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Hatfield, J.L
Crowther, J
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Authors
Hatfield, J.L
Parkin, T.B
Crowther, J
Parrish, J
Ferguson, R
Luck, J
Glewen, K
Shaver, T
Krull, D
Thompson, L
Mueller, N
Krienke, B
Mieno, T
Ingram, T
Parrish, J
Ferguson, R
Luck, J
Glewen, K
Thompson, L
Krienke, B
Mueller, N
Ingram, T
Krull, D
Crowther, J
Shaver, T
Mieno, T
Topics
Type
Oral
Year
2011
2017
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1. Nitrogen Management: Unraveling the Effect of Timing and Form

Improvement of nitrogen use efficiency by co rn production would decrease the potential for nitrogen loss into the environment. A study ha s been conducted in Ames, Iowa on 16 different forms and rates of nitrogen in both a continuous corn and corn-soybean production systems. There were differences among treatments; howeve r, the most consistent treatment was the SuperU applied as a 150 lb A -1 preplant or as 50 lb A-1 preplant and 100 lb A-1 sidedress and UAN with Agrotain adde d to both the 50...

2. Integrating Management Zones and Canopy Sensing for Improved Nitrogen Recommendation Algorithms

Active crop canopy sensors have been studied as a tool to direct spatially variable nitrogen (N) fertilizer applications in maize, with the goal of increasing the synchrony between N supply and crop demand and thus improving N use efficiency (NUE). However, N recommendation algorithms have often proven inaccurate in certain subfield regions due to local spatial variability. Modifying these algorithms by integrating soil-based management zones (MZ) may improve their accuracy... J. Crowther, J. Parrish, R. Ferguson, J. Luck, K. Glewen, T. Shaver, D. Krull, L. Thompson, N. Mueller, B. Krienke, T. Mieno, T. Ingram

3. Comparison of Ground-Based Active Crop Canopy Sensor and Aerial Passive Crop Canopy Sensor for In-Season Nitrogen Management

Crop canopy sensors represent one tool available to help calculate a reactive in-season nitrogen (N) application rate in corn. When utilizing such systems, corn growers must decide between using active versus passive crop canopy sensors. The objectives of this study was to 1) determine the correlation between N management by remote sensing using a passive sensor and N management using proximal sensing with an active sensors. Treatments were arranged as field length strips in a randomized complete... J. Parrish, R. Ferguson, J. Luck, K. Glewen, L. Thompson, B. Krienke, N. Mueller, T. Ingram, D. Krull, J. Crowther, T. Shaver, T. Mieno