Accelerated breeding for winter wheat profitability in a wheat-soybean double-crop system

Berger Munaro, Lucas

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https://hdl.handle.net/2142/132468 Copy

Description

Title

Accelerated breeding for winter wheat profitability in a wheat-soybean double-crop system

Author(s)

Berger Munaro, Lucas

Issue Date

2025-09-29

Director of Research (if dissertation) or Advisor (if thesis)

Rutkoski, Jessica E

Doctoral Committee Chair(s)

Rutkoski, Jessica E

Committee Member(s)

• Lipka, Alexander E
• Arbelaez Velez, Juan D
• Martin, Nicolas F

Department of Study

Crop Sciences

Discipline

Crop Sciences

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Plant breeding
• Wheat
• Genomic selection
• Selection index
• Crop Sciences

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 the selection cohort is genetically represented in the training set, with multi-trait models providing consistent, though typically modest, improvements over single-trait approaches. Adding small, largely unreplicated early-stage phenotypes provides mixed benefit, whereas assembling larger, multi-year, genetically connected training sets reliably improves accuracy. Collectively, the dissertation provides an operational framework for earlier, more accurate selection in SRWW: use MTME models and an optimal economic index for multi-trait selection, and design training sets that maintain temporal and genetic connectedness to selection candidates. These principles support faster, more profitable varietal advancement in the Midwest double-crop system and are transferable to other winter wheat programs with similar constraints.

Graduation Semester

2025-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/132468

Copyright and License Information

Copyright 2025 Lucas Berger Munaro

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