Advancing national greenhouse gas inventories for agriculture in developing countries:
improving activity data, emission factors and software technology
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IOP PUBLISHING ENVIRONMENTAL RESEARCH LETTERS
Environ. Res. Lett. 8 (2013) 015030 (8pp) doi:10.1088/1748-9326/8/1/015030
Advancing national greenhouse gas
inventories for agriculture in developing
countries: improving activity data,
emission factors and software technology
Stephen M Ogle1,2, Leandro Buendia3, Klaus Butterbach-Bahl4,5,
F Jay Breidt6, Melannie Hartman1, Kazuyuki Yagi7, Rasack Nayamuth8,
Shannon Spencer1, Tom Wirth9 and Pete Smith10
1 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA
2 Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins,
CO, USA
3 2113-A Pula Street, College Ville, Brgy. Putho-Tuntungin, Los Banos, Laguna, 4030, Philippines
4 Institute for Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology,
Garmisch-Partenkirchen, Germany
5 International Livestock Research Institute, Old Naivasha Road, Nairobi 00100, Kenya
6 Department of Statistics, Colorado State University, Fort Collins, CO, USA
7 National Institute for Agro-Environmental Sciences, Tsukuba, Japan
8 Mauritius Sugarcane Industry Research Institute, Le Reduit, Mauritius
9 United States Environmental Protection Agency, Washington, DC 20460, USA
10 Institute of Biological & Environmental Sciences, University of Aberdeen, 23 St Machar Drive,
Aberdeen AB24 3UU, UK
E-mail: stephen.ogle@colostate.edu
Received 11 September 2012
Accepted for publication 15 February 2013
Published 7 March 2013
Online at stacks.iop.org/ERL/8/015030
Abstract
Developing countries face many challenges when constructing national inventories of
greenhouse gas (GHG) emissions, such as lack of activity data, insufficient measurements for
deriving country-specific emission factors, and a limited basis for assessing GHG mitigation
options. Emissions from agricultural production are often significant sources in developing
countries, particularly soil nitrous oxide, and livestock enteric and manure methane, in
addition to wetland rice methane. Consequently, estimating GHG emissions from agriculture
is an important part of constructing developing country inventories. While the challenges may
seem insurmountable, there are ways forward such as: (a) efficiently using resources to
compile activity data by combining censuses and surveys; (b) using a tiered approach to
measure emissions at appropriately selected sites, coupled with modeling to derive
country-specific emission factors; and (c) using advanced software systems to guide compilers
through the inventory process. With a concerted effort by compilers and assistance through
capacity-building efforts, developing country compilers could produce transparent, accurate,
complete, consistent and comparable inventories, as recommended by the IPCC
(Intergovernmental Panel on Climate Change). In turn, the resulting inventories would provide
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Environ. Res. Lett. 8 (2013) 015030 S M Ogle et al
the foundation for robust GHG mitigation analyses and allow for the development of
nationally appropriate mitigation actions and low emission development strategies.
Keywords: national greenhouse gas inventory, agricultural greenhouse gas emissions,
emission factors, activity data, soil nitrous oxide, rice methane, enteric methane, manure
methane, soil organic carbon
1. Introduction
National greenhouse gas (GHG) inventories are essential for
public policy planning to mitigate GHG emissions because
it is not possible to develop an informed plan without
first knowing the emissions. Inventories provide essential
information and enhance environmental integrity in planning
and development of GHG mitigation policy, such as baseline
and mitigation scenarios for nationally appropriate mitigation
actions (NAMAs), and low emission development strategies
(LEDS). Furthermore, GHG inventories track the trends in
emissions after actions and strategies are implemented, and
can be used to assess the outcomes. While this is generally
true for all countries and sectors of commodity production,
our objective is to discuss ways of advancing GHG inventories
in the agricultural sector for developing countries to support
policy planning and assessment.
The 2006 IPCC National Greenhouse Gas Inventory
Guidelines (IPCC 2006) describe the sources of emissions
in livestock and crop production systems. Our discussion
will focus on emissions directly from soils, animals, crops
and forages, although this does not discount the importance
of fossil fuel combustion and other sources of emissions
associated with agricultural production. Emissions from
livestock management include methane (CH4) from enteric
fermentation (mostly from ruminants such as cattle, buffalo,
and sheep), and CH4 and nitrous oxide (N2O) from
manure management. Burning of savanna and agricultural
residues releases non-CO2 greenhouse gases such as CH4,
N2O, carbon monoxide, and oxides of nitrogen. Nitrogen
fertilizer and other N-containing amendments when applied
to agricultural soils are sources of direct and indirect N2O
emissions. Liming of agricultural soils is an activity leading
to carbon dioxide (CO2) emissions, while CH4 is emitted
from wetland rice production systems. Land use change and
management can increase or decrease biomass, litter and soil
carbon stocks (e.g., Houghton et al 1999, Bondeau et al 2007,
Ogle et al 2010).
The hallmark of a high quality inventory is that it
follows good practice according to the IPCC guidelines, and
the key components of good practice is that the inventory
is transparent to others including reviewers, government
personnel, non-governmental organizations (NGOs) and
others actively involved in GHG mitigation planning; has
accurate and complete emissions estimates for all gases,
sources and sinks; has consistent application of methods
across a times series; and is comparable to inventories
from other countries (IPCC 2006). While even developed
countries struggle with implementation of good practice,
most can produce reasonably high quality inventories (e.g.,
US-EPA 2012). However, the task is even more challenging
in developing countries with few and sporadic surveys of
agricultural production systems, lack of country-specific data
on emission rates, and limited resources to complete the
task. The following are more specific challenges commonly
encountered by developing countries in preparing national
GHG inventories:
• No clear roles and responsibilities of relevant ministries
and agencies in preparing a national GHG inventory and
lack of institutional arrangements;
• small teams with limited resources and multiple responsi-
bilities;
• difficulty in retaining expertise;
• incomplete or non-existent activity data, and lack of
experimental data for developing country-specific emission
and stock change factors;
• insufficient documentation and absence of an archiving
system from previous inventories;
• no quality assurance and quality control (QA/QC) plan;
and
• no concrete plan to improve future national GHG
inventory.
To some, these problems may seem insurmountable, but
here we discuss several options that through a concerted
effort by national inventory teams can lead to inventories
that are produced with good practice. We focus on
three main challenges, improving activity data compilation,
producing more accurate country-specific emission factors,
and application of advanced software systems to help manage
the inventory data and conduct mitigation analyses. Moreover,
if developing countries can produce high quality inventories,
the governments would be able to more fully and effectively
participate in the development of NAMAs and LEDS, as
well as the negotiations for the United Nations Framework
Convention on Climate Change (UNFCCC), which could
ultimately provide the foundation for participation in
incentive programs to reduce GHG emissions.
2. Activity data compilation: challenges and the way
forward
To compile GHG inventories, a variety of data is needed about
activities that influence GHG emissions (IPCC 2006). Activity
data for agriculture includes a range of diverse information,
such as the number of livestock in the country, quantity of
nitrogen fertilizers applied to soils, and tillage practices used
in field preparation for growing crops. These data can be
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Environ. Res. Lett. 8 (2013) 015030 S M Ogle et al
obtained from one of several sources, including a census,
survey, farmer interviews, field observations, remote sensing
and other geospatial products, expert knowledge, or some
combination of these sources.
Fortunately, most countries have some information on
agricultural production from a census or survey. Other data
sources also exist from organizations such as the International
Fertilizer Industry Association (IFIA) (www.fertilizer.org/ifa/
statistics.asp) and the Food and Agriculture Organization of
the United Nations (www.faostat.fao.org/; IPCC 2010) that
can be used in a national GHG inventory. ‘Mining’ of these
data sources is the first step in compiling an inventory, but
activity data gaps still exist in most countries, and represent
a significant challenge to producing a complete inventory of
GHG emissions.
In general, the challenge of filling activity data gaps
may be achieved through a census or a probability survey,
each of which seeks to make inference about an entire
population, such as all farmers or fields in a region or
country, or all experts in a group. The key distinction is that
a probability survey selects a subset of the population to
make inference about the entire population, while a census
selects the entire population (e.g., Sarndal et al 1992, Lohr
2009). Both censuses and surveys are subject to non-sampling
errors, including under-coverage errors (in which part of
the population cannot be selected; e.g., unlisted farmers,
unmapped fields or unidentified experts), non-response errors
(in which not all selected population elements yield complete
responses; e.g., cloud-covered pixels), measurement errors,
and processing errors. Surveys are further subject to sampling
error, which is due to the fact that the selected sample is
not the entire population. However, sampling error is easily
quantified using the sample itself and standard statistical
techniques (Sarndal et al 1992, Lohr 2009). By enumerating
only a subset of the entire population, surveys save resources
that can then be devoted to reducing non-sampling errors,
and therefore are preferable in situations where resources
are limited. In addition, non-sampling errors are often larger
and more difficult to quantify than sampling errors, and so
surveys are often considered a better approach in practice for
data collection than a census. Examples of probability surveys
for agricultural resources include the US National Resources
Inventory (Nusser and Goebel 1997, Nusser et al 1998) and
the European Land Use and Cover Area Frame Statistical
Survey (Gallego and Delince 2010).
Geospatial products for land use, land management, soils
and climate are key datasets for an agricultural inventory; the
availability of these data was recently reviewed by Smith et al
(2012). Spatial data for soils and weather/climate are available
at sufficient resolution for GHG inventories in developing
countries. More specifically, soil data are available from the
Harmonized World Soils Database (FAO, IIASA, ISRIC,
ISSCAS and JRC 2012) and GlobalSoilMap.net project,
which will likely provide an even better global soil map in the
near future (Sanchez et al 2009). There are various sources of
weather/climate data at fine resolutions, such as WorldClim,
NEO and CRU (e.g., Dee et al 2011). However, one of
the key gaps in datasets for agricultural GHG inventories
are spatial data on land use change, which are adequate
for some developed countries (e.g., Homer et al 2007), but
are almost universally poor for developing countries (Smith
et al 2012). Land cover databases include GLCC-IGBP,
GLC2000, MODIS and other combined products reviewed
in Smith et al (2012), but these data do not always
provide information on land use change that is sufficient
for GHG inventories. In addition, obtaining reliable activity
data on agricultural management in developing countries
remains one of the greatest challenges (Smith et al 2012).
For example, estimating CH4 emissions from wetland rice
cultivation requires annual harvested area of rice by different
ecosystems, water management regimes, type and amount of
organic amendments, and other conditions that influence CH4
emissions (IPCC 2006).
One way forward is to obtain the required activity data
on land use and management by applying well-developed
statistical methods that combine censuses and surveys (e.g.,
Sarndal et al 1992). In particular, relatively inexpensive
remote sensing data, which is analogous to a census with
fully-processed ‘wall-to-wall’ coverage, can be combined
with more expensive field measurements or surveys of land
use and management practices to optimize resource use
(e.g., Nusser et al 1998, Gallego and Delince 2010). The
National Resources Inventory in the United States is a
good example of this approach (Nusser et al 1998). Land
use is classified using aerial photography in this survey.
In subsequent special studies of tillage, irrigation, or other
management practices, a subset of locations is visited by
field crews to collect additional data. For example, sampling
can be targeted toward rare but important environmental
conditions, such as highly erodible land. Similarly, in the
Land Use/Cover Area frame Survey (LUCAS) conducted
by Eurostat, remotely-sensed data are used to partition a
large initial sample into preliminary classes, allowing efficient
selection of subsequent sample sites for field visits (Gallego
and Delince 2010).
Alternatively, expert knowledge could be used to acquire
activity data. While expert knowledge may seem inadequate
to some, it is a type of data collection recommended by the
IPCC (2006) when other sources of data are not available. Use
of expert knowledge to obtain data for estimating emissions
from a GHG source is a better option than for a country
to have no estimate for reporting. An example is provided
in a Brazilian study evaluating soil carbon stock changes in
lower Amazon that utilized an expert knowledge survey to
compile information on crop rotations and tillage practices in
the region (Maia et al 2010). Surveys were given to experts
in the extension service, and responses were combined to
produce not only estimates for the use of various management
practices, but also uncertainty based on the variability in
responses. By combining remote sensing data, farm surveys
and/or expert knowledge, data gaps can be filled for the
required information related to management of crops and
livestock.
Capacity-building efforts could focus on assisting
national inventory compilers and associated government
agencies and ministries with data collection by providing
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Environ. Res. Lett. 8 (2013) 015030 S M Ogle et al
guidance on the design, implementation and interpretation
of data from farmer interviews, field observations, remote
sensing and other geospatial products, and expert knowledge.
For example, regional centers could assist with development
and application of remote sensing products, such as the
Regional Center for Mapping of Resources for Development
in Africa (www.rcmrd.org/). Such centers would assist remote
sensing experts in the countries with acquisition, processing,
classification, and evaluation of remote sensing products.
Assisting the compilers through this entire process will allow
them to learn by ‘doing’ and potentially provide a more
valuable experience than only generalized training on the
methods for data collection. It is important to note that
activity data are likely to be useful for more than just GHG
inventories, and so activity data collection should consider the
broader set of policy and resource management needs when
compiling this information.
3. Country-specific emission factors: challenges and
the ways forward
Emission factors are the rate of emissions associated with
the different environmental conditions (e.g., climatic and soil
variation) and practices for managing livestock and crop
production systems. The emission factors are essentially
multiplied by the activity data, discussed in section 2,
to estimate the total GHG emissions in a country. The
IPCC (2006) provides default factors for all emission
source categories (e.g., enteric methane) in a national GHG
inventory. The purpose of the default factors is to ensure
that every country can conduct an inventory and report
emissions. However, these factors are not very precise or
possibly even accurate for application in certain countries
or regions due to unique conditions that are not always
represented by the default factors. For example, default
enteric CH4 emission factors are identical for all countries
within large continental-scale regions (IPCC 2006). There
is more variation in emissions from livestock production
across a continent, or even a country, due to differences in
environmental conditions, as well as the specific livestock
breeds and management practices that are used (e.g., US-EPA
2012). In general, assigning factors that differ across the
range of variability in a country for both livestock and crop
production would improve the accuracy of the GHG inventory.
As such, the IPCC (2006) considers it good practice to
develop country-specific emission factors for key sources of
GHG emissions (i.e., the emission sources that account for
95% of total emissions in a country), and this is another
critical challenge for inventory compilers.
The least expensive way forward is for compilers
to obtain emission factors that are more appropriate for
their country through the IPCC Emission Factor Database
(EFDB) (www.ipcc-nggip.iges.or.jp/EFDB/main.php). The
EFDB provides emission factors and other parameters
with background documentation and technical references.
However, the EFDB is not a comprehensive database, and
will not provide alternative factors that are appropriate for all
conditions under which human activities are influencing GHG
emissions in a country or region of a country.
Similar to activity data, the next option in developing
country-specific emission factors is ‘mining’ of existing
emissions data. In particular, there are regional research
centers which have conducted GHG measurements and
produced results that can be used to derive emission
factors. For example, the International Rice Research Institute
(IRRI) has conducted more than 10 years of CH4 emission
measurements, using automated chamber techniques to
collect hourly data for rice growing seasons from major
rice producing countries, including China, India, Indonesia,
Thailand, and the Philippines. The data are applicable
to different rice ecosystems, water regimes, and organic
amendments, and to specific countries or regions with similar
environments as described in the experiments (Wassmann
et al 2000a, 2000c, 2000b). The IRRI studies have also
produced sampling strategies, which are analyzed through
regression equations, and can be applied to improve the
estimates of CH4 emissions when using manual sampling
techniques (Buendia et al 1998).
Several governments in Southeast Asia have used rice
methane emissions data to derive country-specific emission
factors, such as the Philippines, Indonesia and Thailand.
In the Philippines, country-specific emission factors were
developed using experimental data collected by IRRI (Corton
et al 2000, Wassmann et al 2000b). The emission factor for
continuously flooded systems with no organic amendment
was estimated at 1.46 kg CH4 ha−1 d−1 for the dry
season, and 2.95 kg CH4 ha−1 d−1 for the wet season. The
country-specific factors reduce bias associated with the IPCC
default value of 1.3 kg CH4 ha−1 d−1 (IPCC 2006), which
under-estimates emissions in the Philippines and does not
incorporate the differences between the wet and dry seasons.
If a country has identified that a category is a key
source of emissions, and there are insufficient research or
other emissions data in the literature or EFDB to support
development of country-specific emission factors, then it
will be necessary for a county to undertake a measurement
programme. In doing this, the compilers have to take into
consideration the available resources and methodologies to
perform measurements in an effective and robust manner.
For example, soil N2O emissions are a key source of
GHG emissions in many countries, largely due to the use
of mineral fertilizers in agriculture. It is estimated that the
amount of N2O present in the atmosphere has been increasing
at the rate of 0.2–0.3% per annum (Granli and Bockman
1994), and these rates are expected to further increase
as more developing countries use fertilizers to enhance
agricultural production and achieve food security. However,
N2O emissions from soils are notoriously variable, and flux
differences between observational points in time and space
may span several magnitudes (Stehfest and Bouwman 2006).
The huge variability of fluxes mirrors the complexity of the
underlying microbial processes involved in the formation and
consumption of N2O, predominantly the oxidative process of
nitrification and the reductive process of denitrification, which
in turn, depend on microbial activities and nitrogen turnover
in the soil (Butterbach-Bahl et al 2011a). Significant pulse
emissions of N2O are largely driven by abrupt changes in soil
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Environ. Res. Lett. 8 (2013) 015030 S M Ogle et al
Figure 1. Measuring/modeling strategy for producing country-specific emission factors for N2O and other GHG fluxes from
agroecosystems in developing countries.
environmental conditions due to re-wetting of soils following
a drought period (Borken and Matzner 2008), thawing of soils
(Groffman et al 2009, Wolf et al 2010), or changes in the
nutrient status of soils following organic or mineral fertilizer
applications (McSwiney and Robertson 2005). Given the large
variability of N2O fluxes, the planning and implementation
of a measurement programme to develop country-specific
emission factors is challenging.
So what is the way forward in developing country-
specific emission factors for N2O fluxes from measurements
in a region with variable conditions and management
practices influencing the rate of emissions? One way forward
is coupling modeling with a measurement network of
monitoring sites based on a priori spatial analysis and
stratification of the country according to key environmental
and management practices variables influencing emissions,
such as existing land use, nutrient and water management
regimes, climate, and soil properties. While advanced
micrometeorological techniques are available for measuring
N2O across sites (Kroon et al 2010), it is more likely
that a measurement network would use the closed chamber
techniques (Hutchinson and Mosier 1981) due to costs and
maintenance. With the closed chamber technique, fluxes are
typically calculated from the temporal changes in headspace
gas concentration with time using preferable non-linear
approaches (Butterbach-Bahl et al 2011b).
To address both temporal as well as spatial variability
of N2O fluxes, a tiered measuring approach is likely
needed consisting of (a) experiments for identifying emission
potentials and driving factors with intact soil cores taken from
various sites or in situ measurements, (b) manual chamber
measurements for different land uses and management
practices associated with the spatial stratification of the
country, (c) automated chamber measurements for a few
selected potential hotspot sites; and (d) ancillary data
collection for characterizing the biophysical environmental
conditions for assignment of the new factors to the
country (e.g., yields, biomass, soil properties) (figure 1).
N2O measurements from experiments (a) and additional
monitoring sites based on the spatial stratification (b) and
(c) are complementary and allow the inventory compiler
to estimate emission factors across a wide variety of
land uses and/or test biogeochemical models for deriving
emission rates across a larger region. It is important to
recognize that the in situ N2O measurements are only
done at a restricted number of sites, and moreover it
may be necessary for compilers to collaborate with other
countries to implement the network of measurements
given the resource needs. Research institutes, universities
5
Environ. Res. Lett. 8 (2013) 015030 S M Ogle et al
and staff from NGOs could facilitate measurement and
modeling activities. Furthermore, this approach has been
realized in recent studies for quantifying the regional
soil GHG emissions of livestock systems in steppe
environments (Yao et al 2010a, 2010b, Wolf et al
2010).
Similar to soil N2O, recognized methods for measuring
methane emissions from rice paddies include the closed
chamber, open chamber and micrometeorological methods.
The selection of the optimum method is dependent on the
objective of the measurement (IAEA 1992, IGAC 1994), but
ultimately a tiered approach with a network of sites is likely
the most feasible and robust way forward, as discussed for
soil N2O. A tiered approach could also be used in livestock
and manure management systems, with a focus on methods
that are appropriate for livestock production systems (e.g.,
Johnson and Johnson 1995), and ancillary data related to key
variables in these systems (e.g., types of livestock and breeds,
age and gender classes, feeding situation, forage quality,
manure handling and storage).
Lokupitiya and Paustian (2006) found that few countries
estimate soil organic C (SOC) stock changes in agricultural
systems, even though the potential for carbon sequestration in
agricultural lands has been considered the most cost-effective
method for reducing emissions from agriculture (Smith et al
2007). While there are global datasets with default stock
change factors for SOC (Ogle et al 2005, Smith et al 2008),
the coverage of experiments from which these factors were
derived is patchy, with the poorest coverage over developing
countries (Smith et al 2012). The uncertainty associated with
some agricultural management practices is large, and the 95%
confidence interval can, in some cases, cross the line between
gains to losses of SOC (Ogle et al 2005). It has also been
widely recognized that the efficacy of mitigation practices are
very site specific, and that application of default IPCC stock
change factors at fine spatial scales is not advisable (Smith
et al 2008, 2012), while the stock change factors are more
robust at larger scales (Ogle et al 2006).
An integrated framework combining a network of
measurement sites with modeling is arguably the most
efficient and feasible way forward to improve estimates of
SOC stock changes in developing countries (Conant et al
2011, Spencer et al 2011, Smith et al 2012). A case of a
country combining measurement with modeling to reduce
uncertainty is provided by Del Grosso et al (2011) who
compare the use of default and country-specific factors
to the same SOC inventory. In this example, the Century
process-based model was used to estimate SOC stock changes
in croplands of the United States after evaluation with SOC
measurements from about 50 experimental sites with over 800
treatments (Ogle et al 2010). Uncertainty was reduced by 43%
as a result of advancing beyond the default factors with a
method combining measurements and modeling (Del Grosso
et al 2011). In some cases, compilers may want to develop
regional cooperatives and pool resources to facilitate the
development of a measurement network that could underpin a
model analysis. As with N2O and CH4, research institutions,
universities and NGOs could facilitate these activities.
4. Advancing software systems for inventory
compilation
One of the decisions from the UNFCCC Conference
of the Parties meeting in Durban (Decision 2/CP17)
revises the national GHG inventory reporting requirements
for non-Annex 1 countries (developing countries) to a
biennial cycle (Least Developed Countries and small island
developing states may submit biennial update reports at
their discretion), and includes monitoring, reporting, and
verification components (MRV). These requirements pose
a challenge to developing countries and require effective
data management to facilitate biannual reporting. Advanced
inventory software systems are a way forward to assist
compilers with overcoming this challenge.
A variety of software systems exist than can be used
to estimate GHG emissions, and these tools operate across
a range of scales with different capabilities (Crosson et al
2011, Colomb et al 2012, Cowie et al 2012). It is important to
note that these GHG emission software systems are designed
for managing activity data and emissions factors, but not the
equally important component of establishing a sustainable
GHG inventory system. Other guidance tools have been
developed for this purpose such as the US-EPA inventory
management template workbook, ‘Developing a National
Greenhouse Gas Inventory System’ (US-EPA 2011).
For national inventories, compiling and managing large
amounts of data over time is probably the most important
capability of an advanced software system for the estimation
of GHG emissions, particularly with agriculture and land
use, which arguably require more data and inter-relationships
among those data than found in other emission reporting
sectors (e.g., energy). A relational database structure is
an efficient approach for storing large volumes of data.
Another key feature of an advanced software system is a
graphical user interface that allows compilers to extract and
enter data into the database without the need to develop
computer programming code. Such a system can guide
compilers through the process of activity data compilation,
emission factor assignment and emission calculations through
the interface, while storing, processing, and analyzing data,
in addition to producing reports. An example of a tool
developed for this purpose is the Agriculture and Land Use
National Greenhouse Gas Inventory (ALU) Software (www.
nrel.colostate.edu/projects/ALUsoftware/).
Consequently, advanced software systems have advan-
tages over the spreadsheet approaches that are commonly
used by national inventory compilers in developing countries
(e.g., UNFCCC software, http://unfccc.int/resource/cd roms/
na1/ghg inventories/index.htm). Spreadsheets certainly pro-
vide the basic functionality for completing an inventory, but
with the technology available today, more advanced systems
can be developed to manage the inventory data.
An advanced software system can promote the use
of good practice from the IPCC guidelines, ensuring
transparency, accuracy, completeness, comparability, and
time series consistency (IPCC 2006). Transparency and
comparability of results is provided through detailed reports
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Environ. Res. Lett. 8 (2013) 015030 S M Ogle et al
on calculations that show all activity data and emission
factors, and associated documentation about data sources
and emission factors. Accuracy is promoted by an integrated
QA/QC process and range checking of data during entry.
Furthermore, accuracy is improved through the use of
country-specific factors, and therefore software should
accommodate new factors and associated documentation
with multiple levels of stratification to allow variation in
assignment of emission factors for different conditions in
a country. Facilitating stratification will complement the
tiered design for developing country-specific emission factors
(figure 1) (e.g., Yao et al 2010a, 2010b, Wolf et al 2010).
Completeness of data can be reinforced by data validation
at each data entry step in the software, such as ensuring all
land area is included, assigned to a land use, and that the
management is described. Completeness can also be achieved
by allowing compilers to ‘track and check’ the completeness
of activity data and emission factor assignments in each GHG
emissions category and sub-category. Using an advanced
inventory system promotes consistent time series where all
inventory years are calculated using the same method and data
sources in all years. In addition, multi-year reports can be
compared by the software system to identify inconsistencies
within a time series of activity data or anomalies in annual
GHG emission trends.
While an inventory is an important component to meet
reporting requirements to the UNFCCC, the inventory is
also the means to analyze mitigation potentials and consider
options for NAMAs and LEDS in developing countries
(i.e., how these data will be used in development of domestic
policy and international programs promoting mitigation in
developing countries). Consequently, mitigation potentials
can be estimated using the inventory data as a baseline for
projecting emission trends associated with ‘business as usual’
practices and management alternatives. An advanced software
system could be informed by various drivers of emissions,
including population growth, mitigation technologies and
economic trends, and by integrating mitigation into the
system, the tool will be of more utility for the compiler than a
simple calculation of emissions.
5. Conclusions
National GHG inventories are an essential component of
climate change policy development and negotiations among
party countries to the UNFCCC. Developing countries will
be able to more fully participate in the UNFCCC climate
change negotiations as well as develop more effective policies
for their agricultural sector with inventories that follow good
practice as defined by the IPCC (2006). Good practice
will ultimately lead to transparent, accurate, complete,
consistent and comparable inventories. More importantly
the resulting inventories can be used with confidence in
development of NAMAs and LEDS. In turn, the proposed
strategies and actions can form the foundation for incentive
programs to reduce GHG emissions in developing countries
through international cooperation, and improve practices in
the agricultural sector from the small stakeholder to the
commercial farmer.
Acknowledgments
The lead author is grateful to the US Environmental
Protection Agency for funding to support preparation of
the manuscript and journal page charges (Agreement No.
EP-W-08-013/0014). We thank Mausami Desai for comments
on the manuscript. The contribution of Pete Smith is part of
his research on the Defra GHG Platform Projects AC0114 and
AC0116 and the EU-funded project GHG-Europe. Pete Smith
is a Royal Society-Wolfson Research Merit Award holder.
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