As recommended by Del Grosso (2011), the first step of the simulation entailed the site-characterization process, providing the model with information on site latitude, weather statistics and soil horizonation. The final stage of the site-characterization was to parameterize and schedule the crop management options, such as crop rotations, tillage, irrigation, fertilization, planting, harvesting and burning. After the system characterization it is advocated to simulate at least 1000 years of native vegetation (also referred as spin-up simulation) followed by plough out and historical land use (implementation of base simulation) (Del Grosso et al., 2011). This procedures guarantees that the base simulation is implemented starting from a soil with relatively stable intermediate and slow SOM pools.To initialize the SOM and nutrient pools the spin-up simulation was run for 2000 years assuming a mixed ecotype formed by eucalyptus trees, scrubs a and grassland. To implement both spin-up and base simulations for Kingaroy field trial, a weather file starting in 1957 was obtained using data from two weather stations situated within 3 km from the plots. Soil physical properties were parameterized using measured bulk density and texture in order to replicate the soil water content dynamics recorded by the FDR probes during the cropping season. The base simulation was started in 1970, when the native vegetation was eliminated via controlled burning and the soil ploughed for the first time. Management practices, including N fertilization rates, irrigation amounts and crop rotations were simulated according to the information provided by the Department of Agriculture, Fisheries and Forestry (DAFF). To assess the reliability of predicted N2O fluxes, ancillary parameters such as soil NH4-N and NO3-N content, soil water content, above ground biomass and grain yield were also compared to measured data using Semaphore software.This software is used to automate importing output data from Century/Daycent modelling to Excel spreadsheets for further processing and/or visualization. Instead of manually opening each output file and converting it to a sheet, use this tool to import all the available output files in a specified directory with a single click. Each file will be converted into a separate sheet on the active workbook.

Using the default model parameters to run the spin-up and base simulation resulted in a significant overestimation of soil C content. For this reason the site potential parameter (SITPOT) and the maximum fraction of C allocated to fine roots under maximum nutrient stress (TFRTCN (1)) in TREE.100, as well as the atmospheric N inputs (EPNFA) in SITE.100, were adjusted to meet the total soil C content measured at the beginning of the wheat season. As recommended by the model developer, the following step entailed the calibration of soil water dynamics: the water holding capacity in the initial layers of the soil was reduced correcting the maximum decomposition rate of soil organic matter with slow turnover (DEC 4) and the potential evapotranspiration rate (FWLOSS (4)) in the FIX.100. The plant growth rates were verified next and resulted that there was little growth response to the different N inputs applied in the four treatments. For this reason, in CROP.100 the sensitivity of maize to N limitation was increased by raising the BIOMAX parameter from 300 to 400 and decreasing PRAMX(1.2) and PRAMN(1,2) to 100 and 50, respectively.As pointed out by Smith (2008b), Jarecki (2008) and Del Grosso (2008), DAYCENT is prone to underestimate N2O emissions from soils with low N content, probably due to an excess in the simulated N plant uptake. This was confirmed by the obtained preliminary results, thereby it was decided to correct to 0.6 the nitrification N2O adjustment factor parameter in SITEPAR.IN. This factor is used as a multiplier on the nitrification rates and can be adjusted to modify the amount of N available to nitrifying bacteria and hence reduce the completion with plant for the N availability. To further increment the amount of N available for nitrification and denitrification, the N leaching rate was reduced via decreasing the FLEACH(3) parameter in FIX.100 to a factor of 0.3.To reproduce the effect of the DMPP nitrification inhibitor, in FERT.100 the NINHIB factor on nitrification rates was reduced from 1 to 0.2. The simulated lasting effect of the nitrification inhibitor was set on 6 weeks with the NINHTM parameter.