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Farmers, food supply companies, and policymakers need practical yet scientifically robust methods to quantify how improved nitrogen (N) fertilizer management can reduce nitrous oxide (N2O) emissions. To meet this need, we developed an empirical model based on published field data for predicting N2O emission from rainfed maize (Zea mays L.) fields managed with inorganic N fertilizer in the United States and Canada. Nitrous oxide emissions ranged widely on an area basis (0.03–32.9 kg N ha−1 yr−1) and a yield‐scaled basis (0.006–4.8 kg N Mg−1 grain yr−1). We evaluated multiple modeling approaches and variables using three metrics of model fit (Akaike information criteria corrected for small sample sizes [AICc], RMSE, and R2). Our model explains 32.8% of the total observed variation and 50% of observed site‐level variation. Soil clay content was very important for predicting N2O emission and predicting the change in N2O emission due to a change in N balance, with the addition of a clay fixed effect explaining 37% of site‐level variation. Sites with higher clay content showed greater reductions in N2O emission for a given reduction in N balance. Therefore, high‐clay sites are particularly important targets for reducing N2O emissions. Our linear mixed model is more suitable for predicting the effect of improved N management on N2O emission in maize fields than other published models because it (a) requires only input data readily available on working farms, (b) is derived from field observations, (c) correctly represents differences among sites using a mixed modeling approach, and (d) includes soil texture because it strongly influences N2O emissions. [ABSTRACT FROM AUTHOR]

We modeled the expected range of seasonal and annual N2O flux from temperate, grain agroecosystems using Monte Carlo sampling of N2O flux field observations. This analysis is complimentary to mechanistic biogeochemical model outcomes and provides an alternative method of estimating N2O flux. Our analysis produced a range of annual N2O gas flux estimates with mean values overlapping with results from an intermodel comparison of mechanistic models. Mean seasonal N2O flux was 1–4% of available N, while median seasonal N2O flux was less than 2% of available N across corn, soybean, wheat, ryegrass, legume, and bare fallow systems. The 25th–75th percentile values for simulated average annualized N2O flux rates ranged from 1 to 12.2 kg N ha−1 in the conventional system, from 1.3 to 8.8 kg N ha−1 in the cover crop rotation, and from 0.8 to 9.3 kg N ha−1 in the legume rotation. Although these modeling techniques lack the seasonal resolution of mechanistic models, model outcomes are based on measured field observations. Given the large variation in seasonal N gas flux predictions resulting from the application of mechanistic simulation models, this data-derived approach is a complimentary benchmark for assessing the impact of agricultural policy on greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Abstract: Though field data indicate that biogeochemical properties are heterogeneous, biogeochemical models are often applied without regard to local variation. In this paper, we examine how the spatial scale used to represent heterogeneity in ecosystem modeling affects aggregated forest stand nitrogen (N) dynamics. We conduct this spatial analysis using measured local variation in forest carbon and nitrogen distributions. By coupling field and modeling approaches, we address spatial scaling in the context of natural ecosystem dynamics. We model spatially explicit N dynamics in Douglas-fir (Pseudotsuga menziesii) forests using reaction-diffusion equations, in which Michaelis–Menten functions represent nitrogen transfer between plant, microbial, and soil pools, and nitrogen transport is approximated using diffusion. A stability analysis of the reaction terms in the absence of diffusion shows the model is very resilient to perturbation; consequently limit-cycle or oscillatory dynamics are not observed. The model is sensitive to variation in the maximum rate of plant nitrogen uptake as well as the Michaelis–Menten half-saturation constant for plant N uptake. The spatial scale of modeled heterogeneity does not significantly affect aggregated N dynamics in the Douglas-fir system; spatial resolution mainly affects the variance of stand nitrogen values. Analysis of post-perturbation dynamics shows that stand productivity is significantly reduced under frequent perturbations, especially when microbial biomass pools are seriously degraded. [Copyright &y& Elsevier]