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
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
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
,
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
Aerial imagery of the Purdue Agronomy Center for Research and Education (ACRE) – 1963
10.4231/ZBHC-3K73
Darrell G Schulze
,
Shams Rahman R Rahmani
01/26/2023
Aerial imagery from 1963 of the Purdue Agronomy Center for Research and Education (ACRE), West Lafayette, IN, USA.
Aerial imagery of the Purdue Agronomy Center for Research and Education (ACRE) - 1976
10.4231/V168-XW88
Darrell G Schulze
,
Shams Rahman R Rahmani
01/26/2023
Aerial imagery from 1976 of the Purdue Agronomy Center for Research and Education (ACRE), West Lafayette, IN.
Aerial imagery of the Purdue Agronomy Center for Research and Education (ACRE) – 2013
10.4231/74KH-8X07
Darrell G Schulze
,
Shams Rahman R Rahmani
01/27/2023
Aerial imagery from 2013 of the Purdue Agronomy Center for Research and Education (ACRE), West Lafayette, IN.
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
Measuring N2O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model
10.4231/JE4B-8J88
Cheng-Hsien Lin
,
Cliff Johnston
,
Richard H Grant
09/21/2020
This file includes the data used in the figures of the manuscript entitled 'Measuring N2O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model'.
A backward Lagrangian stochastic (bLS) dispersion model Agronomy Atmospheric measurements Greenhouse Gas Emissions Maize multiple emission sources N2O OP-FTIR
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