Accelerated breeding for winter wheat profitability in a wheat-soybean double-crop system
type: CreativeWork
description: Soft red winter wheat (Triticum aestivum L.) (SRWW) in the U.S. Midwest is frequently produced in a wheat–soybean (Glycine max L.) double-crop system, where profitability depends on combining high grain yield with acceptable test weight and sufficiently early maturity to enable timely soybean planting. This dissertation aims to understand and accelerate genetic gain for SRWW under these agronomic and economic constraints using genetic trend analyses, multi-trait genomic prediction of total net merit, and training-set design optimization for early-stage advancement. Three questions organize the research. First, what has been the realized genetic trend for key traits over two decades of selection in the University of Illinois breeding program? Second, can an optimal genomic selection index improve expected economic return relative to single-trait strategies? Third, which genomic prediction scenarios and training-set configurations maximize predictive ability for early-stage line selection? To address these questions, Chapter 2 quantifies realized genetic change using long-term yield-trial data and control-population methods. Chapter 3 develops a profit-maximizing genomic selection index that incorporates grain price, test-weight discounts, and soybean yield penalties associated with later heading; the index is evaluated with multi-trait multi-environment (MTME) models across multiple traits and environments. Chapter 4 compares genomic prediction scenarios that vary in training-set size, connectedness, inclusion of early-stage trial data, and whether the selection cohort is represented in the training set, benchmarking multi-trait against single-trait models. Notable results include favorable realized genetic change for target traits over the past 21 years of breeding; MTME-based index selection increases predicted economic return while distributing gains more evenly across yield, test weight, and maturity; and predictive ability for early advancement is highest when t…
abstract: Soft red winter wheat (Triticum aestivum L.) (SRWW) in the U.S. Midwest is frequently produced in a wheat–soybean (Glycine max L.) double-crop system, where profitability depends on combining high grain yield with acceptable test weight and sufficiently early maturity to enable timely soybean planting. This dissertation aims to understand and accelerate genetic gain for SRWW under these agronomic and economic constraints using genetic trend analyses, multi-trait genomic prediction of total net merit, and training-set design optimization for early-stage advancement. Three questions organize the research. First, what has been the realized genetic trend for key traits over two decades of selection in the University of Illinois breeding program? Second, can an optimal genomic selection index improve expected economic return relative to single-trait strategies? Third, which genomic prediction scenarios and training-set configurations maximize predictive ability for early-stage line selection? To address these questions, Chapter 2 quantifies realized genetic change using long-term yield-trial data and control-population methods. Chapter 3 develops a profit-maximizing genomic selection index that incorporates grain price, test-weight discounts, and soybean yield penalties associated with later heading; the index is evaluated with multi-trait multi-environment (MTME) models across multiple traits and environments. Chapter 4 compares genomic prediction scenarios that vary in training-set size, connectedness, inclusion of early-stage trial data, and whether the selection cohort is represented in the training set, benchmarking multi-trait against single-trait models. Notable results include favorable realized genetic change for target traits over the past 21 years of breeding; MTME-based index selection increases predicted economic return while distributing gains more evenly across yield, test weight, and maturity; and predictive ability for early advancement is highest when t…
author: Berger Munaro, Lucas
language: en
keywords: Plant breeding, Wheat, Genomic selection, Selection index, Crop Sciences
date published: 2025-12
date modified: 2026-07-22T22:25:07Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois Urbana-Champaign

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2142/132468