Guidelines for Measuring CH4 and N2O
Emissions from Rice Paddies by a Manually
Operated Closed Chamber Method

Version 1
August, 2015
National Institute forAgro-Environmental
Sciences, Japan

Guidelines for Measuring CH4 and N2O Emissions
from Rice Paddies by a Manually Operated Closed
Chamber Method
Version 1, August 2015

Lead authors:
Kazunori Minamikawa (Japan), Takeshi Tokida (Japan), Shigeto Sudo (Japan), Agnes
Padre (Philippines), Kazuyuki Yagi (Japan)

Contributing authors:
Prihasto Setyanto (Indonesia), Tran Dang Hoa (Vietnam), Amnat Chidthaisong(Thailand), Evangeline B. Sibayan (Philippines), Yusuke Takata (Japan), Takayoshi
Yamaguchi (Japan)

Reviewers:
Kazuyuki Inubushi (Japan), Reiner Wassmann (Philippines/Germany), Tetsuhisa Miwa
(Japan), Ngonidzashe Chirinda (Colombia/Zimbabwe)

These guidelines should be cited as:
Minamikawa, K., Tokida, T., Sudo, S., Padre, A., Yagi, K. (2015) Guidelines for measuring CH4 andN2O emissions from rice paddies by a manually operated closed chamber method. National
Institute for Agro-Environmental Sciences, Tsukuba, Japan.

Acknowledgements
These guidelines were commissioned by the Secretariat of the Agriculture, Forestry and
Fisheries Research Council of the Ministry of Agriculture, Forestry and Fisheries of Japanthrough the international research project “Technology development for circulatory foodproduction systems responsive to climate change (Development of mitigation option forgreenhouse gas emissions from agricultural lands in Asia)” (known as the MIRSA-2 project) to
support the goals and objectives of the Paddy Rice Research Group of the Global Research
Alliance on Agricultural Greenhouse Gases (PRRG-GRA). The authors thank to the provider of
unpublished field data, Dr. Seiichi Nishimura (NARO Hokkaido Agricultural Research Center,
Japan).

Publisher details
National Institute for Agro-Environmental Sciences
3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan
Tel +81-29-838-8180; Fax +81-29-838-8199
Copies can be downloaded in a printable pdf format fromhttp://www.niaes.affrc.go.jp/techdoc/mirsa_guidelines.pdf
This document is free to download and reproduce for educational or non-commercial
purposes without any prior written permission from the authors. Authors must be duly
acknowledged and the document fully referenced. Reproduction of the document forcommercial or other reasons is strictly prohibited without the permission of the authors.
ISBN 978-4-931508-15-6 (online)

Disclaimer
While every effort has been made through the MIRSA-2 project to ensure that the
information in this publication is accurate, the PRRG-GRA does not accept any responsibility
or liability for any error of fact, omission, interpretation, or opinion that may be present, nor
for the consequences of any decisions based on this information. The views and opinions
expressed herein do not necessarily represent the views of the PRRG-GRA.

Table of contents

Table of contents
Table of contents ........................................................................................................................................ 1
Preface............................................................................................................................................................ 4
Recommendations..................................................................................................................................... 6
Experimental design ............................................................................................................................. 6
Chamber design ..................................................................................................................................... 7
Gas sampling ............................................................................................................................................ 7
Gas analysis .............................................................................................................................................. 9
Data processing ................................................................................................................................... 10
Auxiliary measurements ................................................................................................................... 10
Evolving issues ......................................................................................................................................... 12
1. Introduction.......................................................................................................................................... 13
1.1. Background and objectives..................................................................................................... 13
1.2. Biogeochemical mechanisms of CH4 emissions from rice paddies ......................... 14
1.2.1. Microbial mechanisms of CH4 production................................................................. 14
1.2.2. Sources of organic matter for CH4 production ........................................................ 15
1.2.3. Emission pathways of CH4 to the atmosphere......................................................... 16
2. Experimental design .......................................................................................................................... 17
2.1. Introduction .................................................................................................................................. 172.2. Research objectives.................................................................................................................... 17
2.3. Field preparation ......................................................................................................................... 172.4. Arrangement of replicated experimental plots ............................................................... 18
2.4.1. Introduction........................................................................................................................... 18
2.4.2. Experimental factors .......................................................................................................... 19
2.4.3. Randomized block design ............................................................................................... 19
2.4.4. Split-plot design .................................................................................................................. 20
2.4.5. Completely randomized design .................................................................................... 21
2.4.6. Pseudoreplication ............................................................................................................... 21
2.4.7. Multiple comparisons ........................................................................................................ 21
2.5. Terminology for experimental errors................................................................................... 22
3. Chamber design ................................................................................................................................. 24
3.1. Introduction .................................................................................................................................. 24
3.2. Material........................................................................................................................................... 24
3.3. Shape and size ............................................................................................................................. 24
3.4. Base .................................................................................................................................................. 27

1

2

Table of contents

3.5. Other components ..................................................................................................................... 28
3.6. Evolving issues ............................................................................................................................. 29
3.6.1. Chamber color ...................................................................................................................... 29
3.6.2. Area covered by a chamber vs. plot area ................................................................... 30
4. Gas sampling ........................................................................................................................................ 31
4.1. Introduction .................................................................................................................................. 31
4.2. Period .............................................................................................................................................. 31
4.3. Time of day ................................................................................................................................... 32
4.3.1. CH4 flux during the flooded growing period ........................................................... 32
4.3.2. CH4 flux during a temporary drainage period during the growing season.. 34
4.3.3. N2O flux during the flooded growing period ........................................................... 34
4.3.4. CH4 and N2O fluxes during dry fallow periods ........................................................ 35
4.4. Frequency ...................................................................................................................................... 35
4.4.1. CH4 fluxes during the growing period ........................................................................ 35
4.4.2. N2O fluxes during growing period ............................................................................... 36
4.4.3. CH4 and N2O fluxes during dry and wet fallow periods....................................... 37
4.5. Chamber deployment duration and number of gas samples ................................... 38
4.6. Instruments ................................................................................................................................... 39
4.6.1. Gas collection ........................................................................................................................ 39
4.6.2. Gas storage ............................................................................................................................ 40
4.6.3. How to prepare evacuated glass vials ......................................................................... 41
4.6.4. Gas replacement method ................................................................................................. 43
4.7. Notes on manual chamber operation ................................................................................ 44
4.8. Evolving issues ............................................................................................................................. 45
4.8.1. Uncertainty of diurnal CH4 and N2O flux patterns ................................................. 45
4.8.2. Effect of human-induced CH4 ebullition on the number of gas samples...... 45
5. Gas analysis ........................................................................................................................................... 46
5.1. Introduction .................................................................................................................................. 46
5.2. GC requirements ......................................................................................................................... 46
5.2.1. CH4 ............................................................................................................................................ 46
5.2.2. N2O ........................................................................................................................................... 47
5.2.3. Maintenance ......................................................................................................................... 49
5.3. Gas injection.................................................................................................................................. 50
5.4. Standard gases............................................................................................................................. 52
5.5. GC repeatability ........................................................................................................................... 53
5.5.1. Causes of errors ................................................................................................................... 53
5.5.2. Limit of detection and limit of quantification in GC analyses ............................ 53
5.6. Evolving issues ............................................................................................................................. 54

Table of contents

6. Data processing .................................................................................................................................. 56
6.1. Introduction .................................................................................................................................. 56
6.2. Calculation of hourly gas fluxes and cumulative emissions ....................................... 56
6.2.1. Hourly gas flux ..................................................................................................................... 56
6.2.2. Significance of linear regression ................................................................................... 58
6.2.3. Cumulative gas emission .................................................................................................. 58
6.3. Limit of quantification for the gas flux ............................................................................... 59
6.3.1. Introduction........................................................................................................................... 59
6.3.2. Detailed procedure ............................................................................................................. 59
6.4. Evolving issues ............................................................................................................................. 60
6.4.1. Correction for inadequate chamber area................................................................... 60
6.4.2. Correction for a missing flux peak ................................................................................ 61
6.4.3. The significance of linear regression and/or LOQflux ............................................. 62
7. Auxiliary measurements .................................................................................................................. 63
7.1. Introduction .................................................................................................................................. 63
7.2. Experimental conditions........................................................................................................... 63
7.3. Agricultural management practices .................................................................................... 64
7.4. Rice growth and yield ............................................................................................................... 65
7.5. Specific measurements............................................................................................................. 65
7.5.1. Soil redox chemistry........................................................................................................... 65
7.5.2. Soil temperature and moisture ...................................................................................... 67
7.5.3. Soil C and N contents ........................................................................................................ 67
References ................................................................................................................................................. 68
Authors’ affiliations ................................................................................................................................ 75

3

4

Preface

Preface
Since Prof. Ralph J. Cicerone and his colleagues have covered rice plants with gas
collectors at an experimental rice field located in the University of California at Davisin the late summer of 1980, closed chamber methods have been used for measuring
methane emissions from rice paddies at numerous paddy fields in various parts of theworld. The database used for estimating emission and scaling factors for methanefrom rice cultivation in the 2006 IPCC Guidelines compiled more than 1000 data ofseasonal measurements by closed chamber methods at over 100 different sites in 8
Asian countries. Closed chamber measurements are being conducted at various
paddy fields in these and other countries up to the present date, in order to study
mechanisms of material cycling in the ecosystems or to estimate specific emission
factors for developing a greenhouse gas inventory.
The research community doing these measurements often discuss about
identifying both “best practice” and gaps in the current methodologies of measuring
gas emissions, because inter-comparisons of the methods used among different
research groups are limited and assessment of the reliability and uncertainty
associated with the results have not been comprehensively discussed. The need forstandardized guidelines for measuring greenhouse gas emissions from rice paddies
have been recognized from these discussions.
The United Nations Framework Convention on Climate Change (UNFCCC) has
introduced in the Bali Action Plan in 2007, the actions and commitments ofmeasuring, reporting and verification (MRV), which is now recognized to be one of
the most important building blocks to reduce greenhouse gas emissions fromdifferent sources. The MRV framework encompasses submitting national greenhousegas inventories, undergoing international consultation and analysis, and setting up
nationally appropriate mitigation actions (NAMAs). For implementing MRV at the
local and national levels, standardized guidelines for measuring, and also forreporting and verifying, greenhouse gas emissions are strongly requested to beprovided. The methodology registered for Methane emission reduction by adjusted
water management practice in rice cultivation at the UNFCCC Clean development
mechanisms (CDM) recommends to carry out measurements using the closed
chamber method by providing simple Guidelines for measuring methane emissions
from rice fields.
This document, “Guidelines for Measuring CH4 and N2O Emissions from Rice

Preface

Paddies by a Manually Operated Closed Chamber Method”, is a product of
discussions in the international science communities, especially that in the Paddy Rice
Research Group of the Global Research Alliance on Agricultural Greenhouse Gases(PRRG-GRA) since it was established in 2011. Much of the style and composition of
the document follows the preceding publication by the Livestock Research Group of
GRA, “Nitrous Oxide Chamber Methodology Guidelines”.
As mentioned in the Introduction section of the text, the guidelines have beendeveloped to provide “recommended” protocols based on current scientificknowledge. We tried to provide as much scientific evidences that support therecommendations as possible. In addition, we tried to provide a user-friendly
structure of the document by conveying practical and technical "know-how," anddefining minimum requirements for the measurements. Nevertheless, there still exist
some gaps and uncertainties of the methodologies mainly due to current lack of ourknowledge. Therefore, we hereby publish this document as version 1, or bestpractices at this moment, and hope to make revisions in the future by collecting
further knowledge and experiences.

July 2015

Kazuyuki Yagi
Principal Research Coordinator
National Institute for Agro-Environmental Sciences (NIAES)

5

6

Recommendations

Recommendations
Here we summarize the minimum requirements (written in upright letters) andrecommendations (written in italics) of each chapter.

Experimental design
Chapter 2 of these guidelines outlines a basic design for comparative field experiments. For
best results, it is important to work out a detailed plan and to prepare a field withhomogeneous properties before beginning field measurements.
Category

Minimum requirements and recommendations

Researchobjectives

Set research objectives and a plan for their achievement before
beginning the field experiment.



Repeat all measurements multiple times (e.g., over 2–3 years) with thesame design to obtain representative estimates of greenhouse gas(GHG) emissions and the average effects of experimental factors in afield.

Prepare alternatives or countermeasures in case the experiment does
not go as planned.

Field



preparation

Select a field that is homogeneous with respect to agricultural
practices (e.g., organic amendment) and soil properties.



Determine a suitable size for individual plots given the research
objectives.



To prevent physical disturbance of the soil and artificial CH4
ebullition when operating the chambers, set up scaffolding in eachplot.

Arrangement ofArrange

replicatedexperimental

plots

accordingtothereplicatedpredetermined method of statistical analysis (e.g., analysis ofexperimental

variance [ANOVA]).

plots



Avoid pseudoreplication.

Use a post hoc test (e.g., the Tukey-Kramer method) for multiple
comparisons.



Use a randomized block design if any heterogeneity exists (e.g., in
the chamber deployment sequence).

Use at most three factors for ANOVA.

Recommendations

Chamber design
Chapter 3 of these guidelines outlines the features of an ideal chamber that can be used byevery researcher. Several design options are acceptable, taking into account local availabilityof materials and equipment.
Category

Minimum requirements and recommendations

Material

Use lightweight material that is break resistant and inert to CH4 andN2O (e.g., acrylic and PVC).

Shape and sizeUse a rectangular chamber for transplanted rice fields.



The area covered by the chamber (i.e., its footprint) should be amultiple of the area occupied by a single rice hill.

At least two transplanted rice hills should be covered by eachchamber.

Either a cylindrical (e.g., made from a trash container) or rectangularchamber can be used in fields seeded by direct broadcasting.



Record the seed/plant density inside the chamber.



Make sure that the chamber height will always be higher than therice plant.



Measure at least three points in each plot.

Adjust the planting density to one suitable for the chamber size, if thechamber size is already fixed.

Base



Use a double- or triple-deck chamber with adjustable height.



Use a water seal between the base and the chamber to ensuregas-tight closure.



Minimize the aboveground height of the base.



Determine a belowground depth of the base suitable for the soil
hardness (e.g., 5-10 cm).

Other



components(1) Install a small fan, (2) install a thermometer inside the chamber,and (3) drill a vent hole and install a vent stopper.

Equip the chamber with a gas sampling port (e.g., a flexible tube
connected to a valve) that is separate from the chamber body.

Install an air buffer (e.g., a 1-L Tedlar® bag) inside the chamber.

Gas sampling
Chapter 4 of these guidelines outlines a gas sampling schedule and instruments that should
be used during chamber deployment to obtain reliable GHG flux data. These procedures and7

8

Recommendations

recommendations should be applied regardless of the chamber shape.
Category

Minimum requirements and recommendations

Period

Determine the measuring period according to the research
objectives.



The measurement period should encompass the entire rice growing
period for the estimation of seasonal emissions of CH4 and N2O.

In accordance with IPCC recommendations, to calculate the N2O
emission factor, measurements should be obtained throughout a year.

Time of dayMid-morning during flooded rice-growing periods (measure once
daily to obtain the daily mean CH4 flux).

Measure all treatments at the same timing.

Daytime during temporary drainage events during the rice growing
period.

Frequency



Late morning during dry fallow periods.

Measure the N2O flux concurrently with the CH4 flux.

At least weekly during flooded rice-growing periods.

More frequently during agricultural management events (e.g.,
irrigation, drainage, and N fertilization) and some natural events (e.g.,heavy rainfall).



Weekly or biweekly during dry fallow periods.

Chamber

Deploy chamber for 20–30 min during rice-growing periods.

deployment

Obtain at least three gas samples per deployment depending ontime andnumber of gas

sampling and analytical performance.
Use a longer deployment time (up to 60 min) during fallow periods.



Use a syringe or a pump for gas sampling, depending on thesamples
Instruments

required sample volume.
Use plastic or glass containers for the gas samples, taking into
account the allowable storage period.

Use an evacuated glass vial equipped with a butyl rubber stopper forgas storage.

Use a vacuuming machine to prepare evacuated glass vials, instead ofmanually evacuating the vials.



Use a gas replacement method if the use of evacuated glass vials is
impractical.

Notes for



Check the water volume for water seal in the chamber base.

Recommendations

manualFill soil cracks up with kneaded soil collected from outside the plot.

operationPrevent water from overflowing the base when the field is drainedBe gentle when placing the chamber on and removing it from the
base.



Avoid placing items on top of the chamber and avoid directly
touching the chamber body.

Avoid dead volume in the gas sampler.

Store each gas sample in an evacuated vial under pressurized
conditions.



Replace the inside air of the chamber after each measurement by
tipping it sideways for a few minutes.



Use an elastic cord to gently bind the rice plants inside the chamber
together and then remove the cord before the chamber is closed.



Check the degree of inflation of the air buffer bag (if one is used).

Gas analysis
Chapter 5 of these guidelines outlines a standard method for analyzing GHG concentration
using gas chromatography (GC). Typical GC settings and routine operation are described.
Stable GC conditions should be maintained for consistent and accurate analysis of thesampled gases.
Category

Minimum requirements and recommendations

GC requirements 

Use a commercially made GC instrument equipped with a flame
ionization detector (FID) and an electron capture detector (ECD) foranalysis of CH4 and N2O, respectively.

Use packed separation columns to separate the target gas fromother gases.

Gas injectionUse pre-cut filters to remove expected contaminants.

Regularly maintain the GC system (e.g., column conditioning).



Use a gas-tight glass syringe or a gas sample loop for manual
injection.



Avoid using a plastic syringe for the direct injection.



An automated gas sampler can be used to minimize the volume andstroke errors associated with manual gas injection.

Standard gasCalibrate the GC before every analysis.

Use certified standard gases.

910

Recommendations



Use two concentration levels that are outside the expected observed
range.

GC repeatabilityMaximize repeatability by fine-tuning GC settings and operation
procedures.

Calculate the limit of quantification (LOQ) of the GC analysis byrepeated analyses of a gas of known concentration (i.e., 10 ×standard deviation).

Data processing
Chapter 6 of these guidelines outlines acceptable methods for calculating hourly GHG fluxes
and cumulative GHG emissions from the analyzed gas concentrations.
Category

Minimum requirements and recommendations

Calculation ofchamber against time to calculate the hourly flux.

gas fluxes andcumulative

Normally use linear regression of the gas concentration inside theIdentify the reasons of non-linearity (if exists) for the validation andcorrection of calculated flux (see Chapter 6.2).

emissions

Use trapezoidal integration to calculate cumulative gas emissions
from the hourly flux data.

Limit of

quantification
for gas flux

Calculate the limit of quantification (LOQ) of the gas flux to identify
meaningful (i.e., non-zero) flux values (see Chapter 6.3).

Determine how flux data below the LOQ will be handled.

Auxiliary measurements
Chapter 7 of these guidelines outlines auxiliary measurements that provide supporting
evidence for interpreting and generalizing (modeling) the observed GHG emissions. In
addition, collection of field metadata (i.e., data about field data) is helpful for secondary usersof the field data.
Category

Minimum requirements and recommendations

Experimental



conditions

Collect

data

on

the

field

location(atminimum,

country,

province/state, nearest city, and latitude/longitude).
Collect data on weather conditions (at minimum, climate zone,
wet/dry seasons, precipitation, and air temperature).

Collect data on the water and soil environment (at minimum, thewater supply source, soil taxonomy, total C and N contents, plow

Recommendations

layer depth, bulk density, and texture).


Meteorological data collected at a nearby weather station can be used.

Collect information on the field drainage condition, especially if water
management is a focus of the research.

AgriculturalCollect records of cultivation history from at least the preceding 3

management

years [Name of crop(s), number of crops per year, organic

practicesamendments (type and rate), and soil water status during fallow
periods].


Record all current agricultural management practices throughout theyear (date/duration, method/type, and rate/amount of eachmanagement event).

Measure the surface water depth frequently to ensure proper water
management practices (automated sensors and loggers can be used).

Rice growth and

Record the denomination of rice variety.

yield



Measure the yields of grain and straw.



Calculate yield-scaled GHG emissions.



Record disease and insect damage to rice.

Regularly measure plant height, number of tillers/ears per unit area or
per hill, aboveground biomass, and (optional) root biomass.



A yield component analysis is helpful for further investigation.

Compare rice growth and yield between plants growing inside andoutside of chambers.

Specificmeasurements

Measure soil redox potential and/or soil Fe(II) content during flooded
periods.

Monitor soil temperature and moisture throughout the year at 1-hourintervals with automated sensors/loggers.

Conduct long-term measurements of total carbon and nitrogencontents in the upper soil layer (to at least 30 cm depth).



Periodically monitor soil inorganic nitrogen content (ammonium andnitrate).

11

12

Evolving issues

Evolving issues
At this time (i.e., the time of production of these guidelines), there is a lack of consensus onsome issues, and others have yet to be explicitly considered.
Issue

Current status and prospects

Equipmentavailability

For various reasons, it is not always possible to procure the required
equipment, so measurement procedures need to be flexible and,thus, may not be uniform.

Standard gases



It is sometimes difficult to obtain certified standard gases.



If necessary, standards of the required concentrations can beproduced by diluting high-concentration standard gas with an inert
gas (He or N2) with proper checking of the accuracy of the dilution.

Compressed air can be used as a working standard gas after
determination of the target gas concentrations.

Chamber

Chamber transparency (or opacity) remains an open question.

transparencyBoth transparent and opaque materials have advantages anddisadvantages, but which type of material is used often depends on
what is available.

Chamber areaThe area covered by each chamber (i.e., its footprint) and the number

and number ofof chambers that should be deployed within a plot depend on thechambers withinrequired measurement accuracy.

a plot

The larger the chamber area and the greater the number ofchambers deployed, the more reliable the gas flux data will be.

However, practically, the chamber area and the number of chambers
may be limited by the number of people available to carry out themeasurements.



There is no consensus as to what percentage of the plot area should
be covered to obtain a representative gas flux value.

Interpolation toInsufficient gas flux data collected during drainage or after N

fill gaps in thefertilization may lead to considerable over- or underestimation ofgas flux data

total emissions.


Any such gaps in the measurements should be recorded.

The gaps may be filled by interpolation by making some reasonableassumptions.

1. Introduction

1. Introduction
1.1. Background and objectives
Rice (Oryza sativa) paddies act as an interface for gaseous carbon compounds between theatmosphere and the land. Photo-assimilation of atmospheric CO2 by rice plants provides
staple food for half the world's population (GRISP, 2013), and decomposition of organic
materials in the paddy soil can result in the production of CH4, a potent greenhouse gas andthe second largest contributor to historical global warming after CO2 (Myhre et al., 2013).
Although 90% of the world's rice paddies are located in Asia, they are a globally
important CH4 source (Smith et al., 2014). Estimates based on IPCC guidelines (IPCC, 2006)
indicate that CH4 emissions from rice paddies total 33–40 Tg year–1, or 11% of total
anthropogenic emissions (Ciais et al., 2013 and references therein). However, these estimates
include considerable uncertainty, because of large uncertainties in emission factors and thepoor availability of activity data (e.g., water regimes and residue management practices),
which can significantly affect emission strength (Blanco et al., 2014).
Field measurements of CH4 emissions are the basis of CH4 emissions estimates and ameans of evaluating possible countermeasures for reducing emissions. Most fieldmeasurements are obtained by the manually operated closed chamber method, because of
its ease of implementation in the field due to the low cost and high logistical feasibility of
implementation.

Drawbacksofthemethodincludelow

spatialandtemporal

representativeness of the measured data, which is limited by chamber size and measurement
frequency. Alternatively, micrometeorological techniques can provide near-continuous,
spatially averaged estimates, but these methods require a large, homogenous field.
Consequently, the manual closed chamber method is often virtually the only available option
for comparing emissions between experimental plots in which different agronomical practices
are used. Therefore, the manual closed chamber method is expected to continue to have acentral role, especially in studies investigating management options for reducing CH4emissions.
Numerous studies have used the closed chamber method to measure GHGs, and their
protocols are reported in the Materials and Methods section of many journal papers.
However, the published information is usually limited in nature, and it is difficult for
non-experts to carry out these protocols on the basis of the provided descriptions alone.
Alternatively, reference can be made to more methodology-oriented documents on chamber
design (e.g., IAEA, 1992, Figure 1.1) and the standardized protocol developed for a specificproject (i.e. IGAC, 1994, Figure 1.1). To our knowledge, however, no single document
comprehensively presents the detailed information necessary for implementing CH4 emission
measurements from rice paddies using the chamber method. Moreover, it should be notedthat the recommended protocols for upland fields (e.g., Parkin and Venterea, 2010; de Klein

13

14

1. Introductionand Harvey, 2012) cannot be simply applied to rice paddy studies, because the presence of
surface water and rice plants significantly alters the physical mechanisms of gas emissions.
Thus, the measurement scheme and assumptions used for flux calculations must also differ
considerably.
This document, “Guidelines for Measuring CH4 and N2O Emissions from Rice Paddies by a
Manually

Operated

ClosedChamber

Method”

hasbeen

developed

toprovide“recommended” protocol of the closed chamber method for rice paddy studies based on
current scientific knowledge. Furthermore, we wish to convey practical and technical"know-how," which is seldom described in detail in journal articles. In addition, we haveattempted to define minimum requirements, which may be useful when, for financial or
logistic reasons, full implementation of the recommended protocols is not feasible.

Figure 1.1. Examples of published protocols for the chamber measurement in a rice paddy.

1.2. Biogeochemical mechanisms of CH4 emissions from rice paddies
In this subsection, we briefly overview CH4 biogeochemistry in rice paddies because
knowledge of them is necessary to establish proper measurement protocols for CH4 emission
by the manual closed chamber method.
1.2.1. Microbial mechanisms of CH4 production
CH4 is an end product of the organic C decomposition cascade under anoxic conditions,starting with the hydrolysis of macromolecules (e.g., polysaccharides) and followed byprimary and secondary (syntrophic) fermentation to produce hydrogen (H2), C1 compounds,

1. Introductionor acetate, which then behave as electron donors for CH4 production (Conrad, 2002). The
whole CH4 production process can be expressed as reduction and oxidation of two moleculesof a simple hydrocarbon, one of which is reduced to CH4 and the other of which is oxidized to
CO2 (Tokida et al., 2010): 2CH2O → CO2 + CH4.
CH4-producing Archaea (methanogens) are responsible for only the final reaction, i.e.,the conversion of simple compounds, mainly H2 + CO2 and acetate, to CH4 (Takai, 1970).
Various contingent and collaborative decomposition reactions associated with diverse
microbes occur during the course of organic matter (OM) decomposition (Kato and
Watanabe, 2010; Schink, 1997).
The proportion of OM converted to CH4 (rather than CO2) depends primarily on whether
other microbes can harvest more energy by using alternative electron acceptors such as O2,
nitrate, Fe(III), Mn(IV), and sulfate (Takai and Kamura, 1966). If these electron acceptors areavailable, then microbial competitors of methanogens convert organic C into CO2, reducing
the production of CH4. As predicted by thermodynamic theory, these microbial competitors
can produce energy at lower substrate concentrations, and hence prevail. Fe(III) reducers(geobacters) (Balashova and Zavarzin, 1980; Lovley and Phillips, 1988), in particular, canstrongly suppress methanogenesis in paddy soils (Kamura et al., 1963) owing to anabundance of ferric oxides: Fe(III) reduction often accounts for half or more of total
electron-donor consumption in paddy soils (Yao et al., 1999). Consequently, organic C
oxidation is often coupled with Fe(III) reduction, rather than with methanogenesis, in the earlyphase of rice growth in irrigated paddies (Eusufzai et al., 2010; Tokida et al., 2010).
The strict requirement of anoxic condition for CH4 production points to the importanceof proper water management; for example, unintended drainage of surface water, even if fora short period of time, may lead to serious and unrecoverable reduction in the rate of CH4
production and hence the emissions.
1.2.2. Sources of organic matter for CH4 production
Methanogenesis ultimately depends on primary production and the input of OM into soils.
Sources of OM include soil, organic fertilizers, and crop residues (Aulakh et al., 2001; Kimura
et al., 2004). The latter two are applied to and subsequently becomes incorporated into thesoil. In addition, living rice can be a major source of OM for CH4 production (Dannenberg andConrad, 1999; Tokida et al., 2011; Watanabe et al., 1999; Yuan et al., 2012): some portion of
the current-season photosynthates is supplied to the soil via either root exudation from livingroots or root turnover (sloughing of cells and root decay, collectively referred to as
rhizodeposition).
The relative contributions of these sources to CH4 production depend not only on
management practices such as manure application and tillage but also on the rice growthstage (Hayashi et al., 2015). The contribution of applied OM is large during the early rice

15

16

1. Introduction

growing season, when the rice plants are still small, and the amount of root exudation
increases as the rice grows. The root biomass usually peaks at flowering, after which virtually
no further roots grow. Therefore, after flowering, root decay may become a major componentof rhizodeposition. The relative contribution of soil OM is small compared with thecontribution of other sources, but it plays an important role in reducing alternative electron
acceptors,

mostimportantly

Fe(III).

Integrated

over

the

entiregrowingseason,rhizodeposition can account for more than half of total CH4 production (Tokida et al., 2011;
Watanabe et al., 1999).
Because the contribution of rhizodeposition is often very significant, changes in growthand physiology of rice plant from those under ambient condition may lead to divergence insubstrate availability and hence may introduce biases in the estimated CH4 fluxes. Attention istherefore necessary to minimize interfering effects on rice growth during the course of themeasurement period.
1.2.3. Emission pathways of CH4 to the atmosphereCH4 produced in paddy soils enters the atmosphere either through aerenchyma tissue of therice plants (Nouchi et al., 1990; Wang et al., 1997), or ebullition of CH4-containing gas bubbles(Schütz et al., 1989; Wassmann et al., 1996). Molecular diffusion of dissolved CH4 across thewater-atmosphere can also occur, but the contribution is usually negligible (Butterbach-Bahl
et al., 1997; Schütz et al., 1989) because CH4 is only a sparsely soluble gas (Clever and Young,
1987; Wilhelm et al., 1977) and diffusion in soil solution is four orders of magnitude smaller
than in the gas phase (Himmelblau, 1964). In addition, 80–100% of CH4 diffusing through theoxidative soil-water interface is oxidized by methanotrophic bacteria before reaching theatmosphere (Banker et al., 1995; Frenzel et al., 1992). It is well documented that rice-plant
mediated transport is the dominant pathway, accounting for >90% of total emissions when
the rice plant develops its root system (Cicerone and Shetter, 1981; Denier van der Gon andvan Breemen, 1993; Holzapfel-Pschorn et al., 1986). This fact clearly requires investigators toinclude rice plants in their chamber measurements; exclusion of rice plants may results insevere underestimation of the estimated CH4 fluxes.
In rice paddies entrapped gas bubbles (rather than dissolved CH4 in soil solution) havebeen shown to represent a major CH4 inventory, even in soil that is regarded aswater-saturated (Tokida et al., 2013). Many studies have shown a very high CH4 mixing ratio in
the bubbles in rice-paddy soils (Byrnes et al., 1995; Holzapfel-Pschorn and Seiler, 1986;
Rothfuss and Conrad, 1998; Uzaki et al., 1991; Watanabe et al., 1994). Accordingly release ofCH4-containing gas bubbles can be a major emission pathway at early vegetative stage when
the rice plant is still small (Schütz et al., 1989; Wassmann et al., 1996). Also at grain-filling tomaturity stages, ebullition could be a dominant pathway because senescence and decay ofroot system reduce the ability of rice to transfer CH4 (Tokida et al., 2013).

2. Experimental design

2. Experimental design
2.1. Introduction
To obtain the best results from a comparative study based on statistical analysis, it isimportant to work out a detailed experimental design and to prepare homogeneous fields
before measurements are carried out. For example, heterogeneous soil properties can mask
the effect of experimental factor(s) in the statistical analysis owing to other influential
factor(s). Because it can be difficult to prepare homogeneous plots in an actual field, the aim
should be to maximize labor efficiency, especially when preparing a new field or conducting a
new experiment. This chapter provides basic design recommendations for field experiments
and discusses appropriate experimental designs for statistical analysis.

2.2. Research objectives
We conduct field experiments to achieve specific research objectives. Therefore, theobjectives should be precisely defined before the experiment is performed. Moreover, toachieve the research objectives, it is essential to prepare an achievement plan before theexperiment. For example, to estimate representative GHG emissions and the average effects
of experimental factors in a field, we recommended that the measurements be repeatedmultiple times (e.g., over 2–3 years) using the same experimental design.
Sometimes, an experiment may not go as planned. Therefore, we recommend the
preparation of countermeasures and alternative procedures for dealing with problems. Of
course, plans can be changed or extended after an experiment has been started, but
implementation of the changes may increase soil disturbance or be limited by a lack ofmaterials or space.

2.3. Field preparation
Heterogeneity of soil and field properties (among experimental plots) can confound theeffects of experimental factors. For example, different rates of organic amendment in thepreceding rice cultivation may alter the amount of carbon substrate available for CH4
production in the soil (see Chapter 7.3). In addition, the experiment field should be level,
especially if water management regimes are being compared among plots. We should
therefore select or prepare fields that are homogeneous with respect to agricultural practices
and soil properties.
The optimal size of a plot depends on the objectives of the study and on labor
availability. For example, it is appropriate to use an entire field as a plot if the aim is to

17

18

2. Experimental design

estimate mean GHG emissions on a catchment or basin scale. On the other hand, theminimum plot area required for comparing the effects of experimental factors is several
square meters (e.g., 5 m × 5 m for comparing GHG emissions with rice growth and yield).
Scaffolding should be set up on the plots to prevent physical disturbance of the soil andartificial CH4 ebullition while measurements are being carried out (Figure 2.1). In addition, toprevent uneven horizontal flow of surface and soil waters, a waterproof sheet can be installedaround the edges of each plot (Figure 2.2).

Figure 2.1. Scaffolding (boardwalks) installed for chamber access.

Figure 2.2. Installation of a waterproof sheet around the edges of an experimental plot.

2.4. Arrangement of replicated experimental plots
2.4.1. Introduction
Analysis of variance (ANOVA) is commonly adopted as the statistical technique for comparing2. Experimental design

target gas emissions among treatments. Thus, a plot arrangement appropriate for theapplication of this technique to the data is required. The arrangement should be based on
the three principles proposed by Fisher (1926): local control, randomization, and replication.
For field experiments to determine GHG emissions, at least three replicates of each treatment
should be prepared. Although theoretically two replicates might be adequate for statisticalanalysis, in practice if only two replicates are used, (1) it is difficult to detect significant
differences and (2) editors and reviewers of peer-reviewed journals may doubt the reliabilityof the measurement data. Statistical significance level is generally set at p < 0.05 for GHG
studies, but the term “marginal difference” (e.g., p < 0.1) may be useful to explain the resultswith large variation. Here, we give examples of suitable plot arrangements for ANOVA.
2.4.2. Experimental factors
The number and type of experimental factors used for ANOVA are constrained by the plot
arrangement. Therefore, when the experiment is being designed, the experimental factors
and the appropriate plot arrangement should be considered together.
Table 2.1 defines some statistical terms used in ANOVA. At most three factors should be
evaluated by ANOVA in a paddy-field experiment. Although, theoretically, more than three
could be evaluated, it is difficult to interpret statistically significant interactions among morethan three factors and to arrange plots.
Table 2.1. Explanation of statistical terms in ANOVA
TermExplanation

Factor

A factor is a selected causal variable that may affect the target response variable (e.g.,
GHG emissions).

Level

Levels are the different settings of a factor.

Treatment

Treatments are combinations of factors and levels. To evaluate the effects of two
factors, each with three levels, nine treatments are necessary. If the effect of only one
factor is being evaluated, then the number of levels and treatments is the same.

2.4.3. Randomized block design
Two plot arrangements often used in field experiments are the randomized block design andthe split-plot design. A randomized block design (Figure 2.3) is used when someheterogeneity is unavoidable, that is, when it cannot be removed during field preparation. For
example, unidirectional surface water flow may be unavoidable in irrigated fields. Such a fieldshould be divided into blocks from the water inlet to its outlet. Another example is the use ofmultiple fields; in this case, each field is considered a block.
The reason that most often requires an arrangement of randomized blocks to be
adopted is sequential chamber measurement, especially when human resources are limited.

19

20

2. Experimental design

Because CH4 fluxes show substantial diurnal variation (see Chapter 4.3.1), it is often necessary
to consider the chamber deployment time as a block (e.g., Figure 2.3). In the illustrated case,a different person is in charge of performing measurements in each row, and themeasurements are conducted in sequence from block 1 to block 3 (see Table 4.3 for an
example of a detailed time schedule). Note that if there are more than two heterogeneous
properties, it may be impossible to interpret the reason of the significant block effect (if one
exists) with a randomized block design.

Figure 2.3. Example of a randomized block design for one factor with three levels (treatments) andthree replicates.

2.4.4. Split-plot design
A split-plot design is used for a field experiment when the random arrangement of multiple
experimental factors is impractical. This design can incorporate blocking, but blocking is notalways needed. For example, if the experimental factors being evaluated are water
management and fertilizer application rate, a random arrangement is impractical because
each treatment would require its own water inlet and outlet. In this case, water management
should be considered as a main-plot factor and fertilizer application rate as a sub-plot factor(e.g., Figure 2.4).

Figure 2.4. Example of a split-plot design for two factors with three main plots, each with three
sub-plots, and three replicates.

2. Experimental design

2.4.5. Completely randomized design
A completely randomized design is the simplest design (Figure 2.5). However, for the reasonsdescribed in Chapters 2.4.3 and 2.4.4, this design is seldom suitable for studies of GHG
emissions under paddy-field conditions.

Figure 2.5. Example of a completely randomized design for one factor with three levels (treatments)
and three replicates.

2.4.6. Pseudoreplication
We occasionally see published in peer-reviewed journals experiments with an incorrect plot
arrangement. For example, in an experiment with one factor and three levels, thecombination of the use of one plot for each treatment and the deployment of threechambers within each treatment plot does not provide three independent replicates of eachtreatment (level) (Figure 2.6). Rather, it is an example of pseudoreplication. Although it ispossible to perform ANOVA on the resulting data using PC software, the pseudoreplication
makes the ANOVA result meaningless. See Hurlbert (1984) for more examples.

Figure 2.6. Example of an incorrect arrangement of treatment chambers (Ch) in three plots for a one
factor experiment with three levels.

2.4.7. Multiple comparisonsMultiple comparisons are comparisons performed after ANOVA to find which means are
significantly different from each other. A post hoc pairwise comparison is a typical example.
Here we present three parametric methods that are often used for multiple comparisons inpeer-reviewed journals (Table 2.2).

21

22

2. Experimental design

Table 2.2. Features of three multiple comparison methods
MethodFeatures

Tukey-Kramer



Most common and recommended.

Requires homogeneity of variance.

Samples do not need to be the same size.

Result is conservative if sample sizes are unequal.



Small chance of a type I error

Not recommendable because the possibility of making a type I error isFisher’s protectedlarge.

least significant


Can be applied when the ANOVA result is significant.

Should not be applied when the number of treatments is 4 or more.

Easy to detect a significant difference.

Duncan’s new

Not recommendable.

multiple range

Often used in agricultural research.

test



Type II errors are unlikely, but the risk of a type I error is high.

difference (PLSD)

2.5. Terminology for experimental errors
Here we follow the terminology of ISO 5725-1:1994 "Accuracy (trueness and precision) of
measurement methods and results — Part 1: General principles and definitions" assummarized by Wikipedia (Wikipedia contributors, 2015). “Trueness” is the closeness of themean of a set of measurement results to the actual (true) value, and “precision” is thecloseness of agreement among a set of results (Figure 2.7). "Accuracy" is the closeness of ameasurement to the true value, and consists of “trueness” and “precision” (Figure 2.7).

Figure 2.7. Schematic diagram for explaining “accuracy”, “trueness”, and “precision”.

2. Experimental design

Measurement errors can be divided into two components: random error (variability) andsystematic error (bias). Random error relates to “precision” and is an error in measurementthat leads to measurable values being inconsistent when a constant attribute or quantity ismeasured repeatedly. Systematic error relates to “trueness” and is an error that is notdetermined by chance but is introduced by an inaccuracy inherent in the system.

“Precision” can be further stratified into “repeatability” and “reproducibility”.
"’Repeatability' is variation arising when all efforts are made to keep conditions constant byusing the same instrument and operator, and repeating during a short time period.
'Reproducibility' is the variation arising using the same measurement process among
different instruments and operators, and over longer time periods" (Wikipedia contributors,2015). See Chapter 5.5 for an example of the repeatability of GC analysis.

23

24

3. Chamber design

3. Chamber design
3.1. Introduction
Ideally, disturbance of the environmental conditions around the rice plants should be
avoided during chamber deployment. Provided that such disturbance is minimal, any
chamber design is acceptable if it is suitable for the local rice phenology and weather
conditions. However, because it is often difficult for various reasons to obtain necessary
equipment, we focus here on the minimum chamber design requirements that must be met
to obtain scientifically sound measurements. This chapter provides recommendations for
preparing an acceptable chamber, focusing in particular on chamber shape. See Chapter 4.7
for notes on manual operations during chamber deployment.
During dry fallow periods, we recommend using low-height chambers to detect small
exchanges of CH4 and N2O. See Parkin and Venterea (2010) and Clough et al. (2012) for thedesign of low-height chambers. However, although low-height chambers without covering
rice plants may also improve the detectability of small N2O exchange during a flooded period,
its usage is not encouraged because of (1) limited human resource and (2) quantitatively little
importance (see Chapter 4.1).

3.2. Material
It is essential to use a material, such as acrylic or PVC, that is inert to the target gases (CH4
and N2O). In addition, the material should be lightweight and break resistant. Whether thechamber material should be transparent or opaque is still a subject of discussion (see Chapter
3.6.1). Therefore, we recommend the use of any available material that is otherwise suitable(if possible, acrylic plate) without regard to its degree of transparency.

3.3. Shape and size
The chamber cross-sectional shape often depends on the materials that are available.
However, the interior volume of the chamber must be known. Chambers with rectangularcross sections are usually made of acrylic plates (optionally with a stainless steel frame forreinforcement and bonding), whereas one with a round cross section can easily be madefrom a trash can composed of a suitable material (Figure 3.1). An appropriate thickness foracrylic or PVC plates is usually 3–5 mm.
The larger the area that is covered by the chamber, the more reliable the gas flux data
will be. The maximum chamber size is constrained, however, by the need for portability, andits minimum size is constrained by the need to obtain representative measurements and byrice plant height (see Chapter 3.6.2).

3. Chamber design

Figure 3.1. Examples of chambers with rectangular or round cross sections.

In general, the method used to sow the rice plants in the field determines therecommended chamber shape. A chamber with a rectangular footprint should be used intransplanted rice fields, and the area it covers should be a multiple of the area occupied by
one rice plant (hill). For example, a chamber with a 40 cm × 40 cm footprint is required tocover four hills, each occupying an area of 20 cm × 20 cm (Figure 3.2). This recommendationis consistent with IGAC (1994) recommendations. Otherwise, the area-scaled gas flux will beover- or underestimated, unless a post hoc correction is applied (see Chapter 6.4.1). If thechamber footprint size is fixed, the planting density should be adjusted as necessary toachieve the recommended relationship.

Figure 3.2. Examples of correct (left) and incorrect (right) chamber sizes (cross-sectional area) in atransplanted rice paddy.

25

26

3. Chamber design

With regard to chamber portability, a 60 cm × 60 cm chamber, regardless of its height, isthe maximum size that can be carried, even by two people. At least two rice hills should becovered by a rectangular chamber, because the compensatory effect can be expected on rice
growth, reducing the spatial variability in the gas flux. Measurement at one point (onechamber) in each replicated plot allows statistical comparison of the plots, but at least threepoints in a plot are recommended for chambers of the usual size. Having more measurement
points (1) enables the spatial variability within the plot to be checked and (2) increases thespatial representativeness of the measurements.
For fields seeded by direct broadcasts, chambers with either a round or a rectangular
footprint can be used. However, the actual seed or plant density inside the covered area must
be recorded because this information is useful for interpreting spatial variations in the gas
fluxes.
The top of chamber should always be higher than the rice plant height so that rice
growth will not be suppressed. However, the lower the height of the chamber, the morereliable the gas measurement will be (see Chapter 6.3). Therefore, the use of a double- or
triple-deck chamber whose height is adjustable is recommended (Figure 3.3). Althoughchamber height criteria for upland field plants have been proposed (Clough et al., 2012;
Rochette and Eriksen-Hamel, 2008), it may not be appropriate to apply the same criteria to apaddy field. Because a chamber deployed in a paddy is usually equipped with an inside fan,rice height should probably be the primary criterion used to determine chamber height.

Figure 3.3. Examples of double-deck chambers.

3. Chamber design

3.4. Base
The chamber base (1) provides a gas-tight means of chamber closure and (2) prevents soil
disturbance during chamber deployment. The base should be equipped with a water seal toensure gas-tight closure (Figure 3.4). The base usually remains installed throughout the rice
growing period.
The installation of the chamber base inevitably disturbs the environmental conditions
around the rice plants to some degree. The aboveground height of the base should be
minimal (usually less than 5 cm) so that the base does not interfere with solar radiation. The
belowground depth (usually 5–10 cm) depends on the soil hardness and structure, andartificial CH4 ebullition must be avoided during chamber deployment. Gas leakage throughsoil crack should be avoided during a (temporal) drained period (see Chapter 4.7). A greater
belowground depth may affect rice root growth and soil water and gas dynamics. Four cornerpillars (e.g., PVC pipes) inserted as far as the plow pan may help support the chamber when
the field is flooded (Figure 3.5).

Made of alminium
Figure 3.4. Examples of bases for chambers with round and rectangular footprints.

Figure 3.5. PVC tubes installed in flooded soil.

27

28

3. Chamber design

3.5. Other components
During chamber deployment, the internal environment of the chamber should be maintained
under conditions as close as possible to ambient conditions. To achieve this, the inside of thechamber should be equipped with (1) a small fan, (2) a thermometer, (3) a vent hole, and,optionally, (4) an air buffer bag (Figure 3.6).
A small battery-driven fan is used to thoroughly mix the gases in the chamber, so thatthe target gas concentrations will be uniform (IGAC, 1994). In upland fields, headspace mixing
may cause gas flow through the soil (Bain et al., 2005; Xu et al., 2006), but in paddies, a fanshould be used because (1) mixing the inside air scarcely affects the air–water–soil gas
concentration gradient, (2) rice plants often obstruct air circulation, and (3) little natural
mixing occurs in tall chambers.
An air buffer bag (e.g., a 1-L Tedlar® bag) can compensate for both higher air pressurescaused by increased temperatures and lower air pressures caused by gas sampling. Although
the effect of change in inside air pressure on gas fluxes from a flooded paddy soil remains
unsolved, the pressure change should be minimized to maintain the ambient conditions. We
therefore recommend using a buffer bag that has been partially inflated before chamber
deployment. A vent hole with a rubber stopper is used to prevent drastic changes in inside airpressure during chamber deployment. Chambers used for upland fields occasionally are
equipped with thin vent tubes, but their use is still being debated. A vent tube prevents apressure gradient between the interior and exterior of the chamber from influencing gas
exchange (Clough et al., 2012). See Hutchinson and Mosier (1981) for more detailed
information on chamber requirements and design.
A thermometer is essential, because temperature data are necessary for calculating
hourly gas fluxes (see Chapter 6.2.1). The sensor should not be exposed to direct sunlight. A
digital thermometer is recommended because if an analog glass thermometer breaks it cancontaminate the soil.

Figure 3.6. Examples of various components installed on the chamber top.

3. Chamber design

The gas sampling port should be separate from the chamber body to prevent thechamber from possibly being shaken during the sampling. We recommend attaching aflexible tube (20–30 cm long) fitted with a valve to the chamber body (Figure 3.7). The gas
within the tube should be replaced by several syringe strokes before each sampling. A ruler(sticker) affixed on the bottom sidewall is useful to easily check the effective chamber height
during placement (Figure 3.8).

Figure 3.7. Examples of gas sampling ports connected to the chamber body.

Figure 3.8. Examples of a ruler for reading the effective chamber height.

3.6. Evolving issues
3.6.1. Chamber color
Chamber opacity/transparency remains an open question. Each has both advantages anddisadvantages (Table 3.1). In a rice paddy, the chamber covers rice plants through which CH4
is emitted, so possible effects of chamber opacity/transparency on rice growth and gas fluxes
need to be considered. However, better understanding of the relationship between gas fluxes
and rice photosynthesis and inside temperature under various climatic conditions is needed
to settle this question.

29

30

3. Chamber design

At present, opacity/transparency and shape are often inseparable, and they usuallydepend on the available material (see Chapter 3.2). In our experience, researchers prefer to
use transparent acrylic plates if they are available. The use of opaque acrylic plates in a rice
paddy has never been reported to our knowledge. In practice, material availability prevails in
selecting between opacity and transparency. However, use of a transparent material is notnecessary in the case of drained, unplanted soil; in that case, use of an opaque or reflective
material is recommended to prevent temperature increases within the chamber.
Table 3.1. Comparison of opaque and transparent chambers
Subject

Transparent

OpaquePhotosynthesis

Maintained

Restricted

Temperature

Increased

Maintained

Inside visibility

High

None

Chamber operability

High

Low

Material price

High

Low

Material availability

Low

High

3.6.2. Area covered by a chamber vs. plot area
Gas fluxes from a soil generally have high spatial variability, mainly because of heterogeneityof soil properties and rice growth. There is no consensus as to what percentage of the plot
area should be covered by chambers to obtain representative gas fluxes from a plot. This
problem is relevant to how much accuracy (i.e., the combination of trueness and precision)
we require for the estimation of gas emissions.
The percentage of the plot area covered by chambers is determined from the plot area,
the chamber area, and the number of chambers deployed in each plot. The use of small plots
may not be consistent with research objectives (see Chapter 2.2). Increasing the area coveredby each chamber is limited by practical considerations (see Chapter 3.3). The number ofchambers deployed simultaneously is limited by human resource availability. As a practicalexample, if the chamber area is 40 cm × 40 cm, three chambers cover only 1.92% of a 5 m ×
5 m plot (and, of course, even less of a larger plot). Sass et al. (2002) measured CH4 fluxes atmultiple points within a rice paddy and estimated that the fluxes were within ±20% of theactual field values within a 95% confidence interval. Khalil and Butenhoff (2008) reported,
based on the results of a model simulation, that gas sampling at three points within a fieldleads to a large uncertainty (40%–60%) in the calculated CH4 flux. Additional studies areneeded to answer the question of what percentage of plot area should be covered bychambers.

4. Gas sampling

4. Gas sampling
4.1. Introduction
There are substantial seasonal and diurnal variations in gas fluxes. We therefore need toconsider these dynamics in planning an appropriate schedule of gas sampling. Of course, themore frequent the measurements are, the higher the time resolution will be, regardless of theresearch objective. This is true in particular when studying how short-term gas flux variations
are affected by an agricultural management event. However, the frequency of manual gas
sampling is limited by human resource availability and by the need to minimize physicaldisturbance of the rice plants. Here we provide a practical low-intensity sampling schedule
for obtaining gas flux/emissions data with acceptable reliability. In addition, tips about how
to best perform manual operations are included. Because the CO2-equivalent N2O emissions
from a rice paddy are quite low compared to those of CH4, even under high-N-input
conditions (e.g., 11%; Linquist et al., 2012), we prioritize accurate measurement of the CH4
flux.

4.2. Period
The appropriate period of gas flux measurement depends on the research objectives.
Moreover, it may differ between CH4 and N2O, even if the objectives are the same, because ofdifferences in emission processes between the two gases. The IPCC (2006) recommended
that CH4 emission factors be applied only during the rice growing period (with notes for wet
fallow periods, see Chapter 4.4.3). In contrast, the IPCC recommends continuing N2O flux
measurements for an entire year (including dry and wet fallow periods) to derive the emission
factor with comparing N-applied with zero-N plots (IPCC, 2006). Therefore, researchers
should determine in advance the measurement period that is adequate for their specificresearch objectives (Table 4.1).
For example, when measuring both CH4 and N2O fluxes to estimate seasonal cumulativeemissions, measurements should start before the first agricultural management event of therice growing season (e.g., tillage, basal fertilization, or organic amendment), before actual ricecultivation begins, and should continue until harvest. We sometimes see seasonal CH4 flux
data in which the initial flux value is already high (i.e., not near zero). Such data are difficult tointerpret because the actual start (and end) of the rice growing season cannot be determined.
Similarly, a substantial N2O flux after harvest may be caused by, for example, theincorporation of rice straw into the soil. The definition of the rice growing season depends on
local practices, the annual number of rice crops, etc.

31

4. Gas sampling

Table 4.1. Examples of measuring periods for CH4 and N2O in a rice paddy
Objective

Period for CH4

Seasonal

From an agriculture management event From an agriculture management event

cumulativethat precedes the rice growing season that precedes the rice growing season

emissionsthrough the rice growing season until the through the rice growing season until the

Period for N2O

CH4 flux ceases after harvest

N2O flux ceases after harvest

AnnualThroughout an entire year, including One entire year

cumulative

wet/dry fallow period(s) and in theemissionsseedling nursery (if one is used)

IPCC emission

Rice growing season(s)One entire year

factorShort-term

E.g., several days for diurnal variation, as E.g., several days during drainage, andvariation

well as several days during drainage

several days after N fertilization

4.3. Time of day
4.3.1. CH4 flux during the flooded growing period
CH4 fluxes vary considerably diurnally — they tend to be high in the daytime and low at night.
Figure 4.1 shows a typical diurnal pattern measured after the heading stage in two ricepaddies in temperate Japan (Minamikawa et al., 2012). The daily mean flux was obtained ataround 10:00 and around 19:00 at both sites. A similar diurnal pattern has also been
observed in tropical regions (e.g., in India: Adhya et al., 1994; Satpathy et al., 1997).
160
Relative CH4 flux (%)

32

140
120

Site A
Site B100
80
60
00 2 4 6 8 10 12 14 16 18 20 22 24
Time of day

Figure 4.1. Mean diurnal variations of CH4 fluxes at two Japanese sites (modified from Minamikawa etal., 2012). The dotted line indicates the relative daily mean flux. Bars indicate standard deviations.

How many times a day should gas fluxes be measured? By re-analyzing two datasets ofseasonal CH4 fluxes measured by an automated closed chamber system in Japan,

4. Gas sampling

Minamikawa et al. (2012) determined that measurements performed once per day during
mid-morning always resulted in acceptable estimates (i.e., ±10%) (Table 4.2). Therefore, we
recommend conducting measurements in mid-morning to obtain the daily mean CH4 flux. In
particular, in temperate parts of Asia, measurement at approximately 10:00 (09:00–11:00)
local mean time is recommended. The time window recommended here is consistent with
common practice (Sander and Wassmann, 2014). Although twice-per-day measurement can
improve trueness (Table 4.2), measurements in the early morning (when the plants may bewet) and at night (when it is dark) in a rice paddy are not recommended. It should be notedthat the above analysis was conducted in fields in a temperate climate (in Japan), so further
investigation is required to determine the best schedule for fields in other climate regions(see Chapter 4.8.1).
Table 4.2. Effect of the number of measurements per day on the estimation of seasonal CH4 emissions
Number per day

Site A

Site B

Continuous

Midseason

Without rice

With rice

flooding

drainage

straw

straw

Once at 08:00-09:59

93

86

87

85

Once at 10:00-11:59

96

93

102

106

Twice (10:00-11:59 and 18:00-19:59)

a

101

96

112

103

b

Twice (06:00-07:59 and 12:00-13:59)

102

100

96

101

Three times (06:00-07:59, 12:00-13:59,

93

91

94

84

c

and 18:00-19:59)

Modified from Minamikawa et al. (2012). All times are local time.
Values are total CH4 emissions estimated as a percentage of emissions measured by the automatedclosed chamber method.
The measurement interval was weekly in all cases.
abcReported by Parkin and Venterea (2010), IGAC (1994), and Buendia et al. (1998).

For various reasons, it may not always be possible to collect gas samples at a fixed timeof day. In such cases, as proposed by Sander and Wassmann (2014), the data can becorrected if detailed information on the diurnal pattern in the field (as in Figure 4.1) isavailable. However, because the actual diurnal pattern on the measurement day cannot beknown, we recommend conducting measurements at a fixed time of day if at all possible. The
measurement time or times and any correction applied should be reported along with thedata.
From a practical standpoint, it is often the case that not all of the chambers can be
deployed simultaneously at multiple sampling points, for lack of personnel or because thenumber of available chambers may be insufficient. In such cases, it is necessary to determine

33

4. Gas samplinga suitable chamber measurement sequence within an appropriate time window (Table 4.3).
The chamber measurement sequence can be regarded as a block effect in the statisticalanalysis (see Chapter 2.4.3).
Table 4.3. Example of an appropriate time schedule for one person performing three measurements
during a 30-min closure at three positions with three chambers.
st

2

nd

rd

Chamber #Placement

1 sampling

1

10:00

10:01

10:16

10:31

2

10:04

10:05

10:20

10:35

3

10:08

10:09

10:24

10:39

sampling

3 sampling

4.3.2. CH4 flux during a temporary drainage period during the growing season
CH4 stored in flooded soil is released directly to the atmosphere when the field is drained,
and its contribution to the total seasonal emissions is often not negligible (e.g., 5-14%,
Adviento-Borbe et al., 2015; 6–16%, Weller et al., 2015; 15-16%, Yagi et al., 1996). In addition,
Minamikawa et al. (2012) observed no clear diurnal pattern during the non-flooded growing
period in two Japanese paddies (Figure 4.2). In such situations, regular measurement at a
fixed time of day may not provide reliable results. Therefore, we recommend measuring CH4at least once during the daytime; the measurement frequency (i.e., the measurement interval
during the growing period) is discussed in Chapter 4.4.1.
150
CH4 flux (mg CH4 m-2 d-1)

3480Site A
Continuous flooding plot

125
100
75

Site BNo rice straw plot

60
40

50

20

25
0

0
102

103

104

105

101

102

103

104

Days after transplanting

Figure 4.2. Temporal CH4 flux patterns during a drainage event at two Japanese sites (modified from
Minamikawa et al., 2012).

4.3.3. N2O flux during the flooded growing period
To date, few studies have reported the diurnal pattern of N2O fluxes from a rice paddy (seeChapter 4.8.1). Hou et al. (2000) reported that N2O fluxes during the flooded growing period
were higher in the daytime and lower in the nighttime, like the diurnal pattern of the CH4 flux.

4. Gas sampling

However, our preliminary analysis of N2O flux data measured by the automated closed
chamber method in Japan did not show any typical diurnal pattern (Figure 4.3). The lack of aconsistent pattern is partly attributable to the application level of N, which is conventionally
low in Japan generally (in this field it was 90 kg N ha–1). Therefore, we recommend measuring
the N2O flux at the same time as the CH4 flux, that is, in mid-morning. For measurement
during temporary drainage, see Chapter 4.4.2.

N2 O flux (μg N m-2 h-1 )20

Plot 2/2

Plot 1/2

15
10
5
0
-5
-10

0

4

8

12

16

20

0

4

8

12

16

20

24

Time of day
Figure 4.3. Diurnal N2O flux patterns during the flooded rice-growing period in two plots in Japan(Nishimura, unpublished data).

4.3.4. CH4 and N2O fluxes during dry fallow periods
Rochette et al. (2012) suggested that the flux between 10:00 and 12:00 reflects the dailymean N2O flux in upland fields. Although the diurnal patterns of CH4 and N2O fluxes should
be determined at each specific site, here, following Rochette et al. (2012), we recommend alate-morning measurement time during dry fallow periods (i.e., the same as therecommended CH4 flux measurement time during the flooded rice-growing period).

4.4. Frequency
4.4.1. CH4 fluxes during the growing period
CH4 fluxes generally increase after flooding because reductive soil conditions develop.
Minamikawa et al. (2012) reported that both weekly and biweekly measurement yielded an
acceptable estimation (i.e., ±10%) (Table 4.4). However, large fluctuations in the CH4 flux canoccur even under flooded conditions, as a result of, for example, changes in weather
conditions. In such cases, biweekly measurement may miss considerable changes in the CH4flux. Therefore, we recommend measuring the CH4 flux at least once a week during theflooded growing period. This measurement interval is consistent with common practice(Sander and Wassmann, 2014).

35

36

4. Gas sampling

Table 4.4. Effect of gas sampling frequency on the estimation of seasonal CH4 emissions
Frequency

Site A

Site B

Continuous flooding Midseason drainage Without rice straw With rice straw
Daily

100

99

108

108

Every other day

98

92

103

105

Semiweekly

101

94

104

105

Weekly

96

93

102

106

Biweekly

97

106

104

101

Modified from Minamikawa et al. (2012).
Values are total CH4 emissions estimated as a percentage of emissions measured by the automatedclosed chamber method.
Measurements were performed once per day in the10:00–11:59 time window (local time).

As explained in Chapter 4.3.2, drainage events can cause the CH4 flux to increase sharply
because of the direct release of the CH4 stored in the flooded soil. Therefore, a different
measurement-frequency schedule is needed during a temporary drainage period.
Minamikawa et al. (2012) reported that measurement at regular intervals did not yieldsatisfactory flux estimations during the non-flooded growing period, because themeasurements did not adequately detect drastic fluctuations in the CH4 flux over a period ofa few days. Therefore, we recommend measuring the CH4 flux at least every other day during
the drainage period until the CH4 flux ceases (i.e., for 5–7 days). In addition, measurements
should be performed just before drainage to obtain a better estimate of the cumulativeemissions (see Chapter 6.4.2).
4.4.2. N2O fluxes during growing period
To our knowledge, no recommendations for a particular frequency of N2O flux measurement
in a rice paddy have been reported. However, the seasonal N2O flux pattern is known to beevent-driven and sporadic. Generally, during flooded rice-growing periods N2O fluxes remainquite low unless the N input is extremely high. For example, Nishimura et al. (2004) reported
that N2O emissions measured by an automated chamber method during the flooded growing
period accounted for 4.3% of the annual emissions (single cropping followed by dry fallow).
Therefore, we recommend weekly measurement of the N2O flux during flooded periods, inconjunction with the CH4 flux measurement. Although negative N2O flux values may beobtained during flooded periods, they should be interpreted with due consideration of thegas measurement precision (see Chapter 6.3).
Agricultural management and natural events affecting the N2O flux include chemical andorganic N fertilization, drainage and re-flooding, tillage, and rainfall during fallow periods.
These are the same factors that influence soil N dynamics and redox conditions. For example,

4. Gas sampling

flux peaks appear for only a few hours to a few days after flooding following basal N
application (Figure 4.4), mainly as a result of bacterial denitrification (Yano et al., 2014).
Therefore, we recommend increasing the measurement frequency during these events (e.g.,
at least every other day until the flux ceases). In addition, for better estimation of cumulativeemissions, measurements should be performed just before each agricultural management

250

Treatment A(rep 1)Treatment B(rep 1)Treatment A(rep 2)Treatment B(rep 2)200
Flux (μg N m-2 h-1 )

150
100
50
0
250
200
150
100
50
0

-4

-2

0

2

4
6 -4
-2
Days after flooding

0

2

4

6

60
50
40
30
20
10
0
60
50
40
30
20
10
0

Conc. (μg N L-1 )event (see Chapter 6.4.2).

Figure 4.4. Short-term variations in N2O fluxes and dissolved N2O concentrations in the surface water
after flooding of the field following basal N fertilization at a Japanese site (Nishimura and Minamikawa,
unpublished data).

4.4.3. CH4 and N2O fluxes during dry and wet fallow periods
Generally, in a dry soil CH4 is slightly consumed by methanotrophs, whereas a substantial
amount of N2O is produced and emitted. If annual exchanges of CH4 and N2O between theatmosphere and soil are being examined, we recommend measuring their fluxes weekly or
biweekly during dry fallow periods. It is troublesome if wet conditions occur during fallow
periods, such as in a lowland field, during the rainy season, or during a short interval between
two consecutive rice growing seasons, because such conditions may cause significant
emissions of CH4 and N2O. Therefore, we recommend measuring both fluxes frequently
during wetting events so that possible peaks will not be missed. IPCC (2006) also
recommends obtaining measurements during wet (flooded) fallow periods when estimating
the CH4 emission factor.

37

38

4. Gas sampling

4.5. Chamber deployment duration and number of gas samples
The duration of chamber deployment and the number of gas samples collected during each
deployment affect the accuracy (and statistical significance) of the calculated gas fluxes (seeChapter 6). A shorter deployment time is preferred for healthy rice growth, because the airtemperature becomes elevated within the closed chamber and the CO2 concentration
decreases. On the other hand, a longer deployment time and a greater number of gas
samplings improve the accuracy of the flux calculation. According to Sander and Wassmann(2014), most researchers deploy the chamber for 30 min and collect gas samples three orfour times per deployment. On the basis of empirical knowledge, we recommend deploying
each chamber for 20–30 min during the rice growing period so as to not interfere with rice
growth. On the other hand, a longer time (<60 min) is acceptable during fallow periods toimprove the accuracy of the gas analysis.
How many gas samples should be collected per deployment? Katayanagi and Tokida (inpreparation) analyzed the effect of the number of gas samplings on the precision of the flux
calculation by using a Monte Carlo simulation. They found that increasing the number ofsamplings increases the precision of the flux calculation but the degree of improvement israther limited (Table 4.5). For example, if errors associated with gas sampling and analysis are
10% (CV), estimated error for the CH4 flux based on 2 samplings is 22.5% (CV) and that basedon 5 samplings is still 19.7%. Here it is noteworthy that the fraction of ‘statistically significant’fluxes increases markedly (28.3% for n = 3, and 91.5% for n = 5, Table 4.6), although theactual precision for the estimated fluxes does not change much. These results suggest thatthe p value of the slope itself does not provide information regarding the precision if thenumber of sample is different.
Table 4.5. Precision of the estimated CH4 flux (shown as CV, %) for different number of gas samples perchamber deployment based on Monte Carlo simulation (each for 10,000 runs).
Sampling and analytical error

Number of samples per chamber deployment
2

3

4

5

8

10

1%

2.2

2.2

2.1

2.0

1.7

1.5

5%

10.9

11.2

10.5

9.9

8.3

7.7

10%

22.5

22.4

21.1

19.7

17.0

15.6

20%

44.7

45.3

41.7

39.7

33.4

30.7

Modified from Katayanagi and Tokida (in preparation).
In the simulations, we assume that each gas sample has the same degree of relative error associatedwith gas sampling and analysis regardless of the absolute concentration (i.e. CV is constant).

4. Gas sampling

Results of Katayanagi and Tokida (in preparation) clearly point to the importance of
reducing random errors associated with gas sampling and analysis if precision of theestimated flux is to be improved. Decreasing the random error from 10% to 5% is far more
effective way than increasing the number of gas samples from 2 to 10 (Table 4.5). If random
errors are small, the error of the estimated flux is satisfactory small even when only 2-3
samples are taken. Considering these results, we recommend collecting three samples per
20–30 min chamber deployment. We also suggest that if analytical error is large (e.g. beyond5%), efforts in reducing the analytical error deserves first priority before taking more gas
samples per chamber deployment. However, as a compromise during the improvement ofsampling and analytical performance, collecting more than four samples per chamber
deployment can be adopted (see also Chapter 6.2).
Table 4.6. Percentage of statistically significant flux (non-zero slope, p < 0.05) for the same data-set asin Table 4.5 (10,000 Monte Carlo simulations for each settings).
Sampling and analytical error

Number of samples per chamber deployment
2

3

4

5

8

10

1%

–

100

100

100

100

100

5%

–

53.1

98.7

100

100

100

10%

–

28.3

70.9

91.5

99.8

100

20%

–

14.2

30.0

44.6

71.9

82.4

Modified from Katayanagi and Tokida (in preparation).
Note that statistical significance cannot be calculated for 2 points samplings.

4.6. Instruments
4.6.1. Gas collection
Generally, gas samples can be collected from a chamber with a syringe or with a pump
(Figure 4.5). The instrument chosen depends partly on the storage container used. A plastic
syringe (e.g., 25–50 mL) should be used if the samples are to be stored in (evacuated) glass
vials (e.g., 10–30 mL). If the storage container will be a plastic bag, it is necessary to collect a
relatively large volume (e.g., >0.5 L) to minimize gas contamination. In this case, abattery-operated pump is helpful, although use of a plastic syringe is also possible. To avoid
a drastic change in air pressure inside the chamber, collecting a smaller gas volume isrecommended.

39

40

4. Gas sampling

Figure 4.5. A plastic syringe (left) and a battery-operated pump (right) for gas collection.

4.6.2. Gas storage
During gas storage, gas leakage and contamination must be avoided. There are three known
methods of storing collected gas samples until GC analysis. In each case, it is necessary tocheck the allowable period of sample storage to avoid significant gas leakage orcontamination.
Gas storage in a plastic syringe (the one used for collection) or a plastic bag (such as a
Tedlar® bag, Figure 4.6) requires close attention to the possibility of gas leakage orcontamination. Generally, the gas permeability of plastic materials is quite high, so a longstorage period (e.g., more than 1 day) should be avoided. If long-term storage before GC
analysis is unavoidable, we recommend using an evacuated glass vial equipped with a butyl
rubber stopper and a cap (a plastic screw top or an aluminum cap; Figure 4.7). Generally,rubber stoppers can be re-used several times; however, their condition should be checked
before re-use. Silicone rubber stoppers should not be used, because of their high gas
permeability. Note also that butyl rubber stoppers made by different companies differ
considerably in gas permeability.

Figure 4.6. Gas being pumped into a plastic bag for storage.

4. Gas sampling

Figure 4.7. A glass vial and plastic screw cap (left); butyl rubber stoppers (middle); and aluminum caps,
crimper, and decapper (right).

4.6.3. How to prepare evacuated glass vials
You can prepare evacuated glass vials yourself, as explained below, or you can purchasecommercially prepared vials (e.g., Vacutainer®). To evacuate glass vials, an evacuation
apparatus and a manometer (Figure 4.8) are needed. The volume of the vial should be muchgreater than the volume of gas required for analysis (e.g., 10–30 mL if 1–2 mL of gas isneeded for the GC analysis).

Figure 4.8. A portable manometer. The needle is inserted into an evacuated vial through a rubber
stopper to check the degree of vacuum.

We describe here three types of evacuation apparatus. (1) A vacuum freeze dryer
equipped with a stopper-closing function (i.e., trays that can be moved up and down) (Figure4.9) can be used to prepare ~300 twenty milliliter vials at a time in 30 min. (2) A cylindricalapparatus equipped with an oil pump (Figure 4.10) can be used to prepare 50 twenty milliliter
vials at a time in ~10 min (depending on the capacity of vacuum pump). (3) Multiple vials canbe connected to a vacuum pump via a vacuum manifold (Figure 4.11).

41

42

4. Gas sampling

Figure 4.9. A vacuum freeze drier (left) with two trays for closing the vials with stoppers (right top andbottom).

Figure 4.10. An apparatus specifically designed to prepare evacuated vials. After the evacuation iscomplete, the inner plate is moved down manually to close the vials with stoppers.

4. Gas sampling

Figure 4.11. Glass vials connected to a self-made vacuum manifold. A manometer monitors the degreeof vacuum online.

4.6.4. Gas replacement method
One problem with preparing evacuated vials oneself is that additional equipment is required
to create the vacuum. If the necessary equipment is not available, a gas replacement method
can be used instead of evacuated vials. In brief, a double-needle technique is used to replace
the air in a non-evacuated vial with a sufficient volume of sampled gas (Table 4.7). A
tank-in-series model has shown that theoretically gas replacement of more than 4.5 times thevolume of the container leaves less than 1% of the original air remaining. However, the actual
accuracy will vary depending on the skill level of the operator, so the results should beverified by GC before the actual experiment is carried out.
Table 4.7. A gas replacement procedure with a 10-mL vial used for gas storage
Step

Detail1

Plug a 10-mL non-evacuated vial with a butyl rubber stopper.

2

First, insert a needle (for degassing) into the vial through the stopper.

3

Second, insert the needle of a 50-mL syringe containing 50 mL of gas sample.

4

Inject 45 mL of the gas sample into the vial, replacing the original air.

5

Quickly remove the first needle.

6

Inject the remaining 5 mL of gas sample to establish a pressurized condition.

43

44

4. Gas sampling

4.7. Notes on manual chamber operation
Manual chamber operation consists of the following steps: (1) advance preparation, (2)chamber placement, (3) gas sampling and storage, and (4) chamber removal. Here, we
present our recommendations for each step and tips on how best to carry them out.
Advance preparation. Check the water depth in the chamber base to ensure that a water seal
will be obtained. If the base is installed and removed every flux measurement, it should beinstalled at least 24 hours before. If the paddy field is drained, carefully check the interface
between the soil and the base and make sure it is airtight. If obvious soil cracks are found, werecommend filling them with kneaded soil collected from outside the plot. In addition, avoid
letting water overflow the base when the field is being drained. If the rice plants are tall, it
may be hard to cover rice plants with the chamber and extension column without physically
disturbing the plants. In such cases, an elastic cord can be used to gently push the rice plants
into a bunch; this cord should be removed before the chamber is covered.
Chamber placement. Partially inflate the air buffer bag (if one is used), because both positive
and negative pressures may occur inside the chamber (see Chapter 3.5). The chamber shouldbe placed gently on the base to prevent increasing the initial CH4 concentration by ebullition
from the soil. If failed, we recommend removing the chamber and placing it again. In addition,
permanent placement throughout the rice growing season should be avoided not to affectadversely rice growth.
Gas sampling and storage. To prevent CH4 ebullition during sampling, avoid placing
measurement components on top of the chamber (see Chapter 3.5). For the same reason,
avoid directly touching the chamber body. Note that gas sampling at constant intervals is notnecessary for calculation of the gas flux (see Chapter 6.2). Therefore, if the regular samplingtime is missed (see Table 4.3), gas can be collected at a different time (which must berecorded). Avoid dead volume in the gas sampler (i.e., syringe or pump) so that the gas
concentration in the sampler will be in equilibrium with that in the chamber. If a syringe isused, after it is connected to the chamber via the sampling tube (Figure 3.7), pump theplunger several times to flush the barrel. The collected gas should be stored in an evacuatedvial under a pressurized condition. For example, if a 20-mL vial is being used, manually inject~30 mL of gas while minimizing leakage or contamination; this also allows the gas
concentration to be analyzed several times. If a pump is used, the first several seconds ofcollected gas should be discharged, before the storage container is filled.
Chamber removal. First, the vent plug should be removed and then the chamber should begently lifted off the base. If failed when the field is drained, water in the base may overflow

4. Gas sampling

and moisten the soil. After the chamber is removed from the base, we recommend tipping it
sideways for a few minutes to replace the air inside with ambient air, to prevent an initial highCH4 concentration during the next deployment of the chamber.

4.8. Evolving issues
4.8.1. Uncertainty of diurnal CH4 and N2O flux patterns
There are few reports about the diurnal CH4 flux pattern in a tropical climate. Therefore, it is
not possible to recommend in this chapter a particular time of day for obtaining the dailymean flux in the tropics. Wassmann et al. (2000) reported that significant ebullition occurredat the beginning of rice cultivation in the Philippines, caused both by the application of straw
immediately preceding cultivation and by hot weather. This ebullition is not the case for thetemperate region. On the basis of a literature survey, Sander and Wassmann (2014) reported
that in most studies sampling is carried out in late morning, regardless of the climatic zone.
Further investigation is required to elucidate diurnal flux patterns in the tropics and theunderlying mechanisms.
Another unresolved issue is the diurnal N2O flux pattern during the flooded rice-growing
period. As mentioned in Chapter 4.3.3, contradictory results have been obtained. The N2O
flux is generally low when the level of N application is low, and thus N2O flux data fromhigh-N-application fields will be helpful to elucidate the mechanisms underlying the
temporal pattern of N2O flux under flooded conditions.
4.8.2. Effect of human-induced CH4 ebullition on the number of gas samples
Theoretically, a minimum of two gas samples during each chamber deployment is needed forthe flux calculation. Mathematically, two and three gas samples yield the same flux estimation(but not for R2) if linear regression is used (Katayanagi and Tokida, in preparation). Therefore,
it would be possible to recommend two, instead of three, samplings, provided that the skill of
the individual doing the sampling and GC performance level had been quantitatively
evaluated. This reduction would save labor, enabling the number of replicates (chambers) pertime window to be increased. However, although our understanding of the spatiotemporal
pattern of natural CH4 ebullition is improving, in practice, using the concentration data only,
it is difficult to explicitly distinguish between human error and a natural event as the cause ofebullition. Accordingly, in the current guidelines we recommend collecting at least three gas
samples during chamber deployment. Collection of more samples per chamber deployment
and also during the flooded growing period improves our understanding of the timing andfield conditions of CH4 ebullition observed at our own site (see also Chapter 6.2). The
accumulation of such fragmentary data will eventually help elucidate the conditions that arenecessary and sufficient for adopting the minimum number of samples (i.e., 2).

45

46

5. Gas analysis

5. Gas analysis
5.1. Introduction
Several methods are available for analyzing the concentrations of GHGs, including GC with aselective detector, GC with mass spectrometry, GC-less mass spectrometry, and laser-based
spectrometry. The optimal method should be selected from the viewpoint of cost, required
accuracy (i.e., the combination of trueness and precision), time consumption, and so on. Here
we describe the use of GC with a selective detector, because it is the most often used method
and it is relatively cheap. This chapter introduces a standard method for analyzing
concentrations of CH4 and N2O using a GC with a difference detector. Typical settings androutine operations are described.

5.2. GC requirements
5.2.1. CH4
A flame ionization detector (FID), which uses a hydrogen flame to detect ionizedhydrocarbons (HCs), is the most suitable for the detection of CH4. A FID uses H2 and air (O2)
as a supplemental fuel and a carrier gas. Atmospheric air contains not only CH4 but also otherHCs, so the FID signal obtained from air is a mixture of signals from CH4 and other HCs.
Therefore, CH4 and other HCs should be separated from each other so that the target CH4
concentration can be analyzed precisely.
Packed separation columns are commonly used to separate CH4 from other componentsof the gas sample. All of the materials listed in Table 5.1 can theoretically be used to separate
CH4, but with some of these materials, a single analysis may take more than 30 min tocomplete. On the other hand, materials requiring a shorter time for CH4 separation may notalways ventilate other HCs. Generally, the retention times of CH4 and other HCs can beshortened by increasing the column temperature. However, higher temperatures (>150°C)
risk an increase in signal noise due to the discharge of particulate matter. Therefore, we
recommend using a column temperature between 50 and 130°C for CH4 separation.
The random signal noise level should be reduced to achieve a signal-to-noise (S/N) ratioof more than 10. We recommend the following to reduce the noise level.
 Use He or N2 (99.999% purity) as the carrier gas.
 Use a charcoal filter to maintain the high purity of the carrier gas.
 Sufficiently dehumidify the air from the compressor used for supplemental combustion in
the FID by using a membrane filter and a silica-gel moisture trap.
 Use a catalytic combustor to eliminate HCs contained in this dehumidified air.
 Allow an idling time of at least 30 min after ignition of the FID.
 Even when the FID is not being used, we recommend maintaining a continuous flow of the5. Gas analysis

carrier gas at a low rate (up to 10 mL min–1).
The installation of a high-throughput analytical system is desirable if a large number ofgas samples will be analyzed. The following settings are recommended to complete CH4
analysis of one sample in 5 min without deploying a “pre-cut” flow-changing technique.
(Pre-cut is a method of rapidly venting long-retention-time species with a counterflow ofcarrier gas by changing the position of gas-flow switching valves.)
 Use a combination of two stainless columns, one packed with Porapak Q (3 mm o.d., 2
mm i.d., 1.5 m long) and the other with Porapak N (3 mm o.d., 2 mm i.d., 1.5 m long). A
single 3-m-long column packed with Porapak Q or Porapak N can also be used.
 The optimal particle size of the column fill is 80/100 mesh.
 Set the GC column temperature to 70–90°C. At temperatures below 70°C, the retention
times of water vapor and HCs are longer.
 The optimum flow rate of the carrier gas is between 20 and 40 mL min–1.
 Install a moisture trap filled with granular magnesium perchlorate downstream of the gas
injection port.
 (optional) Use dual column and detection systems.
 (optional) Use a pre-cut system with a switching valve to prevent the entry of non-CH4
species into the main separation columns.
Table 5.1. Typical packed column materials for separation of target gases
Materials

SystemTarget gases

Molecular sieve 5AZeolites, porous

inert gases, CO, CH4

Molecular sieve 13X

Zeolites, porous

inert gases, CO, CH4

AluminaAl2O3

inert gases, CO, CH4, CO2, low-carbon-number HCs

Active carbon

Charcoal

inert gases, CO, CH4, lower HCs

Unibeads C

Carbon, porous

inert gases, CO, CH4, CO2, N2O

Porapak Q
Porapak N
Porasil D

Polymer, porous

inert gases, CO, CH4, CO2, N2O, H2O, halocarbons,
lower HCs

5.2.2. N2O
The molecular weight of N2O (44.0) is nearly the same as that of CO2, so the retention timesof both gases are almost the same. Therefore, it is not possible to separate N2O and CO2 byusing a column packing in which the elution order corresponds to the molecular weight. The
two gases have different molecular polarities, however, so separation can be achieved byusing a column filler that retains compounds according to their molecular polarity. A thermal
conductivity detector (TCD), which is a non-selective detector, is not adequate for measuring

47

48

5. Gas analysis

the atmospheric level of N2O because its concentration in air is only about one thousandththat of CO2. Therefore, an electron capture detector (ECD), which is a selective detector, iscommonly used to measure atmospheric N2O. Because an ECD has high sensitivity forchemical substances with a relatively high electron affinity, the target N2O must first be
separated from residual species.
A carrier gas of N2 or Ar is ionized in the ECD cell by a beta ray source (63Ni). In principle,
charged electrons of the carrier gas are captured by N2O with a negative electron affinity andthe charged carrier transfer is detected electrically. Therefore, the carrier gas needs to beadequately ionized by the beta ray. It is crucial to first separate contaminants, such as O2,
chlorofluorocarbons, halogens, and oxygen compounds from N2O in a column, because thesecontaminants also have a substantial ECD response. It is empirically known that the additionof CH4 or an Ar-CH4 gas mixture to the ECD as a make-up gas effectively stabilizes andenhances the N2O detection response when the carrier gas is N2. Figure 5.1 shows a typicalarrangement of an ECD-GC system for measuring the atmospheric level of N2O. The majoradvantage of this system is perfect separation of atmospheric level of O2, because its longretention time (so-called tailing peak) can overlap the peak of N2O.

Figure 5.1. A typical ECD-GC arrangement for accurately separating N2O. Residual contaminants with along retention time are discharged from the ventilation flow (CC2), which is controlled by counterflow
in a pre-cut column (PC). Oxygen and CH4 that enter the main column 1 (MC1) before the first valve's
switching time of 1.5 min after injection are introduced into another discharge flow path (CC3),
followed by the second valve switching 2.5 min later to introduce N2O into the main column 2 (MC2).
By a three-stage separation technique, N2O is separated into MC2. The same column filler must be
used in PC and DC1 and in MC2 and CC3. The function of columns CC1, CC2, and CC3, is tocompensate for the column-end pressure caused by changing of the switching valve position.

5. Gas analysis

The following ECD-GC settings are recommended for measuring the atmospheric level of
N2O.
 Use N2 (purity > 99.999%) or a mixture of 95% Ar (purity > 99.999%) and 5% CH4 as thecarrier gas.
 Purify the carrier gas with a charcoal filter and a moisture filter.
 Set the temperature of the ECD to >300°C to obtain a sufficient N2O peak.
 We recommend using Porapak Q (4 mm o.d., 3 mm i.d., 1.0 m long) or Porapak N (4 mmo.d., 3 mm i.d., 1.0 m long) columns, with a total column length of 3–5 m (i.e., the total
length of the eight columns in Figure 5.1), to analyze N2O in one sample in 10 min.
 The optimal particle size of the column fill is 60/80 or 80/100 mesh.
 Set the flow rate of the carrier gas to between 20 and 40 mL min–1.
 Set the column temperature to between 70 and 90°C.
When the column temperature is less 70°C, some water vapor and HCs will persist in thecolumn, increasing the analytical time to longer than 10 min. On the other hand, it is difficult
to separate the N2O completely if the column temperature is higher than 90°C.
5.2.3. MaintenanceContinuous good operation of GCs requires maintenance, such as replacement of the
moisture absorbent (silica gel) and the rubber septum of the injection port, cleaning thegas-flow channel, and column conditioning (Figure 5.2). Routine procedures should be
carried out at regular intervals.

Figure 5.2. Examples of GC maintenance. Left, glass tube cleaning; top right, replacement of silica gel;bottom right, checking the water level in the hydrogen generator.

49

50

5. Gas analysis

Column conditioning is required in a non-flow-change system (i.e., no pre-cut orback-flush system) to remove the residual contaminants that gradually accumulate in thecolumn. If conditioning is not performed regularly, the baseline of the GC will be unstable.
Therefore, we recommend performing the conditioning regularly after a certain number ofgas sample injections. The conditioning temperature of the column should be lower than thecolumn’s maximum limit, while taking into account the temperature limits of other
instruments. The conditioning should be conducted with a continuous carrier gas flow for
about 24 h to ventilate residual species.

5.3. Gas injection
It is necessary to minimize fluctuation of the gas injection volume to reduce the uncertaintyof the gas analysis (see Chapter 5.5). Several gas injection methods are commonly used, suchas manual injection using a glass syringe, manual injection using a gas sample loop, and an
automated injection system (ready-made or custom-made). If a gas injection port is used, therubber septum of the GC port should be replaced after every 100 samples to avoid
contamination by the ambient air.
For manual gas injection, the use of a gas-tight glass syringe (ideally equipped with an
open-shut valve/screw) is recommended (Figure 5.3). Use of a glass syringe with an
open-shut valve allows the gas sample to be stored in a pressurized state — the pressure isreleased just before the injection by opening the valve. A plastic syringe is not recommended
for manual gas injection because precise reading of the scale is difficult and gas leakage may
occur. A sample loop with a known volume can also be used for manual gas injection (Figure5.4). A sample loop is particularly useful when a glass syringe is not available. An automatedgas sampler can minimize errors in stroke volume (Figures 5.5 and 5.6). However, anautomated system is expensive, and the injector (syringe and needle) sometimes must be
replaced because of damage caused by a system malfunction.

Figure 5.3. A gas-tight glass syringe with an open-shut valve.

5. Gas analysis

Figure 5.4. A gas sample loop and a manually switched injection valve.

Figure 5.5. Examples of automated gas injection systems for glass vials.

51

52

5. Gas analysis

Figure 5.6. An example of automated gas injection system for plastic bags.

5.4. Standard gases
The output of a GC is the peak area or height of each separated gas. The peak area must be
converted to concentration with reference to a known concentration of the target gas (i.e., astandard gas). We recommend using certified standard gases to calibrate GCs. It is also
encouraged to cross-check the concentration between gas cylinders. Because the conditionof a GC changes from day to day, it should be calibrated before every analysis.
Calibration at two concentration levels is adequate for FID-GC and ECD-GC whenanalyzing gas concentrations obtained by chamber measurement (i.e., at around theatmospheric concentration). The two calibration points of the standard gases should beoutside the expected observed range (Figure 5.6). For example, suitable CH4 standard gas
concentrations are ~1.8 ppm (ambient level) and ~50 or 100 ppm (above the maximum level
expected in the chamber).
It is sometimes difficult to obtain standard gases. If that is the case, it may be necessary
to use working standard gases or to dilute a high-concentration standard gas. See Chapter
5.6 for further information.

Figure 5.6. Examples of well-chosen and poorly chosen standard gas concentrations.

5. Gas analysis

5.5. GC repeatability
5.5.1. Causes of errors
Multiple injections of even the same gas volume often give different peak areas. The
repeatability (precision) of the analysis is increased by minimizing this error by improving GC
settings and operation (see Chapters 5.2 and 5.3). However, it must be understood that someerror is more or less inevitable due to the cumulative effect of the following errors.
First, the condition of a GC changes from moment to moment because of, for example,
changes in column purity and the gas flow rate and purity. It is usually not possible to fix
such instabilities on the spot, but they can be reduced by continuous maintenance andimproving the GC settings.
Second, manual collection of the standard gas from the gas cylinder affects the trueness.
We usually use a syringe, a plastic bag, or an evacuated vial to subdivide and temporarily
store the standard gas, but this handling may cause contamination with ambient air. Thiserror can be reduced by consistently collecting the standard gas with the same instruments
each time.
Finally, during manual injection of the standard gas with a glass syringe, the gas may
become contaminated with ambient air (affecting trueness). In addition to using appropriate
methods and instruments (see Chapter 5.4), we should inject the gas at a constant stroke
speed and push the start button of the GC after a fixed time (to increase precision). Thus,
information on the GC performance and on the skill of manual operations is useful for GC
maintenance and improvement.
5.5.2. Limit of detection and limit of quantification in GC analyses
The accuracy of an instrumental analysis is commonly represented by the limit of detection(LOD) and the limit of quantification (LOQ). The LOD is the lowest detectable quantity,
whereas the LOQ is the lowest quantifiable quantity. Generally, the LOD is defined as 3 ×standard deviation (σ) of repeated blank tests, and the LOQ is defined as 10σ. It should be
noted, however, that many definitions of the detection limit have been proposed.
Here we apply the concepts of LOD and LOQ to GC analyses targeting GHG emissions
from a rice paddy. LOD for GC analysis (LODgc) denotes the lowest detectable difference
between the target gas concentration in a sample and its concentration in the ambient air.
LODgc is defined as 3σ of repeated analysis of ambient air (standard gas). Similarly, LOQ for
GC analysis (LOQgc) is the lowest quantifiable difference between the target gas
concentration in a sample and its concentration in the ambient air. LOQgc is defined as 10σ of
repeated analyses of ambient air (standard gas).
To determine LODgc and LOQgc, the gas analysis should be repeated 10–20 times in thesame way. Ambient air (standard gas) samples should be stored in the same gas containersthat are usually used for gas sampling. LOQgc is then used to calculate LOQ for gas flux

53

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5. Gas analysis

measurement (see Chapter 6.3). Table 5.2 shows two examples of the calculation of LODgc
and LOQgc. In case 1, a CH4 concentration of >2.79 ppm (i.e., mean 1.79 + LOQgc 1.00) is
considered quantifiable when compared with ambient air, whereas in case 2, a concentrationof >2.20 ppm (i.e., mean 2.00 + LOQgc 0.20) is quantifiable when compared with ambient air.
These examples show that GC repeatability greatly affects LODgc and LOQgc.
Table 5.2. Template for calculating LODgc and LOQgc of CH4 concentration analysis (ppm)
Replication number

Case 1Case 2Function in MS-Excel

1

1.89

1.97

2

1.89

1.95

3

1.93

2.01

4

1.79

2.01

5

1.70

2.00

6

1.83

1.99

7

1.66

2.01

8

1.75

2.01

9

1.71

2.05

10

1.60

1.98

11

1.75

2.00

12

1.90

1.99

13

1.88

2.01

14

1.82

2.03

15

1.79

2.01

Mean

1.79

2.00

=average(#1:#15)

SD

0.10

0.02

=stdev.s(#1:#15)

CV (%)

5.59

1.00=SD/Mean*100

LODgc

0.30

0.06

=SD*3

LOQgc

1.00

0.20

=SD*105.6. Evolving issues
It may not be possible to obtain certified standard gases because of their cost, or because it
takes too much time to import them from abroad by ship (air transportation is often
prohibited). If evacuated glass vials are used, it may be possible to store the collected gas
samples for a long time (up to 1–2 months). However, a chronic inability to obtain standard
gases cannot be solved by long-term storage. Here we discuss two possible stopgap
measures.

5. Gas analysis

Gas dilution. If a cylinder of certified standard gas with a concentration higher than the target
range is available (Figure 5.6), the standard gas can be diluted as necessary with an inert gas(He or N2). For example, three plastic bags can be connected to a plastic syringe: one
containing an inert gas, one containing the high-concentration standard gas, and an empty
bag to receive the diluted standard gas. Then the syringe is used to inject the standard gas
and the inert gas in the necessary ratio into the empty bag (Figure 5.7). Diluted gases should
be used soon or temporarily stored in evacuated glass vials. However, the accuracy of thedilution depends greatly on manual skill and must be checked by GC analysis.

2
3

1

Figure 5.7. A gas dilution tool consisting of a plastic syringe and three plastic bags. Bag 1 contains aninert gas; bag 2 contains a high-concentration standard gas; and bag 3 receives the diluted gas(low-concentration standard gas).

Working standard gas. If no certified standard gas is available, temporarily or chronically, air
can be used as a working standard gas. However, this method requires a large-volume gas
canister. The ambient air should be introduced into the canister in a pressurized state. The
GHG concentrations in the gas in the canister should be analyzed at some later time by using
certified standard gases. Evacuated glass vials should be used for long-term storage of theworking standard gas.

55

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6. Data processing

6. Data processing
6.1. Introduction
Data processing involves (1) the calculation of the hourly gas flux from the analyzed gas
concentrations over time in the chamber and (2) the calculation of seasonal or annual
cumulative gas emissions from the calculated hourly gas flux. The common method for theflux calculation adopted by most researches may lead to under- or overestimation of the flux
in some cases. This chapter explains how to accurately calculate hourly gas fluxes and total
emissions from the gas concentrations determined by GC.

6.2. Calculation of hourly gas fluxes and cumulative emissions
6.2.1. Hourly gas flux
Linear regression is the recommended method for calculating the hourly CH4 flux from a rice
paddy. This method is based on the principle that the concentration gradient of CH4 between
flooded soil and the atmosphere is quite large, so that CH4 can be considered to be emitted
at a constant rate (Figure 6.1). The hourly fluxes of CH4 (mg CH4 m–2 h–1) and N2O (μg N m–2
h–1) are calculated as follows:

C V
273
 
t A
273  T
C V
273
28
FluxN 2O 
 

t A
273  T 44

FluxCH 4 –1

where ∆C/∆t is the concentration change over time (ppm-CH4 or ppb-N2O h ); V is chamber volume
3

2

–3

–3

(m ); A is chamber area (footprint; m ); ρ is gas density (0.717 kg m for CH4 and 1.977 kg m for N2O
at 0°C); and T is the mean air temperature inside the chamber (°C).

Figure 6.1. Temporal changes in the gas concentration inside the chamber, showing a large
concentration gradient between the soil and the atmosphere (left) and one that is too small (right).

6. Data processing

The assumption of a large concentration gradient is often not justified in the case of theN2O flux from a non-flooded soil. In an upland field, long-term chamber placement can cause
saturation of the N2O concentration inside the chamber (Parkin and Venterea, 2010) (Figure6.1). Therefore, the pattern of observed concentration increases should be checked under
every situation and for every gas to determine the appropriate method. For other calculation
methods, see Parkin and Venterea (2010) and Venterea et al. (2012).
Linear regression is not always the best method, and it may be theoreticallyinappropriate, for calculating the CH4 flux from a flooded soil. For example, the rate ofchange in the concentration might change at mid-morning, or an unpredictable natural CH4
ebullition may occur during chamber deployment, causing the linearity to be poor (Figure6.2). In such cases, we can use a concentration difference method (i.e., two-point linear
regression using the concentrations of the initial and last gas samples) instead. For instance,in the case of CH4 ebullition, using the inappropriate calculation method can cause aconsiderable error (11%) (Table 6.1). It should be noted that adoption of the concentrationdifference method is appropriate on the assumption that the measurements were conducted
properly. Errors from human-induced CH4 ebullition, for example, cannot be avoided by using
this alternative method (see Chapter 4.8.2). In addition, when the original number of gas
samples per chamber deployment is three, the calculated flux becomes the same between
the linear regression and the concertation difference method.

Figure 6.2. Examples of three observed patterns of CH4 concentration changes over time.
Table 6.1. CH4 fluxes calculated by two methods for each of the three cases shown in Figure 6.2
Variables

Case 1Case 2

Case 3

15.97

13.62

11.68

1.00

0.86

0.85

<0.001

0.014

0.017

Flux calculated from the concentration difference (B, mg CH4 m h )

15.63

13.97

10.39

B/A

0.98

1.03

0.89

–2

–1

Flux calculated from linear regression (A, mg CH4 m h )Adjusted R2

p value
–2

–1

57

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6. Data processing

6.2.2. Significance of linear regression
One criterion used to check whether the number of gas samples collected during chamber
deployment is adequate is the statistical significance of the linear regression of gas
concentration over time. The use of this criterion is based on the assumption that the gas isemitted at a constant rate (via diffusion through the rice plants, not via ebullition). To analyze
the significance of the regression slope, at least three samples are needed. However, thecoefficient of determination (R2) must be very high to detect significance with three samples
per chamber deployment (Table 6.2). Therefore, as discussed in Chapter 4.5, we recommend
collecting at least three samples during a 20–30-min chamber deployment (see also Chapter
4.8.2). In this case, it is possible to disregard outliers of the gas concentration data based on
experience and judgment of past performance of the site, instrument function, or chamber
efficacy (Parkin and Venterea, 2010).
Although some researchers use the statistical significance of the regression slope todecide whether the calculated flux is acceptable or not (i.e., considered as zero or other fixedvalue), the relationship can be non-significant for various reasons, as explained in Chapter
6.2.1. Natural or human-induced ebullition can cause the regression to be non-significant
even when the gas flux is high. If ebullition occurs naturally, the rejection of a non-significant
flux can cause the data to be misinterpreted. Similarly, a very low gas flux can lead tonon-significance, especially when the GC performance is poor (see Chapter 5.5). For ways toidentify low but meaningful fluxes, see Chapter 6.3. Thus, the reason for non-linearity should
be identified before statistical significance is used as the criterion for determining whether
the number of gas samples is adequate and whether the calculated flux is acceptable.
2

Table 6.2. Values of the coefficient of determination (R ) needed to detect significance (p < 0.05 or p <0.1) for different numbers of gas samples in a linear correlation
Significance level3 samples

4 samples

5 samples

6 samplesp < 0.05

0.994

0.903

0.773

0.659

p < 0.1

0.976

0.810

0.650

0.533

6.2.3. Cumulative gas emission
Use of a trapezoidal integration method (i.e., linear interpolation and numerical integration
between sampling times) is recommended for calculating cumulative gas emissions. There
are three steps to this calculation (Figure 6.3). First, calculate the daily gas flux by multiplying
the daily mean hourly gas flux by 24. Second, calculate the emission between every twoconsecutive measurements using the trapezoidal rule. Third, sum up the areas of all the
trapezoids. It should be noted that low-frequency (i.e., long interval) flux measurements
during agricultural management events (e.g., drainage and N topdressing) may cause underor overestimation of the cumulative emissions (see Chapter 6.4.2).

6. Data processing

Figure 6.3. A schematic diagram of the trapezoidal integration method.

6.3. Limit of quantification for the gas flux
6.3.1. Introduction
Systematic and random errors associated with gas flux measurements vary acrosslaboratories, because the errors depend on relative instrumental performance, the analysts
who perform the analyses, and the methods used. For (1) standardized reporting of a MRV(measurement, reporting, and verification) project and (2) comparative assessment ofmultiple locations, it is essential to assess the analytical uncertainty associated with all
sources of variability in the observed data.
In Chapter 5.5.3, we introduced the concepts of LOD and LOQ and applied them to GC
analysis of GHG emissions from a rice paddy (LODgc and LOQgc). Here we further apply theseconcepts to the gas flux calculation. Our proposed method for estimating LOD and LOQ of
the gas flux (LODflux and LOQflux) quantifies the overall precision and sensitivity of themethods used, from gas sampling to GC analysis. That is, we accept the measured gas flux asa significant value if the difference in the measured gas concentration between the initialsample and the last sample during the chamber deployment is higher than the LOQgc.
However, it should be noted that human-caused errors, such as CH4 ebullition and use of a
non-airtight chamber, are not captured by the LOQflux calculation.
6.3.2. Detailed procedure
LOQflux of CH4 (mg CH4 m–2 h–1) is calculated as follows:

LOQ flux  LOQgc 

V
273
60


A
273  T t32–3

where V is chamber volume (m ); A is chamber area (m ); ρ is gas density (0.717 kg m at 0°C); T is themean air temperature inside the chamber (°C); and t is the chamber deployment time (min).

59

60

6. Data processing

This equation is almost the same as that for the flux calculation (see Chapter 6.2.1), and it
shows that a shorter deployment time and/or a higher chamber height (V/A) increases LOQflux(Table 6.3). Furthermore, a more accurate GC analysis (LOQgc) is obviously important forlowering LOQflux.
At present there is no single way to handle data below the LOQflux in a scientific paper.
Parkin et al. (2012) suggested three possible ways to manage flux data below a detection
limit (here LOQflux). First, the data can be presented "as is" (without any correction); second,
they can be presented with a disclaimer; third, they can be set to zero. Whichever way is
chosen should be indicated in the methods section of a scientific paper. LOQgc and LOQflux
should be determined at least once every cropping season to check the overall method
performance.
Table 6.3. Examples of calculating LODflux and LOQflux under different conditions
Case 1Case 2

Case 3

Case 4

LODgc (ppm)

0.30

0.06

0.06

0.06

LOQgc (ppm)

1.00

0.20

0.20

0.20

-2

-1

0.39

0.08

0.12

0.14

-2

-1

1.31

0.26

0.39

0.47

V (m )

0.18

0.18

0.18

0.22

2

0.18

0.18

0.18

0.18

ρ (kg m )

0.717

0.717

0.717

0.717

T (°C)

25

25

25

25

t (min)

30

30

20

20

LODflux (mg CH4 m h )LOQflux (mg CH4 m h )3A (m )-3

Gray shading highlights large differences between two adjacent cells.

6.4. Evolving issues
6.4.1. Correction for inadequate chamber area
As described in Chapter 3.3, the chamber cross-sectional area (its footprint) should generally
be a multiple of plant density for transplanted fields. This simplifies determination of the unit
area when calculating the gas flux. If the chamber area is not a multiple of plant density, acorrection needs to be applied in the flux calculation. Because the emission pathway differs
between CH4 and N2O, and between flooded and drained periods, the unit-area correctionused for each is based on different assumptions.
For CH4 flux in a flooded field, a chamber that is the wrong size is not a critical problem,
because most CH4 is emitted via the rice plants (Figure 6.4). For example, if a chamber with a
30 cm × 30 cm footprint covers four plants, each occupying an area of 20 cm × 20 cm (i.e.,6. Data processing

total area of 40 cm × 40 cm), the area covered by the chamber can be assumed to be 40 cm
× 40 cm when calculating the area-based gas flux. On the other hand, for the CH4 flux in adrained field and the N2O flux in flooded or drained fields, the gases are emitted directly(exchanged) from the drained soil or the surface water to the atmosphere (Figure 6.4).
Therefore, the original chamber footprint (30 cm × 30 cm in the above example) should be
used when calculating the area-based gas flux.

Figure 6.4. Main emission pathways of CH4 and N2O during flooded and drained periods.

6.4.2. Correction for a missing flux peak
As described in Chapter 4.4, we recommend measuring gas fluxes just before agricultural
management events and then frequently until the flux peak has passed. Lack of gas flux datafrom just before and during temporary drainage and N fertilization events may cause
considerable over- or underestimation of the cumulative emissions (Figure 6.5). Such gaps in
the measurements should be recorded. At least in the case of CH4 and N2O fluxes just before
the drainage and N2O flux just before N topdressing, the flux levels are not likely to differ
drastically from the preceding measurement. In such cases, it can be assumed that the gas
flux just (1 day) before the agricultural management event was the same as the one justpreceding it.

Figure 6.5. Examples of the consequences of inadequate gas sampling before and during agricultural
management events.

61

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6. Data processing

6.4.3. The significance of linear regression and/or LOQflux
By itself, the significance of the linear regression (see Chapter 6.2.2) is not a sufficient
criterion for determining whether the measured gas flux is acceptable. Because thesignificance is strongly affected by the LOQgc (see Chapter 5.5), it is difficult to tell whether
the non-significance is due to poor GC performance or to human error when the measured
gas concentration is near the ambient level. Therefore, when the measured gas concentrationis near the ambient level, we recommend using both the significance of the regression and
LOQflux as criteria. That is, if the measured gas flux fails to pass either criterion, it should be
set to zero.
Non-significance accompanying high positive or negative fluxes indicates human error or
natural CH4 ebullition, because GC performance is better at a high concentration range.
Human error should be avoided by careful sample collection and adherence to GC protocols.
However, if such error cannot be avoided, we recommend prioritizing LOQflux as the criterion
for determining the acceptability of measured fluxes (i.e., ignore the linear regression result).
As described in Chapter 6.2.1, the concentration difference method can be used for the flux
calculation in such cases. If a high but non-significant gas flux is regarded as zero or otherfixed value, the cumulative emissions are likely to be misestimated.

7. Auxiliary measurements

7. Auxiliary measurements
7.1. Introduction
Auxiliary data are essential to provide supporting evidence for the interpretation andgeneralization (modeling) of observed GHG emissions from a rice paddy. These data
generally include rice productivity, soil and water environment, and weather conditions. Someof these data are commonly collected in agricultural studies, but others are specific to GHG
studies, thereby requiring additional research instruments and skills. Provision of field
metadata (i.e., data about field data) is helpful for secondary users of the field data (i.e., formeta-analyses, model simulations, etc.). This chapter introduces recommended and optional
measurements.

7.2. Experimental conditions
Table 7.1 lists basic information that should be collected for field GHG studies. Weather data
can be recorded by an automated meteorological monitoring system throughout the year.
Nearby weather station data can also be used. Weekly weather data is not adequate for fullinterpretation of the measured GHG fluxes. Daily solar radiation and/or hours of sunshine areuseful for model simulations.
The type of water supply (i.e., rice ecosystem type) may be, for example, irrigated,flood-prone rainfed, or dry-prone rainfed. Soil C and N contents are related to soil C
sequestration, another option for the mitigation of GHG emissions from agricultural soils (seeChapter 7.5.3). In addition to common soil properties, we recommend collecting information
on field drainage conditions, especially if water management is the research subject.
Table 7.1. Examples of basic information on field location, weather, and the soil and water environment
Category

Item

Location

Country, province/state, (nearest) city, site name, latitude/longitude, topography(elevation)

Weather

Climate zone, wet and dry seasons, precipitation, air temperature, daily solar radiation,sunshine hours

Water

Type of water supply, groundwater level, water percolation rate (hydraulic conductivity)

Soil

Soil taxonomy (World Reference Base for Soil Resources and/or USDA Soil Taxonomy),
total C and N contents, plow layer depth, bulk density, texture (sand, silt, clay), free ironcontent

Italics indicate optional items.

63

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7. Auxiliary measurements

7.3. Agricultural management practices
Table 7.2 lists information related to agricultural management practices that should berecorded. The field cultivation history covering at least the three preceding years should becollected. This is particularly important for CH4 emissions if non-rice crops have beencultivated (paddy-upland rotation; Nishimura et al., 2011). In addition, information about all
agricultural practices used throughout the year should be recorded. Details of water
management during the rice growing period are especially important because water
management drastically alters soil redox conditions. Therefore, the surface water depth
should be measured frequently manually (Figure 7.1) or automatically (Figure 7.2) to ensurethat the stated practice is being adhered to.
Table 7.2. Examples of information related to basic agricultural practices
Category

Item

Cultivation history

Name of crop(s), number of crops per year, organic amendments (type andrate), soil water status in fallow

Current practices

Date/duration, method/type, or rate/amount of each

Water management

Surface water level, water intake, surface water drainage, pipe/tile drainage

Italics indicate optional items.

Figure 7.1. PVC pipes with multiple holes on the sidewall that installed in a paddy soil to monitor thesurface water depth.

7. Auxiliary measurements

Figure 7.2. Automated sensors for surface water level: left, capacitance type; right, ultrasonic type.

7.4. Rice growth and yield
Record the denomination of rice variety. Periodically (e.g., biweekly) (1) plant height, (2) the
number of tillers/ears per unit area or per hill, (3) aboveground biomass, and (4) root biomass(optional) should be measured. In addition, records of the rice growth stages, especially thedate of heading, are useful for interpreting the seasonal pattern of CH4 fluxes (Minamikawa etal., 2012). Disease and insect damage to rice should be observed and recorded. Yields ofgrain and straw should be measured by a quadrate sampling method. These enablecalculation of yield-scaled GHG emissions (i.e., GHG intensity), which has recently been a
topic of interest (e.g., Linquist et al., 2012). In addition, rice growth and yield inside andoutside of the chamber should be compared to evaluate the so-called chamber effect(Nishimura et al., 2004). Yield component analysis data are useful for further investigations.

7.5. Specific measurements
7.5.1. Soil redox chemistry
Sequential reduction of soil oxidants after flooding controls paddy soil biogeochemistry.
Redox potential is a useful measure of reductive conditions in a flooded paddy soil. To
measure soil redox potential, platinum-tipped electrodes and a portable meter with areference electrode are usually used (Figure 7.3). Before using the equipment on-site, the

65

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7. Auxiliary measurements

output of each electrode should be checked by using a standard chemical (e.g., quinhydrone),because there can be substantial differences in output among electrodes. Data are generallycollected at a soil depth of 5 cm. Props can be used to support the electrode and minimize
shaking when it is being connected to the meter. Because of the high spatial variability of soil
redox status, at least three electrodes should be used per plot. It should be noted thatdrainage events can cause the soil to crack and expose the platinum tip of the electrode towater or air.

Figure 7.3. Platinum-tipped electrodes (left), electrodes installed in a flooded paddy soil (right), and aportable redox potential meter (bottom).

Iron is the dominant redox substance in a paddy soil. After a field is flooded, soil ferrous
iron (Fe(II), a reduced form of iron) increases and reaches a plateau. Although the biologicallyreducible soil Fe(II) can be regressed from the empirical equation with various forms of Fe(Cheng et al., 2007; Smakgahn et al., 2009), soil Fe(II) can be determined by colorimetry usingo-phenanthroline. See Tokida et al. (2010) for the detailed method.

7. Auxiliary measurements

7.5.2. Soil temperature and moisture
Soil temperature can be recorded by inserting a thermometer to a depth of 5 cm in flooded
or drained soil. The soil moisture content under the non-flooded condition is a driving factorof the N2O flux. The measurement depth should be the same as for soil temperature. As
mentioned in Chapter 7.2, we recommend monitoring both at 1-hour intervals throughout
the year by using automated sensors/loggers. However, if automated instruments are notavailable, manual monitoring each time the gas flux is measured still provides useful
information.
7.5.3. Soil C and N contents
Soil C sequestration is one of the most effective options for mitigating GHG emissions fromagricultural soils. IPCC (2006) recommends investigating soil C storage down to 30 cm depth.
However, because soil thickness is changed by agricultural practices and natural events, soil
mass equivalent (Ellert and Bettany, 1995) can be used for offsetting their effects. The
product of the soil bulk density and the soil CN content is the CN storage. We recommend
measuring them at various depths (e.g., 2.5–7.5 cm for 0–10 cm layer, 12.5–17.5 cm for 10–20
cm layer, and 22.5–27.5 cm for 20–30 cm layer) (Figure 7.4). A long-term (i.e., several years)
investigation is required to accurately evaluate storage changes.
N substrates for soil N2O production include ammonium and nitrate. Frequent
measurement of soil inorganic N contents is useful for interpreting the seasonal N2O flux
pattern. Soil extracted with KCl solution can be analyzed for ammonium and nitrateconcentrations by using, for example, a continuous flow analyzer.

Figure 7.4. Stainless coring tubes (100 mL, left) and soil collection using a coring tube (right).

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Authors’ affiliations

Authors’ affiliations
Lead authors
Kazunori Minamikawa
National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japan
minakazu@affrc.go.jp
Takeshi Tokida
National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japan
tokida@affrc.go.jp
Shigeto Sudo
National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japanssudo@affrc.go.jp
Agnes Padre
International Rice Research Institute, Los Baños, Laguna, Philippinesa.padre@irri.org
Kazuyuki Yagi
National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japan
kyagi@affrc.go.jp

Contributing authors
Prihasto Setyanto
Indonesian Agricultural Environment Research Institute, Pati, Central Java, Indonesia
prihasto_setyanto@yahoo.com
Tran Dang Hoa
Hue University of Agriculture and Forestry, Hue, Vietnam
trandanghoa@huaf.edu.vn
Amnat Chidthaisong
The Joint Graduate School of Energy & Environment, King Mongkut’s University ofTechnology Thonburi, Bangkok, Thailand
amnat_c@jgsee.kmutt.ac.th

75

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Authors’ affiliations

Evangeline B. Sibayan
Philippine Rice Research Institute, Muñoz, Nueva Ecija, Philippines
eb.sibayan@philrice.gov.ph
Yusuke Takata
National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japan
takatay@affrc.go.jp
Takayoshi Yamaguchi
National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japan
ladakh2008@affrc.go.jp

Reviewers
Kazuyuki Inubushi
Graduate School of Horticulture, Chiba University, Matsudo, Chiba, Japaninubushi@faculty.chiba-u.jp
Reiner Wassmann
International Rice Research Institute, Los Baños, Laguna, Philippines
R.Wassmann@irri.org
Tetsuhisa Miwa
National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japanmiwa@niaes.affrc.go.jp
Ngonidzashe Chirinda
International Center for Tropical Agriculture, Cali-Palmira, Colombia
n.chirinda@cgiar.org