Cyril Bauland

This paper demonstrates that UAV-based phenomic selection can predict field performance of nursery-grown maize and pea genotypes without genotyping.

The paper investigates how genetic variation in common bean affects plant-plant interactions with maize, showing that effects depend on neighbor identity and environmental context.

The paper introduces a genomic prediction model that forecasts yield performance across environments and future climates and identifies adapted maize landraces by integrating genomic offset and gene diversity.

This study investigates maize-bean intercropping through on-farm surveys and field assays, finding competition dominates over cooperation, primarily affecting bean yield and gene expression.

The paper evaluates maize resistance to European Corn Borer by linking palatability to metabolomic profiles, identifying key metabolites correlated with resistance.

The paper develops a DNA bulk SNP genotyping method for landraces and applies it to maize, showing accurate allelic frequency estimation and low ascertainment bias.

The paper uses SNP genotyping of DNA pools to identify landraces and genomic regions with untapped genetic diversity for maize breeding.

The paper introduces a GWAS method to identify QTLs with group-specific allele effects in admixed populations, applied to maize flowering time data.

The paper compares genotyping-by-sequencing and microarrays for detecting QTLs, showing they complement each other by tagging different haplotypes and improving detection power and localization.