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«Global Climate Change: Carbon Reporting Initiative The AFOLU Carbon Calculator AFOLU CARBON CALCULATOR THE GRAZING MANAGEMENT TOOL: UNDERLYING DATA ...»

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United States Agency for International Development

Cooperative Agreement No. EEM-A-00-06-00024-00

Global Climate Change:

Carbon Reporting Initiative

The AFOLU Carbon Calculator




Winrock International

December, 2014

This publication was produced for review by the United States Agency for International Development.

Prepared by Winrock International under the Cooperative Agreement No. EEM-A-00-06-00024-00.






Cite report as:


Winrock International. 2014. AFOLU Carbon The author’s views expressed in this publication do Calculator. The Grazing Tool: Underlying Data and not necessarily reflect the views of the United Methods. Prepared by Winrock International under States Agency for International Development or the Cooperative Agreement No. EEM-A-00-06the United States Government. 00024-00.



List of Tables

1. Scope

2. Applicability

3. Approach to the Grazing Tool

4. Data Sources

4.1. Soil carbon management

4.2. Livestock managment

4.3. Rewetting organic soils

5. Uncertainty of Estimates

5.1 Combining uncertainties for multiplication

5.2 Combining uncertainties for addition and subtraction

6. Calculation Methods

6.1 Soil carbon management

6.2 Livestock management

6.3 Rewetting organic Soils

6.4 Hypothetical example

7. Overriding Default Data

8. References


Table 1: Default reference soil organic carbon stocks (SOCREF) for mineral soils (t C ha-1 in 0-30 cm depth) (Adapted from 2006 IPCC Guidelines for National Greenhouse Gas Inventories)

Table 2: Relative stock change factors (fLU, fMG, and fI) for grassland management (net effect over a period of 20 years) Adapted from 2006 IPCC Guidelines for National Greenhouse Gas Inventories)...... 5 Table 3: Emission factors for enteric fermentation from non-cattle livestock (kg CH4 head-1 yr-1)............ 6 Table 4: Emission factors for enteric fermentation from cattle (kg CH4 head-1 yr-1)

Table 5: Default carbon accumulation rate following rewetting of drained organic soils

Table 6: Key parameters used to estimate the carbon benefits of grazing activities and an assessment of their uncertainties


1. SCOPE This document describes the underlying data sources and calculation methods employed in the Grazing Management (GM) tool of the AFOLU Carbon Calculator (http://afolucarbon.org/). The GM tool is designed for project activities that aim at improving the management of grazing lands and grazing practices to reduce GHG emissions.


The activities applicable under the GM tool through which GHG emissions could be reduced are:

 improved grassland management;

 livestock management; and  rewetting organic soils.


To provide an estimate of the GHG emission related to grazing management, this study employed methodologies from the IPCC (2006) Guidelines for the Agriculture, Forestry, and Other Land Uses (AFOLU)1, by using country-specific activity data and default emission factors provided in these IPCC Guidelines. The GHG accounted for are: soil carbon from fertilizer usage, rewetting of organic soils, and methane from livestock enteric fermentation. All GHG emissions and removals are expressed in tons of CO2e.

4. DATA SOURCES The greenhouse gas benefit of management activities represents the sum of benefits from soil carbon sequestration, from reduced livestock enteric fermentation emissions, and from carbon accumulation in rewet organic soils. The sections below describe how the underlying data for each of the parameter used in the calculations were derived.

4.1. SOIL CARBON MANAGEMENT Soil carbon stocks before conversion to cropland were derived from the default SOCREF numbers given by the IPCC (2006), Table 2.3. These stocks were then projected on to the administrative units as follows: Major soil types from the Harmonized World Soil Database (HWSD)2 and IPCC (2006) climate zones were re-grouped to satisfy the soil and climate regime category in Table 2.3. These datasets were combined with the grassland and cropland category from the MODIS 2009 and cover dataset and boundaries for the first level administrative units to link the climate region and soil class per Available at: http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol1.html Available at: http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/


administrative unit with the reference soil organic carbon SOCREF value (Table 1). Due to the different spatial resolution between the datasets, some administrative units were not covered by all of the three raster datasets (climate, soil, grassland and cropland); as a result some manual edits were performed adopting the value of neighboring administrative units. Users can override the default value with a site specific carbon stock value.

Table 1: Default reference soil organic carbon stocks (SOCREF) for mineral soils (t C ha-1 in 0-30 cm depth) (Adapted from 2006 IPCC Guidelines for National Greenhouse Gas Inventories)

–  –  –

(a) Soils with high activity clay (HAC) minerals are lightly to moderately weathered soils, which are dominated by 2:1 silicate clay minerals (in the World Reference Base for Soil Resources (WRB) classification these include Leptosols, Vertisols, Kastanozems, Chernozems, Phaeozems, Luvisols, Alisols, Albeluvisols, Solonetz, Calcisols, Gypsisols, Umbrisols, Cambisols, Regosols; in USDA classification includes Mollisols, Vertisols, high-base status Alfisols, Aridisols, Inceptisols);

(b) Soils with low activity clay (LAC) minerals are highly weathered soils, dominated by 1:1 clay minerals and amorphous iron and aluminium oxides (in WRB classification includes Acrisols, Lixisols, Nitisols, Ferralsols, Durisols;

in USDA classification includes Ultisols, Oxisols, acidic Alfisols);

(c) Includes all soils (regardless of taxonomic classification) having 70% sand and 8% clay, based on standard textural analyses (in WRB classification includes Arenosols; in USDA classification includes Psamments);

(d) Soils exhibiting strong podzolization (in WRB classification includes Podzols; in USDA classification Spodosols);

(e) Soils derived from volcanic ash with allophanic mineralogy (in WRB classification Andosols; in USDA classification Andisols)


Soil carbon stocks after forest conversion to cropland were based on specific soil stock change factors for land use, management and inputs (fLU, fMG, fI, respectively) listed in Table 6.2 of the IPCC (2006).

Relevant factors are listed in Table 2. Stock change factors were selected for each land cover type and multiplied by reference soil carbon stocks. Following the IPCC (2006) Guidelines, the total difference in carbon stocks before and after activity implementation is averaged over 20 years.

Table 2: Relative stock change factors (fLU, fMG, and fI) for grassland management (net effect over a period of 20 years) Adapted from 2006 IPCC Guidelines for National Greenhouse Gas Inventories)

–  –  –

As a default it is assumed that there are low inputs to the grasslands both with and without activity implementation and that management switches from moderately degraded grassland to improved grassland.

Users have the option to specify the grassland management both before and after activity implementation and the level of inputs to grasslands both with and without activity implementation.


4.2. LIVESTOCK MANAGMENT Users are able to enter two types and the respective numbers of head of livestock both with and without activity implementation. The emission factors attributed to each non-cattle livestock subcategory for enteric fermentation are based on IPCC (2006) default values ascribed to developing countries (Table 3). Default IPCC emission factors for dairy and other cattle are divided by geographical region (Table 4).

Table 3: Emission factors for enteric fermentation from non-cattle livestock (kg CH4 headyr-1) <

–  –  –

4.3. REWETTING ORGANIC SOILS Changes in soil carbon stocks with organic soil rewetting were calculated based on Section of the 2006 IPCC (2006). The assumption with rewetting is that accumulation will occur at a rate equal to the rate of loss with initial drainage (Table 5).


Table 5: Default carbon accumulation rate following rewetting of drained organic soils

–  –  –


Uncertainty is a property of a parameter estimate and reflects the degree of lack of knowledge of the true parameter value because of factors such as bias, random error, quality and quantity of data, state of knowledge of the analyst, and knowledge of underlying processes. Uncertainty can be expressed as the size of the half width of a specified confidence interval as a percentage of the mean value. For example, if the area of forest land converted to grazing land (mean value) is 100 ha, with a 95% confidence interval ranging from 90 to 110 ha, we can say that the uncertainty in the area estimate is ±10% of the mean (from GOFC-GOLD 2013).

Uncertainty is an unavoidable attribute of practically any type of data including land area and estimates of carbon stocks and many other parameters used in the estimation of the AFOLU carbon benefits from activities on the land. Identification of the sources and quantification of the magnitude of uncertainty will help to better understand the contribution of each source to the overall accuracy and precision of the final estimate.

The proper manner of dealing with uncertainty is fundamental in the IPCC and UNFCCC contexts. The IPCC defines estimates that are consistent with good practice as those which contain neither over- nor underestimates so far as can be judged, and in which uncertainties are reduced as far as practicable. The first step in an uncertainty analysis is to identify the potential sources of uncertainty. Many sources are possible including measurement errors due to human errors or errors in calibration; measurement errors in the predictor variables; modelling errors due to inability of the model to fully describe the phenomenon; parameter and residual uncertainty; erroneous definitions or classifications that lead to double-counting or non-counting; unrepresentative samples; and variability resulting from the use of samples rather than censuses. In this section, the potential sources of uncertainty are identified and an assessment of their likely range of uncertainties used in the calculation of the carbon benefit in this tool is presented (Table 6). A brief primer of the steps involved in assessing total uncertainties for each carbon benefit estimate is provided with a couple of simple examples to demonstrate the process. The reader is referred to the GOFC-GOLD 2013 sourcebook for more details on all sources of uncertainty and how to reduce them. These analyses are not provided in the tools.

In addition to the uncertainties associated with each parameter, when parameters are combined as in e.g. estimating emissions from combining area grazed and emission factors for livestock, then overall error of the product will change. Uncertainties in individual parameter estimates can be combined using


either (1) error propagation (IPCC Tier 1) or (2) Monte Carlo simulation (IPCC Tier 2). Tier 1 method is based on simple error propagation, and cannot therefore handle all kinds of uncertainty estimates.

The key assumptions of Tier 1 method are (from GOFC-GOLD 2013):

 estimation of carbon emissions and removals is based on addition, subtraction and multiplication  there are no correlations across parameters (or if there is, they can be aggregated in a manner that the correlations become unimportant)  none of the parameter estimates has an uncertainty greater than about ±60%  uncertainties are symmetric and follow normal distributions However, even in the case that not all of the conditions are satisfied, the method can be used to obtain approximate results. In the case of asymmetric distributions, the uncertainty bound with the greater absolute value should be used in the calculation. The Tier 2 method is based on Monte Carlo simulation, which is able to deal with any kind of models, correlations and distribution. However, application of Tier 2 methods requires more resources than that of Tier 1.

The key parameters are of medium uncertainty. The other parameter used in the calculations is area grazed—it is assumed that this will be well known with an uncertainty of about 5% or less.

Table 6: Key parameters used to estimate the carbon benefits of grazing activities and an assessment of their uncertainties.

–  –  –


The simple error propagation method is based on two equations: one for multiplication and one for

addition and subtraction of uncertainties. The equation to be used in case of multiplication is:

U total  U 12  U 2 ....  U n


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