Datasets

subject: Sorghum creator: Mitchell R Tuinstra, 0000-0002-5322-6519

Total is 5 Results
Row Selection in Remote Sensing for Maize and Sorghum

10.4231/PF9S-4G38

Mitchell R Tuinstra ORCID logo , Seth A Tolley ORCID logo

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

Expression of Stay-Green in Dhurrin-Free Sorghum

10.4231/4ABA-TW22

Mitchell R Tuinstra ORCID logo , 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.

Agronomy dhurrin NDVI Plant Breeding Sorghum stay-green

Preference of dhurrin-free sorghum by ewes

10.4231/A2WW-EF74

Mitchell R Tuinstra ORCID logo , 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.

dhurrin ewes Forage Preference Nutritive value Sorghum

Stability of dhurrin and HCN release in dried sorghum samples

10.4231/SDJ6-9C84

Keith Johnson , Mitchell R Tuinstra ORCID logo , 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.

Agronomy dhurrin Forage quality Sorghum

Multi-Species Prediction of Physiological Traits with Hyper-Spectral Modeling

10.4231/FPHP-0153

Meng-yang Lin , Mitchell R Tuinstra ORCID logo

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

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