Tags: Agronomy

All Categories (1-20 of 61)

  1. 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

    2019-12-10 16:31:04 | Datasets | Contributor(s): Jeffrey J. Volenec, Sylvie M. Brouder | doi:10.4231/WWSK-RA65

    Maize was grown in 2006 in plots differing in soil test P and K. Yield and plant composition were determined.

    https://purr.purdue.edu/publications/3356

  2. 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

    2019-12-09 21:36:40 | Datasets | Contributor(s): Jeffrey J. Volenec, Sylvie M. Brouder | doi:10.4231/C1W0-8F75

    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.

    https://purr.purdue.edu/publications/3355

  3. Busia Digital Soil Mapping

    2019-12-04 16:09:46 | Datasets | Contributor(s): Joshua O Minai, Darrell G Schulze | doi:10.4231/E81F-NX21

    This study is to bring legacy soil data from the Busia area of Kenya ‘back to life’ using digital soil mapping techniques.

    https://purr.purdue.edu/publications/3338

  4. Soil Property Data for Spatial Prediction of Soil Properties for the Busia Area, Kenya

    2019-11-08 15:21:48 | Datasets | Contributor(s): Joshua Minai, Darrell Schulze | doi:10.4231/00R1-HM25

    Soil property data mined from the Reconnaissance Soil Survey of the Busia Area (quarter degree sheet No. 101) for digital soil mapping.

    https://purr.purdue.edu/publications/3273

  5. Principal Components for Stepwise Multiple Linear Regression

    2019-11-08 15:21:31 | Datasets | Contributor(s): Darrell Schulze, Joshua Minai | doi:10.4231/0FGR-Z715

    Independent predictor variables for stepwise multiple linear regression.

    https://purr.purdue.edu/publications/3272

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

    2019-11-08 15:21:14 | Datasets | Contributor(s): Joshua Minai, Darrell Schulze | doi:10.4231/4DBT-2W68

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

    https://purr.purdue.edu/publications/3268

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

    2019-11-08 15:20:52 | Datasets | Contributor(s): Joshua Minai, Darrell Schulze | doi:10.4231/7F9R-4W74

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

    https://purr.purdue.edu/publications/3267

  8. Busia Soil Class Map

    2019-11-08 15:20:32 | Datasets | Contributor(s): Joshua Minai, Darrell Schulze | doi:10.4231/6D95-RH34

    This is a disaggregated soil map of the Busia area.

    https://purr.purdue.edu/publications/3265

  9. Busia Land Quality Maps

    2019-11-08 15:20:16 | Datasets | Contributor(s): Joshua Minai, Darrell Schulze | doi:10.4231/KYJ5-S732

    Independent diagnostic criteria reflecting limitations for land use.

    https://purr.purdue.edu/publications/3271

  10. Busia K-Means Cluster Map

    2019-11-08 15:20:02 | Datasets | Contributor(s): Joshua Minai, Darrell Schulze | doi:10.4231/4Q9T-FT90

    Map that mimics the geometry of 'fully developed slopes'.

    https://purr.purdue.edu/publications/3270

  11. Busia Fuzzy Soil Class Map

    2019-11-08 15:19:47 | Datasets | Contributor(s): Darrell Schulze, Joshua Minai | doi:10.4231/TDN8-PM14

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

    https://purr.purdue.edu/publications/3269

  12. Busia Crop Suitability Maps

    2019-11-08 15:19:15 | Datasets | Contributor(s): Joshua Minai, Darrell Schulze | doi:10.4231/VA77-JB85

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

    https://purr.purdue.edu/publications/3266

  13. Potential Suitability for Subirrigation in the U.S. Midwest

    2019-11-05 15:07:45 | Datasets | Contributor(s): Feng Yu, Jane Frankenberger, Jason Ackerson, Benjamin Reinhart | doi:10.4231/10ES-AH67

    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).

    https://purr.purdue.edu/publications/3263

  14. Mutations in sorghum SBEIIb and SSIIa affect alkali spreading value, starch composition, thermal properties and flour viscosity

    2019-07-19 17:57:19 | Datasets | Contributor(s): Stefanie Griebel, Richard Westerman, Adedayo Adeyanju, Charles Addo-Quaye, Bruce A. Craig, Clifford Weil, Suzanne M. Cunningham, Bhavesh Patel, Osvaldo H. Campanella, Mitchell Tuinstra | doi:10.4231/3GBF-RS75

    Seven novel alleles of SBEIIb and one allele of SSIIa co-segregated with the ASV phenotype and contributed to distinct starch quality traits important for food processing applications.

    https://purr.purdue.edu/publications/3238

  15. Data For Relationships of Nitrous Oxide Emissions to Fertilizer Nitrogen Recovery Efficiencies in Rain-fed Corn Systems: Research Foundation Building

    2019-07-05 13:44:28 | Datasets | Contributor(s): Rex Omonode, Tony Vyn | doi:10.4231/NSKN-0502

    Tillage affected relationships between N2O and NRE; stronger negative linear relationships under no-till and strip-till compared to moldboard. Ecological intensification increased grain yield without significant increase of N2O emission.

    https://purr.purdue.edu/publications/3117

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

    2019-06-06 04:00:00 | Datasets | Contributor(s): Cheng-Hsien Lin, Cliff Johnston, Richard Grant, Albert Heber | doi:10.4231/06W5-J904

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

    https://purr.purdue.edu/publications/3216

  17. Uniform Soybean Tests Northern Region 2014

    2019-05-01 16:04:40 | Datasets | Contributor(s): David H. Schlueter, Steve R. Scofield | doi:10.4231/R7TH8JQB

    This dataset contains tables prepared for the 2014 Uniform Soybean Tests Northern Region Report.

    https://purr.purdue.edu/publications/2568

  18. Uniform Soybean Tests Northern Region 2018

    2019-05-01 15:34:20 | Datasets | Contributor(s): Guohong Cai, Gary Nowling | doi:10.4231/6MHS-FZ56

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

    https://purr.purdue.edu/publications/3196

  19. Reducing Nitrous Oxide Emissions with Enhanced Efficiency Nitrogen Fertilizers in Wheat Production in Manitoba, Canada

    2019-02-13 21:17:16 | Datasets | Contributor(s): Mario Tenuta, Matthew Wood, Xiaopeng Gao | doi:10.4231/FXR5-F070

    This project was conducted to quantify and compare the application time (fall, spring) and source (EEF, non-EEF) effects of nitrogen fertilizers on nitrous oxide emissions and agronomic measurements of spring wheat in southern Manitoba, Canada.

    https://purr.purdue.edu/publications/3122

  20. Evaluation of nitrogen stabilizers - nitrogen use efficiency and greenhouse gas emissions in Alberta cereal crops

    2019-02-06 16:29:39 | Datasets | Contributor(s): Miles Dyck, Len Kryzanowski, Yuanpei Gao, Dick Puurveen | doi:10.4231/MDJM-0529

    These data files summarize spring wheat yields and annual soil N2O emissions (non-steady-state chamber measurements) at two sites in Alberta, Canada (Lethbridge and Ellerslie) over 2016 and 2017.

    https://purr.purdue.edu/publications/3115

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