Jode W. Edwards

This paper introduces a regression-based framework to predict flowering time from sparse UAV observations, reducing the need for repeated data collection.

Drone imagery from 356 flights in 19 Genomes to Fields maize environments; vegetation-index functional principal components complement genomic kernels, predicting yield of held-out hybrids at r up to 0.501.

This study evaluates genomic prediction models for maize hybrids using GBLUP-based multi-kernel models to estimate genetic variance components and predict hybrid performance.

The paper investigates how host genotype and environment interact to shape bacterial endophyte communities in maize stalks, finding that gene-by-environment interactions significantly influence microbial communities.

This paper introduces distributional data analysis to plant phenomics, showing it captures novel heritable features in cotton and maize imagery that improve yield prediction and explain genetic variation.

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

This study identifies selection signatures in maize genes associated with reduced tassel size during hybrid breeding.

This study identifies quantitative trait loci (QTL) for lodging in maize by evaluating diverse inbred lines across multiple environments, finding overlaps with genes involved in lignin and cellulose synthesis.