|Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures|
R Howard, AL Carriquiry, WD Beavis
G3: Genes, Genomes, Genetics 4 (6), 1027-1046, 2014
|Increasing genomic‐enabled prediction accuracy by modeling genotype× environment interactions in Kansas wheat|
D Jarquín, C Lemes da Silva, RC Gaynor, J Poland, A Fritz, R Howard, ...
The plant genome 10 (2), 1-15, 2017
|Genome-wide analysis of grain yield stability and environmental interactions in a multiparental soybean population|
A Xavier, D Jarquin, R Howard, V Ramasubramanian, JE Specht, ...
G3: Genes, Genomes, Genetics 8 (2), 519-529, 2018
|Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype× environment interaction on prediction accuracy in chickpea|
M Roorkiwal, D Jarquin, MK Singh, PM Gaur, C Bharadwaj, A Rathore, ...
Scientific reports 8 (1), 1-11, 2018
|Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region|
FAM Tenorio, AJ Eagle, EL McLellan, KG Cassman, R Howard, FE Below, ...
Field Crops Research 240, 185-193, 2019
|Increasing predictive ability by modeling interactions between environments, genotype and canopy coverage image data for soybeans|
D Jarquin, R Howard, A Xavier, S Das Choudhury
Agronomy 8 (4), 51, 2018
|Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery|
J Li, AN Veeranampalayam-Sivakumar, M Bhatta, ND Garst, H Stoll, ...
Plant Methods 15 (1), 123, 2019
|Application of response surface methods to determine conditions for optimal genomic prediction|
R Howard, AL Carriquiry, WD Beavis
G3: Genes, Genomes, Genetics 7 (9), 3103-3113, 2017
|Genomic prediction using canopy coverage image and genotypic information in soybean via a hybrid model|
R Howard, D Jarquin
Evolutionary Bioinformatics 15, 1176934319840026, 2019
|Response surface analysis of genomic prediction accuracy values using quality control covariates in soybean|
D Jarquín, R Howard, G Graef, A Lorenz
Evolutionary Bioinformatics 15, 1176934319831307, 2019
|Enhancing hybrid prediction in pearl millet using genomic and/or multi-environment phenotypic information of inbreds|
D Jarquin, R Howard, Z Liang, SK Gupta, JC Schnable, J Crossa
Frontiers in Genetics 10, 2019
|Joint USE of genome, pedigree, and their interaction with environment for predicting the performance of wheat lines in new environments|
R Howard, D Gianola, O Montesinos-López, P Juliana, R Singh, J Poland, ...
G3: Genes, Genomes, Genetics 9 (9), 2925-2934, 2019
|Evaluation of parametric and nonparametric statistical methods in genomic prediction|
|Predicting Yield by Modeling Interactions between Canopy Coverage Image Data, Genotypic and Environmental Information for Soybeans|
D Jarquin, R Howard, A Xavier, SD Choudhury
Intelligent Image Analysis for Plant Phenotyping, 267-286, 2020
|Genomic Prediction Enhanced Sparse Testing for Multi-environment Trials|
D Jarquin, R Howard, J Crossa, Y Beyene, M Gowda, JWR Martini, ...
G3: Genes, Genomes, Genetics 10 (8), 2725-2739, 2020
|The local stability of a modified multi-strain SIR model for emerging viral strains|
M Fudolig, R Howard
A Xavier, W Beavis, J Specht, B Diers, R Howard, W Muir, K Rainey, ...
|Achieving Higher Genetic Gain by Enhancing Precision through Genomic Selection Breeding in Chickpea|
M Roorkiwal, N Santantonio, D Jarquin, B Chellapilla, M Singh, PM Gaur, ...
Plant and Animal Genome XXVII Conference (January 12-16, 2019), 2019
|Principal Variable Selection to Explain Grain Yield Variation in Winter Wheat from UAV-derived Phenotypic Traits|
J Li, M Bhatta, ND Garst, H Stoll, AN Veeranampalayam-Sivakumar, ...
2019 ASABE Annual International Meeting, 1, 2019
|Response Surface Methodology in Genomic Selection|
Plant and Animal Genome XXIV Conference, 2016