The first paradigm of plant breeding involves direct selection-based phenotypic observation,
followed by predictive breeding using statistical models for quantitative traits constructed …

Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction
methods have been proposed including the standard additive genetic effect model for which …

LightGBM is an ensemble model of decision trees for classification and regression
prediction. We demonstrate its utility in genomic selection-assisted breeding with a large …

Crop production systems need to expand their outputs sustainably to feed a burgeoning
human population. Advances in genome sequencing technologies combined with efficient …

Technological developments have revolutionized measurements on plant genotypes and
phenotypes, leading to routine production of large, complex data sets. This has led to …

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement
in plants. Traditional methods typically use linear regression models with clear assumptions; …

Warming and drought are reducing global crop production with a potential to substantially
worsen global malnutrition. As with the green revolution in the last century, plant genetics …

Studying the genetics of adaptation to new environments in ecologically and industrially
important tree species is currently a major research line in the fields of plant science and …

Genomic selection (GS) is transforming the field of plant breeding and implementing models
that improve prediction accuracy for complex traits is needed. Analytical methods for …

Due to their enhanced predictive capabilities, noninterpretable machine learning (ML)
models (eg deep learning) have recently gained a growing interest in analyzing and …