Subject
- Agronomy 6
Creator
- Seth A Tolley, 0000-0001-9664-3534 4
- Shelby M Gruss 3
- Keith Johnson 1
- Meng-yang Lin 1
- Shelby M. Gruss 1
Type
- Dataset 9
Date
creator: Mitchell R Tuinstra, 0000-0002-5322-6519 type: dataset
Total is 9 Results
Expression of Stay-Green in Dhurrin-Free Sorghum
10.4231/4ABA-TW22
Mitchell R Tuinstra, 0000-0002-5322-6519 Shelby M Gruss
07/15/2022
This data examines the effects dhurrin production has on the stay-green trait by utilizing near isogenic lines, near isogenic backcrosses, and near isogenic hybrids varying in dhurrin production.
Preference of dhurrin-free sorghum by ewes
10.4231/A2WW-EF74
Mitchell R Tuinstra, 0000-0002-5322-6519 Shelby M. Gruss
10/16/2023
The preference of dhurrin-free sorghum was evaluated using ewes in a free choice grazing trial comparing a dhurrin-free hybrid to commercial hyrbids, and a set of near isogenic lines varying in dhurrin production.
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, 0000-0002-5322-6519 Seth A Tolley, 0000-0001-9664-3534
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
Multi-Species Prediction of Physiological Traits with Hyper-Spectral Modeling
10.4231/FPHP-0153
Meng-yang Lin, Mitchell R Tuinstra, 0000-0002-5322-6519
02/11/2022
High-throughput hyperspectral imaging in corn and sorghum can be used in multi-species models to predict water and nitrogen status of plants within and across these crop species.
Abiotic stress Agronomy Corn Ecophysiology High-throughput Phenotyping Machine Learning nitrogen content partial least square regression relative water content Remote Sensing Sorghum
Row Selection in Remote Sensing for Maize and Sorghum
10.4231/PF9S-4G38
Mitchell R Tuinstra, 0000-0002-5322-6519 Seth A Tolley, 0000-0001-9664-3534
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
Seedling growth and fall armyworm feeding preference influenced by dhurrin production in sorghum
10.4231/3PQE-NP07
Mitchell R Tuinstra, 0000-0002-5322-6519 Shelby M Gruss
06/15/2021
Dhurrin plays a key role in host-plant defense of sorghum. Studies of genetic mutants coupled with nondestructive phenotyping techniques revealed a significant metabolic tradeoff between dhurrin production and plant growth in sorghum seedlings.
Agronomy dhurrin High-throughput Phenotyping Spodoptera frugiperda
Stability of dhurrin and HCN release in dried sorghum samples
10.4231/SDJ6-9C84
Keith Johnson, Mitchell R Tuinstra, 0000-0002-5322-6519 Shelby M Gruss
05/31/2022
Dhurrin is a cyanogenic glucoside of sorghum. Dhurrin content is thought to decline when making sorghum hay. Contrary to expectations, this study demonstrated that dhurrin was stable in sorghum tissues during the hay drying and curing process.
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, 0000-0002-5322-6519 Seth A Tolley, 0000-0001-9664-3534
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, 0000-0002-5322-6519 Seth A Tolley, 0000-0001-9664-3534
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
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Results 1 - 9 of 9