Datasets

subject: Maize type: dataset

Total is 16 Results
Agronomic responses of soybean to long-term implementation of tillage and crop rotation systems in Indiana from 1975 to 2022

10.4231/2KQ5-KR50

D R Griffith , Garrett S Verhagen , J V Mannering , Terry D West , Tony J Vyn ORCID logo

02/02/2023

This dataset includes soybean population, heights, grain moisture at harvest, and grain yield measured in the Long-Term Tillage (LTT) study at the Agronomy Center for Research and Education (ACRE) in West Lafayette, IN, USA from 1975 to 2022.

Agronomy chisel-plow conservation tillage Corn crop rotation grain moisture Grain Yield Maize moldboard-plow no-till plant height plant population planting date Purdue University ridge-till Soybean strip-till

Agronomic responses of corn to long-term implementation of tillage and crop rotation systems in the US Corn Belt, from 1975 to 2022

10.4231/E031-BS21

D R Griffith , Garrett Verhagen , J V Mannering , Terry D West , Tony Vyn ORCID logo

02/02/2023

This dataset includes corn population, heights, grain moisture at harvest, and grain yield measured in the Long-Term Tillage (LTT) study at the Agronomy Center for Research and Education (ACRE) in West Lafayette, IN, USA from 1975 to 2022.

Agronomy chisel-plow conservation tillage Corn crop rotation grain moisture Grain Yield Maize moldboard-plow no-till plant height plant population planting date Purdue University ridge-till Soybean strip-till

Multi-Year Study Maize Agrivoltaics Soil Moisture Data

10.4231/M2Z8-NT18

Geoffrey Alistair Sanchez ORCID logo , Peter Bermel ORCID logo

03/07/2024

Volumetric water content (m^3/m^3) data for agrivoltaic experimental setup. Here will also be the data which was inputed based off a k-fold Bayesian Regularization Neural Network.

agrivoltaics Bayesian Regularization Neural Network Maize Soil Moisture

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

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 ORCID logo , Seth A Tolley ORCID logo

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

Genetic Parameters and Multi-trait Genomic Prediction of Grain Yield on a Plot and Ear Basis in Temperate and Tropical Maize

10.4231/PQFT-7G59

Mitchell R Tuinstra ORCID logo , Seth A Tolley ORCID logo

05/02/2023

The objective of this study was to assess genetic parameters and perform single- and multi-trait genomic prediction of grain yield and yield components assessed through ear photometry in three testcross populations of either temperate or tropical descent.

Ear Photometry Ear photometry in maize testcrosses Genomic Prediction Grain Yield Maize Multi-Trait Genomic Prediction Single-Trait Genomic Prediction Temperate Germplasm Tropical Germplasm Yield components

Measuring N2O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model

10.4231/JE4B-8J88

Cheng-Hsien Lin ORCID logo , Cliff Johnston , Richard H Grant ORCID logo

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

Display #

Results 1 - 10 of 16