Subject
- Crop Science 47
- Agriculture 41
- Plant Pathology 34
- Botany 33
- Soil Science 13
- Geographic Information Systems (GIS) 4
Creator
- Wad D. Crochet 12
- Darrell Schulze, 0000-0001-9278-2457 9
- J.R. Wilcox 9
- Joshua Minai 9
- Mitchell Tuinstra, 0000-0002-5322-6519 9
- Gary Nowling 7
- Jeffrey J. Volenec, 0000-0002-5455-6157 7
- Sara Peel 7
- Stanislav Pejša, 0000-0001-5057-262X 7
- Sylvie M. Brouder, 0000-0002-7785-5336 7
Type
Date
Total is 104 Results
Quantifying physiological trait variation with automated hyperspectral imaging in rice
10.4231/SY0A-W347
Augusto Souza
,
Carmela R. Guadagno
,
Chris Hoagland
,
Diane Ran Wang
,
Rachel Katrina Imel
,
To-Chia Ting
,
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
,
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.
Genetic and environmental variation in alfalfa forage yield from variety testing experiments conducted in North America between 1986 to 1999
10.4231/QS1J-6J77
Daniel Wiersma
,
Jeffrey Volenec
,
Stanislav Pejša
,
Sylvie Brouder
,
Wayne G. Hartman
05/25/2023
The dataset contains data used to analyze genetic and environmental effects on alfalfa yield and agronomic performance. Data were compiled from alfalfa variety tests conducted by University researchers in the US and Canada from 1986 through 1999.
Agriculture Agronomy Alfalfa alfalfa_db Forage yield Genetic improvement Genotype x environment Germplasm Lucerne Medicago Variety testing
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
Display #
Results 101 - 104 of 104