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1. Use of Nitrogen Fertilizer Sources to Enhance Tolerance and Recovery of New Corn Hybrids from Early Season Soil WaterloggingCorn (Zea mays L.) production losses due to temporarily flooded or saturated soils resulting from excessive precipitation are a persistent problem in Missouri and the Midwest Region of the United States. In 2011 alone, monetary losses for corn and soybean (Glycine max) production due to excessive flooding in the Midwest were calculated to be more than $1.6 billion. Application of different sources of nitrogen (N) fertilizer may promote increased flood tolerance and recovery in interaction with different... |
2. An Environmental Assessment of Sensor-Based Variable-Rate Nitrogen Management in CornIn order to address the problem of nitrate contamination of surface and ground waters, various methods have been used to try to account for spatial variability of N within agricultural fields. One approach to account for this variability and thereby reduce nitrate pollution is in-season site- specific N application according to economic optimal N rate (EONR). Recently, active crop canopy sensors have been tested for mid-season, on-the-go N fertilizer application in corn. This 2004 and 2005 study... |
3. What are the Benefits of Canopy Sensing for Variable-Rate Nitrogen Corn Fertilization?Canopy reflectance sensing for assessing crop N health has been proposed as a technology on which to base top-dress variable-rate N applicat ion. The objective of this research in Missouri was to evaluate the economic a nd environmental benefit of activ e-light crop-canopy reflectance sensors for corn N rate decisions. A total of 16 field-scale experiments were conducted over four seasons (2004-2007) in three major soil areas. Mu ltiple blocks of randomized N rate response plots traversed the length... |
4. Irigated Soybean Response to Nitroen Applied During Early Pod FormationHigh yield soybean ( Glycine max L.) has a high rate of N uptake during grain fill with maybe 2/3 of the N derived from the atmosphere. The rema ining needs to come from the soil. Previous research has found that the probability of response to N applied at early pod development (R3) for yield trials, including 44 with mean yield >60 bu/A, we re conducted in Nebraska to determine effect on soybean yield of applying N and S to the soil at R3. With 27 lb/A N applied and >60 bu/A yield, mean yield increases... |
5. Adapt-N: A Computational Tool for Precise N Management in CornCurrent approaches to estimation of optimum N fertilizer rates are based on mass balances, average expected economic return based on field experiments, soil N tests, an d crop leaf or canopy sensing. However, denitrification and leaching losses of nitrogen may occur from dynamic and complex interactions among weather, soil hydrology, crop water and N uptake, and management practices , and result in high variability in annual crop N needs in maize ( Zea mays L.) production. W eather impacts the soil... |
6. Integrating Management Zones and Canopy Sensing for Improved Nitrogen Recommendation AlgorithmsActive 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 |
7. Comparison of Ground-Based Active Crop Canopy Sensor and Aerial Passive Crop Canopy Sensor for In-Season Nitrogen ManagementCrop 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 |