Department of Plant & Environmental Sciences, Clemson University, Florence, SC 29506, USACereal Grains Breeding and Genetics, Pee Dee Research and Education Center, Department of Plant & Environmental Sciences, Clemson University, Florence, SC 29506, USACereal Grains Breeding and Genetics, Pee Dee Research and Education Center, Department of Plant & Environmental Sciences, Clemson University, Florence, SC 29506, USAAdvanced Plant Technology Program, Clemson University, Clemson, SC 29634, USACereal Grains Breeding and Genetics, Pee Dee Research and Education Center, Department of Plant & Environmental Sciences, Clemson University, Florence, SC 29506, USACrop and Soil Environmental Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USACrop and Soil Environmental Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USADepartment of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USASchool of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge, LA 70803, USADepartment of Crop Science, North Carolina State University, Raleigh, NC 27695, USAUSDA-ARS, Department of Plant Pathology, North Carolina State University, Raleigh, NC 27695, USACereal Grains Breeding and Genetics, Pee Dee Research and Education Center, Department of Plant & Environmental Sciences, Clemson University, Florence, SC 29506, USA<i>Fusarium</i> head blight (FHB) is one of the most economically destructive diseases of wheat (<i>Triticum aestivum</i> L.), causing substantial yield and quality loss worldwide. <i>Fusarium graminearum</i> is the predominant causal pathogen of FHB in the U.S., and produces deoxynivalenol (DON), a mycotoxin that accumulates in the grain throughout infection. FHB results in kernel damage, a visual symptom that is quantified by a human observer enumerating or estimating the percentage of <i>Fusarium</i>-damaged kernels (FDK) in a sample of grain. To date, FDK estimation is the most efficient and accurate method of predicting DON content without measuring presence in a laboratory. For this experiment, 1266 entries collectively representing elite varieties and SunGrains advanced breeding lines encompassing four inoculated FHB nurseries were represented in the analysis. All plots were subjected to a manual FDK count, both exact and estimated, near-infrared spectroscopy (NIR) analysis, DON laboratory analysis, and digital imaging seed phenotyping using the Vibe QM3 instrument developed by Vibe imaging analytics. Among the FDK analytical platforms used to establish percentage FDK within grain samples, Vibe QM3 showed the strongest prediction capabilities of DON content in experimental samples, <i>R</i><sup>2</sup> = 0.63, and higher yet when deployed as FDK GEBVs, <i>R</i><sup>2</sup> = 0.76. Additionally, Vibe QM3 was shown to detect a significant SNP association at locus S3B_9439629 within major FHB resistance quantitative trait locus (QTL) <i>Fhb1</i>. Visual estimates of FDK showed higher prediction capabilities of DON content in grain subsamples than previously expected when deployed as genomic estimated breeding values (GEBVs) (<i>R</i><sup>2</sup> = 0.71), and the highest accuracy in genomic prediction, followed by Vibe QM3 digital imaging, with average Pearson’s correlations of <i>r =</i> 0.594 and <i>r =</i> 0.588 between observed and predicted values, respectively. These results demonstrate that seed phenotyping using traditional or automated platforms to determine FDK boast various throughput and efficacy that must be weighed appropriately when determining application in breeding programs to screen for and develop resistance to FHB and DON accumulation in wheat germplasms.https://www.mdpi.com/2073-4395/12/2/532<i>Fusarium</i> head blight<i>Fusarium</i>-damaged kernelsdeoxynivalenolDON resistancemanual sortingvisual estimation |
| spellingShingle | Arlyn J. Ackerman Ryan Holmes Ezekiel Gaskins Kathleen E. Jordan Dawn S. Hicks Joshua Fitzgerald Carl A. Griffey Richard Esten Mason Stephen A. Harrison Joseph Paul Murphy Christina Cowger Richard E. Boyles Evaluation of Methods for Measuring <i>Fusarium</i>-Damaged Kernels of Wheat Agronomy <i>Fusarium</i> head blight <i>Fusarium</i>-damaged kernels deoxynivalenol DON resistance manual sorting visual estimation |
| title | Evaluation of Methods for Measuring <i>Fusarium</i>-Damaged Kernels of Wheat |
| title_full | Evaluation of Methods for Measuring <i>Fusarium</i>-Damaged Kernels of Wheat |
| title_fullStr | Evaluation of Methods for Measuring <i>Fusarium</i>-Damaged Kernels of Wheat |
| title_full_unstemmed | Evaluation of Methods for Measuring <i>Fusarium</i>-Damaged Kernels of Wheat |
| title_short | Evaluation of Methods for Measuring <i>Fusarium</i>-Damaged Kernels of Wheat |
| title_sort | evaluation of methods for measuring i fusarium i damaged kernels of wheat |
| topic | <i>Fusarium</i> head blight <i>Fusarium</i>-damaged kernels deoxynivalenol DON resistance manual sorting visual estimation |
| url | https://www.mdpi.com/2073-4395/12/2/532 |
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