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Updated: Jun 24, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

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Published on: August 5, 2020

Enhancing winter wheat prediction with genomics, phenomics and environmental data.

Osval A Montesinos-López¹, Andrew W Herr², José Crossa^3,4

¹Facultad de Telemática, Universidad de Colima, Colima, 28040, México.

|May 31, 2024

Summary

This summary is machine-generated.

Integrating environmental data significantly boosts genomic selection accuracy in wheat breeding. This approach improves prediction across diverse environments, enhancing overall selection efficiency for plant breeding programs.

Keywords:

Environmental information Genomic prediction Genomics Integrating additional inputs Multi-environment trails Phenomics

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Area of Science:

Agricultural Science
Plant Breeding
Genetics

Background:

Genomic selection (GS) efficiency is limited in multi-environment prediction due to genotype by environment interactions.
Improving prediction accuracy requires integrating diverse data types like phenomics, environmental, and omics data.

Purpose of the Study:

To evaluate the impact of incorporating environmental information into prediction models.
To assess the improvement in prediction accuracy for soft white winter wheat across multiple environments.

Main Methods:

Utilized five datasets of soft white winter wheat.
Integrated genomic, phenomic, and environmental data into prediction models.
Measured prediction accuracy using normalized root mean square error (NRMSE).

Main Results:

Incorporating environmental information significantly improved prediction accuracy.
Average gain in prediction accuracy was 49.19% in terms of NRMSE across datasets.
Prediction accuracy gains ranged from 5.68% to 60.36% depending on the dataset.

Conclusions:

Environmental data integration substantially enhances prediction accuracy in multi-environment trials.
Plant breeding programs can improve selection efficiency by combining genomic, phenomic, and environmental data.
This integrated approach is crucial for effective breeding across diverse agricultural locations.