Subject
- Agronomy 2
Creator
- Seth A Tolley, 0000-0001-9664-3534 4
- Elizabeth K. Grubbs 1
- Geoffrey Alistair Sanchez, 0009-0002-0604-260X 1
- Margaret W Gitau, 0000-0002-4931-7047 1
- Peter Bermel, 0000-0001-7140-0667 1
- Rakesh Agrawal, 0000-0002-6746-9829 1
- Shelby Gruss 1
- Val Schull 1
Type
- Dataset 5
Date
subject: Maize creator: Mitchell R Tuinstra, 0000-0002-5322-6519
Total is 5 Results
Multi-Year Study Maize Agrivoltaics Soil Moisture and Maize Information
10.4231/0JKD-A159
Elizabeth K. Grubbs
,
Geoffrey Alistair Sanchez
,
Margaret W Gitau
,
Mitchell R Tuinstra
,
Peter Bermel
,
Rakesh Agrawal
,
Shelby Gruss
,
Val Schull
11/18/2024
Volumetric water content (m^3/m^3) data, maize yield, silking/anthesis, height data for agrivoltaic experimental setup. Here will also be the data which was inputed based off a k-fold Bayesian Regularization Neural Network.
agrivoltaics Bayesian Regularization Neural Network Crop yield Maize Soil Moisture
Row Selection in Remote Sensing for Maize and Sorghum
10.4231/PF9S-4G38
Mitchell R Tuinstra
,
Seth A Tolley
07/26/2023
Remote sensing data evaluates all row segments of a plot, but the repeatability of traits from different row segments has not been evaluated. We evaluated which row segments provide the best repeatability and yield prediction of remote sensing traits.
Border effect High-throughput Phenotyping hyperspectral LiDAR Maize Plot trimming Predictive modelling Remote Sensing RGB Sorghum UAV
Investigating the Genomic Background and Predictive Ability of Genotype-by-environment Interactions in Maize Grain Yield Based on Reaction Norm Models
10.4231/0C1Q-2G44
Mitchell R Tuinstra
,
Seth A Tolley
05/12/2023
Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. The main objectives of this study were to evaluate genetic parameters and perform genomic prediction using a reaction norm model.
Agronomy G2F Genome-Wide Association Study Genomes 2 Fields Genomic Prediction Genotype-by-environment Interaction Grain Yield GxE Maize Multi-environment Trial Reaction Norm Model
Genetic Parameters and Multi-trait Genomic Prediction of Grain Yield on a Plot and Ear Basis in Temperate and Tropical Maize
10.4231/PQFT-7G59
Mitchell R Tuinstra
,
Seth A Tolley
05/02/2023
The objective of this study was to assess genetic parameters and perform single- and multi-trait genomic prediction of grain yield and yield components assessed through ear photometry in three testcross populations of either temperate or tropical descent.
Ear Photometry Ear photometry in maize testcrosses Genomic Prediction Grain Yield Maize Multi-Trait Genomic Prediction Single-Trait Genomic Prediction Temperate Germplasm Tropical Germplasm Yield components
Best linear unbiased predictions (BLUPs) for ear photometry traits of 831 testcross maize hybrids. This dataset was used in ANOVA and tukey testing to differentiate maize heterotic groups.
10.4231/D2JJ-Y263
Mitchell R Tuinstra
,
Seth A Tolley
10/12/2020
Ear photometry was used to characterize 298 ex-PVP inbred lines and 274 Drought Tolerant Maize for Africa (DTMA) inbred lines when crossed to Iodent (PHP02) and/or Stiff Stalk (2FACC) testers for 25 yield-related traits in 2017 and 2018.
Agronomy Ear photometry in maize testcrosses heat-tolerant maize Maize
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Results 1 - 5 of 5