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International Journal of Agricultural
Science and Research (IJASR)
ISSN(P): 2250-0057; ISSN(E): 2321-0087
Vol. 5, Issue 6, Dec 2015, 31-44
© TJPRC Pvt. Ltd.
LOW-COST QUANTIFICATION OF GREENHOUSE GAS EMISSIONS IN
SMALLHOLDER AGRO-ECOSYSTEM: A COMPARATIVE
ANALYSIS OF METHODS
TEK B SAPKOTA, P. KAPOOR & ML JAT
International Maize and Wheat Improvement Centre (CIMMYT), New Delhi, India
ABSTRACT
Quantification of greenhouse gas (GHG) exchanges between agricultural field and the atmosphere is essential
for understanding the contribution of various production systems to the total emissions, develop mitigation options and
policies, raise awareness and encourage adoption. But, GHG quantification from smallholder agricultural landscape is
challenging primarily because of the heterogeneity of production systems. Various methods have been developed over
years to quantify GHG fluxes between agricultural ecosystem and atmosphere. In this paper, we reviewed and analysed
the common methods with regard to their scale and precision of quantification, cost effectiveness, prospects and
limitations focusing mainly on smallholder production systems. As most of the quantification methods depend on
ground data and due to data deficit for smallholder systems, field measurement must be an essential part of GHG
emission inventories under such systems. Chamber-based method is a principal approach for field level quantification
under smallholder production system mainly because of its cost effectiveness, portability and adoptability under diverse
field conditions. However, direct measurement of GHG for all mosaics of smallholder production landscape is
impractical and therefore use of models becomes imperative. Here, selection of suitable models and their rigorous
parameterization, calibration and validation under various production environments are necessary in order to obtain
meaningful emission estimation. After proper validation, linking dynamic ecosystem models to geographic information
system (GIS) helps estimating GHG emission within reasonable time and cost. Integration of different approaches such
as chamber-based measurement to generate field data, simulation modelling by using empirical as well as process-based
models coupled with use of satellite imagery may provide a robust estimate of GHGs emission than use of a single
approach.
KEYWORDS: Greenhouse Gas, Smallholder Production Systems, Agriculture, Climate Change, Modelling
Received: Oct 02, 2015; Accepted: Oct 13, 2015; Published: Oct 18, 2015; Paper Id.: IJASRDEC20155
INTRODUCTION
Agricultural sector is one of the major emitters of GHGs accounting for 14% of total anthropogenic
emission (Schaffnit-chatterjee, 2011).Developing countries currently account for about three-quarters of direct
emissions and are expected to be the most rapidly growing emission sources in the future. Expansion of agricultural
land also remains a major contributor of GHGs, with deforestation largely linked to clearing of land for cultivation or
pasture, generating 80% of emissions from developing countries (Hosonuma et al., 2012). Unnecessary tillage for
land preparation and planting, indiscriminate irrigation and fertilizer application are the main sources of GHG
emission from agricultural production systems. Methane and nitrous oxide are the main agricultural GHGs
Original Article
32 Tek B Sapkota, P. Kapoor & Ml Jat
Impact Factor (JCC): 4.7987 NAAS Rating: 3.53
accounting for 10%–12% of total global anthropogenic emissions (Smith et al., 2008)mainly through direct N
2
O emissions
from soils, CH
4
emission from enteric fermentation, biomass burning, rice production, and manure management
(Vermeulen, Campbell, & Ingram, 2012). At the same time, agriculture is part of the solution in mitigating climate change:
by reduction of GHG emission into the atmosphere as well asabsorption of atmospheric carbon into plant biomass and soil.
Therefore, agricultural production system can be either a net sources or sinks of GHGs depending on the management
practices.
Understanding the dynamics of fluxes between agricultural fields and the atmosphere is essential for knowing the
contribution of various production systems to the total GHGs emission. This helps farmers, researchers and policymakers
to understand how mitigation can be integrated into policy and practice. Quantification of GHG emissions from
agricultural production systems is also important to guiding national planning for low-emissions development, generating
and trading carbon credits, certifying sustainable agriculture practices, informing consumers' choices with regard to
reducing their carbon footprints and supporting farmers in adopting less carbon-intensive farming practices (Olander,
Wollenberg, Tubiello, & Herold, 2013). Better information on greenhouse-gas (GHG) emissions and mitigation potential
in the agricultural systems also help manage these emissions and identify solutions that are consistent with the food
security and economic development priorities of countries.
With these realizations, quantification of GHGs from agricultural production systems has been the subject of
intensive scientific investigation recently. This need has driven the development of different methods for measuring
exchanges of GHGs between agricultural landscape and atmosphere. Denmead (2008) broadly classified them into two
categories: chamber and micrometeorological methods. With rapid development of technologies, use of models and remote
sensing for GHG quantification is increasing. This review focuses mainly on the methods applicable to the smallholder
production systems with regards to their operations aspects along with their strengths and weaknesses. The aim is to
provide users with helpful information for choosing the most appropriate methods based suitable for the objective, scale
and cost.
CHAMBER METHOD
Chambers are classified as flow through or non-flow through i.e. closed chamber (Rochette & Eriksen-Hamel,
2008). In a flow-through chamber, a constant flow of outside air is maintained through the headspace of the chamber and
the difference in gas concentration between the air entering and leaving the headspace is measured. In a closed chamber,
on the other hand, there is no or a very small replacement of air in the headspace so that the gas concentration increases
continuously. The closed-chamber method described by Rolston et al. (1978) is the most common method used for
measuring gas exchange between the soil and the atmosphere. Close-chamber techniques have been used to estimate soil
respiration for more than eight decades and still remain the most commonly used approach (Rochette & Eriksen-Hamel,
2008; Rochette & McGinn, 2004). This method permits measurement of very small flux, is relatively inexpensive and can
be adapted to a wide range of field conditions and experimental objectives (Sapkota et al., 2014).With this method, flux
measurements can be taken multiple times during the year for estimating seasonal or annual flux. This method is very
useful for quantifying the impact of various treatments but their coverage is limited over space and time.
The operating principle of close chamber method is to restrict the volume of air with which gas exchange occurs
so as to magnify changes in concentration of gas in the headspace (Denmead, 2008). The increase in gas concentration
over time indicates the amount of flux from the soil. For this method, chambers are placed in specific locations on the
Low-cost Quantification of Greenhouse Gas Emissions in 33
Smallholder Agro-Ecosystem: A Comparative Analysis of Methods
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agriculture field. At certain time intervals, air samples are physically extracted from the chamber headspace employing
either manual or automated system. The concentration of GHGs in the air samples is quantified in a gas chromatograph.
Soil flux is then determined through the relationship of headspace gas concentration with time.
More than 95% of the thousands of published studies on GHG emission used chamber methodologies (Rochette,
2011). Chamber-based method with manual system has particular advantage in smallholder production system of
developing countries because they are low cost, portable, and require no power in the field. The drawbacks include its
inability to capture all spatio-temporal variability of episodic emissions such as nitrous oxide due to limited replication and
logistical (time and human labour) constraints. Further, chambers can alter the soil environment and microclimate,
potentially introducing biases and artifacts to the soil fluxes(Glenn, Amiro, Tenuta, Stewart, & Wagner-Riddle, 2010).
Nevertheless, given its cost-effectiveness, versatility and adoptability, chamber method is suitable for the smallholder
system of developing countries. Adoption of this approach to quantify N
2
O emission in irrigated rice(Kumar, Jain, Pathak,
Kumar, & Majumdar, 2000; Majumdar, Kumar, Pathak, Jain, & Kumar, 2000; Malla et al., 2005)and leguminous crops
(Ghosh, Majumdar, & Jain, 2002), to quantify methane emission from rice-wheat cropping system(Pathak et al., 2003) and
to quantify GWP of rice-wheat system(Bhatia, Pathak, Jain, Singh, & Singh, 2005) are some of the examples of use of
chamber-based method for GHG quantification in small-holder production systems.
MICROMETEOROLOGICAL METHODS
Micrometeorological approaches assume that fluxes are nearly constant with height and that concentrations
change vertically not horizontally. The flux at particular height ‘z’ depends on whether the ground is source or sink.
Various micrometeorological methods such as eddy covariance (Burba, Madsen, & Feese, 2013), flux gradient (Glenn et
al., 2010; Pattey et al., 2007), eddy accumulation (Desjardins, Buckley, & Amour, 1984) and backward Lagrangian
dispersion (Flesch, Wilson, & Yee, 1995) with various degree of complexity have been developed to determine the net
exchange of GHGs between landscape and atmosphere. These methods have advantage of providing continual
measurement and can take into account temporal and spatial variability of flux. But generally, they require large footprint
area of similar landscape and depend on many assumptions, violation of which may result into serious errors in
measurement and interpretation. Further, these methods are expensive; require sophisticated instrumentation and high
technical capacity all of which may be prohibitive for its adoption in smallholder production system of developing
countries.
MODELLING
Direct measurement of GHG emissions for all landscape types in smallholder production systems is impractical as
it would require many measurements to be made over large areas and for long periods of time. Therefore, development and
use of model to predict GHGs emission is imperative. The models not only allows the simulation of agricultural GHGs
emission at a range of scales i.e. from field through landscape to national and regional scale, but also the exploration of
potential mitigation strategies. They are particularly essential for landscape scale assessments (Conant, Ogle, Paul, &
Paustian, 2010; Eggleston, Buendia, Miwa, Ngara, & Tanabe, 2006). Considering these needs, a number of models have
been developed for assessing GHG emissions from agricultural production systems besides IPCC’s work and progress on
methodological issues. Different authors have classified modeling approaches differently. For example Babu et al. (2006)
classified GHG quantification models into empirical, semi-empirical, regression and process-based models whereas Denef
et al. (2012) have broadly classified them into calculators, protocols, guidelines and models. Based on the approach taken
34 Tek B Sapkota, P. Kapoor & Ml Jat
Impact Factor (JCC): 4.7987 NAAS Rating: 3.53
for GHG quantification, we categorize them into three groups i.e. a) guidelines, b) empirical models, tools & calculators
and c) process based models. The former two groupsare based on the emission-factor associated with activities whereas
third group takes into account interaction between different processes within the systems. Each group has its own
advantage and disadvantage with regards to time taken for the study, data requirement, cost effectiveness as well as
accuracy and reliability of the estimates.
Intergovernmental Panel on Climate Change (IPCC) Guidelines
In order to meet United Nations Framework Convention on Climate Change (UNFCC) reporting requirement for
37 industrialized countries, IPCC published guidelines for calculating national inventories in 1996(Houghton et al., 1997).
These were subsequently revised in 2000, 2003 and 2006 and allowed for quantification of national emissions based on
readily available activity data such as power usage, fossil fuel consumption, fertilizer use, animal number, land use change,
as well as associated emission factor for each activity (Crosson et al., 2011). IPCC classifies GHG accounting systems into
three i.e. Tier 1, Tier 2 and Tier 3 approaches. Tier 1 is a general approach with average emission factors provided for large
eco-regions of the world. To estimate CO
2
emission from energy consumption and all N
2
O and CH
4
emission, this method
considers multiplying activity data by its specific emission factor for each source (Colomb et al., 2012). Tier 2 is also
similar to Tier 1 but use state or region specific data, with more accurate emission factors when available. Tier 3, on the
other hand, is a very detailed approach usually including biophysical modelling of GHG processes. IPCC methodologies
provide set of generalized guidelines for estimating GHG emissions at various time also under smallholder production
system but use of too generalized emission factor may mask the considerable variability which occurs among the
smallholder farms (Crosson et al., 2011).
Empirical Models, Tools and Calculators
These are automated web-, excel-, or other software-based tools and mathematical equations developed for
estimating GHG fluxes or emission reduction from agricultural and forest activities. These models are less complicated and
require less amount of dataset as input to predict GHG emission from a product, production system and management
practices. Most of them are based on the activity data (inventory) and associated emission factors developed elsewhere but
some of them also take into account pedo-climatic condition. Various organizations and individuals have developed these
models or calculators with diverse objectives such as raising awareness, GHG inventory, product footprint calculation,
project evaluation and so on and they are suitable for defined geographical coverage. Therefore, users should choose the
appropriate calculator based on the objective of the study and major factors contributing to the total emission. Further,
direct comparison between studies done using different calculators is impossible. Detailed review of major GHG
calculators in agriculture and forestry sector can be found in Colomb et al. (2012).
EX-ACT (Ex-Ante Carbon balance Tool), Holos and CBP (Carbon Benefit Project) are useful tool to evaluate
GHG emission of various development and sustainable land use project. For example, EX-ACT has been widely used
including a large scale ex-ante assessment of two rural development projects in Brazil dominated by smallholder farmers
(Branca, Lipper, McCarthy, & Jolejole, 2013). EX-ACT allows the user to analyse any mosaic of land as the inputs and
outputs are not spatially explicit. The CBP tool allows a more spatially explicit approach as the user can divide a landscape
into numerous adjacent sub-units and enter detailed land management information for each of these before carrying out an
integrated analysis which gives spatially explicit output (Milne et al., 2013). Some calculators such as carbon calculator
NZ, CALM (Carbon Accounting for Land Manager) are specifically developed to increase climate change awareness to
Low-cost Quantification of Greenhouse Gas Emissions in 35
Smallholder Agro-Ecosystem: A Comparative Analysis of Methods
www.tjprc.org editor@tjprc.org
farmers and land managers and to test the impact of various environmental schemes from the perspective of GHG
emission. Few calculators (e.g. CFT, CoolFarmTool) are product oriented to calculate environmental footprint a production
system or product. The result coming from such calculators contains three types of uncertainties i.e. uncertainties related to
farm inventory, uncertainties related to climate induced variation and uncertainties due to emission factors. Together, these
uncertainties can be very high particularly in the mosaic landscape of small-holder production systems. Therefore, the
results out of these calculators should also include these uncertainties and interpreted accordingly.
Good part of using empirical tools or calculators in smallholder production system is they require less data and
almost all data are available at least at farm level. The accuracy level is sometime questionable but active research is on-
going and most developers are frequently updating their calculators. Given the level of information that is available, these
calculators can be promising tools for quantification GHG emission under smallholder farming condition of developing
countries. Further, almost all the calculators are available on their website or asking the developer free of cost. Many also
provide detailed guidelines on how to use them and related assumptions. Although, there is a huge prospect of using GHG
calculators in developing countries, most available calculators are developed in economically well-off countries; about
80% of the commonly used calculators are developed in USA and Australia. So, more calculators should be developed
taking into account the smallholder production environments of developing countries.
Process-Based Models
One distinction between emission factor-based calculators and process-based models is that the former are stock-
taking approaches whereas the latter are based on flows between different compartments of the system. This allows
process-based models to simulate emission pathways and make predictions about the future for a variety of cases whereas
other instruments often treat the time between two stock-taking exercises as black boxes and can only make predictions
that are based on past emission trajectories. Process-based models are dynamics and take into account many management
practices such as tillage, fertilizer, irrigation, crop protection etc. as well as their interaction with soil, climate and other
management practices. Inclusion of these processes in modelling can enhance extrapolation reliability making it possible to
model at ecosystem level.
Over the time, a number of process-based models have been developed to quantify GHG emission from
agricultural production systems. For example, DAISY (Hansen, Jensen, Nielsen, & Svendsen, 1990) and CENTURY
(Parton et al., 1993) describe the soil carbon dynamics in detail. The DayCent model is the daily time-step version of the
CENTURY which reliably simulates fluxes of C and N between atmosphere, vegetation and soil under various native and
managed systems (Del Grosso et al., 2002). The denitrification-decomposition (DNDC) model, originally developed to
simulate biogeochemical cycling of nitrogen, also simulates C and N dynamics from agricultural landscapes (Giltrap, Li, &
Saggar, 2010; Li, Frolking, & Frolking, 1992; Li, 2000). Cao, Gregson, & Marshall (1998) developed a process-based CH
4
emission model to predict CH
4
emission from rice fields. Matthews, Wassmann, & Arah (2000) simulated CH
4
emission
from rice fields in China, India, Indonesia, Philippines and Thailand by using process-based ‘Methane Emission from Rice
Ecosystem’ (MERES) model.Aggarwal, Kalra, Chander, & Pathak (2006) developed InfoCrop to simulate the effect of
weather, soils, agronomic management practices such as planting, nitrogen, residue and irrigation and major pests on crop
yield as well as C, N and water dynamics. Some researchers in South Asia(Pathak, Saharawat, Gathala, & Ladha, 2011;
Saharawat et al., 2011) are using the InfoRCT (Information on Use of Resource-Conserving Technologies) simulation
model to estimate GHGs emission fromrice-wheat production system. This model integrates biophysical, agronomic, and
36 Tek B Sapkota, P. Kapoor & Ml Jat
Impact Factor (JCC): 4.7987 NAAS Rating: 3.53
socioeconomic factors to estimate GHG emission by establishing input-output relationship related to water, fertilizer,
labour and biocide uses.
Process-based models have the advantage of describing the underlying dynamics of a system. For example,
process-based models use complex functions to describe the temporal dynamics of SOC through different pools and
include sub-models of plant productivity, water movement and the turnover of plant nutrients. A major benefit of using
processed-based models for scaling purposes is their ability to estimate several measurable variables at the same time
(Turner, Ollinger, & Kimball, 2004) . Each model has its own strategy and philosophy. The extrapolation reliability and
simulation power of the model depend on the mechanistic understanding and sub-modeling of the individual processes and
driving variables involved. These models, when parameterized correctly, have been shown to decrease uncertainties in
estimates, compared to estimations made using the IPCC equations and empirical models(Del Grosso, Ogle, & Parton,
2011)
The use of process-based ecosystem models linked to GIS for landscape scale GHG assessment involves a certain
level of expertise in ecosystem modeling and GIS. This can prohibit the use by farmers’ groups or extension workers in
developing countries, making many of the calculators, based on simple computational methods, more accessible. Although
most models can estimate GHG emission at one site with reasonable accuracy, their potential for simulating emissions at
other sites with different management practices remains unknown. This requires large number of validation test across
different mosaics of the landscape before such models can be used for landscape level quantification.
LIFE CYCLE OR WHOLE FARM APPROACH
Environmental analysis of product using life cycle assessment (LCA) takes into account the entire production
system. The most specific characteristics of the LCA methodology is the “life cycle thinking” i.e. to consider the entire
network of main and sub-processes relevant to the production(Brentrup, Küsters, Kuhlmann, & Lammel, 2001). Because of
the integrated nature of the agricultural production system, whole farm and life-cycle approach can be used focussing on
GHG quantification until farm gate of the production system. Here, emissions from the different components are summed
up to total GHG budget. The main phases of LCA are goal and scope definition, life cycle inventory analysis, life cycle
impact assessment and life cycle interpretation. Here, carbon footprint of the production system or farm can be calculated
on per unit of area as well as per unit of final product. One can define the system boundary of the analysis at the beginning
which generally includesproduction of agricultural inputs, field production processes and soil processes leading to change
in soil C and N pool (Fig. 1;Brentrup et al., 2001). In this approach, production inputs such as fertilizer, compost, manure,
machinery and other chemicals as well as management information such as tillage, cover crops, and residue are obtained
from within the pre-defined system boundary. The emission factor associated with these inputs and management practices
can be obtained from published literature. Similarly direct and indirect emission of GHGs can be estimated following
published literature( e.g. Eggleston et al., 2006). As many agricultural production systems produce more than one product,
it is necessary to attribute environmental impact to each product from the system using appropriate allocation approach.
Based on the availability of activity data this approach can be applied in smallholder production system with varied degree
of detail.
Low-cost Quantification of Greenhouse Gas Emissions in 37
Smallholder Agro-Ecosystem: A Comparative Analysis of Methods
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Figure 1: Basic Elements Estimating GHG Emission by Life Cycle or Whole Farm Approach in a Crop Prodution
System
REMOTE SENSING
Remote sensing (RS) has been used for the past several decades to monitor land cover and land cover change
throughout the tropics (Skole & Tucker, 1993). There are a variety of sensors used in making earth observations that are
either active or passive sensors. Active sensors include LIDAR (light detection and ranging) and RADAR (radio detection
and ranging) that emit energy and measure attributes of the returned energy. Passive sensors detect reflected radiation from
a landscape or radiation emitted by landscape features. The primary uses for remote sensing in quantifying landscape GHG
emissions/removals in the agriculture, forestry and other land use (AFOLU) sector are to measure the extent of land cover
and its changes, and stratification of the landscape prior to conducting ground inventories (Hairiah et al., 2011). The land
cover changes due to human actions or consequences of those actions influence GHG emission rates(Eggleston et al.,
2006). The availability of fine resolution satellite data allows for determination of heterogeneous landscape in smallholder
production system. Crown attributes measured by satellites can then be related directly to above ground biomass through
specialized allometric equations.
Remote sensing techniques are increasingly being used to estimate landscape carbon density and carbon stocks—a
type of IPCC emissions factor that is also required for calculations of landscape GHG emissions (Goetz et al., 2009). Here,
soil reflectance values from satellite imagery can be correlated with laboratory measured reflectance values from near
infra-red spectroscopy of SOC to map these SOC stocks across large agricultural landscapes (Aynekulu, Sherpherd, &
Winowiecki, 2011). A field-based carbon inventory of heterogeneous landscape in smallholder production systems
requires a large financial expenses and this may become cost prohibitive in many developing countries. In such contexts,
use of remote sensing could be a low-cost option to quantify carbon stock and its change over time at landscape level.
These inventory data can be uploaded into an online GIS that calculates C stocks and emissions associated with current
land cover and potential land cover changes.
Historically, high cost of satellite remote sensing data has been a barrier to adoption for researchers in both
developed and developing countries. But, with the availability of multiple data sources (e.g. MODIS, NASA) which
provide free or low cost satellite data, use of remote sensing for carbon stock studies may be particularly important in the
smallholder production systems of the developing countries. However, technical capacity to store large datasets and
process them still remains as a barrier for researchers and government agencies working in smallholder production
38 Tek B Sapkota, P. Kapoor & Ml Jat
Impact Factor (JCC): 4.7987 NAAS Rating: 3.53
systems.
INTEGRATED APPROACH
Quantification of GHGs from mosaic of smallholder production landscape is challenging because of variable
conditions and management practices influencing the rate of emission. Selection of the suitable method depends on the
type of production system, availability of database, desired precision of estimate and resources available. Unavailability of
field-scale data from smallholder production conditions necessitates the use of plot-based measurement techniques (such as
chamber) not only to develop emission factor for inventory preparation but also to calibrate and validate suitable models
for landscape level quantification. However, field-based measurement of GHGs from heterogeneous landscape in
smallholder production systems requires a large financial expenses and this may become cost prohibitive in many
developing countries. Therefore, integration of different approaches may provide better, reliable and cost-effective
estimates of GHGs emission than by adopting a single approach. An integrated framework coupling modelling with a
measurement in key monitoring sitesis a way forward in smallholder quantification of GHG in developing countries(Ogle
et al., 2013; Smith et al., 2012). The key measurement sites or ‘hotspots’ of such measurement should be determined based
on a priori spatial analysis and stratification of landscape according to key environmental and management practices
influencing emissions. Identification of hotspots based on what matters for emission, at what scale and boundaries help
targeting the most important source of emission rather than measuring everywhere thereby making it cost-effective yet
providing meaningful data. The results coming out of the measurement are fed into the model to improve the assumptions
and emission factors of the model while the output of model can also help improve the process studies. 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., 2012). The use of process-based ecosystem models linked
to GIS may be the future of landscape scale GHG quantification. With the availability of multiple sources of satellite data
at low or no cost, integration of remote sensing with modelling efforts could also be low cost quantification approach under
smallholder production condition.
Critical Analysis and Comparison GHG Quantification Methods with Regards to Cost, Scale and Accuracy
A multitude of approaches are available for quantification of GHG from agricultural production systems with
potential to use under smallholder conditions. A choice of method depends on the objective and desired level of precision,
scale of estimation and available resources. Advantages and disadvantages of common method under smallholder crop
production systems along with their cost effectiveness and scale of estimation is summarized in table 1. Chamber-based
method permits measurement of very small flux, is relatively inexpensive and adapted to wide range of production
environment. However, they cover small soil surface area and many chambers are required for a representative emissions
estimate. Further, it is not possible to have continuous measurement with chamber based method possibly missing some
peaks of episodic emission (e.g. N
2
O) unless automatic chambers are used. IPCC developed guidelines for calculating
national GHG inventories in a consistent and standard framework. Although appropriate for national level accounting
purposes, Tier 1 and Tier 2 methodologies lack the farm level resolution (Crosson et al., 2011) and use of too generalized
emission factor may mask the considerable variability, a typical characteristics of smallholder production systems.
Micrometeorological methods offer the possibility of continuous measurement and achieving spatial integration of fluxes,
but they are generally expensive and require large footprint area of homogeneous landscape. Therefore, use of
micrometeorological approaches has limited scope under smallholder production systems from practical, technological and
Low-cost Quantification of Greenhouse Gas Emissions in 39
Smallholder Agro-Ecosystem: A Comparative Analysis of Methods
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financial perspective. Life-cycle or whole farm approach of GHG quantification provide the overall footprint of a
particular product, production system or whole farm which can be useful not only for understanding the wider
consequences but also for raising awareness, demonstration and encouraging adoption. Considering the integrated nature of
smallholder production systems, this can be useful tool to take into account various processes within the system boundary.
However, the precision of the output from this approach depends on the degree of details of the activity data.
Since direct measurement of GHGs for inventory purpose is impractical as it would require many measurements
to be made over large areas and for long, use of simulation models become essential component of smallholder
quantification. Empirical models and calculators are relatively simple to build and develop and may be considered as
decision support tools for farmers and policy makers at field and farm level. Various calculators and tools have been
developed with different objectives and assumptions and many of them may be location specific. Therefore, users should
choose the appropriate calculators based on objectives and major factors contributing to the emission. Use of empirical
model can be a cost-effective approach to estimate GHG emission from smallholder production system where minimum
datasets are available to run the model. Process-based models have the advantage of describing the underlying dynamics of
a system. They take into account many management practices and their interaction with soil and climate. Inclusion of these
processes in modelling can enhance extrapolation reliability making it possible to simulate at ecosystem level. However,
they are very complex and require huge amount of data input. Further, the extrapolation reliability and simulation power of
these models depend on the mechanistic understanding and sub-modelling of the individual processes and driving variables
involved. Therefore, they are more oriented to research and refining emission factors. Remote sensing techniques are
increasingly being used to estimate landscape carbon density and carbon stocks. Sampling based carbon inventory may be
cost prohibitive in many developing countries. In such context and with the availability of low-cost or free satellite data,
use of remote sensing could be a cost effective option to quantify carbon stocks under smallholder production systems.
However, the resolution of available satellite imagery (pixel size) may not be sufficient to capture possible variabilities in
smallholder production systems.
Table 1: Comparison of Different Methods of GHG Quantification for Smallholder Production Systems
Approaches Cost
effectivenes
s
Scale of
estimation Precision Limitations References
Chamber
Method
Cost
effective,
Labour
intensive
Plot level,
sometime
field level
Possible to
measure very
small flux.
Difficult to take into
account temporal and
spatial variability.
Disturbs the system
being measured.
(Denmead
2008; Glenn et
al. 2010;
Rochette 2011)
Micrometeor
ological
approaches
Very
expensive
Field to
landscape
level
Gives precise
estimation
and also
accounts for
temporal and
spatial
variability.
Requires high
technical skills and
uniform landscape.
Not suitable for
smallholder condition.
(Pattey et al.
2007; Denmead
2008; Burba et
al. 2013)
Life cycle or
whole plot
approach
Moderately
cost
effective
Whole
farm or
production
system
Precision
depends on
the type of
sub-modules
for different
sub-systems
Time consuming.
Requires large amount
of data from different
sub-systems of farm
or production system.
(Brentrup et al.
2001; Knudsen
et al. 2014)
40 Tek B Sapkota, P. Kapoor & Ml Jat
Impact Factor (JCC): 4.7987 NAAS Rating: 3.53
Table 1 – Cond.,
Modelling
Cost
effective
Plot, field
and
landscape
level
Moderately
precise if
adequately
parameterized
and validated
across
different
production
environments.
Its accuracy
depends on
model and
input data.
For example,
it can be low
while using
Tier 1
Approach
Requires technical
expertise. Process-
based models need
detailed input data.
(Conant et al.
2010; Del
Grosso et al.
2011; Colomb
et al. 2012)
Remote
Sensing
Moderately
cost
effective.
Landscape
level
Its accuracy
is variable
depending on
land cover.
Chances of
errors if high
resolution
images are
not available
Requires expertise for
data processing.
(Goetz et al.
2009;
Aynekulu et al.
2011; Hairiah
et al. 2011)
CONCLUSIONS AND WAY FORWARD
Here, we presented various approaches for quantification of GHG from smallholder production systems along
with their comparative analysis of cost effectiveness, scale of estimation and precision. Quantification of GHGs from
mosaic of smallholder production landscape is challenging because of variable pedo-climatic conditions and management
practices influencing the rate of emission. Use of generalized emission factor for GHG estimation for such production
system will mask the variability occurring amongst the farms. Chamber methods are cheap, simple and easy to operate but
they fail to take into account spatial and temporal variability of emission and also pose disturbance on the system being
measured. Micrometeorological methods offer the possibility of undisturbed and continuous measurement but they are
expensive, technologically complex and require large footprint area. Use of models allows simulation of agricultural GHG
emission under wide range of conditions and makes it possible to scale up estimation to landscape, national, regional and
global level. However, choice of appropriate model and its parameterization as well as validation under different
production systems is necessary for the model to adequately simulate GHGs under variable production environment of
smallholder systems. With the advancement of technology, linking dynamic ecosystem models to GIS and development of
user friendly tools can make quantification of environmental footprint of product and production system cost-effective and
reliable. At the moment, two main barriers to extending such tools to smallholder areas in developing countries are: a) a
lack of default data with relevance to the land management systems in smallholder areas and b) lack of accessible systems
which are comprehensive enough to allow smallholders to input their own data. Therefore, considering the dependence of
quantification approaches on data and the current data deficit for smallholder systems, it is clear that in-situ measurement
must be the core part of initial and future strategies to improve GHG inventories and develop mitigation measures for
Low-cost Quantification of Greenhouse Gas Emissions in 41
Smallholder Agro-Ecosystem: A Comparative Analysis of Methods
www.tjprc.org editor@tjprc.org
smallholder agriculture. Many a times, integration of different approaches such as chamber-based measurement, modelling
and use of satellite imagery can provide better and reliable estimates of GHGs emission from smallholder production
systems than by adopting a single approach. Quantification of GHGs and its mitigation from certain production system
should, however, be assessed taking into account the household benefits such as resilience led-productivity enhancement
and input use efficiency.
ACKNOWLEDGEMENTS
This review was carried out as part of pro-poor mitigation program of CGIAR’s research program (CRP) on
Climate Change Agriculture and Food Security (CCAFS) in CIMMYT.
REFERENCES
1. Aggarwal, P. K., Kalra, N., Chander, S., & Pathak, H. (2006). InfoCrop: A dynamic simulation model for the assessment of
crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description.
Agricultural Systems, 89(1), 1–25
2. Aynekulu, E. V., Sherpherd, K., & Winowiecki, L. (2011). A Protocol for Measurement and Monitoring Soil Carbon Stocks in
Agricultural Landscapes (Version 1.). World Agroforestry Centre, Nairobi, Kenya.
3. Babu, Y. J., Li, C., Frolking, S., Nayak, D. R., & Adhya, T. K. (2006). Field Validation of DNDC Model for Methane and
Nitrous Oxide Emissions from Rice-based Production Systems of India. Nutrient Cycling in Agroecosystems, 74(2), 157–174
4. Bhatia, A., Pathak, H., Jain, N., Singh, P. K., & Singh, A. K. (2005). Global warming potential of manure amended soils under
rice–wheat system in the Indo-Gangetic plains. Atmospheric Environment, 39(37), 6976–6984.
5. Branca, G., Lipper, L., McCarthy, N., & Jolejole, M. C. (2013). Food security, climate change, and sustainable land
management. A review. Agronomy for Sustainable Development, 33(4), 635–650
6. Brentrup, F., Küsters, J., Kuhlmann, H., & Lammel, J. (2001). Application of the Life Cycle Assessment methodology to
agricultural production: an example of sugar beet production with different forms of nitrogen fertilisers. European Journal of
Agronomy, 14(3), 221–233.
7. Burba, G., Madsen, R., & Feese, K. (2013). Eddy Covariance Method for CO2 Emission Measurements in CCUS
Applications: Principles, Instrumentation and Software. Energy Procedia, 40, 329–336
8. Cao, M., Gregson, K., & Marshall, S. (1998). Global methane emission from wetlands and its sensitivity to climate change.
Atmospheric Environment, 32(19), 3293–3299.
9. Colomb, V., Bernoux, M., Bockel, L., Chotte, J. L., Martin, S., Martin-Phipps, C., … Touchemoulin, O. (2012). Review of GHG
Calculators in Agriculture and Forestry Sectors: A Guideline for Appropriate Choice and Use of Landscape Based Tools.
FAO, Eco&Sols and ADEME ClimAgri.
10. Conant, R. T., Ogle, S. M., Paul, E. A., & Paustian, K. (2010). Measuring and monitoring soil organic carbon stocks in
agricultural lands for climate mitigation. Frontiers in Ecology and the Environment, 9(3), 169–173
11. Crosson, P., Shalloo, L., O’Brien, D., Lanigan, G. J., Foley, P. a., Boland, T. M., & Kenny, D. a. (2011). A review of whole
farm systems models of greenhouse gas emissions from beef and dairy cattle production systems. Animal Feed Science and
Technology, 166-167, 29–45
12. Del Grosso, S., Ogle, S. M., & Parton, W. J. (2011). Soil organic matter cycling and greenhouse gas accounting
methodologies. In L. Guo, A. Gunasekara, & L. McConnel (Eds.), Understanding Greenhouse Gas Emissions from
42 Tek B Sapkota, P. Kapoor & Ml Jat
Impact Factor (JCC): 4.7987 NAAS Rating: 3.53
Agricultural Management e (pp. 3–13). Americal Chemical Society, Washington DC
13. Del Grosso, S., Ojima, D., Parton, W., Mosier, A., Peterson, G., & Schimel, D. (2002). Simulated effects of dryland cropping
intensification on soil organic matter and greenhouse gas exchanges using the DAYCENT ecosystem model. Environmental
Pollution, 116 Suppl , S75–83. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11833921
14. Denef, K., Paustian, K., Archibeque, S., Biggar, S., & Pape, D. (2012). Report of greenhouse gas accounting tools for
agriculture and forestry sectors. Interim Report to USDA under Contract No. GS23F8182H
15. Denmead, O. T. (2008). Approaches to measuring fluxes of methane and nitrous oxide between landscapes and the
atmosphere. Plant and Soil, 309(1-2), 5–24
16. Desjardins, R. L., Buckley, D. J., & Amour, G. S. (1984). Eddy flux measurements of CO2 above corn using a microcomputer
system. Agricultural and Forest Meteorology, 32(3), 257–265
17. Eggleston, S., Buendia, L., Miwa, K., Ngara, T., & Tanabe, K. (2006). IPCC guidelines for national greenhouse gas
inventories. Institute for Global Environmental Strategies, Hayama, Japan
18. Flesch, T. K., Wilson, J. D., & Yee, E. (1995). Backward-time Lagrangian stochastic dispersion models and their application
to estimate gaseous emissions. Journal of Applied Meteorology, 34(6), 1320–1332.
19. Ghosh, S., Majumdar, D., & Jain, M. (2002). Nitrous oxide emissions from kharif and rabi legumes grown on an alluvial soil.
Biology and Fertility of Soils, 35(6), 473–478
20. Giltrap, D. L., Li, C., & Saggar, S. (2010). DNDC: A process-based model of greenhouse gas fluxes from agricultural soils.
Agriculture, Ecosystems & Environment, 136(3-4), 292–300
21. Glenn, A. J., Amiro, B. D., Tenuta, M., Stewart, S. E., & Wagner-Riddle, C. (2010). Carbon dioxide exchange in a northern
Prairie cropping system over three years. Agricultural and Forest Meteorology, 150:7-8
22. Goetz, S. J., Baccini, A., Laporte, N. T., Johns, T., Walker, W., Kellndorfer, J., … Sun, M. (2009). Mapping and monitoring
carbon stocks with satellite observations: a comparison of methods. Carbon Balance and Management, 4, 2-6
23. Hairiah, K., Dewi, S., Agus, F., Valarde, S., Ekadinata, A., Rahayu, S., & Noordwijk, M. (2011). Measuring Carbon Stocks
Across land use systems: A Manual (p. 154). World Agroforestry Centre (ICRAF), SEA Regional Office, Bogor, Indonesia
24. Hansen, S., Jensen, H. E., Nielsen, N. E., & Svendsen, H. (1990). DAISY: Soil plant atmosphere system model. NPO Report
No. A10. The National Agency for Environmental Protection, Copenhagen, p: 272
25. Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., … Romijn, E. (2012). An assessment of
deforestation and forest degradation drivers in developing countries. Environmental Research Letters, 7(4), 044009
26. Houghton, J. T., Mieira Filho, L. G., Lim, B., Treanton, K., Mamaty, I., Bonduki, Y., … Callender, B. A. (Eds.). (1997).
Revised 1996 IPCC guidelines for national greenhouse gas inventories. UK Meteorological Office, Bracknell, UK
27. Kissinger, G., Herold, M., Sy, V. De, Angelsen, A., Bietta, F., Bodganski, A., … Wolf, R. (n.d.). Drivers of Deforestation and
Forest Degradation.
28. Kumar, U., Jain, M. C., Pathak, H., Kumar, S., & Majumdar, D. (2000). Nitrous oxide emission from different fertilizers and
its mitigation by nitrification inhibitors in irrigated rice. Biology and Fertility of Soils, 32(6), 474–478
29. Li, C. (2000). Modeling trace gas emissions from agricultural ecosystems. Nutrient Cycling in Agroecosystems, (2), 259–276
30. Li, C., Frolking, S., & Frolking, T. A. (1992). A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model
structure and sensitivity. Journal of Geophysical Research: Atmospheres (1984–2012), 97(D9), 9759–9776
Low-cost Quantification of Greenhouse Gas Emissions in 43
Smallholder Agro-Ecosystem: A Comparative Analysis of Methods
www.tjprc.org editor@tjprc.org
31. Majumdar, D., Kumar, S., Pathak, H., Jain, M. C., & Kumar, U. (2000). Reducing nitrous oxide emission from an irrigated
rice field of North India with nitrification inhibitors. Agriculture, Ecosystems & Environment, 81(3), 163–169.
32. Malla, G., Bhatia, A., Pathak, H., Prasad, S., Jain, N., & Singh, J. (2005). Mitigating nitrous oxide and methane emissions
from soil in rice–wheat system of the Indo-Gangetic plain with nitrification and urease inhibitors. Chemosphere, 58(2), 141–
147
33. Matthews, R. B., Wassmann, R., & Arah, J. (2000). Using a crop/soil simulation model and GIS techniques to assess methane
emissions from rice fields in Asia. I. Model development. Nutrient Cycling in Agroecosystems, 58(1-3), 141–159
34. Milne, E., Neufeldt, H., Rosenstock, T., Smalligan, M., Cerri, C. E., Malin, D., … Paustian, K. (2013). Methods for the
quantification of GHG emissions at the landscape level for developing countries in smallholder contexts. Environmental
Research Letters, 8(1), 015019
35. Ogle, S. M., Buendia, L., Butterbach-Bahl, K., Breidt, F. J., Hartman, M., Yagi, K., … Smith, P. (2013). Advancing national
greenhouse gas inventories for agriculture in developing countries: improving activity data, emission factors and software
technology. Environmental Research Letters, 8(1), 015030
36. Olander, L., Wollenberg, E., Tubiello, F., & Herold, M. (2013). Advancing agricultural greenhouse gas quantification.
Environ. Res. Lett., 8(1), 11002
37. Parton, W. J., Scurlock, J. M. O., Ojima, D. S., Scholes, R. J., Kirchner, T., Seastedt, T., & Garcia, E. (1993). Observation and
modeling of biomass and soil organic matter dynamics for the gransland biome worldwide, 7(4), 785–809
38. Pathak, H., Prasad, S., Bhatia, A., Singh, S., Kumar, S., Singh, J., & Jain, M. C. (2003). Methane emission from rice–wheat
cropping system in the Indo-Gangetic plain in relation to irrigation, farmyard manure and dicyandiamide application.
Agriculture, Ecosystems & Environment, 97(1), 309–316
39. Pathak, H., Saharawat, Y. S., Gathala, M., & Ladha, J. K. (2011). Modeling and Analysis Impact of resource-conserving
technologies on productivity and greenhouse gas emissions in the rice-wheat system, 17, 1–17
40. Pattey, E., Edwards, G. C., Desjardins, R. L., Pennock, D. J., Smith, W., Grant, B., & MacPherson, J. I. (2007). Tools for
quantifying N2O emissions from agroecosystems. Agricultural and Forest Meteorology, 142(2-4), 103–119
41. Rochette, P. (2011). Towards a standard non-steady state chamber methodology for measuring soil N2O emissions. Animal
Feed Science and Technology, 167, 141–146
42. Rochette, P., & Eriksen-Hamel, N. S. (2008). Chamber measurements of soil nitrous oxide flux: are absolute values reliable?
Soil Science Society of America Journal, 72(2), 331–342
43. Rochette, P., & McGinn, S. M. (2004). Methods for measuring soil-surface gas fluxes. In J. Alvarez-Benedi & R. Munoz-
Carpena (Eds.), Soil–Water–Solute Processes: An Integrated Approach (p. 816). CRC Press, Boca Raton, FL
44. Rolston, D. E., Hoffman, D. L., & Toy, D. W. (1978). Field measurement of denitrification: I. Flux of N2 and N2O. Soil
Science Society of America Journal, 42(6), 863–869
45. Saharawat, Y. S., Ladha, J. K., Pathak, H., Gathala, M. K., Chaudhary, N., & Jat, M. L. L. (2011). Simulation of resource-
conserving technologies on productivity , income and greenhouse gas GHG emission in rice-wheat system. Journal of Soil
Science and Environmental Management, 3(12), 9–22
46. Sapkota, T. B., Rai, M., Singh, L. K., Gathala, M. K., Jat, M. L., Sutaliya, J. M., … Sharma, D. K. (2014). Greenhouse gas
measurement from smallholder production systems: guidelines for static chamber method. International Maize and Wheat
Improvement Center (CIMMYT), New Delhi, India.
44 Tek B Sapkota, P. Kapoor & Ml Jat
Impact Factor (JCC): 4.7987 NAAS Rating: 3.53
47. Schaffnit-chatterjee, C. (2011). Mitigating climate change through agriculture. (S. Schneider, Ed.). Deutsche Bank, Frankfurt
am Main, Germany
48. Skole, D., & Tucker, C. (1993). Tropical deforestation and habitat fragmentation in the Amazon: satellite data from 1978 to
1988. Science (New York, N.Y.), 260(5116), 1905–10
49. Smith, P., Davies, C. a., Ogle, S., Zanchi, G., Bellarby, J., Bird, N., … Braimoh, A. K. (2012). Towards an integrated global
framework to assess the impacts of land use and management change on soil carbon: current capability and future vision.
Global Change Biology, 18(7), 2089–2101
50. Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H., Kumar, P., … Smith, J. (2008). Greenhouse gas mitigation in
agriculture. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 363(1492), 789–813
51. Turner, D. P., Ollinger, S. V., & Kimball, J. S. (2004). Integrating Remote Sensing and Ecosystem Process Models for
Landscape- to Regional-Scale Analysis of the Carbon Cycle. BioScience, 54(6), 573
52. Vermeulen, S. J., Campbell, B. M., & Ingram, J. S. I. (2012). Climate Change and Food Systems. Annual Review of
Environment and Resources, 37(1), 195–222. doi:10.1146/annurev-environ-020411-130608
