Mark Cooper

This study develops an ensemble model using UAV-derived photogrammetric height data to predict plot-level lodging severity in sorghum breeding trials, achieving high accuracy and robustness across sites.

This study identifies genetic loci controlling tillering responsiveness to neighbour proximity in sorghum using GWAS and a diversity panel.

This study shows that ectopic ARGOS8 expression in maize improves silk elongation under water deficit, increasing silk emergence and reproductive performance.

The paper investigates whether higher clustering in Watts-Strogatz networks reduces attractor periods, finding a robust 13-15% decrease per 0.10 increase in clustering coefficient.

The paper introduces an additivity index [A]g to quantify local fitness landscape curvature and identifies additive channels where linear genetic predictions are effective for short-term evolutionary response.

This paper improves genomic prediction accuracy for maize flowering-time traits using an ensemble approach, revealing novel insights into trait genetic variation.

The paper investigates how environmental changes affect genetic gain in plant breeding by simulating germplasm structure and selection strategies.

This study evaluates graph attention networks (GAT) for genomic prediction in crops, finding that ensembles of GAT models outperform single models using gene network structures.

This study evaluates three weight optimisation approaches for genomic prediction ensembles in maize, finding that weighted models improve prediction performance when weights differ from equal weights, but no single method clearly outperforms others.

EasiGP is a tool that interprets genomic prediction models by visualizing marker effects on trait phenotypes, revealing both known and novel genetic regions in maize.

This study uses envirotyping to analyze genotype x environment interactions in wheat, showing stay-green traits improve yield across environments.

The paper shows that combining multiple genomic prediction models improves accuracy and reduces errors in predicting crop traits, supporting the Diversity Prediction Theorem.

The paper shows that maize breeding in the US corn-belt has increased genetic gain over time, adapting to climate change and improving yield despite environmental changes.

The paper discusses the importance of the MET-TPE relationship in genomic prediction for improving breeding outcomes in target populations of environments.

The paper develops a gene-to-phenotype network for shoot branching in plants to explore how genetic canalization affects selection responses and trait prediction across different contexts.

The paper uses NK models and Boolean logic to predict trait phenotypes from gene network topology in microorganisms through numerical simulations.

This study uses simulations to show that constraining genetic space and reducing population size in hybrid breeding improves heritable gains, explaining the success of traditional breeding strategies.

The paper explores using crop models as gene-to-phenotype link functions to optimize selection in plant breeding programs by integrating quantitative genetic theory and trait modeling.

This paper integrates crop growth models with whole genome prediction to improve phenotypic prediction by accounting for genotype-by-environment-by-management interactions in maize breeding.

This paper integrates crop growth models with whole genome prediction using approximate Bayesian computation to improve prediction accuracy for non-additive gene effects and genotype by environment interactions.