More than a third of all CH4 emissions come from soils, as a result of the microbial breakdown of organic compounds in strictly anaerobic conditions. This process occurs in natural wetlands (Chapter 3), in flooded rice fields and in landfill sites rich in organic matter (Chapter 11), as well as in the gut of some species of soil-dwelling termites (Chapter 5). Rice fields make a significant contribution to anthropogenic CH4 emissions, which are responsible for the concentration in the atmosphere more than doubling since the pre-industrial era, when, on the basis of evidence from ice core analysis, it was only about 0.7pmol mol1 (Prather et al, 1995). Early estimates suggested that rice production contributes about a quarter of the total anthropogenic CH4 source and is of similar strength to the ruminant source or the energy sector (Fung et al, 1991; Hein et al, 1997). However, estimates have declined substantially with time, to values mostly between 25 and 50Tg CH4 year1. Higher values generally originate from inverse modelling of observed fluctuations in atmospheric CH4 mixing ratios (top-down method) (for example Hein et al, 1997; Chen and Prinn, 2006), whereas lower values are obtained by scaling up field observations (bottom-up method) (Figure 8.1).
As far as the bottom-up estimates are concerned, the trend towards smaller emission estimates reflects the increasing level of our understanding of the processes governing emissions, aided by a growing number of measurements and modelling exercises. It is not related to any changes that may have occurred over the same period in actual cultivation methods or cultivated area. The experimental and modelling studies show that emissions may be affected by the continuity, or lack of it, of the flooding regime, the extent of incorporation of organic residues into the soil, the general level of productivity of the crop, the cultivar used and other factors. Improved understanding of the fundamental processes controlling CH4 production, and knowledge about how they are influenced by agricultural management, not only gives greater confidence in the emission estimates, but also indicates possible ways to reduce the emissions.
Note: Large open symbols represent estimates from global inverse modelling (top-down method). Source: Adapted from Sass (2002), with inclusion of the estimates by Fung et al (1991), Hein et al (1997), Olivier etal (1999, 2005), Scheehle et al (2002), Wang et al (2004), Mikaloff Fletcher et al (2004), Chen and Prinn (2006) and Yan et al (2009)
Was this article helpful?