Datasets

subject: Agronomy type: dataset date: 2023

Total is 10 Results
Aerial imagery of the Purdue Agronomy Center for Research and Education (ACRE) – 1963

10.4231/ZBHC-3K73

Darrell G Schulze, 0000-0001-9278-2457, Shams Rahman R Rahmani, 0000-0001-6246-2786

01/26/2023

Aerial imagery from 1963 of the Purdue Agronomy Center for Research and Education (ACRE), West Lafayette, IN, USA.

ACRE aerial photography Agronomy Remote sensing imagery

Aerial imagery of the Purdue Agronomy Center for Research and Education (ACRE) - 1976

10.4231/V168-XW88

Darrell G Schulze, 0000-0001-9278-2457, Shams Rahman R Rahmani, 0000-0001-6246-2786

01/26/2023

Aerial imagery from 1976 of the Purdue Agronomy Center for Research and Education (ACRE), West Lafayette, IN.

ACRE aerial photography Agronomy Remote sensing imagery

Aerial imagery of the Purdue Agronomy Center for Research and Education (ACRE) – 2013

10.4231/74KH-8X07

Darrell G Schulze, 0000-0001-9278-2457, Shams Rahman R Rahmani, 0000-0001-6246-2786

01/27/2023

Aerial imagery from 2013 of the Purdue Agronomy Center for Research and Education (ACRE), West Lafayette, IN.

ACRE aerial photography Agronomy Remote sensing imagery

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, 0000-0002-5455-6157, Stanislav Pejša, 0000-0001-5057-262X, Sylvie Brouder, 0000-0002-7785-5336, 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, 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

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