Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information
Abstract Enhancing predictive modeling accuracy in wheat (Triticum aestivum) breeding through the integration of high‐throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. In this study, spanning four locations in the southeastern United States over 3 yea...
Saved in:
| Main Authors: | Jordan McBreen, Md. Ali Babar, Diego Jarquin, Naeem Khan, Steve Harrison, Noah DeWitt, Mohamed Mergoum, Ben Lopez, Richard Boyles, Jeanette Lyerly, J. Paul Murphy, Ehsan Shakiba, Russel Sutton, Amir Ibrahim, Kimberly Howell, Jared H. Smith, Gina Brown‐Guedira, Vijay Tiwari, Nicholas Santantonio, David A. Van Sanford |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
|
| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20532 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Soft Red Winter Wheat Elite Germplasm Screening and Evaluation for Stripe Rust in the US Southeast Region
by: Ehsan Shakiba, et al.
Published: (2024-11-01) -
Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi‐kernel genomic prediction models integrating secondary traits and environmental covariates
by: Sudip Kunwar, et al.
Published: (2025-06-01) -
Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials
by: Jordan McBreen, et al.
Published: (2025-05-01) -
Enhancing genomic‐based forward prediction accuracy in wheat by integrating UAV‐derived hyperspectral and environmental data with machine learning under heat‐stressed environments
by: Jordan McBreen, et al.
Published: (2025-03-01) -
Advances in Seasonal Predictions of Arctic Sea Ice With NOAA UFS
by: Jieshun Zhu, et al.
Published: (2023-04-01)