Methane Emission from Flooded Rice Cultivation

Methods 5.0

The annual methane emission (kg CH₄) from flooded soils in rice production.

Introduction

In rice production, the annual methane (CH₄) emission (kg CH₄) from soils results from the balance of two processes: methanogenesis, which occurs under anaerobic conditions, and methanotrophy, which is the dominant process under aerobic conditions. The magnitude of both processes depends on the length of flooding periods during cropping season and other management practices.

Work at CIAT’s headquarters in Colombia, to measure the greenhouse gas emissions of rice production. Credit: Neil Palmer.

Methods

For FPv5, we revised the FP v4.2 method with the one from Ogle et al. (2024). The proposed solution to account for rice ratooning is described in Section 3. The revised method removes the impact of rice cultivars and adds the options of pre-season water management and length of the growing season.

The method for estimating CH₄ emissions from rice fields is formulated on a baseline emission factor \(EF_i\), at which CH₄ is produced daily by unit of land for rice production with continuously flooded conditions and no organic amendments. The baseline emission factor is scaled according to the practices and conditions for the land parcel, including water management, organic amendments, use of sulfur products, residue amount, and seeding practices.

The method described below is based on IPCC’s Tier 2 method with specific factors for the two major rice production regions in the U.S.: the Mid-South (Arkansas, Louisiana, Mississippi, and some counties in Missouri and Texas) and California.

In case of ratooning (see Section 3), CH₄ flux is computed as if the two rice crops (main crop and ratooning crop) were one crop by aggregating the management variables applied to each crop (fertilizers, etc.). In the case of fertilizers, since both crops share the same area, the rates would be expressed as the simple average among the individual rates. The same approach would be used for yield. In contrast, the length of the cultivation period would be the sum of each cultivation period.

Although not ideal, this approach would enable the accounting for whole field emissions of both crops within the same scenario. The following equations are used to calculate the CO₂e emissions for both crops (main and ratooning crop if present) combined. At the end, if ratooning is present, per area and per crop production unit estimates are adjusted by the corresponding area and production levels.

Annual total (whole field) CH₄ flux

\[ [CH_4]^{total} = EF \times t \times A \]

• \([CH_4]^{total}\) = the annual total CH₄ flux (kg CH₄)
• \(EF\) = integrated daily emission factor based on management for the growing season (kg CH₄ ha^-1 day^-1)
• \(t\) = cultivation period for the entire growing season (days). If ratooning crop is present, it would be the days between the planting date of the main crop and the harvest of the ratooning crop.
• \(A\) = the area of the land parcel (ha)

The emission factor for each growing season (\(EF_i\)) is estimated using the following equation:

\[ EF_i = EF_{base} \times SF_w \times SF_p \times SF_o \times SF_s \times SF_r \times SF_e \]

where:

• \(EF_{base}\) = baseline emission factor for continuously flooded fields (kg CH₄ ha^-1 day^-1)
• \(SF_w\) = scaling factor for water regime during the cultivation period (dimensionless)
• \(SF_p\) = scaling factor to account for the differences in water regime in the preseason before the cultivation period (dimensionless)
• \(SF_o\) = scaling factor for both type and amount of organic amendment applied (dimensionless)
• \(SF_s\) = scaling factor for sulfur amendments to soils (dimensionless)
• \(SF_r\) = scaling factor for residue litter amount (dimensionless)
• \(SF_e\) = scaling factor for seeding method in California (dimensionless)

The \(EF_{base}\) for continuously flooded fields can be estimated using region-specific conditions using the following equation:

\[ EF_{base} = \{ EF_{sa} - [ (Clay-BPC ) \times C_f ] \} \times CP^{-1} \]

where:

• \(EF_{base}\) = baseline emission factor for continuously flooded fields (kg CH₄ ha^-1 day^-1)
• \(EF_{sa}\) = average seasonal CH₄ emissions (kg CH₄ ha^-1 season^-1)
• \(Clay\) = percent clay associated with the soil texture (percentage); percent clay values that are greater than 54% are assigned a value of 54%
• \(BPC\) = base percent clay (percentage)
• \(C_f\) = clay factor (kg CH₄ ha^-1 season^-1)
• \(CP\) = average cultivation period for the seasonal CH₄ emissions (days)

The following equation estimates the scaling factor for sulfur amendments \(SF_s\) as a function of the sulfur amendment rate (\(SR\)):

\[ SF_s = \begin{cases} 1 & \text{for $SR = 0$ kg S ha}^{-1}\\ 1 - (SR \times 0.00133) & \text{for 0 $\lt$ $SR$ $\le$ 338 kg S ha}^{-1}\\ 0.55 & \text{for $SR > 338$ kg S ha}^{-1} \end{cases} \]

where:

• \(SR\) = sulfur application rate (kg S ha^-1)

Sulfur application rate is computed by adding up the sulfur inputs entered by a Fieldprint Platform user.

\[ SR = [\sum_i^n (FR_i \times S^{prop}_i) ] \times 10^3 \]

• \(FR_i\) = the annual applied rate of fertilizer i^th (kg ha^-1)
• \(S^{prop}\) = the proportion of sulfur in fertilizer i^th [kg sulfur (kg fertilizer^-1)]

The following equation estimates the scaling factor for organic amendments \(SF_o\) as a function of the type and rate of amendments (\(ROA\)):

\[ SF_o = [ 1 + \sum_i^n (ROA_i \times 10^{-3} \times CFOA_i) ] ^ {0.59} \]

• \(ROA_i\) = rate of application of the i^th organic amendment type (kg ha^-1)
• \(CFOA_i\) = conversion factor for the i^th organic amendment type (dimensionless)

Annual total CH₄ flux per area and per crop production unit

Methane emissions per area and crop production unit are estimated by the following equations:

\[ [CH_4]^{area} = [CH_4]^{total} \times (A \times i)^{-1} [CH_4]^{prod} = [CH_4]^{total} \times (Y \times A \times i)^{-1} \]

where:

• \([CH_4]^{total}\) = the annual total CH₄ emissions (kg CH₄)
• \([CH_4]^{area}\) = the annual total CH₄ emissions per area (kg CH₄ ha^-1)
• \([CH_4]^{prod}\) = the annual total CH₄ emissions per crop production unit (kg CH₄ [crop prod unit]^-1)
• \(A\) = the land parcel area (ha)
• \(Y\) = the crop yield (crop production units ha^-1)
• \(i\) = the number of rice crops during the season, 1 for a single crop and 2 for a main crop followed by ratooning (dimensionless)

Conversion of CH₄ to CO₂e

Finally, methane flux can be expressed in units of CO₂e as follows:

\[ \begin{align} [CO_2\text{e}]^{total} &= [CH_4]^{total} \times [CH_4]^{gwp} \\ [CO_2\text{e}]^{area} &= [CH_4]^{area} \times [CH_4]^{gwp} \\ [CO_2\text{e}]^{prod} &= [CH_4]^{prod} \times [CH_4]^{gwp} \end{align} \]

where:

• \([CO_2\text{e}]^{total}\) = the annual total CO₂e flux (kg CO₂e)
• \([CO_2\text{e}]^{area}\) = the annual total CO₂e flux per area (kg CO₂e ha ^-1)
• \([CO_2\text{e}]^{prod}\) = the annual total CO₂e flux per crop production unit (kg CO₂e [crop production units]^-1)
• \([CH_4]^{gwp}\) = the global warming potential factor for CH₄ (kg CO₂e/kg CH₄)

Producing a rice crop with ratooning

Rice producers, mostly in Texas and Louisiana, can benefit from harvesting two rice crops from the same planted seeds. This presents a unique situation for which there is little guidance for GHG emission accounting methodologies, and it blurs the lines of crop interval delineation. We are faced with three complications:

1. The method for CH₄ emissions from flooded rice cultivation, as published by Ogle et al. (2024), does not contain guidance about how to account for ratoon emissions.

2. Rice ratooning cannot be classified strictly as a double crop because it is harvested from the regrowth of the first rice crop planted at the beginning of the season. It is also not a given that both crops will be managed with the same level of inputs, such as fertilizers.

3. The guidance published in IPCC (2019) indicates that the area harvested for the main crop and the ratoon crop should be summed together, which leads to having to sum together the crop production outputs from the two harvests (primary rice crop + ratoon crop).

To solve these challenges, Field to Market proposes using the following approach until better guidance emerges:

• If a field produces annual rice with no ratooning, the method CH₄ emissions from flooded rice cultivation is run, and the accounting of energy use and GHG emissions is similar to an annual cash crop.

• If a field produces rice with a ratoon crop, the method CH₄ emissions from flooded rice cultivation is run twice (once for the first rice crop and once for the ratoon crop), and the estimated energy use and GHG emissions are summed together. This will require filling in some assumptions. Taking this approach will typically result in higher emissions per area (e.g., kg CO₂e / ha) and lower emissions per crop production unit (e.g., kg CO₂e / kg crop) compared to producing a single rice crop per year.

• If a field produces rice with a ratoon crop, the method CH₄ emissions from flooded rice cultivation is run aggregating inputs and outputs for both rice crops, and extending the season length from the planting of the first rice crop to the harvest of the ratoon crop (approximately 133 days + 60 days). The estimated energy use and GHG emissions are representative of both crops. This will require filling in some assumptions. Taking this approach will typically result in higher emissions per area (e.g., kg CO₂e / ha) and lower emissions per crop production unit (e.g., kg CO₂e / kg rice crop) compared to producing a single rice crop per year.

The following figure demonstrates the crop intervals for a rotation with rice ratooning. To be concise, many field activities were omitted from the timeline of operations.

Figure 1: Crop interval for rice with ratooning

For the crops with complete information shown above, the crop intervals would be delineated as follows:

• 2020 rice with ratooning: 10/16/2019 to 10/15/2020

References

IPCC. 2019. “2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.”

Ogle, Stephen M, Paul R Adler, Gary Bentrup, Justin Derner, Grant Domke, Stephen Del Grosso, Johannes Lehmann, Michele Reba, and Dominic Woolf. 2024. “Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems.” In: Hanson, Wes L.; Itle, Cortney; Edquist, Kara, Eds. Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry: Methods for Entity-Scale Inventory. Technical Bulletin Number 1939, 2nd Edition. Washington, DC: US Department of Agriculture, Office of the Chief Economist. 6-1-6-23. Chapter 3. 1939: 31.