Advancing national greenhouse gas inventories for agriculture in developing countries: improving activity data, emission factors and software technology

Ogle, Stephen M.; Buendia, L. V.; Butterbach‐Bahl, Klaus; Breidt, F. Jay; Hartman, Melannie D.; Yagi, Kazuyuki; Nayamuth, R.; Spencer, Shannon; Wirth, Tom; Smith, Pete

doi:10.1088/1748-9326/8/1/015030

Cited by 36 publications

(38 citation statements)

References 36 publications

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“…The national GHG emissions inventory forms a good basis for government climate change and mitigation strategies [32]. Thus, the inventory has to be inclusive and needs to cover all the sources and sinks for it to be useful to government agencies.…”

Section: Improvements In Estimation Of Enteric Methanementioning

confidence: 99%

“…Lack of permanent hosting organizations promotes lack of continuity in approaches, resulting in different uses of emission factors and the inconsistent presentation of results [21,22,28,29]. According to FAO [34] and Ogle et al [32], countries should have clear institutional arrangements for the compilation of GHG inventories. Assigned institutions will be able to improve on emission estimates as they will have a mandate to employ specialists for each of the sectors.…”

Section: Improvements In Estimation Of Enteric Methanementioning

confidence: 99%

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Enteric Methane Emissions Estimate for Livestock in South Africa for 1990–2014

2017

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Methane (CH 4 ) from enteric fermentation is one of the main anthropogenic greenhouse gas (GHG) emissions in South Africa. Livestock population data from 1990 to 2014 and emission factors were utilized in estimating CH 4 emissions as per the 2006 IPCC (Intergovernmental Panel on Climate Change) guidelines. CH 4 emissions originating from country-specific emission factors were compared with those calculated using IPCC default emission factors. Trends in emissions were then determined using the Man-Kendall trend test at the 5% significance level. The results showed annual total enteric CH 4 emissions exceeding 1171.56 Gg (in 1995) with an average (1990 to 2014) of 1227.96 Gg. Non-dairy cattle are the highest emitters with an average of 873.07 Gg (71.10%) while sheep are the second highest with 227.61 Gg (18.54%). Other contributors are dairy cattle, goats, horses, pigs and donkeys with an average (percentage contribution) of 85.94 Gg (7.00%), 32.06 Gg (2.61%), 4.86 Gg (0.40%), 2.77 Gg (0.23%) and 1.65 Gg (0.13%), respectively. The trend analysis revealed positive trends for all the livestock categories, except sheep and goats which showed negative trends, consequently balancing out. The results obtained for the year 2014 were 37% higher than the enteric CH 4 emissions in 1990, 1994 and 2000 inventories and the emissions estimated entirely from IPCC default emission factors. This demonstrates the need for the development of Tier 2 emission factors for key category sectors such as cattle and sheep in South Africa. To fully adhere to the principles of GHG inventory accounting, there has to be total livestock inclusivity and major improvements in activity data collection.

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Section: Improvements In Estimation Of Enteric Methanementioning

confidence: 99%

Section: Improvements In Estimation Of Enteric Methanementioning

confidence: 99%

Enteric Methane Emissions Estimate for Livestock in South Africa for 1990–2014

2017

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“…a review by Snyder et al (2009). Some types of emissions have attracted higher attention from the scientific community due to their more complex nature (Ogle et al 2013). For example, Herrero et al (2015) published a review of the problems resulting from the livestock production.…”

Section: Introductionmentioning

confidence: 99%

High-resolution spatial distribution and associated uncertainties of greenhouse gas emissions from the agricultural sector

Charkovska

Horabik-Pyzel

Bun

et al. 2018

Mitig Adapt Strateg Glob Change

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Agricultural activity plays a significant role in the atmospheric carbon balance as a source and sink of greenhouse gases (GHGs) and has high mitigation potential. The agricultural emissions display evident geographical differences in the regional, national, and even local levels, not only due to spatially differentiated activity, but also due to very geographically different emission coefficients. Thus, spatially resolved inventories are important for obtaining better estimates of emission content and design of GHG mitigation processes to adapt to global carbon rise in the atmosphere. This study develops a geoinformation approach to a highresolution spatial inventory of GHG emissions from the agricultural sector, following the categories of the United Nations Intergovernmental Panel on Climate Change guidelines. Using the Corine Land Cover data, a digital map of emission sources is built, with elementary areal objects that are split up by administrative boundaries. Various procedures are developed for disaggregation of available emission activity data down to a level of elementary emission objects, conditional on covariate information, such as land use, observable in the elementary object scale. Among them, a statistical scaling method suitable for spatially correlated areal emission sources is applied. As an example of implementation of this approach, the spatial distribution of methane (CH 4 ) and Nitrogen Oxide (N 2 O) emissions was obtained for areal China University of Mining and Technology, Beijing, China emission sources in the agriculture sector in Poland with a spatial resolution of 100 m. We calculated the specific total emissions for different types of animal and manure systems as well as the total emissions in CO 2 -equivalent. We demonstrated that the emission sources are located highly nonuniformly and the emissions from them vary substantially, so that average data may provide insufficient approximation. In our case, over 11% smaller emission was estimated using spatial approach as compared with the national inventory report where average data were used. In addition, we quantified uncertainties associated with the developed spatial inventory and analysed the dominant components in total emission uncertainties in the agriculture sector. We used the activity data from the lowest possible (municipal) level. The depth of disaggregation of these data to the level of arable lands is minimal, and hence, the relative uncertainty of spatial inventory is smaller when comparing with traditional gridded emissions. The proposed technique allows us to discuss factors driving the geographical distribution of GHG emissions for different categories of the agricultural sector. This may be particularly useful in high-resolution modelling of GHG dispersion in the atmosphere.Mitig Adapt Strateg Glob Change https://doi.org/10.1007/s11027-017-9779-3 Electronic supplementary material

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“…National Greenhouse Gas inventories for quantifying emissions from agriculture and land use change often calculate emissions using default emission factors (EFs) coupled with country specific activity data in the form of land use and management information (IPCC, 2006;Ogle et al, 2013Ogle et al, , 2014. To reduce uncertainty, and calculate estimates on a Tier 2 or 3 level, spatially disaggregated land use and land management information, in combination with country specific EFs, are increasingly being used.…”

Section: Introductionmentioning

confidence: 99%

“…While the Intergovernmental Panel on Climate Change (IPCC) has proposed default EFs, they tend not to reflect local variations in climate and management (Skiba et al, 2012) and there is often not enough long term information to derive these EFs at regional level. Therefore, country specific EFs should be derived through experimental work, which in turn aims to reflect a degree of heterogeneity across the country of interest (Ogle et al, 2013) and also reflect a range of management practices (Bell et al, 2016). However, data from experimental studies are understandably limited, both temporally and spatially, due to the resources required to conduct such experiments, and uncertainties still remain at a site level due to the spatial variability of soil properties (particularly nitrogen (N), carbon (C) and soil wetness).…”

Section: Introductionmentioning

confidence: 99%

Modelling spatial and inter-annual variations of nitrous oxide emissions from UK cropland and grasslands using DailyDayCent

Fitton

Datta

Cloy

et al. 2017

Agriculture, Ecosystems & Environment

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A B S T R A C TAgricultural soils are the primary source of nitrous oxide emissions due to management practices including fertiliser application. While fertiliser rates are one of the main drivers of nitrous oxide emissions, emissions are also dependent on other variables such as climate and soil properties. To understand the spatial and inter-annual variations in emission rate, simulations of N 2 O emissions were made from 2000 to 2010 for UK grass and croplands. In addition, the sensitivity of these emissions to soil and climate inputs was also tested. Emissions of between 0.3 to 3.5 kg N ha −1 yr −1 and 0.7-7 kg N ha −1 yr −1 were simulated across UK croplands and grasslands, respectively. While inter-annual variations can be attributed to climate influences, the primary driver of spatial variations in emissions was soil clay content. However, when the sensitivity of nitrous oxide emissions to soil clay content alone was tested, it was not always the best predictor of emissions, when soil texture is altered outside of the normal range used as inputs to the model from different databases.

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Advancing national greenhouse gas inventories for agriculture in developing countries: improving activity data, emission factors and software technology

Cited by 36 publications

References 36 publications

Enteric Methane Emissions Estimate for Livestock in South Africa for 1990–2014

Enteric Methane Emissions Estimate for Livestock in South Africa for 1990–2014

High-resolution spatial distribution and associated uncertainties of greenhouse gas emissions from the agricultural sector

Modelling spatial and inter-annual variations of nitrous oxide emissions from UK cropland and grasslands using DailyDayCent

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