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
Data for Fertilizer Management and Environmental Factors Drive N2O and NO3 Losses in Corn: A Meta-Analysis
10.4231/R7NS0S57
Alison J Eagle , Ardell D. Halvorson , Bijesh Maharjan , Craig F. Drury , Douglas R. Smith , G. Philip Robertson , John P. Hoben , Rodney T. Venterea , Timothy B. Parkin
11/12/2018
Data used in Eagle et al. 2017 (Soil Sci. Soc. Am. J. 81:1191–1202; doi:10.2136/sssaj2016.09.0281). Fertilizer Management and Environmental Factors Drive N2O and NO3 Losses in Corn: A Meta-Analysis
4R Nutrient Management Agriculture Climate Change Corn Empirical Models Hierarchical Model IPNI Maize Meta-analysis Nitrate Leaching Nitrogen Nitrogen Fertilizer Nitrous Oxide North America Synthesis Water Quality
Maize Response to P and K in 2006. Experiment 1 at Throckmorton Purdue Agricultural Center: Yield, Soil and Tissue P and K, and Seed and Stover Composition
10.4231/C1W0-8F75
Jeffrey J. Volenec
,
Sylvie M. Brouder
12/18/2019
Grain and stover yield and composition were determined for maize grown in soil previously fertilized annually for 8 years with potassium (K, 0 to 400 kg K/ha) and phosphorus (P, 0 to 75 kg P/ha) resulting in large differences in soil test P and K.
Agronomy Biomass Corn Grain Yield Indiana Maize Phosphorus Fertilizer Plant Nutrition Potassium Fertilizer Seed Composition Soil Fertility Stover Composition
Maize Response to P and K in 2006. Experiment 2 at Throckmorton Purdue Agricultural Center: Yield, Soil and Tissue P and K, and Stover and Grain Composition
10.4231/WWSK-RA65
Jeffrey J. Volenec
,
Sylvie M. Brouder
12/18/2019
Maize was grown in 2006 in plots differing in soil test P and K. Yield and plant composition were determined.
Agronomy Corn Grain Yield Indiana Maize Phosphorus Fertilizer Plant Nutrition Potassium Fertilizer Soil Fertility Stover Composition
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
Maize 282 Kernel Ion and Isotope BLUPs
10.4231/R7TT4P5D
05/31/2018
The 282 member maize association panel was grown under irrigated and non-irrigated conditions and kernels were phenotyped for ionome and isotope traits.
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
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
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
,
Seth A Tolley
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
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Results 1 - 10 of 16