Upscaling Regional Emissions of Greenhouse Gases from Rice Cultivation: Methods and Sources of Uncertainty

Abstract

One of the important sources of greenhouse gases is the emission of methane from rice fields. Methane emission from rice fields is the result of a complex array of soil processes involving plant-microbe interactions. The cumulative effects of these processes at the level of individual plants influence the global atmospheric composition and make it necessary to expand our research focus from small plots to large landscapes and regions. However, present extrapolations ('upscaling') are tenuous at best because of methodological and practical problems. The different steps taken to calculate regional emission strengths are discussed and illustrated by calculations for a case-study in the Philippines. The applicability of high quality, process-based, models of methane emission at the level of individual plants is limited for regional analysis by their large data requirements. Simplified models can be used at the regional level but are not able to capture the complex emission situation. Data availability and model accuracy are therefore often difficult to match. Other common sources of uncertainty are the quality of input data. A critical evaluation of input data should be made in every upscaling study to assess the suitability for calculating regional emissions. For the case-study we show effects of differences in input data caused by data source and interpolation technique. The results from the case-study and similar studies in literature indicate that upscaling techniques are still troublesome and a cause of large uncertainties in regional estimates. The results suggest that some of the stumbling blocks in the conventional upscaling procedure are almost impassable in the near future. Based on these results, a plea is made for meso-level measurements to calibrate and validate upscaling methods in order to be better able to quantify and reduce uncertainties in regional emission estimates.

Challenges in using land use and land cover data for global change studies

“… Rice area per municipality based on interpretation of Radar images and as reported in statistical surveys (based on Verburg et al ., 2006). …”

Characterizing Spatiotemporal Dynamics of Methane Emissions from Rice Paddies in Northeast China from 1990 to 2010

“…These ground-based measurements were reliable for understanding the mechanics of CH 4 emission at local scale. Given the emergence of new frameworks for GHGs mitigation, however, it fails to respond to practicable requirements at national, regional, and global levels in the long run for high variations in spatial and temporal pattern of CH 4 emission with changing environmental conditions [11]–[15]. Consequently, based on the extrapolation of the understandings gained at site scale to a large spatial dimension, model simulations were required to meet the demands for spatiotemporal analysis of CH 4 emissions from rice fields.…”

Association between contrasting methane emissions of two rice (Oryza sativa L.) cultivars from the irrigated agroecosystem of northeast India and their growth and photosynthetic characteristics

“…Rice growing in India can be broadly categorized into rainfed and irrigated farming representing about 52 and 48% of the total rice area, respectively (Babu et al 2006). Methane production in rice fields such as these is the result of interactions of soil processes involving plants and microbes (Verburg et al 2006). Flooding the soil promotes anaerobic degradation of photosynthetic carbon supplied by rice plants resulting in methane production whenever redox potentials become sufficiently negative.…”