Summary and conclusions

Approaches estimating direct N2O emissions range from using simple emission factors to statistical and mechanistic models. EFs are based on the relationship between N inputs and N2O emission. Statistical models use different factors that influence N2O emissions and are appropriate to depict spatial variability and differences due to climate, soil and management conditions. Finally, mechanistic models are designed to operate at the field to regional scales, and in general only use parameters that are also available at these larger scales. Improvement in the performance of N2O simulation models at larger scales is not only hampered by insufficient understanding of the various processes, but also by a lack of available data on controlling variables such as hydraulic conductivity and porosity, and their inherent spatial heterogeneity.

A simple decrease in fertilizer-N input would lead to a concurrent reduction of N2O emissions. Depending on the size of the decrease in fertilizer-N input, a dilemma will manifest itself as lower fertilizer-N input will have a negative effect on grain yield. A positive relationship between the amount of N fertilizer input and yield is well established (Cassman et al, 2003). Such a positive relationship between N fertilizer input and yield is also reflected at the global level as worldwide increases in the use of synthetic fertilizer-N have led to a significant increase in total yield and food production (FAO, 2008). Therefore, a significant reduction in N2O emissions through a reduction in fertilizer-N input will most likely lead to lower food production. However, with an expected increase in world population to 8.9 billion in 2075 (Lutz et al, 2008), sustaining or increasing food production will become a necessity.

Simply reducing fertilizer use is therefore not an option, except in regions where there is over-fertilization, i.e. where reducing N inputs has no effect on crop yields. Generally, optimum and near optimum levels of fertilizer-N input in association with best management practices to optimize the yield potential and increase fertilizer-N use efficiency would lead to a reduction in N2O emissions as expressed per unit of grain or food yield. Management practices can be used that increase the crop uptake of N directly, for example plant breeding, matching (and timing of) N supply with crop demand, etc. Other practices aim at reducing NH3 volatilization, denitrification or leaching losses.

Furthermore, fertilizer-N can be replaced by N inputs in the crop rotation from biological N2 fixation by leguminous crops such as soya beans, pulses, alfalfa, etc. However, generally N2O emissions from agricultural systems including leguminous crops are comparable to those systems that rely on synthetic N fertilizer as their source of N. The option of including legumes in the system will reduce the need for synthetic N fertilizer but will not automatically lead to a reduction in N2O emissions.

N fertilizer can also be replaced by better integrating animal manure in the arable production system, or by recycling human N and P. Our simple calculation shows that a considerable 13 per cent reduction of N2O emission from the global arable land is possible by replacing fertilizer by animal manure (Table 5.3). The feasibility of this option depends on the spatial separation of livestock and crop production activities. Finally, measures in the production process in the livestock sector may also have an impact on N2O emissions from cropland. Improving animal diets aimed at improving the N use efficiency by animals and thus reducing the N in animal manure (by 20 per cent) may lead to important reductions (5 per cent) in N2O from arable land. Finally, the use of nitrification inhibitors is an effective way to mitigate perhaps 50 per cent of N2O emissions from arable fields. However, these inhibitors are not used on a large scale yet.

In summary, there is no simple and generic approach for reducing N2O emissions from arable land. Therefore, mitigation strategies need to be adapted to the local socio-economic situation, climate and soil conditions, the level of production and fertilizer input, and integration of livestock and crop production systems. Mitigation strategies should also account for the N-cascade effect, where a reduction in emissions for one part of the system may actually lead to an increase in emissions in another part of the N cascade.

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