215-2 In-Season Decision Support Tools for Estimating Nitrogen Side-Dress Rates for Maize in the Mid-Atlantic Coastal Plain.

See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: Technologies for Determining Nutrient Needs and Improving Nutrient Use Efficiency: Graduate Student Competition
Tuesday, November 4, 2014: 1:15 PM
Long Beach Convention Center, Seaside Ballroom A
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Bee Khim Chim, 417 Smyth Hall, Virginia Tech, Blacksburg, VA, Tyler Kitchen Black, Virginia Tech, Blacksburg, VA, Robert B. Norris, Crop and Soil Environmental Sciences, Virginia Tech, Blacksburg, VA, Wade E. Thomason, Dept. of Crop and Soil Environmental Sciences, Virginia Tech, Blacksburg, VA and Madalyn Lynch, 300 Turner Street NW Mail Code 0312, Virginia Tech, Blacksburg, VA
Nitrogen fertilizer has been synthetically produced to nourish plants, increase yield and improve harvest quality. One of the way to increase NUE is called split application which is apply portion of N fertilizer from the beginning and apply another portion during vegetative stage. Improving accuracy of corn side dress N rate recommendations can improve profitability and reduce potential negative environmental impacts of over fertilization.  The objective of this experiment is to compare yield and NUE of side-dress rates prescribed by: 1) the Virginia Corn Algorithm; 2) the Maize-N computer simulation model; and 3) the Nutrient Expert for Maize computer simulation model to the standard rate growers would currently apply.  Four field experiments were established in 2012 and 2013 with 4 replications in a randomized complete block design. Treatments evaluated included a complete factorial of four different pre-plant rates (0, 44.8, 89.6 and 134.4 kg ha-1) with the three different simulation model-prescribed rates and the standard rate. Expected results are using Nutrient expert has applied lesser N side-dress compare to others.
See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: Technologies for Determining Nutrient Needs and Improving Nutrient Use Efficiency: Graduate Student Competition