Wednesday, November 15, 2006 - 2:20 PM

Application of Dynamic Simulation Modelling for Nitrogen Management in Maize.

Jeff Melkonian1, Harold Van Es2, Arthur T. DeGaetano2, Jean M. Sogbedji3, and Laura Joseph2. (1) Cornell University, Cornell University, 1123 Bradfield Hall, Ithaca, NY 14853, (2) Cornell Univ, 1005 Bradfield Hall, Ithaca, NY 14853, (3) Univ de Lome, Ecole Superieure d'Agronomie, B.P. 1515, Lome, Togo

Denitrification and leaching losses of nitrogen (N) in maize production result from dynamic and complex interactions among weather, soil hydrology, crop water and N uptake, and management practices. Current tools for N management do not directly account for the dynamic behavior of soil N, limiting our ability to more efficiently manage N. Using dynamic simulation models as nutrient management tools represents a major step forward in the management of agricultural nutrient flows.  We have developed the Precision Nitrogen Management or PNM model, composed of a dynamic simulation model of soil N transformations and soil N/water transport (LEACHN; Hutson, 2003) linked to a maize N uptake/growth model (Sinclair and Muchow, 1995). Our goal is to apply the PNM model to improve N use efficiency and reduce N losses in maize production. To achieve this goal, we are developing and testing two N management tools with the PNM model. First, a new Nitrate Leaching Index (NLI) for maize production is being constructed for the Northeast US. The new NLI will be based on multi-year PNM model simulations using archived weather data and input files representing a range of soil types and management practices. Second, we are developing an N management tool for in-season N application guidelines. Current guidelines for in-season N applications don’t account for the well-documented variability in economic optimum maize sidedress N rates. This variability can be quantified with a well-calibrated dynamic simulation model of the soil-maize system. Using the PNM model, we generated adjustments to the recommended in-season N rates for maize in the 2004 and 2005 growing seasons for different climate regions in New York State. We are developing a Web-based version of this tool that will automatically access high resolution climate data (Northeast Regional Climate Center at Cornell University) and allow farm- or field-specific in-season N recommendations