Machine learning algorithms translate big data into predictive breeding accuracy
Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and
environmental data. ML algorithms automatically identify relevant features and use cross …
environmental data. ML algorithms automatically identify relevant features and use cross …
Wheat genetic resources have avoided disease pandemics, improved food security, and reduced environmental footprints: A review of historical impacts and future …
The use of plant genetic resources (PGR)—wild relatives, landraces, and isolated breeding
gene pools—has had substantial impacts on wheat breeding for resistance to biotic and …
gene pools—has had substantial impacts on wheat breeding for resistance to biotic and …
GIS‐based G× E modeling of maize hybrids through enviromic markers engineering
Through enviromics, precision breeding leverages innovative geotechnologies to customize
crop varieties to specific environments, potentially improving both crop yield and genetic …
crop varieties to specific environments, potentially improving both crop yield and genetic …
Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials
Key message Incorporating feature-engineered environmental data into machine learning-
based genomic prediction models is an efficient approach to indirectly model genotype-by …
based genomic prediction models is an efficient approach to indirectly model genotype-by …
Enviromic prediction enables the characterization and map** of Eucalyptus globulus Labill breeding zones
Genotype-environment interaction is pervasive in forest genetics. Delineation of spatial
breeding zones (BZs) is fundamental for accommodating genotype-environment interaction …
breeding zones (BZs) is fundamental for accommodating genotype-environment interaction …
Satellite-enabled enviromics to enhance crop improvement
Enviromics refers to the characterization of micro-and macroenvironments based on large-
scale environmental datasets. By providing genotypic recommendations with predictive …
scale environmental datasets. By providing genotypic recommendations with predictive …
Factor‐Analytic Variance–Covariance Structures for Prediction Into a Target Population of Environments
HP Piepho, E Williams - Biometrical Journal, 2024 - Wiley Online Library
Finlay–Wilkinson regression is a popular method for modeling genotype–environment
interaction in plant breeding and crop variety testing. When environment is a random factor …
interaction in plant breeding and crop variety testing. When environment is a random factor …
Crop genomic selection with deep learning and environmental data: A survey
Machine learning techniques for crop genomic selections, especially for single-environment
plants, are well-developed. These machine learning models, which use dense genome …
plants, are well-developed. These machine learning models, which use dense genome …
Prediction of near‐term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding
Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed.
Grain quality is paramount in determining its value and downstream use. While we know that …
Grain quality is paramount in determining its value and downstream use. While we know that …
GIS-FA: an approach to integrating thematic maps, factor-analytic, and enviroty** for cultivar targeting
Key message We propose an “enviromics” prediction model for recommending cultivars
based on thematic maps aimed at decision-makers. Abstract Parsimonious methods that …
based on thematic maps aimed at decision-makers. Abstract Parsimonious methods that …