Datasets

Subject
Creator
Type
Date

subject: Agronomy type: dataset

Total is 101 Results
Measuring N2O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model

10.4231/JE4B-8J88

Cheng-Hsien Lin, 0000-0001-6580-5457, Cliff Johnston, Richard H Grant, 0000-0001-9464-6942

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, 0000-0002-5322-6519, Seth A Tolley, 0000-0001-9664-3534

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

Uniform Soybean Tests Northern Region 2018

10.4231/6MHS-FZ56

Gary Nowling, Guohong Cai

05/01/2019

This dataset contains tables prepared for the 2018 Uniform Soybean Tests Northern Region Report.

Agriculture Agronomy Botany Crop Science Soybeans USDA-ARS

Application of Open Path Fourier Transform Infrared Spectroscopy (OP-FTIR) to Measure Greenhouse Gas Concentrations from Agricultural Fields

10.4231/06W5-J904

Albert Heber, Cheng-Hsien Lin, 0000-0001-6580-5457, Cliff Johnston, Richard Grant, 0000-0001-9464-6942

06/06/2019

Application of Open Path Fourier Transform Infrared Spectroscopy (OP-FTIR) to Measure N2O and CO2 Concentrations from Agricultural Fields

Agronomy Atmospheric measurements CO2 Corn Greenhouse gases N2O Open-path FTIR spectroscopy Quality Assurance Quality Control

Potential Suitability for Subirrigation in the U.S. Midwest

10.4231/10ES-AH67

Benjamin Reinhart, 0000-0003-1649-3762, Feng Yu, 0000-0002-6505-2288, Jane Frankenberger, 0000-0002-2781-0041, Jason Ackerson, 0000-0001-8123-0200

11/08/2019

This series provides shapefiles showing the potential suitability, and limiting factors, for subirrigation in the U.S. Midwest based on gSSURGO soil data from the Natural Resources Conservation Service (NRCS).

Agricultural and Biological Engineering Agriculture Agronomy Drainage Geographic Information Systems (GIS) gSSURGO Irrigation Midwest Subirrigation Water Water Management

Busia Soil Class Map

10.4231/6D95-RH34

Darrell Schulze, 0000-0001-9278-2457, Joshua Minai

11/18/2019

This is a disaggregated soil map of the Busia area.

Agronomy Busia Area Digital Soil Mapping Disaggregate K-Means Clustering Kenya Multiresolution Ridgetop Flatness Multiresolution Valley Bottom Flatness Planform Curvature Profile Curvature Topographic Position Index

Busia Crop Suitability Maps

10.4231/VA77-JB85

Darrell Schulze, 0000-0001-9278-2457, Joshua Minai

11/18/2019

These are suitability classes defining the requirements for various crops/ land use types.

Agronomy Beans Busia Area Cabbage Cassava Coffee Corn Cotton Crop Suitability Map Decision Matrix Forage Crops Grazing Guavas Kale Kenya Land Suitability Map Unit Millets Onions Peanuts Rice Sorghum Sugarcane Sunflower Tomato

Environmental Covariate Data for Spatial Prediction of Soil Properties for the Busia Area, Kenya

10.4231/7F9R-4W74

Darrell Schulze, 0000-0001-9278-2457, Joshua Minai

11/18/2019

Environmental covariates were carefully selected to represent factors of soil formation: climate, relief, organisms, and time.

Agronomy Busia Area Environmental Covariates Kenya Landsat Terrain Attributes WorldClim

Environmental Covariate Data for the Disaggregation of the Reconnaissance Soil Map of the Busia Area, Kenya

10.4231/4DBT-2W68

Darrell Schulze, 0000-0001-9278-2457, Joshua Minai

11/18/2019

Enviromental covariate data used to develop a digital model that represents the landscape and environmental conditions of the Busia landscape.

Agronomy Busia Area Digital Soil Mapping Disaggregate Geographic Information Systems (GIS) Kenya Soil Land Rule-Based Approach

Busia Fuzzy Soil Class Map

10.4231/TDN8-PM14

Darrell Schulze, 0000-0001-9278-2457, Joshua Minai

11/18/2019

Map based on the concept that soil classes can be spatially inferred from soil-related environmental conditions.

Agronomy Busia Area Digital Soil Mapping Fuzzy Membership Values Geographic Information Systems (GIS) Kenya SoLIM

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