C. Hirsch

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

This study uses high-temporal-resolution plant height data to show that genetic and environmental factors influence plant growth differently across the growing season.

Plant height defined growth curves during vegetative development have the potential to predict end of season maize yield and assist with mid-season management decisions

The paper uses UAV-derived plant height data to predict maize grain yield variation within fields, showing moderate predictive accuracy but limitations in drought-stressed environments.

Importance of genetic architecture in marker selection decisions for genomic prediction

This study evaluates how genetic architecture affects prediction accuracy when using SNPs or SVs for genomic prediction across multiple environments.

Predicting Moisture Content During Maize Nixtamalization Using Machine Learning with NIR Spectroscopy

This study uses machine learning with NIR spectroscopy to predict maize kernel moisture content during nixtamalization, finding environmental factors and complex genetics influence the trait.

Genetic Architecture of Kernel Compositional Variation in a Maize Diversity Panel

This study identifies genetic loci associated with compositional traits in maize using GWAS, revealing significant genetic and environmental influences on grain quality.

Whole Genome Variation of Transposable Element Insertions in a Maize Diversity Panel

This study analyzes TE insertion polymorphisms across 509 maize inbred lines using short read sequencing, revealing patterns of TE frequency, nesting, and SNP tagging.