The Crutzen et al (2008) and IPCC (2006) methods to estimate global nitrous oxide yields
The basis of the Crutzen et al (2008) methodology is that the newly fixed N entering agricultural systems (synthetic fertilizer-N and N from BNF) is regarded as the source of all agriculture-related N2O emissions. These emissions will not all happen in the season when fixation takes place, but will involve longer cycling times (which are nonetheless short compared with the >100 year lifetime of N2O in the environment). We may consider three categories:
1 direct emissions from N-fertilized soils;
2 'secondary' emissions resulting from the complex transformations of N compounds in the various flows within agricultural systems;
3 indirect emissions (in the IPCC meaning of the phrase) arising from leached N leaving agricultural fields and entering water systems, and from volatilized N deposited onto natural ecosystems.
Examples of the 'secondary' emission sources are:
• crop residues ploughed in as fertilizer for a successor crop;
• dung and urine from livestock (both grazing and housed) fed variously on N-fertilized grain crops, feeds containing BNF-N (for example soya bean meal, alfalfa, clover-rich pasture and silage in Europe, and tropical grasses with Azospirillum associations in Brazil);
• N mineralized from soil organic matter and root residues following cultivation or grassland renewal.
In contrast, in the IPCC approach, emissions from crop residues and mineralization are included in the 'direct' emissions and have the same EF; separate EFs are used for emissions from grazing animals, and the N source here is quantified on the basis of the N excreted, and essentially is treated as a 'new' N source, not as fertilizer- or BNF-derived N (see also Chapters 5 and 6). The fractions of the N applied to fields that are lost by leaching, runoff and volatilization have additional EFs applied to them to describe the resulting 'indirect' emissions. The aggregate emissions from agriculture are arrived at by summing all these individual sources. The IPCC's 1 per cent EF for direct N2O emissions contains an uncertainty of one third to three times the default value (IPCC, 2006). The default EF for emissions from cattle, poultry and pigs is 2 per cent of the N excreted, with a range of 0.7 per cent to 6 per cent - again, from one third to three times the default value. The EFs for N derived from N volatilization and re-deposition and N derived from leaching and runoff are 1 per cent (uncertainty range 0.2-5 per cent) and 0.75 per cent (0.05-2.5 per cent), respectively. At default volatilization fractions of 10 per cent (mineral fertilizer) or 20 per cent (animal manure), and the default leaching fraction of 30 per cent, indirect emissions amount to 0.35-0.45 per cent of the N applied to the land. Each of the source terms in the bottom-up IPCC method is very uncertain. However, their sum is not inconsistent with the total derived by the top-down methodology.
As noted above, the IPCC (2006) methodology is based on soil surface gas flux measurements from numerous global sites. Emissions are assumed to be proportional to soil N inputs from various sources (Table 4.1). This method also accounts for emissions from burning crop biomass and from N transformations occurring in manure management systems. The DAYCENT model (Del Grosso et al, 2006) is an example of a more sophisticated bottom-up approach. In addition to N inputs, DAYCENT accounts for the influence of other factors (water, temperature, O2 and labile C availability, and plant N demand) that influence direct soil N2O emissions. Model predictions are evaluated on the basis of soil surface flux measurements. In contrast, the top-down approach infers anthropogenic N2O emissions from changes in atmospheric N2O concentration and N2O removal rates (Crutzen et al, 2008). Using DAYCENT, Del Grosso et al (2008) calculated N2O emissions from agricultural systems in the US and for the entire globe using bottom-up approaches, and compared these results with the range of N2O emissions estimated using the top-down approach of Crutzen et al (2008) that, as explained above, calculates the N2O emissions range as 35 per cent of the combined N inputs from symbiotic N fixation and synthetic fertilizer application. To obtain a value for the US national greenhouse gas inventory, Del Grosso et al (2008) calculated N2O emissions for major crops and grasslands from DAYCENT model simulations, and for other crops and manure management systems using the IPCC (2006) methodology (US EPA, 2008). DAYCENT results for N volatilization and leached/runoff N were combined with IPCC (2006) methodology to estimate indirect N2O emissions. The N2O emission from agricultural systems in the US for 2005, obtained using the bottom-up approaches, was 0.6Tg N yr-1 (Table 4.4), somewhat below the range of 0.8-1.4Tg N yr-1 based on the top-down approach. However, using the IPCC (2006) methodology, Del Grosso et al (2008) also calculated that approximately 5.8Tg of N from N2O are currently emitted annually from agricultural systems at the global scale (Table 4.4). This is close to the middle of the range (4.2-7.0Tg N2O-N yr-1) given by the top-down approach. Del Grosso et al concluded that, at sufficiently large scales, the use of these top-down and bottom-up approaches to calculate N2O emissions from agricultural systems yield similar estimates. They emphasized that the EF for the global top-down approach (35 per cent of N inputs from symbiotic N fixation and synthetic fertilizer production) cannot be compared directly with the emissions factors used in the IPCC (2006) method because the methods consider different sources of N inputs. N2O emissions are highly variable in space and time, and different methodologies have not agreed closely, especially at small scales. However, as scale increases, so does the agreement between estimates based on soil surface measurements (bottom-up approach) and estimates derived from changes in atmospheric concentration of N2O (top-down approach). Del Grosso et al (2008) concluded that:
the convergence of top-down and bottom-up approaches increases confidence in emissions estimates because the methods are based on different assumptions, and this convergence suggests that we have at least a rudimentary understanding of the factors that control emissions at large spatial and temporal scales.
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