Jump to: Methods and sources of activity data | Methods and sources of emissions factors
Flooded rice, a form of paddy rice, releases CH4 due to the anaerobic decomposition of organic material in flooded fields, which is released to the atmosphere primarily by transport through rice plants. CH4 emissions are primarily a function of the duration of the growing period, water management before and during cultivation, and application of soil amendments.
Fertilizers applied to rice are also a source of N2O, which are calculated separately (see emissions from managed soils).
Rice grown under aerobic (non-flooded) conditions is not a major source of methane and is treated like other non-flooded crops.
What data are required
Tier 1
CH4 emissions are estimated by multiplying the cultivation period of rice and annually harvested areas by daily emission factors. In its most simple form, this equation is implemented using national activity data (i.e., national average cultivation period of rice and area harvested) and a single emission factor.
Calculating CH4 emissions and emission factors
- Emissions of CH4= CH4 emissions from paddy rice, Gg CH4 yr-1
- EFi,j,k = a daily emission factor for i, j, and k conditions, kg CH4 ha-1 day-1
- t i,j,k = cultivation period of rice for i, j, and k conditions, day
- A i,j,k = annual harvested area of rice for i, j, and k conditions, ha yr-1
- i, j, and k = represent different ecosystems, water regimes, type and amount of organic amendments, and other conditions under which CH4 emissions from rice may vary
The default value for EFi,j,k is 1.19 kg CH4 ha-1 day-1, varying from 0.65 to 1.27 kg CH4 ha-1 day-1depending on the region (IPCC, 2019; Table 5.11). However, it is preferable to account for this variability of rice-growing practices within a country by disaggregating national total harvested area into sub-units (e.g., harvested areas under different water regimes). Harvested area for each sub-unit is multiplied by the respective cultivation period and an adjusted emission factor that is representative of the conditions that define the sub-unit.
- EFi = adjusted daily emission factor for a particular harvested area
- EFc = baseline emission factor for continuously flooded fields without organic amendments (IPCC, 2019; Table 5.11)
- SFw = scaling factor to account for the differences in water regime during the cultivation period (IPCC, 2019; Table 5.12)
- SFp = scaling factor to account for the differences in water regime in the pre-season before the cultivation period (IPCC, 2019; Table 5.13)
- SFo = scaling factor should vary for both type and amount of organic amendment applied (see below equation) (IPCC, 2019; Table 5.14)
- SFs,r = scaling factor for soil type, rice cultivar, etc., if available
The scaling factor for type and amount of organic amendment is calculated by multiplying the application rate by a conversion factor.
- SFo = scaling factor for both type and amount of organic amendment applied
- ROAi = application rate of organic amendment i, in dry weight for straw and fresh weight for others, tonne ha-1
- CFOAi = conversion factor for organic amendment i (in terms of its relative effect with respect to straw applied shortly before cultivation) (IPCC, 2019; Table 5.14)
Tier 2
The Tier 2 methodology for rice uses the same equation as Tier 1 but substitutes country-specific emission factors and scaling factors. Activity data requirements are the same.
Tier 3
Tier 3 includes models and monitoring networks tailored to address national circumstances of rice cultivation, repeated over time, driven by high-resolution activity data (e.g. satellite-based and in-situ measurement) and disaggregated at the sub-national level. Models can be empirical or mechanistic, but in either case, need to be validated with independent observations from country- or region-specific studies (Li et al., 2004; Pathak et al., 2005). Tier 3 methodologies may also take into account inter-annual variability triggered by typhoons, flooding, drought, etc. A few countries have used Tier 3 method in their national communications to the UNFCCC (e.g., China and Japan used CH4MOD (Huang et al., 2004) and DNDC-Rice models (Katayanagi et al., 2017), and the USA used DayCent (Cheng et al. 2013)).
Methods and sources of activity data
Cultivation and number of paddy rice cropping seasons
RiceAtlas (Laborte et al., 2017) is an open-source data set that provides data on cultivation period, number of rice seasons, planting method, and rice production for 115 countries, and a subnational resolution for 77 countries. The data on production could also be used to estimate organic matter inputs of rice straw.
Water management
Collecting subnational data on water regimes (i.e. level and period of flooding before and during cultivation) can be challenging, particularly at the level of resolution needed to determine mitigation. For national inventories, cultivated rice areas are often assigned to a given water management regime based on expert judgment. India’s GHG inventory, for example, Manjunath et al. (2006) assigned areas with sandy soils and high evapotranspiration to an intermittent flooding regime on the assumption that such areas were not conducive to maintaining continuous standing water. Other areas were assigned to continuous flooding. Japan’s GHG inventory relied on data from rice cooperatives. In countries with well-developed irrigation systems, local irrigation officials or water management associations are a further source of information. Remote sensing using drones or even satellites may be used to detect water levels eventually, although the technology for satellite detection of water under the rice plant canopy is still under development.
New precision irrigation technologies that employ low-cost sensors and the Internet of Things to allow farmers to remotely control irrigation water also hold promise as sources of activity data. The same technology that collects and transmits data from soil moisture sensors telling farmers when to irrigate could also collect and aggregate data on irrigation patterns, providing highly accurate activity data. Such applications are still theoretical.
Methods and sources of emission factors
- Sanchis E, et al. 2012. Effect of Water and Straw Management Practices on Methane Emissions from Rice Fields: A Review Through a Meta-Analysis. Environmental Engineering Science.
- Yen BT, Wassmann R, Sander BO. 2019. Methane Emission Factors for Rice Production in the Mekong River Delta. CCAFS Info Note.