Research graph
References from Stacked enviromic–genomic models improve prediction of genotype performance in new environments. Local targets link to admitted publications; unresolved targets remain external evidence.
GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting
10.1007/s00122-024-04579-z · 2024 · External reference
Optimizing soybean variety selection for the Pan-African trial network using factor analytic models and envirotyping
10.3389/fpls.2025.1594736 · 2025 · External reference
Prediction of maize single-cross performance using RFLPs and information from related hybrids
10.2135/cropsci1994.0011183x003400010003x · 1994 · External reference
Unresolved reference
2017 · External reference
Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods
10.1073/pnas.2317284121 · 2024 · External reference
Stacking ensemble learning for genomic prediction under complex genetic architectures
10.3390/agronomy16020241 · 2026 · External reference
Employing factor analytic tools for selecting high-performance and stable tropical maize hybrids
10.1002/csc2.20911 · 2023 · External reference
Genomes to fields 2024 maize genotype by environment prediction competition
10.1186/s13104-026-07629-5 · 2026 · External reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · 2016 · External reference
Accuracy of genomewide selection for different traits with constant population size, heritability, and number of markers
10.3835/plantgenome2012.11.0030 · 2013 · External reference
Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction
10.1007/bf01240919 · 1994 · External reference
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
10.1038/s41437-020-00353-1 · 2021 · External reference
Envrtype: a software to interplay enviromics and quantitative genomics in agriculture
10.1093/g3journal/jkab040 · 2021 · External reference
A uniform, objective, and adaptive system for expressing rice development
10.2135/cropsci2000.402436x · 2000 · External reference
Deep kernel for genomic and near infrared predictions in multi-environment breeding trials
10.1534/g3.119.400493 · 2019 · External reference
On the design of early generation variety trials with correlated data
10.1198/108571106x154443 · 2006 · External reference
A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation
10.1093/g3journal/jkaf095 · 2025 · External reference
Unresolved reference
2025 · External reference
Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials
10.1007/s00122-024-04687-w · 2024 · External reference
The analysis of adaptation in a plant-breeding programme
10.1071/ar9630742 · 1963 · External reference
Regularization paths for generalized linear models via coordinate descent
10.18637/jss.v033.i01 · 2010 · External reference
Model selection and validation for yield trials with interaction
10.2307/2531585 · 1988 · External reference
Unresolved reference
External reference
Integrating envirotyping and genetic modeling to dissect crossover G×E interaction and stability in maize
10.1590/1984-70332026v26n1a3 · 2026 · External reference
Regression approaches for modeling genotype–environment interaction and making predictions into unseen environments
10.1007/s00122-026-05188-8 · 2026 · External reference
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
10.1007/s00122-013-2243-1 · 2014 · External reference
Predicting maize phenology: intercomparison of functions for developmental response to temperature
10.2134/agronj14.0200 · 2014 · External reference
Yield–trait performance landscapes: from theory to application in breeding maize for drought tolerance
10.1093/jxb/erq329 · 2011 · External reference
Prediction of total genetic value using genome-wide dense marker maps
10.1093/genetics/157.4.1819 · 2001 · External reference
Artificial intelligence meets genomic selection: comparing deep learning and gblup across diverse plant datasets
10.3389/fgene.2025.1568705 · 2025 · External reference
Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
10.1534/g3.118.200728 · 2018 · External reference
Enhancing genomic prediction with stacking ensemble learning in Arabica coffee
10.3389/fpls.2024.1373318 · 2024 · External reference
Genome-wide regression and prediction with the bglr statistical package
10.1534/genetics.114.164442 · 2014 · External reference
Analysis of a randomized block design with unequal subclass numbers
10.2134/agronj1997.00021962008900050002x · 1997 · External reference
PaCMAP-embedded convolutional neural network for multi-omics data integration
10.1016/j.heliyon.2023.e23195 · 2024 · External reference
Consolidating 23 years of historical data from an irrigated subtropical rice breeding program in Uruguay
10.1002/csc2.20955 · 2023 · External reference
Satellite-enabled enviromics to enhance crop improvement
10.1016/j.molp.2024.04.005 · 2024 · External reference
GIS-based G × E modeling of maize hybrids through enviromic markers engineering
10.1111/nph.19951 · 2025 · External reference
Enviromics in breeding: applications and perspectives on envirotypic-assisted selection
10.1007/s00122-020-03684-z · 2021 · External reference
Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend
10.1111/j.0006-341x.2001.01138.x · 2001 · External reference
Plant breeding selection tools built on factor analytic mixed models for multi-environment trial data
10.1007/s10681-018-2220-5 · 2018 · External reference
nasapower: a NASA power global meteorology, surface solar energy and climatology data client for R
10.21105/joss · 2018 · External reference
Efficient methods to compute genomic predictions
10.3168/jds.2007-0980 · 2008 · External reference
On the additive and dominant variance and covariance of individuals within the genomic selection scope
10.1534/genetics.113.155176 · 2013 · External reference
Understanding how dimension reduction tools work: an empirical approach to deciphering t-SNE, UMAP, TriMAP, and PaCMAP
2021 · External reference
Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates
10.1093/genetics/iyae195 · 2025 · External reference
Stacked generalization
10.1016/s0893-6080(05)80023-1 · 1992 · External reference
Unresolved reference
1932 · External reference
Envirotyping for deciphering environmental impacts on crop plants
10.1007/s00122-016-2691-5 · 2016 · External reference
Unresolved reference
1981 · External reference
EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models
10.1093/bib/bbaf414 · 2025 · External reference
Employing factor analytic tools for selecting high-performance and stable tropical maize hybrids
10.1002/csc2.20911 · ExternalCitation · doi-reference
Consolidating 23 years of historical data from an irrigated subtropical rice breeding program in Uruguay
10.1002/csc2.20955 · ExternalCitation · doi-reference
Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction
10.1007/bf01240919 · ExternalCitation · doi-reference
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
10.1007/s00122-013-2243-1 · ExternalCitation · doi-reference
Envirotyping for deciphering environmental impacts on crop plants
10.1007/s00122-016-2691-5 · ExternalCitation · doi-reference
Enviromics in breeding: applications and perspectives on envirotypic-assisted selection
10.1007/s00122-020-03684-z · ExternalCitation · doi-reference
GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting
10.1007/s00122-024-04579-z · ExternalCitation · doi-reference
Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials
10.1007/s00122-024-04687-w · ExternalCitation · doi-reference
Regression approaches for modeling genotype–environment interaction and making predictions into unseen environments
10.1007/s00122-026-05188-8 · ExternalCitation · doi-reference
Plant breeding selection tools built on factor analytic mixed models for multi-environment trial data
10.1007/s10681-018-2220-5 · ExternalCitation · doi-reference
PaCMAP-embedded convolutional neural network for multi-omics data integration
10.1016/j.heliyon.2023.e23195 · ExternalCitation · doi-reference
Satellite-enabled enviromics to enhance crop improvement
10.1016/j.molp.2024.04.005 · ExternalCitation · doi-reference
Stacked generalization
10.1016/s0893-6080(05)80023-1 · ExternalCitation · doi-reference
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
10.1038/s41437-020-00353-1 · ExternalCitation · doi-reference
The analysis of adaptation in a plant-breeding programme
10.1071/ar9630742 · ExternalCitation · doi-reference
Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods
10.1073/pnas.2317284121 · ExternalCitation · doi-reference
EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models
10.1093/bib/bbaf414 · ExternalCitation · doi-reference
Envrtype: a software to interplay enviromics and quantitative genomics in agriculture
10.1093/g3journal/jkab040 · ExternalCitation · doi-reference
A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation
10.1093/g3journal/jkaf095 · ExternalCitation · doi-reference
Prediction of total genetic value using genome-wide dense marker maps
10.1093/genetics/157.4.1819 · ExternalCitation · doi-reference
Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates
10.1093/genetics/iyae195 · ExternalCitation · doi-reference
Yield–trait performance landscapes: from theory to application in breeding maize for drought tolerance
10.1093/jxb/erq329 · ExternalCitation · doi-reference
Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend
10.1111/j.0006-341x.2001.01138.x · ExternalCitation · doi-reference
GIS-based G × E modeling of maize hybrids through enviromic markers engineering
10.1111/nph.19951 · ExternalCitation · doi-reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · ExternalCitation · doi-reference
Genomes to fields 2024 maize genotype by environment prediction competition
10.1186/s13104-026-07629-5 · ExternalCitation · doi-reference
On the design of early generation variety trials with correlated data
10.1198/108571106x154443 · ExternalCitation · doi-reference
Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
10.1534/g3.118.200728 · ExternalCitation · doi-reference
Deep kernel for genomic and near infrared predictions in multi-environment breeding trials
10.1534/g3.119.400493 · ExternalCitation · doi-reference
On the additive and dominant variance and covariance of individuals within the genomic selection scope
10.1534/genetics.113.155176 · ExternalCitation · doi-reference
Genome-wide regression and prediction with the bglr statistical package
10.1534/genetics.114.164442 · ExternalCitation · doi-reference
Integrating envirotyping and genetic modeling to dissect crossover G×E interaction and stability in maize
10.1590/1984-70332026v26n1a3 · ExternalCitation · doi-reference
Regularization paths for generalized linear models via coordinate descent
10.18637/jss.v033.i01 · ExternalCitation · doi-reference
nasapower: a NASA power global meteorology, surface solar energy and climatology data client for R
10.21105/joss · ExternalCitation · doi-reference
Predicting maize phenology: intercomparison of functions for developmental response to temperature
10.2134/agronj14.0200 · ExternalCitation · doi-reference
Analysis of a randomized block design with unequal subclass numbers
10.2134/agronj1997.00021962008900050002x · ExternalCitation · doi-reference
Prediction of maize single-cross performance using RFLPs and information from related hybrids
10.2135/cropsci1994.0011183x003400010003x · ExternalCitation · doi-reference
A uniform, objective, and adaptive system for expressing rice development
10.2135/cropsci2000.402436x · ExternalCitation · doi-reference
Model selection and validation for yield trials with interaction
10.2307/2531585 · ExternalCitation · doi-reference
Efficient methods to compute genomic predictions
10.3168/jds.2007-0980 · ExternalCitation · doi-reference
Artificial intelligence meets genomic selection: comparing deep learning and gblup across diverse plant datasets
10.3389/fgene.2025.1568705 · ExternalCitation · doi-reference
Enhancing genomic prediction with stacking ensemble learning in Arabica coffee
10.3389/fpls.2024.1373318 · ExternalCitation · doi-reference
Optimizing soybean variety selection for the Pan-African trial network using factor analytic models and envirotyping
10.3389/fpls.2025.1594736 · ExternalCitation · doi-reference
Stacking ensemble learning for genomic prediction under complex genetic architectures
10.3390/agronomy16020241 · ExternalCitation · doi-reference
Accuracy of genomewide selection for different traits with constant population size, heritability, and number of markers
10.3835/plantgenome2012.11.0030 · ExternalCitation · doi-reference
