Genomic prediction for targeted populations of environments in oat (Avena sativa)

Authors: SANDRO, PABLO - University Of Wisconsin; BHATTA, MADHAV - University Of Wisconsin; BOWER, ALISHA - Practical Farmers Of Iowa; CARLSON, SARAH - Practical Farmers Of Iowa; Jannink, Jean-Luc; WARING, DAVID - Cornell University; Birkett, Clayton; SMITH, KEVIN - University Of Minnesota; WIERSMA, JOCHUM - University Of Minnesota; CAFFE, MELANIE - South Dakota State University; KLEINJAN, JONATHAN - South Dakota State University; MCMULLEN, MICHAEL - North Dakota State University; ENGLISH, LYDIA - Practical Farmers Of Iowa; GUTIERREZ, LUCIA - University Of Wisconsin

Submitted to: Crop and Pasture Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/7/2024
Publication Date: 4/30/2024
Citation: Sandro, P., Bhatta, M., Bower, A., Carlson, S., Jannink, J., Waring, D.J., Birkett, C.L., Smith, K., Wiersma, J., Caffe, M., Kleinjan, J., Mcmullen, M.S., English, L., Gutierrez, L. 2024. Genomic prediction for targeted populations of environments in oat (Avena sativa). Crop and Pasture Science. Volume 75. https://doi.org/10.1071/CP23126.
DOI: https://doi.org/10.1071/CP23126

Interpretive Summary: This study looked at how to improve the process of predicting how well different oat varieties will grow in various environments. Scientists often test oat varieties in different locations over many years to see how they perform. However, these tests are usually not balanced because some varieties are removed from the tests after a few years. The goal of this study was to find the best way to predict which oat varieties will grow best in different environments. The researchers used a lot of data from past experiments to make their predictions. They found that using this information helped them make more accurate predictions, especially when they included how different environments affect the varieties. This research can help farmers and plant breeders choose the best oat varieties for their specific areas, leading to better crops.

Technical Abstract: Long-term multi-environment trials (METs) could improve genomic prediction models for plant breeding programs by better representing the target population of environments (TPE). The goals of this study were to compare strategies for modeling genotype × environment interaction (GEI) in genomic prediction using large METs from oat breeding programs in the Midwest United States, and to develop a variety decision tool for farmers and plant breeders. The performance of genotypes in TPEs was predicted using different strategies for handling GEI in genomic prediction models. The study showed that modeling GEI was beneficial in small but not in large mega-environments. High predictive ability, accuracy, and reliability were obtained when large datasets were used in TPEs. The deployment of historical datasets can be accomplished through meaningful delineation and prediction for TPEs.