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Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
Published on: March 13, 2020
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Spectral-genomic chain-model approach enhances the wheat yield component prediction under the Mediterranean climate
Roy Sadeh1, Roi Ben-David2, Ittai Herrmann1
1The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel.
Physiologia Plantarum
|August 26, 2024
Summary
Genomic selection aids crop breeding for food security. Integrating spectral data for wheat spike number estimation significantly improved grain yield prediction accuracy in a novel chain-model workflow.
Area of Science:
- Plant breeding and genetics
- Agricultural science
- Remote sensing in agriculture
Background:
- Climate change threatens global food security, necessitating enhanced crop breeding strategies.
- Genomic selection (GS) is a powerful tool for accelerating genetic gains in crop improvement.
- Predicting grain yield is complex due to genetic factors, environmental interactions, and trait complexities.
Purpose of the Study:
- To improve the prediction accuracy of wheat grain yield using genomic selection.
- To evaluate the effectiveness of a chained model approach integrating spectral data.
- To explore the utility of spectral estimation of spike number as a secondary trait for yield prediction.
Main Methods:
- Phenotyped a diversity panel across three Mediterranean environments for morpho-physiological and yield traits.
- Employed a chained model approach, breaking down complex prediction tasks.
- Utilized machine learning and unmanned aerial vehicle (UAV)-based hyperspectral reflectance for spike number estimation.
- Integrated spectral-based estimated spike number as a secondary trait in a multi-trait genomic selection model.
Main Results:
- Multi-environment models showed improved prediction accuracy for most traits compared to single-environment models.
- Initial attempts to predict grain yield directly were not improved.
- Integrating spectral-based estimated spike number significantly enhanced wheat grain yield prediction accuracy.
- Predicted spike number showed consistency across seasons and scalability to different trial sizes.
Conclusions:
- A novel spectral-genomic chain-model workflow effectively improves wheat grain yield prediction accuracy.
- Spectral-based phenotypes, like spike number, can serve as valuable secondary traits in genomic selection.
- This approach offers a promising strategy for accelerating the development of high-yielding crop varieties in the face of climate change.
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