Aaron Joel Lorenz

Inter-variety competition dynamics in US inbred and hybrid maize

This study examines competitive dynamics in maize varieties and finds limited competition effects, no yield penalty in mixtures, and increased yield stability.

Genetic Gains from Sixty Years of Spring Wheat Breeding in the Northern Plains of the US

This study quantifies genetic gains in HRS wheat over 60 years, finding annual yield gains of 0.61% but no improvement in grain protein since 1995.

Genomic and Phenomic Prediction for Soybean Seed Yield, Protein, and Oil

This paper compares genomic and phenomic prediction for soybean seed yield, protein, and oil content, finding phenomic prediction outperforms genomic for yield while genomic is better for protein and oil.

High-throughput phenotyping of soybean transpiration response curves to rising atmospheric drying in a mapping population

This study identifies genetic loci associated with soybean transpiration response to increasing vapor pressure deficit using a mapping population.

Assembly, comparative analysis, and utilization of a single haplotype reference genome for soybean

This paper presents an updated soybean genome assembly (Wm82.a6) and compares it with other assemblies and Fiskeby III, revealing genomic heterogeneity and polymorphisms.

Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean

This study shows genomic selection (GS) is as effective as phenotypic selection (PS) for increasing soybean seed yield, with similar gains and reduced breeding costs.

Development of Molecular Inversion Probes for Soybean Progeny Genomic Selection Genotyping

The paper develops a 1K SNP Molecular Inversion Probes (MIPs) set for soybean genomic selection, achieving high accuracy and cost-effectiveness for breeding programs.

The relevance of dominance and functional annotations to predict agronomic traits in hybrid maize

This study shows dominance and gene annotations improve genomic prediction of agronomic traits in maize hybrids.

Multi-Trait Improvement by Predicting Genetic Correlations in Breeding Crosses

This study predicts genetic correlations in plant breeding crosses to improve multiple traits, finding that cross selection based on predicted correlations increases multi-trait genetic gain.

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

This study evaluates methods for updating training data in genomewide selection over breeding cycles, finding that updating with best predicted lines or both best and worst predicted lines yields high accuracy and genetic gain in the short-term, while smaller, more recent training populations provide a slight advantage long-term.