Subject
- Crop Science 3
- Agriculture 2
- Botany 2
- Plant Pathology 2
Creator
- Mitchell R Tuinstra, 0000-0002-5322-6519 3
- Adam Nicholas Brock 2
- Diane Ran Wang, 0000-0002-2290-3257 2
- Guohong Cai 2
- Rachel Katrina Imel 2
- Richard H Grant, 0000-0001-9464-6942 2
- Shelby M Gruss 2
- Augusto Souza 1
- Carmela R. Guadagno 1
- Chris Hoagland 1
Type
- Dataset 10
Date
- 2022 10
subject: Agronomy type: dataset date: 2022
Total is 10 Results
Southeast Purdue Agricultural Center (SEPAC) drainage research data, 1984-2020
10.4231/CTM1-0Y45
Eileen J Kladivko, 0000-0002-8993-2722, Giorgi Chighladze, 0000-0002-7819-0508, Laura C Bowling, 0000-0002-1439-3154, Lori Abendroth, 0000-0002-0176-7815
12/16/2022
This dataset contains research data obtained during a long-term subsurface drainage research project conducted at the Southeast Purdue Agricultural Center in Jennings County, Indiana, from 1984 to 2020.
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.
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
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.
Surface boundary layer turbulence measurements: Purdue Agronomy Center for Research and Education
10.4231/JP60-BN82
Richard H Grant, 0000-0001-9464-6942
05/19/2022
Dataset of measurements made between 2013 and 2021
Uniform Soybean Tests Northern Region 2020
10.4231/ZWFR-8Y15
Adam Nicholas Brock, Guohong Cai
05/09/2022
This dataset contains tables prepared for the 2020 Uniform Soybean Tests Northern Region Report.
Agriculture Agronomy Botany Botany and Plant Pathology Crop Science Plant Pathology Soybeans USDA-ARS
Uniform Soybean Tests Northern Region 2021
10.4231/8FTE-B575
Adam Nicholas Brock, Guohong Cai
05/09/2022
This dataset contains tables prepared for the 2021 Uniform Soybean Tests Northern Region Report.
Agriculture Agronomy Botany Crop Science Plant Pathology Soybeans USDA-ARS
Surface boundary layer turbulence on an 11% slope.
10.4231/3PT4-GD65
Richard H Grant, 0000-0001-9464-6942
05/31/2022
Surface Boundary Layer Turbulence measurements in presence of mesoscale slope flow of 11% slope.
Agronomy Mesoscale Meteorology planetary boundary layer Slope Position wind
Quantifying physiological trait variation with automated hyperspectral imaging in rice
10.4231/SY0A-W347
Augusto Souza, Carmela R. Guadagno, Chris Hoagland, Diane Ran Wang, 0000-0002-2290-3257, Rachel Katrina Imel, To-Chia Ting, 0000-0001-5273-7520, Yang Yang
12/08/2022
To predict rice physiological traits from automated hyperspectral data, 14 physiological traits were collected when the rice plants were six to 13 weeks old. Concurrently, side-view hyperspectral imaging events took place two to three times per week.
Agronomy Genetic Diversity growth traits Hyperspectral imaging Nitrogen Oryza sativa
Evaluation of water-deficit responses in wild-by-cultivated rice introgression lines
10.4231/0JP3-WK59
Diane Ran Wang, 0000-0002-2290-3257, Rachel Katrina Imel
10/04/2022
Rice introgression lines were analyzed for their response to water-deficit conditions under controlled environments at Purdue University as well as a field setting in Stuttgart, Arkansas.
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