Subject
- Agronomy 10
- Agriculture 1
- Biochemistry 1
Creator
- Mitchell R Tuinstra, 0000-0002-5322-6519 5
- Terry D West 5
- Garrett S Verhagen 4
- Seth A Tolley, 0000-0001-9664-3534 4
- Tony J Vyn, 0000-0001-9860-4475 4
- Alicia West 3
- D R Griffith 2
- Geoffrey Alistair Sanchez, 0009-0002-0604-260X 2
- J V Mannering 2
- Jeffrey J. Volenec, 0000-0002-5455-6157 2
Type
- Dataset 16
Date
Total is 16 Results
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
Best linear unbiased predictions (BLUPs) for ear photometry traits of 831 testcross maize hybrids. This dataset was used in ANOVA and tukey testing to differentiate maize heterotic groups.
10.4231/D2JJ-Y263
Mitchell R Tuinstra
,
Seth A Tolley
10/12/2020
Ear photometry was used to characterize 298 ex-PVP inbred lines and 274 Drought Tolerant Maize for Africa (DTMA) inbred lines when crossed to Iodent (PHP02) and/or Stiff Stalk (2FACC) testers for 25 yield-related traits in 2017 and 2018.
Agronomy Ear photometry in maize testcrosses heat-tolerant maize Maize
Multi-Year Study Maize Agrivoltaics Soil Moisture Data
10.4231/M2Z8-NT18
Geoffrey Alistair Sanchez
,
Peter Bermel
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
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
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
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
Nutrient analysis of corn earleaf at R1 under tillage, crop rotation, nitrogen, and sulfur treatments within a long-term tillage study
10.4231/44K5-EJ32
Alicia West
,
Garrett S Verhagen
,
Terry D West
,
Tony J Vyn
01/11/2023
Nutrient analysis (N, P, K, Ca, Mg, S, Zn, Mn, Fe, Cu, B, and Al) of corn earleaf at R1 under tillage, crop rotation, nitrogen (2017, 2019), and sulfur treatments (2020, 2021). Part of the Long-Term Tillage study in West Lafayette, IN.
Agronomy chisel-plow conservation tillage Corn crop rotation earleaf grain moisture Grain Yield Maize moldboard-plow no-till nutrient data plant height plant population planting date Purdue University ridge-till Soybean SPAD Chlorophyll Meter strip-till
Grain nutrient removal of corn influenced by the addition of nitrogen, sulfur fertilizers to a long-term tillage and crop rotation study conducted in the US Corn Belt
10.4231/9BA5-KK34
Alicia West
,
Garrett S Verhagen
,
Terry D West
,
Tony J Vyn
02/02/2023
Nutrient analysis (N, P, K, Ca, Mg, S, Zn, Mn, Fe, Cu, B, and Al) of corn grain at R6 across seven site-years involving tillage, crop rotation, nitrogen (2015-19), and sulfur treatments (2020-21). Part of the Long-Term Tillage study in West Lafayette, IN.
4R Nutrient Management Agronomy chisel-plow conservation tillage Corn crop rotation grain moisture grain nutrient Grain Yield kernel number kernel weight Maize moldboard-plow no-till nutrient removal plant height plant population planting date Purdue University Soybean strip-till
Grain and stover nutrient content in corn at maturity grown under multiple tillage, crop rotation, and nitrogen treatments
10.4231/GPZW-DR78
Alicia West
,
Garrett S Verhagen
,
Terry D West
,
Tony J Vyn
01/11/2023
Nutrient content (N, P, K, Ca, Mg, S, Zn, Mn, Fe, Cu, B, and Al) of corn grain and shoot at maturity across five site-years (2015-2019) under tillage, crop rotation, and nitrogen treatments. Part of the Long-Term Tillage study in West Lafayette, IN.
Agronomy chisel-plow cob nutrient conservation tillage Corn crop rotation grain moisture grain nutrient Grain Yield Maize moldboard-plow Nitrogen no-till nutrient content nutrient removal nutrient uptake plant height plant population planting date Purdue University ridge-till Soybean stover nutrient strip-till
Row Selection in Remote Sensing for Maize and Sorghum
10.4231/PF9S-4G38
Mitchell R Tuinstra
,
Seth A Tolley
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
,
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
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Results 1 - 10 of 16