Hans‐Peter Piepho

The paper introduces a method to analyze genotype by environment interactions by examining environmental effects across growth stages, finding that early grain fill stages have the greatest impact on yield variance.

This study evaluates UAV-derived canopy traits in cassava to assess their repeatability and heritability across two growing seasons, finding high repeatability but variable heritability among traits.

This paper evaluates robust Random Forests for genomic prediction under contamination, finding data transformation as the most effective strategy for improving performance in contaminated datasets.

This study shows that single-parent expression complementation explains 29% of heterotic variance in maize hybrids and reveals cis/trans eQTL regulation of SPE patterns.

This study shows that the neonicotinoid acetamiprid is highly toxic to plant bugs, with effects up to 10,000 times greater than on honeybees and sex-specific differences in sensitivity.

The paper expands the BonnMu maize mutant collection by generating 8,064 F2-families with 425,924 Mu insertions affecting 36,612 gene models.

This paper evaluates heritability at family and tree levels in blocked tree breeding trials, finding heterogeneity in pairwise heritabilities due to genetic and error variance differences.

The paper investigates how host genotype and source environment influence the microbiome of maize, finding genetic variants linked to microbiome composition and environmental adaptation.

This paper compares machine learning methods for genomic prediction using synthetic and empirical data, finding that no single method outperforms others consistently and that classical methods remain competitive.

The paper evaluates models to estimate genetic gain in soybean seed yield from multi-environment trials and finds all models are biased, recommending multiple models for accurate estimation.

This paper extends Finlay-Wilkinson regression by replacing the environmental index with synthetic covariates derived from observable environmental factors, enabling predictions for new environments.

This paper uses simulation to address two key questions in plant breeding: the number of entries needed to select the best one with a defined probability and the probability of selecting the top entries when some are chosen.

This paper demonstrates that average semivariance can estimate genetic variance components across different modes of inheritance in complex traits.

The paper introduces a P-spline smoothing method for spatial analysis of plant breeding trials, finding that first differences are more practical than second differences for routine use.

The paper introduces a new genomic relationship matrix that improves genomic heritability estimation and breeding value prediction across populations.

This paper identifies a bias in statistical methods for estimating marker heritability and proposes a bias-corrected approach using average semivariance.

The paper evaluates MAGIC population designs and introduces a tool to create and test them, showing that certain designs improve haplotype diversity without requiring large crossing programs.

The paper uses Bayesian probability methods to analyze genotype-by-environment interactions in plant breeding, improving cultivar recommendation by quantifying adaptation and stability probabilities.

The paper evaluates robust and classical methods for estimating heritability and predictive accuracy in plant breeding using simulations and empirical data, finding robust methods more reliable in contaminated data.

The paper introduces a best prediction approach for additive genomic variance in random-effects models, incorporating linkage disequilibrium and improving upon existing methods.

The paper evaluates common misuses of PCA in SNP data analysis and recommends best practices for improving population structure elucidation.

The paper evaluates genomic prediction efficiency for untested single crosses using simulated data, finding high accuracy and coincidence across varying heritability and genetic models.