Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

N 2 O EM IS SI ONS FROM AN IMAL WAS TE
MANAGEM EN T SY STEMSACKNOWLEDGEMENTS
This paper was written by Oene Oenema (The Netherlands), Barbara Amon (Austria), Lambert Gnapelet(Central African Republic), Karin Groenestein (The Netherlands), Otto Heinemeyer (Germany), John van
Aardenne (The Netherlands), and André van Amstel (The Netherlands). The encouragement by Robert Hoppaus,Thomas Martinsen and Carolien Kroeze to write this paper and their suggestions for improvement are highly
appreciated.

ABSTRACT
Livestock and poultry excrements (dung and urine) contain nitrogen that is substrate for microbial nitrificationand denitrification processes. A gaseous nitrogen species that is released from these processes into theatmosphere is nitrous oxide (N2O). This paper provides some background information for the proper estimationof N2O emissions from animal waste (produced by livestock and poultry) during storage and treatment of theanimal waste. The noun “animal waste” encompasses all animal excrements, including possible bedding
material, water and micro-organisms.
The Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC Guidelines) provide atransparent framework to calculate national emissions from animal waste during storage and treatment. Seven
types of Animal Waste Management Systems (AWMS’s) are distinguished. In the IPCC Guidelines, emissionsof N2O from AWMS’s are assumed to be linearly related to the amount of N in the animal waste. This
relationship is expressed as N2O emission(i) = A(i) ● EF(i), where A(i) is the total amount of nitrogen (N) in the
AWMS of type (i), and EF(i) is the emission factor for the AWMS of type (i).
Good practice in inventory preparation involves a series of six consecutive steps:•

Characterization of livestock and poultry population;•

Determination of the mean annual N excretion for each animal category;•

Determination of the amount of animal waste N in the various AWMS’s;•

Correction of the amount of N in the AWMS’s for possible N losses via ammonia volatilization;•

Application of the proper emission factor to each AWMS, and•Calculation of N2O emissions from: N2O(AWMS) =∑(T) [N(T) ● Nex(T) ● AWMS(T) ● EF3(AWMS)]

Good practice in inventory preparation requires that proper activity data and emission factors are used, and thatthe proper procedures for “upscaling” and “quality assurance and quality control” (QA/QC) are applied.
Application of good practice in inventory preparation will improve the transparency and completeness of theinventory and ultimately the accuracy and quality of the estimated total emissions.
So far, the uncertainties in the N2O emission estimates from AWMS’s are large. This is mainly due to theuncertainties in the emission factors and to the uncertainties related to the actual management of the animal
waste in the various AWMS’s. A number of possible categories of biases and errors, as sources of uncertainties
in inventory preparation, have been identified.
In this paper we make a number of suggestions to further improve the IPCC Guidelines. These suggestions
relate to a refinement of the categories “Other animals” and “Other systems”, and to the application of acorrection factor for N losses during storage and treatment of the animal waste in the AWMS’s. Suggestions
were also made to improve the quality of the inventories of livestock, poultry and AWMS’s. There is aconsiderable lack of proper criteria and information about AWMS’s in practice. Finally, we make a plea for acoordinated research action to improve the quality of the emission factors. The uncertainties in the emission
factors contribute most to the uncertainty in the overall N2O emission. Whenever possible, emission factors
should be related to: (i) the aeration states of the animal waste in the AWMS’s, (ii) temperature and (iii) the
duration of the storage process, as these are the major controlling factors.

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1

INTRODUCTION

On a global scale, Animal Waste Management Systems (AWMS’s) are important sources of nitrous oxide (N2O).
It has been estimated that AWMS contribute about a third of the total N2O emission from agriculture, whichamounts to about 6 Tg N2O-N per year (Mosier et al., 1998). Hence, AWMS’s contribute as much as about 14
percent to the estimated total global emissions of about 14 Tg N2O-N into the atmosphere. The term “animal
waste” is used here as a generic noun for all types of animal excrements (dung, urine, waste), including possible
additives (bedding material, straw, water, micro-organisms, etc.). The term “animal waste management system”
is used here as a generic noun for all types of storage and treatment of animal waste, except the application of theanimal waste on the land.
The N2O from AWMS’s originates predominantly from the microbial processes of nitrification anddenitrification (Oenema et al., 2001). Nitrification is the microbial transformation of ammoniacal nitrogen(NH4+-N) into nitrate nitrogen (NO3---N. Denitrification is the transformation of NO3- into nitrogen gas (N2).
Nitrous oxide is released from the nitrification process as a by-product, the amount of which depends on the rateof the nitrification process, oxygen concentration, pH, temperature, and the microbial community. In the
denitrification process, N2O is an intermediate product, which may escape when the rate of N2O production andthe rate of N2O consumption are not well synchronized and synlocalized. The amount of N2O released from thedenitrification process is a function of the amounts of NO3- and metabolizable organic carbon, the oxygen
concentration, pH, temperature, and the microbial community.
Qualitatively, the controlling factors of nitrous oxide production and emission at the process level are rather wellunderstood. This holds as well for nitrous oxide from animal waste at the process level. However, ourquantitative understanding of N2O emissions from the various sources is still far from complete. This holds aswell for N2O emissions from AWMS’s. There is a wide range of N2O emissions in types of animal waste
management systems in practice. The waste and the conditions of the management system may vary from site tosite and also from time to time. In fact, we know surprisingly little about what farmers actually do and how they
manage the storage and utilization of waste, and how this translates into emissions of N2O into the atmosphere.
As a result, the uncertainty in the estimated N2O emission is large (Mosier et al., 1998). The uncertainty isrelated in part to the stochastic nature of the emitting processes in AWMS’s, and in part also to the poor
availability and quality of data and to the methodology applied for calculating areal estimates (e.g. Van
Aardenne, 2002).
The IPCC Guidelines (IPCC/OECD/IEA, 1997) provide a step-by-step guide to countries in the preparation andsubmission of annual greenhouse gas emission inventories, including N2O from AWMS’s. The IPCC Guidelines
provide standard tables, worksheets, units, default values for activity data and emission factors, and timeintervals for reporting the emissions. Countries are invited to use their own estimates for activity data andemission factors, when these values are considered more accurate than the default values provided by the IPCC
Guidelines. These values should be sustained by proper documentation. It is generally agreed that the use ofgood practice procedures in the selection of activity data and emission factors can reduce uncertainties inemission inventories. Application of good practice in inventory preparation will provide the most accurate
estimates of actual emissions. It will also facilitate the process or review to qualify uncertainties and to crosscheck national estimates with independent calculations. Furthermore, it may provide an appropriate basis forcompliance with the commitments under the Kyoto Protocol.
The IPCC Expert Meeting on Good Practice in Inventory Preparation: Agricultural Sources of Methane (CH4)
and Nitrous Oxide(N2O) in Wageningen (February 24-26, 1999) provided a forum of technical experts familiar
with greenhouse gas emissions from agriculture, to discuss and agree upon good practice guidance for CH4 andN2O emissions from agriculture. The present paper has been prepared as a background paper to facilitate thediscussion in the sub-group “Nitrous oxide from animal waste management systems” of the IPCC Expert
Meeting. The purpose of this background paper is to briefly review the guidelines for good practice in national
inventory management of N2O from animal waste management systems, and to suggest improvements of theguidelines, so as to improve the accuracy of national estimates of N2O emissions from animal waste management
systems. It addresses key issues related to inventory preparation, i.e., (i) methodological issues, (ii) completenessof the inventory, (iii) assessment of inventory quality, and (iv) assessment of uncertainties.

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2

CURRENT STATE OF INVENTORYMETHODOLOGIESBasically, there are various options to quantify N2O emissions from AWMS’s, ranging from frequent direct
measurements to approximations, using activity data and emissions factors. Evidently, direct measurements
provide the most accurate estimates of the N2O emissions from AWMS’s, but direct measurements are not anappropriate option for getting a total average emission per country. Nitrous oxide from AWMS’s is the sum of
numerous small and diffusely spread sources all over the world, and these cannot all be measured at the desired
level of accuracy and precision. Furthermore, process-based models are still in the phase of development andtesting (validation), and are not yet a suitable alternative either. Hence, approximations such as those in the IPCC
Guidelines provide the best alternative at this moment.
The IPCC Guidelines provide a transparent framework to calculate national emissions from AWMS’s(IPCC/OECD/IEA, 1997). Seven categories of Animal Waste Management Systems (AWMS’s) are
distinguished in section 4.2 ‘Domestic Livestock’ of Chapter 4 ‘Agriculture’ of the IPCC Guidelines, namely•

anaerobic lagoons;•liquid systems;•

daily spread;•solid storage and drylot;•

pasture range and paddock (grazing);•

used as fuel, and•other systems.

However, for reasons of convenience, the category “used as fuel” is reported henceforth under section 1.3 ofChapter 1 Energy. Furthermore, for the categories ‘daily spread’ and ‘pasture range and paddock’, there isessentially no intermediate storage and treatment of the animal waste, because the dung and urine are directlytransferred to soil. Hence, the categories ‘daily spread’ and ‘pasture range and paddock (grazing)’ are reported
under section 4.6. Agricultural soils of Chapter 4 of the IPCC Guidelines (IPCC/OECD/IEA, 1997).
The IPCC Guidelines assume that a certain fraction of N contained in animal waste in the AWMS’s is released
as N2O into the atmosphere, during storage and treatment of the animal waste. The fraction of N2O emitted, i.e.
the emission factor, is assumed to be linearly related to the amount of N in the animal waste, but it varies for thevarious types of AWMS’s. This can be represented as:
N2O emission(i) = A(i) ● EF(i)Where:
A(i): the total amount of nitrogen in the AWMS of type (i),EF(i): the emission factor for the AWMS of type (i).
Emission factors are expressed as the mass fraction of the N in the waste that escapes as N2O to the atmosphere.
It ranges from 0.00 (zero) for daily spread, to 0.001 for anaerobic lagoons and liquid systems, and to 0.02 fordung and urine from grazing animals and for solid storage of waste and drylots. Different emission factors fordifferent AWMS’s reflect the differences in conditions during storage and treatment of the waste, and incontrolling factors of N2O emissions (i.e. oxygen content, nitrate content, pH, temperature, etc.).
The amounts of N in the AWMS are estimated from the amounts of N excreted by the various types of animals.
Default values of N excretion have been presented by the IPCC Guidelines for 6 categories of animals and forthe 8 major regions in the world. The amount of N excreted by non-dairy cattle, dairy cattle, poultry, sheep,swine and other animals are corrected for N losses by ammonia volatilization (Mosier et al., 1998).
Urine and dung droppings of grazing animals are suggested to be a dominant source of N2O (Mosier et al., 1998;
Oenema et al., 1997). Approximately 80% of the estimated total N2O emission from animal excrements
originates from grazing animals (IPCC/OECD/IEA, 1997). Most of the non-dairy cattle, dairy cattle, sheep andthe category other animals are kept outside in the field. Hence, a large fraction of the N in animal excrements isdeposited on pasture. A number of studies have shown that a relatively large fraction of the N from dung andurine in pastures is emitted as N2O into the atmosphere. The high emission factor has been related to the veryN2O Emission from Animal Waste Management Systems

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high N concentrations in the urine and dung patches, to interactions between nitrogen and carbon cycling ingrazed pastures and to the effects of trampling and compaction of the soil by the grazing animal (Oenema et al.,1997). As noted before, the animal waste from grazing animals directly reaches the soil. Hence, the associated
N2O losses have to be reported under Greenhouse Gas Emissions from Agricultural Soils (Section 4.6)
The IPCC Guidelines provide a transparent framework to calculate national emissions from AWMS’s as briefly
summarized below:
N2O (awms) =∑(t) [N(t) ● Nex(t) ● AWMS(t) ● EF3(awms)]
Where:
N2O(AWMS): N2O emissions from animal Waste Management Systems in the country (kg N/yr)
N(t):number of animals of type t in the country

Nex(t):

N excretion of animals of type t in the country (kg N/animal/yr); (1996 IPCC Guidelines,
Table 4-20)

AWMS(t): fraction of Nex(T) that is managed in one of the different distinguished animal waste
management systems for animals of type t in the country; (see Table 4-21)
EF3(AWMS): N2O emission factor for an AWMS (kg N2O-N/kg of Nex in AWMS); (1996 IPCC Guidelines,
Table 4-22)
t:

type of animal

The estimated uncertainty in the various N2O sources can be as high as two orders of magnitude(IPCC/OECD/IEA, 1997). The uncertainty in total emissions from a single source follows mainly from theuncertainty in the emission factor, and also from the uncertainty about the amount of nitrogen in animal waste ina specific animal waste management system. Generally, there is less uncertainty related to the agriculturalactivity data, i.e. the number and type of animals and amount of N excreted per animal. Yet, deviations of actual
N excretion from default values may be as large as 30 percent for the various categories of animals. Furthermore,
there may be some confusion about different categories of animals (e.g. other animals, dairy cattle versus nondairy cattle) and whether baby animals are included or not (e.g. piglets and pig; lambs and sheep).
The IPCC/OECD/IEA Working Group on developing the IPCC Guidelines recommended that future inventory
methodologies should be based on process-based models because of the many uncertainties related to theapplication of linear emission factors to estimating N2O emissions from agricultural sources,. Process-basedmodels should be developed and tested, to check the accuracy of approximations of the IPCC Guidelines, and tofurther improve the estimates in the near future. However, it was also stated that the present knowledge about thecomplex interactions and about the many controlling factors of N2O emissions from agricultural sources isincomplete to apply these models right now. Furthermore, there is insufficient experimental data to calibrate andvalidate these models, at this moment. Hence, the IPCC Guidelines provide the best alternative for this moment
when proper procedures and guidance are provided.

3

3.1

METHOD FOR DETERMINING N2OEMISSIONS FROM AWMS’S
Framework of the IPCC Guidelines forinventories of N2O emissions from AWMS’s

The method for estimating N2O from animal waste management systems requires 6 steps:
Step 1: Divide the population of domestic animals into categories. Use the categories provided in the chapter“Domestic livestock” of The IPCC Guidelines. To improve the accuracy, we suggest the characterization of eachcategory (average annual population) in terms of age, weight, fodder intake and number of young-ones relative
to adults. We also suggest to characterize and specify the animals in the category “other animals”. Furthermore,determine the number of animals of each category, and calculate three-year averages, based on monthlyweighted sums.

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Step 2: Determine the annual average N excretion rate for each of the distinguished animal categories. Default
values are in the IPCC Guidelines. We suggest that if there is evidence that the N excretion estimates deviate
more than 10 percent from the default values provide in Table 4-20 of the IPCC Guidelines, countries are invited
to apply their own estimates. These adjusted N excretion rates must be based on published sources, e.g. peer
reviewed papers, Organisation for Economic Cooperation and Development (OECD) and the Food andAgriculture Organisation (FAO ) reports.
Step 3: Correct the annual average N excretion rate for each category for N losses via ammonia volatilizationand denitrification during storage and treatment of animal waste. Currently, there is no correction factor for N
losses during storage and treatment in the IPCC Guidelines. However, there is ample evidence that significant N
losses may occur during storage and treatment of animal wastes, depending on storage conditions and storage
duration. Tentatively, a conservative default value of 10% N loss in AWMS’s is proposed. Countries should be
invited to come up with own estimates, which may be dependent on storage conditions (temperature, aeration,
duration). Please note that the amount of N considered in the section “Indirect N2O emissions” (section 4.6 of the
IPCC Guidelines) should be corrected for the amount of N that is lost during storage and treatment in AWMS’s.
Furthermore, note that there is a correction factor for N loss for the animal waste applied to soil.
Step 4: Determine the amount of animal waste N, corrected for N losses via ammonia volatilization anddenitrification, in the various animal waste management systems. It is suggested to characterize each AWMS interms of the operations. Check for consistency in the data; provide N budgets for the animal categories. The sumof the total N intake by the animal via animal feed should equal the sum of the amount of N retained by the
animals, the amount of N in animal products, the amount of N volatilized and the amounts of N in the various
AWMS. Budgets should be provided on an annual basis.
Step 5: Estimate the N2O emission factors for each category of animal waste management system. Default
values are provided in the IPCC Guidelines. However, there is a considerable uncertainty in the default emission
factors, up to two orders of magnitude. We suggest that countries use their own emission factors, wheneverjustified by results of sound experimentation and published in peer reviewed scientific journals. It is likely thatclimate (temperature) and the amount of N in the waste are important modifiers of emission factors. We believethat coordinated research is needed to lower the uncertainty in the emission factors.
Step 6: Multiply the AWMS’s emission factors by the amounts of N in the corresponding AWMS’s to estimate
AWMS emissions. Sum across the AWMS’s to estimate total emissions.

3.2

Recommendation on activity data in thecontext of good practice

Activity data in the context of this paper refer to numbers and categories of animals as well as to the fractions
and types of AWMS’s. Good practice requires that reporting countries supply information along with numbersthat allow an independent authority to check the accuracy and reliability of the data. So far, data on animal
numbers seem more accurate than data on type and management of AWMS. For the majority of countries,
counting the number of animals has been a long standing tradition. There are proper procedures as well as
documents for national (Yearbooks) and international (e.g. to FAO) inventories of livestock and poultry. These
procedures/documents, however, do not include AWMS’s simply because there has been no commercial
incentive to do so.
Evidently, there is an urgent need for accurate information on the management of animal excrements in
AWMS’s in practice, particularly information about N2O emission controlling factors. The development ofguidelines for a proper inventory and monitoring of AWMS’s is therefore necessary. It is also deemed important
to convince the policy makers and administrators that the collection of data on this topic must be included in
census legislation in order to obtain such data on a regular basis for good practice in GHG emissions. An
enforcing argument for this is that such data will not only serve to increase confidence incurrent emission
estimates but will also be needed to document progress in emissions reduction strategies.

3.3

Recommendations on emission factors in thecontext of good practice

The IPCC Guidelines provide default values for emissions factors for the various AWMS’s. They reflect annual
mean losses. As such, the default values may differ significantly from the results of some direct measurements.
While results from direct measurements reflect one distinct situation, frequently read over short periods,

N2O Emission from Animal Waste Management Systems

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emission factors basically integrate possible effects and variations due to time (one year), climate, management
and animal waste compositions.
So far, the default values for the emission factors in the IPCC Guidelines are based on a limited amount ofexperimental data and expert judgement. The uncertainties are large. The AWMS ‘daily spread’ has an emission
factor of 0.0 (no range), simply because there is no storage of the animal waste; it is assumed that time period
between the moment of excretion and the moment of spreading is too short to emit any N2O into the atmosphere.
However, there are no experimental data to confirm this assumption. A relatively high emission factor of 0.02
has been assigned to the AWMS “Solid storage and drylot”, because of the partial oxic conditions in the animal
waste and associated nitrification and denitrification processes, and because of the relatively lengthy storage
time. However, there is little experimental data that confirm this estimate (Mosier et al., 1998). A relatively highemission factor of 0.02 has been also assigned to the AWMS “Pasture range and paddock (grazing)”, based on aliterature review by Oenema et al. (1997; 2001). Those review indicated that the actual emission factor may
range between 0.001 to 0.08. Liquid systems and anaerobic lagoons are suggested to emit little N2O, because of
the anoxic conditions. As a result, there is essentially no nitrification and denitrification going on, except for thesurface layer in contact with the atmosphere. An emission factor of 0.001 has been assigned to these systems, but
again there is little information that can confirm this estimate. Finally, the AWMS category “other systems” hasbeen arbitrarily assigned an emission factor of 0.005, as this category may include completely anoxic systems aswell as systems that contain partly oxic conditions.
A huge effort is needed to increase the accuracy and to decrease the uncertainty. Whenever possible, emission
factors should be related to:•the degree of aeration;•

the storage time, and•the temperature of the animal waste.

These factors are suggested to be the major controlling factors.
Because of the many factors and interactions involved, there exist the possibility that single measurements within acountry for a specific AWMS will not only significantly deviate from the default value, but also from estimatesobtained elsewhere. The underlying questions are how to extrapolate these data to other sites and other AWMS’s aswell as circumventing the fact that emission estimates vary from year to year simply because new data frommeasurements show such variation. A coordinated effort is needed worldwide to obtain more reliable emission
factors for N2O from AWMS’s. So far, there are some doubts on the progress of this type of studies. It is clear that
this type of studies are more of monitoring than conducting scientific research. Funding agencies may be not soeager to fund monitoring tasks, because little novel information is to be expected from such efforts. Furthermore,
the uncertainties that arise from up-scaling remain large. This dilemma requires more attention.

4

COMPLETENESS

Basically, the IPCC Guidelines cover all possible sources of N2O from AWMS’s, hence categories “otheranimals” and “other systems” are added so as to include animals and systems not specified. When not properly
specified, certain categories may be overlooked.

4.1

Other animals

The term “other animals” is a diffuse notation, and risks exist that certain types of animals may be overlooked. The
currently specified animal types were selected in order to comply with the categories used in the FAO statistical
yearbook. To improve the quality of the agriculture activity data, according to good practice, a more detailed
animal categories is considered. Animal categories should be detailed enough to provide accurate estimates ofnumbers and N excretion rates. Moreover, the distinction between agricultural or domestic animals and wildlife
animals is not always clear. As a result, emissions from agriculture and from natural or background sources are notseparated clearly. Furthermore, it is not always clear whether the number of animals and N excretion include thenumbers of young (baby) animals and associated N excretion. Improving the quality of counting the animals in acountry will further improve the quality of N excretion rates, and hence, N2O emission estimates.
There are two listings of animal categories in the IPCC guidelines, i.e. one that includes 10 categories ofanimals, used for the CH4 emission inventories, and one that includes 6 categories of animals, used in the N2O
emission inventories (Table 1). From a comparison of these two categories, the category “other animals” in the354

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Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

N2O emission inventories is found to be an important source of N2O in some African, Asian and Latin Americancountries. Furthermore, there are significant differences in N excretions between animals that are now grouped in
the category ‘other animals’. For example, amounts of N excretion may vary between goats, horses and buffaloes
by a factor of 5. Hence, the use of a more detailed listing for animal categories in Table 1 is recommended.
Furthermore, the extension of Table 4.20 on page 4.99 in Volume 3 of the IPCC Guidelines should include N
excretion rates for goats, camels, horses, mules and asses and buffaloes.

TABLE 1
CATEGORIES OF DOMESTIC ANIMALS INCLUDED IN THE EMISSION INVENTORIES OF N2O AND CH4 FROM ANIMALS AND
ANIMAL WASTE MANAGEMENT SYSTEMS ACCORDING TO THE IPCC GUIDELINES
CH4 emission inventories

N2O emission inventories

Dairy cattle

Dairy cattle

Non-dairy cattle

Non-dairy cattle

Swine

Swine

Sheep

Sheep

Poultry

Poultry

Goats

Other animals

CamelsHorsesMules and Asses
Buffaloes

4.2

Other AWMS’s

In The IPCC Guidelines, a total of seven animal waste management systems (AWMS’s), including the category“other systems”, have been identified for N2O emission inventories, whereas, a total of ten categories have been
identified for CH4 emissions inventories (Table 2). The category “other systems” covers all remaining systems inboth inventories, thereby assuming completeness. When comparing the N2O and CH4 inventories, it follows thatthe category “other systems” in the N2O emission inventory includes pit storage, anaerobic digesters and (deep)
litter houses mentioned in the CH4 emissions inventories. The category “other systems” in N2O emission
inventories also encompasses most of the poultry waste storage, i.e. the semi-dry storage of waste from poultry
in large poultry stables.
TABLE 2
CATEGORIES OF ANIMAL WASTE MANAGEMENT SYSTEMS (AWMS’S) IDENTIFIED IN THE EMISSION INVENTORIES OF N2O
AND CH4 FROM ANIMAL WASTE MANAGEMENT SYSTEMS ACCORDING TO THE IPCC GUIDELINES
CH4 emission inventory

N2O emission inventory

Pasture range and paddock

Pasture range and paddock

Solid storage

Solid storage and drylots

DrylotDaily spread

Dairy spread

Liquid slurry

Liquid slurry

Anaerobic lagoons

Anaerobic lagoons

Pit storage
Anaerobic digester

Burned for fuelBurned for fuelOther systems

Other systems

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The classification of AWMS’s for the N2O inventory in the IPCC Guidelines has been adopted from Safley et al.
(1992). It must be noted that while animals are to some extent standardized by nature, AWMS’s are not. For
example, the AWMS “Solid Storage and Drylot” of farm A may differ significantly from that of farm B. Such
differences can be larger than the differences in, for example, N-excretion from one dairy cow to another dairy
cow. Moreover, census data for the AWMS applied by the farmers are rare. Evidently, efforts are needed fromthe reporting countries to gather quantitative data on amounts and types of AWMS’s. Guidelines for goodpractice should be made for specification of the various AWMS’s; how to make the inventories and according towhich criteria. Reviews on the national AWMS’s can then be used to identify classes, groups and subgroups of
AWMS’s. With this prerequisite fulfilled, meaningful research could lead to appropriate N2O emission factors
with less uncertainty than those associated with the default ones in the IPCC Guidelines.

5

INVENTORY QUALITY

Quality assurance and quality control (QA/QC) is a process that encompasses the whole range of activities andintermediate steps in assuring accurate and high quality data in an inventory (Mangino, 1999). A well-developed
and well-implemented quality assurance programme fosters confidence in the final inventory results. The
common thread throughout the quality assurance process is the need for thorough documentation and complete
transparency (Mangino, 1999). Each group of actors involved in the inventory programme should have thenecessary information and full documentation, with which to fulfill its function. Figure 1 outlines the flow of
information and the inventory quality process at each step (Mangino, 1999). The first component is internal
quality control, which is a system of routine activities to measure and control the quality of the data statistics atfarm level, the direct measurements of emissions (factors), and of the inventory as it is being developed.
Application of internal quality control systems will minimize biases and errors by specifying procedures to checkfor consistency, completeness and correctness of the inventory process.
Prerequisite to quality assurance and quality control in the inventory of N2O emissions from AWMS’s are welldocumented procedures of data acquisition and data handling. Acquisition of activity data can be done atdifferent scales, frequency and level of detail. The farm-level is the most detailed scale and aggregating the datafrom all individual farms will yield ultimately the most accurate estimates of activity data. Activity data can bemonitored on a monthly basis or on an annual basis. Monthly weighted annual averages yield the most accurate
data. In industrialized countries, farms have to report number and type of animals on an annual basis to national
agencies that record farm statistics. These primary data are collected at all farms following standard procedures
on an annual basis. For other countries, activity data are derived from surveys in representative areas in the field.
These are subsequently extrapolated to the whole country. Ultimately, the activity data are compiled in the FAO
statistical yearbook, which provides a uniform format for activity data inventories for all countries. Evidently,
there are many possible steps involved in the acquisitions of activity data.
In short, quality assurance and quality control require that all actors involved in the emissions inventory sharethe information. This will ensure that all animal categories and all animal waste management systems areincluded. The Governmental agencies responsible for emission inventories should carry out the assessments ofdata quality and completeness. Data published in peer-reviewed international journals should provide the basis
for emission factors.

6
6.1

UNCERTAINTY ASSESSMENT
Sources of uncertainty

Uncertainty assessment requires that all possible sources of uncertainty must be evaluated in the complete chain
from very beginning of the measurement of primary data (activity data) and the measurement of emissions in thefield up to the compilation of work sheets and the establishing of emission estimates per country. Uncertainties
arise from biases and errors. Bias is defined here as misrepresentation, leading to a systematic deviation of themeasurement mean from the true (scientific) mean. Error is random variation around the true mean, leading to aconfidence interval around the measurement mean. Bias in emission estimates may lead to wrong conclusions,whilst errors complicate the making of conclusions, and in turn may lead to wrong conclusions as well. The term
'accuracy' is used here as a measure for bias; a high accuracy means that the deviation of the measurement meanfrom the true mean is small. The term 'precision' is used here as a measure for error; a high precision means thatthe variance of repeated measurements (or estimates) is small (Oenema and Heinen, 1999).

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Figure 1

Key steps in the inventory QA/QC process

Internal quality control:

Documentation:

Farm level

Research level

Number and categories ofanimals

Emission factors

N budgets calculations

N Excretion and N2O
budgets

Farm statistics

Peer reviewed papers

Internal quality control

Internal quality control⇓⇓

National Agencies for Farm Statistics
Review/Quality assurance:

Farm-level inputs of activity data

Internal quality control:

Compilation of N activity data from farm data statistics

Documentation:

Results of compilation and results of QA/QC

Reporting:

Submission of data to Government Agency⇓
FAO statistical yearbook
Review/Quality assurance:

Farm-level inputs of activity data

Internal quality control:

Compilation of N activity data from farm data statistics

Documentation:

Results of compilation and results of QA/QC

Reporting:

Submission of data to Government Agency⇓
Governmental Agencies
Review/Quality assurance:

Country Level inputs of activity data and emission factors

Internal quality control:

Compilation of national emission inventory

Documentation:

Results of compilation and results of QA/QC

Reporting:

Submission of data to IPCC/OECD⇓
External review
External Review:

External audit; stakeholders, peer & public review, verification

Documentation:

Results of external review⇓
IPCC/OECD/UNFCCC
Secretariat
External Review:

Requires standard format and transparency;
Ensure consistency with other inventories and external data
Source: Mangino, 1999

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6.1.1

Sources of bias

There are five possible sources of biases, which lead to inaccuracies in emission estimates, i.e. (i) personalbiases, (ii) sampling biases, (iii) measurement biases, (iv) data manipulation biases, including guestimation, and,
finally (v) fraud, which is consciously introducing biases. These categories of biases are briefly explained below.
Personal biases
Personal biases are the most complex source of biases. The conceptual interpretation of emission inventories andof the proposed methodology to calculate the emissions can vary from scientist to scientist especially when no
proper guidelines are provided. In fact, it is the purpose of the guidelines for good practice in inventory
preparation to overcome this type of bias.
Sampling biases
Emission inventories require census data of agricultural activity data, i.e. of type and number of animals, type andnumber of AWMS’s, and accurate estimates of emission factors for each of these AWMS’s. Census data usuallyconsist of a small collection from an aggregate or population. The sample is examined but it is the aggregate or
population that interest the users. The sample can be the animals, N excretion rates and AWMS’s in a certain areas.
Unless samples are representative, facts about the sample cannot be taken as the facts about the aggregate or
population. Generally, there is an endless variety among areas and farms so that successive samples are usuallydifferent. Hence, the key problem is where and when to sample to obtain representative samples. Evidently,
sampling is a large potential source of bias in inventory studies, and proper guidelines are needed. Sampling is also
a possible source of bias in investigations that quantitatively estimate the N2O flux for deriving emission factors.
Measurement biases
Measurements of the amount and N content of animal excretions and of the N2O concentrations in air requirechemical analyses. Potentially, there are a number of artefacts in the chemical analyses that may lead to bias. All
these potential artefacts can be minimized by good laboratory practice and by careful calibration of the methods.
Inclusion of appropriate standard samples in routine analyses and participation in sample exchange programmes
are very useful for identifying and preventing systematic deviations in the chemical analyses.
Data manipulation biases
A potential source of bias is data manipulation, estimating means of replicate analyses and guestimation, which
is making estimates (guesses) in the absence of proper data. Generalizations, averaging and upscaling may also
lead to loss of information as well as bias. Cross-checks should be made when using spread sheets. Rounding off
of figures must be postponed until the last calculation has been performed. Unfortunately, there are no general
guidelines, with respect to data manipulation, except for the standardised IPCC work sheets and the applicationof accepted mathematical rules.
Biases due to fraud
When emission inventories and greenhouse gas budgets are used as control and policy instrument to enforce a
protocol with possible economic consequences for countries, it then becomes obvious that some of thesecountries may try to manipulate the budget so as to minimize the economic consequences. The message
presented here is that biases may be larger in the case of greenhouse gas budgets being used as a regulatory
control instrument than a management instrument.

6.1.2

Sources of errors

Errors occur as a result of random variations, and show up as variance in repeated determinations. Two types oferrors can be distinguished, i.e. sampling and measurement errors. When the precision is low, and determinations
are carried out only once, errors and biases merge into each other.
Sampling error
Sampling errors originate from ‘within-area’ heterogeneity, from spatial variations or temporal variations orboth. Animal wastes are notoriously variable in space and time, even within well-defined plots and areas, andrequire well-designed sampling strategies. Emissions of N2O are often associated with hot spots and with certain
events. As a consequence, frequency distributions of N2O losses in space and time tend to be highly skewed andthe variance becomes extremely large. In such cases, errors are large. Quantification of the sampling error
requires repeated sampling following appropriate sampling designs, and repeated analyses.

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Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

Measurement error
Measurement errors originate from variations introduced during the determinations of the volume andcomposition of the sample, i.e. through sub-sampling, pre-treatment and chemical analysis. The measurement
error shows up as the variance of repeated measurements of one sample. Usually, the measurement error is muchsmaller than the sampling error. Good laboratories usually present results of chemical analyses with a relative
error of less than 5% for nitrogen in waste and N2O in air.
Total error in emission estimates
The total variance in the emission inventory is less than the sum of the variances of the individual inputs andoutputs because of the covariance between the various items in the work sheets of the emission inventories. The
total variance is equal to the sum of the variances of the various items plus twice the covariance of all possible twoway combinations of these items. Because, there is often a negative correlation between the sizes of the various
items, the covariance is negative. In statistical terms, the total variance of the budget on the balance is equal toVar (budget) =var (animals1) + ...+ var (animalsx) + var (N excretion1,x) + .... + var (AWMS1,y) + …. + 2 cov(animals1, animals2,…) + ... + 2 cov (animals1, AWMS1) + ...+ 2 cov (AWMSy-1, AWMSy).
Where:
Animals1, x:

number of animals, a total of x types

N excretion1, x: amount of N excreted per animal type
AWMS1, y:emission factor per type of animal waste management system, with y systems

var:estimated variance, andcov:

estimated covariance

The total variance of the emission estimate heavily depends on the size and variance of large items and on thecovariance between these major items. If precise information on the variance and covariance is lacking, and one
is interested in the possible variance of the total emission estimate, it is recommended to perform uncertainty
analysis. This may be done via a Monte Carlo type of simulation, using frequency distributions for the various
items based on literature data and best guesses (e.g. Van Aardenne, 2002). Again, proper assessment of theinteractions between the various inputs and outputs is crucial for obtaining unbiased and precise estimates of thefrequency distribution of the estimates, and hence of errors.
Summarizing, there are a number of possible sources of biases and errors. Biases are generally a more serious
problem than errors in emission inventories. They must be prevented in order to preclude incorrect conclusions.
It requires in-depth analyses of sources and sinks of N2O emissions and of its controlling factors as well as
testing of assumptions and proper data acquisition and handling. It is therefore the purpose of the IPCC
Guidelines and good practice to minimize biases.

6.2

Default uncertainty for emissions factors

Default emission factors and uncertainty estimates for AWMS’s are presented in Table 4-20 of the IPCC
Guidelines. The uncertainty in these default estimates is large, up to two orders of magnitude. These wide ranges
reflect (i) the lack of quantitative data and (ii) the many complicating interactions due to the interference ofenvironmental and management factors. Emissions of N2O are predominantly the result of the escape of N2O
from the microbial processes nitrification and denitrification. The amount of N2O emitted depends on the factorsthat determine the rate of the processes of nitrification and denitrification, and the rate at which the produced
N2O can escape from the AWMS. There are complex interactions and feed backs, which are not completely
known. Furthermore, there is a lack of quantitative information about the environmental conditions and theactual management of AWMS in practice.
As discussed before, uncertainty assessment must focus on the uncertainty in the major sources (see also Van
Aardenne, 2002). Major sources of N2O, include the AWMS categories urine and dung droppings from grazing
animals, solid storage and drylot, and other systems. The uncertainties presented in Table 4-20 of the IPCC
Guidelines reflect the possible range of emission factors for individual sources; they do not reflect theuncertainty in the mean emission factor for one country.

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7

CONCLUSIONS

On a global scale, animal waste management systems AMWS’s are important sources of N2O. The magnitude of
the N2O emissions is related to the management of the animal waste and to the amount of nitrogen in the animal
waste. There is a wide range in animal waste management systems and also in the amounts of N in the animal
waste. As a result, emissions of N2O from animal waste management systems also differ widely.
The IPCC Guidelines provide a transparent framework and step-by-step guide to calculate national emissions
from animal waste during storage and treatment. Good practice in inventory preparation involves a series of six
consecutive steps to be made. Crucial steps are the accurate estimation of the total amount of nitrogen in the
animal waste per animal waste management system, and the application of the proper emission factors to each of
the animal waste management system. Procedures, data acquisition and data handling must be well-documented,
and whenever possible only data and emissions factors from peer-reviewed literature should be used.
Though the IPCC Guidelines provide a good framework for national N2O emission inventories, there is still a
relatively large uncertainty associated with the estimated total N2O emissions from animal waste management
systems. This uncertainty is mainly related to the uncertainty in the emission factors. A coordinated researchaction is required to further improve the accuracy of the emission estimates.

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