Dayane Cristina Lima

An open field phenomics resource for multimodal maize yield prediction across divergent environments

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.

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.

Gene-by-Environment Interaction Significantly Drives Bacterial Endophyte Communities in Maize Stalks

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.

Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery

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.

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

The paper reports on a competition to predict maize yield using genetic and environmental data, showing that diverse modeling approaches can achieve good results.

Genetic mapping of flowering time and plant height in a maize Stiff Stalk MAGIC population

The paper develops a MAGIC population in maize to map flowering time and plant height, finding that predictive models perform better when trained on direct data rather than indirect data from hybrids.