Wheat Multi-Trait Predictions: A Quantitative, Genotype x Environment (GxE) Approach to Supporting Minnesota Wheat Breeding and Farmer Varietal Selections

Project Description

A perennial challenge faced by wheat breeders and producers is to identify and select the best performing varieties for each location. A high-yielding variety at one location during one season may not perform well at another location and/or another season, exemplifying the strong effects of Variety (Genotype) by Environment (GxE) interactions on crop performance. Limits on experimental land and funding make it impossible to test all varieties at all locations for all years so as to choose the best performing varieties. In this project, researchers at the UMN CFANS GEMS Informatics Center, in collaboration with breeder Dr. Jim Anderson from the Department of Agronomy and Plant Genetics, will develop a wheat trait prediction tool to intelligently combine genomic information, environmental conditions, and their GxE interactions to accurately predict the performance of different varieties under different environments. This project will utilize the rich genotypic and phenotypic data collected through Dr. Anderson’s field trials and the comprehensive weather and soil data provided by the GEMS Informatics Center to build a spatially-explicit, MN-focused wheat trait prediction tool to accelerate wheat breeding programs and assist varietal selections for wheat breeders and farmers across the state.

Time Period

January, 2022 to December, 2023

Sponsoring Organization

Minnesota Wheat Research and Promotion Council

Activities

• Activity 1. Genotype a panel of MN wheat varieties
• Activity 2. Phenotype data from field trials
• Activity 3. Environmental data and crop growth modeling
• Activity 4. Phenotypic prediction model training, testing, and validation