Quality Assurance and Quality Control

The characteristics of the greenhouse gas inventory estimate of Forest Land can have different level of precision, accuracy and levels of bias. Moreover, the estimates are influenced by the quality and consistency of data and information available in a country, as well as gaps in knowledge. In addition, depending on the tier level used by a country, estimates can be affected by different sources of errors, such as sampling errors, assessment errors, classification errors in remote sensing imagery, and modeling errors that can propagate to the total estimation.

It is good practice to execute quality control checks through Quality Assurance (QA) and Quality Control (QC) procedures, and expert review of the emission estimation procedures. Additional quality control checks as outlined in Tier 2 procedures in Volume 1, Chapter 6, and quality assurance procedures may also be applicable, particularly if higher Tier methods are used to estimate emissions. It is good practice to supplement the general QA/QC related to data processing, handling, and reporting and documenting, with source-specific category procedures. QA/QC procedures should be documented separately for Forest Land Remaining Forest Land and for Land Converted to Forest Land.

Agencies which collect data are responsible for reviewing the data collection methods, checking the data to ensure that they are collected and aggregated or disaggregated correctly, and cross-checking the data with other data sources and with previous years to ensure that the data are realistic, complete and consistent over time. FAO data needs to cross checked with other national sources for accuracy and consistency. The basis for the estimates (e.g., statistical surveys or 'desk estimates') must be reviewed and described as part of the QC process. Documentation is a crucial component of the review process because it enables reviewers to identify inaccuracy, gaps and suggest improvements. Documentation and transparency in reporting is most important for highly uncertain source categories and to give reasons for divergences between country-specific factors and default or factors used by other countries. Countries with similar (ecological) conditions are encouraged to collaborate in the refinements of methods, emissions factors and uncertainty assessment.

Activity data check: The inventory agency should, where possible, check data comprising of all managed land areas, using independent sources and compare them. For many countries, FAO database could be the main source and in such a case the data must be cross-checked with other sources. Any differences in area records should be documented for the purposes of review. Activity data area totals should be summed across all land-use categories to ensure that total area involved in the inventory and its stratification across climate and soil types remains constant over time. This ensures that Forest Land areas are neither 'created' nor 'lost' over time, which would result in major errors in the inventory. When using country-specific data (such as data on standing biomass and biomass growth rates, carbon fraction in above-ground biomass and biomass expansion factors, and synthetic fertilizer consumption estimates), the inventory agency should compare them to the IPCC default values or the Emission Factor Database (EFDB) and note the differences.

The country-specific parameters should be of high quality, preferably peer-reviewed experimental data, adequately described, and documented. The agencies performing the inventory are encouraged to ensure that good practice methods have been used and the results peer-reviewed. Assessments on test areas can be used to validate the reliability of figures reported.

internal and external review: The review processes as set out in Volume 1, Chapter 8 should be undertaken by experts preferably not directly involved in the inventory development. The inventory agency should utilize experts in greenhouse gas removals and emissions in AFOLU to conduct expert peer-review of the methods and data used. Given the complexity and uniqueness of the parameters used in calculating country-specific factors for some categories, selected specialists in the field should be involved in such reviews. If soil factors are based on direct measurements, the inventory agency should review the measurements to ensure that they are representative of the actual range of environmental and soil management conditions, and inter-annual climatic variability, and were developed according to recognized standards. The QA/QC protocol in effect at the sites should also be reviewed and the resulting estimates compared between sites and with default-based estimates.

It is good practice that countries using Tier 1 methods review and, if necessary, revise the default assumptions for carbon stocks in litter and dead wood pools which are required for estimation of carbon losses following deforestation. Countries that use higher tier methods are encouraged to calculate intermediate indicators of the models used to develop estimates of DOM stock changes. For example, QA/QC procedures could compare estimates of stock sizes, litterfall inputs, decay losses, etc., against literature values and other peer-reviewed publications. Where possible, it is also good practice to compare model estimates against field measurements and other data sources. One QA/QC check that is easily implemented in modelling systems is to calculate an internal mass balance to ensure that the model neither produces nor loses carbon that is not reported as a source or a sink. For example, conservation of mass requirements include that losses from biomass pools are either accounted as input to the DOM pools, are transferred outside of the forest ecosystem or released to the atmosphere (in case of fire). Further, harvest data can be used to check transfer (stop loss) estimates produced by models. A second QA/QC procedure that can be implemented in countries that use higher Tier estimation methods is to establish upper and lower bounds for DOM pools stratified by regions, forest type, and soil type (organic vs. mineral soils). Any values, reported in inventories or estimated by models that fall outside these bounds can be investigated further.

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