Thursday, 10 November 2005 - 8:45 AM
307-2

Variability in N Availability to Corn for Various Landscape Positions.

John Schmidt1, Arthur Allen2, Peter Kleinman1, M.S. Srinivasan1, and Peter Vadas1. (1) USDA Agricultural Research Service, USDA-ARS-PSWMRU, University Park, PA 16802-3702, (2) University of Maryland, Eastern Shore, 30921 Martin Court/crop & Aquacultu, Princess Anne, MD 21853

Understanding N availability across the landscape is essential to improving N management decisions. This project aims to distinguish and quantify the availability of N to corn at various landscape positions. Two distinctive regions of the Chesapeake Bay watershed were selected for this project, the Maryland Eastern Shore (MES) and the Ridge and Valley (RV) region of Pennsylvania. Four landscape positions were selected at MES; including near a deep (2 – 3 m) ditch, middle of the field, and two near a shallow (0 – 0.3 m) ditch. Two landscape positions were selected at the RV site; head and toe slope positions. Nitrogen treatments at the midfield position (MES) and both positions at RV include: 0, 56, 112, 168, 224, and 268 kg N ha-1 applied at planting. At the three other MES positions, N treatments include 0 and 112 kg N ha-1. Grain yield increased with N fertilizer at a near-shallow-ditch position at MES (8.8 to 11.0 Mg ha-1) and at the head slope position at RV (7.4 to 10.3 Mg ha-1). Leaf N content at silking increased between 0 and 112 kg N ha-1 at all MES positions and for both RV positions. Leaf N content at both near-shallow-ditch positions (MES) indicated that less N was available here (3.0-3.1 %) with 0 kg N ha-1 compared to the midfield position (3.2 %) and the deep ditch position (3.4 %). Although leaf N content responded to N at both RV positions, N content was consistently greater across all treatments for the toe slope position. At MES sandy soils and a shallow water table play a large role in N availability, while soil depth and N mineralization likely contribute to response differences observed at RV. Understanding these mechanisms and quantifying their impact will provide the appropriate input for developing site-specific N recommendation models.

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