Diego Jarquín

Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates

Genetic gains of oat (Avena sativa L.) grain yield have been historically low compared to other major cereal crops. The use of machine learning models to capture complex interactions and leveraging data types other than genomic information in prediction models has great potential...

Non-linear kernel methods for genomic prediction of soybean yield and quality in multi-environment trials

Abstract Key message Nonlinear kernels improve the accuracy of genomic prediction while preserving the inferential framework of quantitative genetics. Abstract Improving the predictive accuracy of genomic prediction (GP) for complex genetic architectures involving non-additive ef...