COMPUTATIONAL AND EXPERIMENTAL APPROACHES RELATED TO NITROUS OXIDE EMISSIONS AND ECONOMIC ANALYSIS OF PRIVATE AND SOCIAL RETURNS FROM MAIZE FERTILIZATION
Continued research and development of computational methods are needed to effectively address both environmental and economic issues related to nitrogen (N) use for maize fertilization. This research consists of three major inter-related components. The first constitutes an experiment in Willsboro, New York to estimate the impact of management practices, especially tillage and timing of N application on nitrous oxide (N2O) emissions for clay loam and loamy sand. The second component includes the use of N2O emissions, and soil physical and chemical data collected from the Willsboro experiment to 1) calibrate the Precision Nitrogen Management (PNM) model, 2) determine the N2:N2O ratio from partial N budgets and incorporate it into the PNM model for N2O losses estimations, and 3) evaluate different combinations of process representations of the PNM model. The final component involves an integration of the PNM model and economic analyses by 1) simulating long-term yield and environmental N losses for maize production on three textural soils, and 2) estimating private and social returns based on PNM-simulated data. Nitrous oxide losses averaged four times higher on the clay loam than the loamy sand soil. Under no-tillage, full fertilizer application at planting resulted in 4.7 and 2.3 kg N ha-1 greater cumulative N2O losses than starter-only fertilizer application on maize after grass and continuous maize plots, respectively. Nitrogen management critically affects the extent of N2O losses, particularly for fine-textured soils under no-tillage, and must be an important consideration in soil and crop management for greenhouse gas (GHG) reduction. With the process complexities in the soil-plant-atmosphere system, modeling of N2O losses was challenging, especially for short-term periods. The incorporation of the biological aspects of the denitrification process is important to capture the dynamics involved in the production of N2O fluxes. Timing of N application affected optimum N rates depending on soil type and weather conditions. The economic modeling effort provided a framework for computations of revenue that incorporates environmental impacts of N fertilizer management. A more sophisticated approach is necessary to 1) increase PNM model accuracy, and 2) refine the calculations on environmental losses and associated damage costs for practical farm application.