Use of Monte Carlo Analysis to Characterize Nitrogen Fluxes in Agroecosystems

Abstract

Intensive agricultural systems are largely responsible for the increase in global reactive nitrogen compounds, which are associated with significant environmental impacts. The nitrogen cycle in agricultural systems is complex and highly variable, which complicates characterization in environmental assessments. Appropriately representing nitrogen inputs into an ecosystem is essential to better understand and predict environmental impacts, such as the extent of seasonally occurring hypoxic zones. Many impacts associated with reactive nitrogen are directly related to annual nitrogen loads, and are not adequately represented by average values that de-emphasize extreme years. To capture the inherent variability in agricultural systems, this paper employs Monte Carlo analysis (MCA) to model major nitrogen exports during crop production, focusing on corn-soybean rotations within the U.S. Corn Belt. This approach yields distributions of possible emission values and is the first step in incorporating variable nutrient fluxes into life cycle assessments (LCA) and environmental impact assessments. Monte Carlo simulations generate distributions of nitrate emissions showing that 80% of values range between 15 and 90 kg NO39-) N/ha (mean 38.5 kg NO3(-) N/ha; median 35.7 kg NO3(-) N/ha) for corn fields and 5-60 kg NO3(-) N/ha (mean 20.8 kg NO3(-) N/ha; median 16.4 kg NO3(-) N/ha) for soybean fields. Data were also generated for grain and residue nitrogen, N2O, NO(x), and NH3. Results indicate model distributions are in agreement with available measured emissions.

Carbon footprint in different beef production systems on a southern Brazilian farm: a case study

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Greenhouse gas assessment of soybean production: implications of land use change and different cultivation systems

“…However, there are significant uncertainties in N 2 O emission calculations (IPCC, 2006), particularly for N 2 O emissions originating in the fraction of N lost via runoff, leaching and volatilization (Reijnders and Huijbregts, 2011). In addition, only a few studies have assessed how this influences the soybean GHG balance (Del Grosso et al, 2009;Smeets et al, 2009;Snyder et al, 2009;Panichelli et al, 2009;Smaling et al, 2008;Reijnders and Huijbregts, 2008;Miller, 2010;Miller et al, 2006).…”

Analysis of parameter uncertainty in hydrological and sediment modeling using GLUE method: a case study of SWAT model applied to Three Gorges Reservoir Region, China

“…It has been shown that parameter uncertainty is inevitable in hydrological modeling and a corresponding assessment should be conducted before model prediction in the decision making process. Studies of parameter uncertainty have been conducted in the area of integrated watershed management (Zacharias et al, 2005), peak flow forecasting (Jorgeson and Julien, 2005), soil loss prediction (Cochrane and Flanagan, 2005), nutrient flux analysis (Murdoch et al, 2005;Miller et al, 2006), assessment of the effect of land use change (Eckhardt et al, 2003;Shen et al, 2010;Xu et al, 2011) and climate change impact assessment (Kingston and Taylor, 2010), among many others. Nevertheless, parameter identification is a complex, non-linear problem and numerous possible solutions might be obtained by optimization algorithms (Nandakumar and Mein, 1997).…”