The probabilities of more than 5 mm of rainfall falling in 24 hours within 1 to 6 days (or more) of a nitrogen application to land (used in calculations of ammonia (NH3) emissions from

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 843 manufactured fertilisers), occurring with equal probability on each day of the year, were calculated for each of the MORECS grid squares. The resulting probabilities were correlated with annual rainfall and downscaled to the reference 10 by 10 km grid cells.
Annual Inventory survey data on agricultural activities The inventory model calculations are primarily driven by three core annual agriculture activity datasets, supplemented by other data inputs from annual or periodic statistical releases and targeted research. The three key annual datasets (described in more detail below) • The June Agricultural Survey, or Census (JAS/C) • The Cattle Tracing System (CTS) • The British Survey of Fertiliser Practice (BSFP) These three sur veys together provide most of the required annual activity data to drive the inventory model estimates, i.e.
of livestock numbers, crop production and fertiliser use. None of the three are comprehensive annual surveys of every UK farm holding; each are stratified surveys aiming to gather data from a large, representative sample of farm holdings from across the country.
There are some minor differences in the precise scope and management of these surveys within the UK, as each is conducted per constituent country, i.e. within England, Scotland, Wales and Northern Ireland (E, S, W, NI) separately, but there is a good level of consistency in data gathering across all of the UK.
Many of the same reporting systems (and survey questions etc) are applied across Great Britain (E, S, W) whilst the Northern Ireland Farm Business Survey (NIFBS) delivers the equivalent data for Northern Ireland. The June Agricultural Survey / Census - description The June Agricultural Survey/Census (JAS/C) provides farm/holding level inf ormation on crop areas and livestock numbers for the UK.
It is the only survey of its kind for the UK, with areas/numbers updated annually; a full census of all farms is conducted infrequently (typically every 10 years). The government statistical authorities for each of the four countries of the United Kingdom (i.e.
England, Wales, Scotland and Northern Ireland) carry out separate surveys every year, with varying levels of sample sizes between the full census years. The statistics teams then use a process called “imputation”, where they extrapolate from the samples to the full set of holdings, using a range of auxiliary government databases in the process.
Overall, the data collected in the JAS/C are broadly similar for each of the four UK countries, with small differences according to types of crops grown and livestock kept in different regions. However, the level of detail of the livestock and crop categories changes between JAS/C years.
For example, some crop types are discontinued as areas diminish over time, and instead are incorporated with a summary category such as “other crops and tillage”. The level of detail may also differ across the four countries to reflect local priorities, e.g.
agriculture in Wales has relatively little cropland, and therefore only the main crops are reported individually, with other crop types only reported at a summary level. A key part of the Inventory Agency role is to harmonise across time series (1990 to present) as well as between countries, to provide the best possible input data for the agricultural emission model.
The JAS/C data are collected annually on 1st June through questionnaires sent to individual agricultural holdings above minimum size thresholds, with each holding being identified with a unique ID, referred to in Great Britain (E, S, W) as the ‘County Parish Holding number’ (CPH). The equivalent ID in Northern Ireland is the “Farming Community Network number” (FCN).
During the period from 1990 to present, the data collection methodology has generally moved from a full census towards more survey -based coverage across the UK, with full censuses only carried out at longer intervals, and varying survey sample sizes in intervening years. Over recent years, there has also been a trend to substitute/combine data fro m other government systems, such as databases for veterinary/animal health and agricultural subsidy systems, thereby

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 844 reducing the need to ask farmers for duplicate information.
This has the additional advantage of reducing uncertainties due to non-returns of the annual census-survey questionnaires. In general, the agricultural statistics departments estimate data for holdings where no returns are received by ‘imputation’, i.e.
using previous data and general trends as substitutes. The main differences in the census-survey methodology used by the four UK countries are described in more detail below, describing the detailed category from 1990 and how to deal with such changes consistently across the inventory timeline.
The JAS/C is used as input to the agricultural emission model for the following ✓ Holding Each holding is assigned to a 10 by 10 km grid square used by the ✓ Arable and grassland The JAS/C records the land area of arable and grassland for each holding, this is used to estimate emissions associated with land use (e.g. emissions ✓ Livestock numbers (for non-cattle livestock): The number of livestock on each holding is  Cattle population data are supplied separately from Cattle Tracing System (CTS).
The Cattle Tracing System (CTS) - description The British Cattle Movement Service 20 (BCMS) administer the Cattle Tracing System (CTS) database, which records the details of all bovine animals in Great Britain; all cattle have a unique ID, held on ear tags and within a cattle passport (held by the animal owner). These cattle numbers / IDs remain with each animal throughout its life and are recorded by the slaughterhouse at death enabling beef to be traceable to its origins.
All GB farmers must notify births, movements and deaths of cattle on their holdings and log information within the CTS databa se. The Inventory Agency receives a complete copy of the CTS dataset annually from the Animal and Plant Health Agency (APHA) which uses CTS for cattle disease tracing and surveillance; the Inventory Agency analyses the CTS data to develop activity data that reflect the GB herd per holding according to cattle breed, type, age, including monthly datasets.
As regards data scope and limitations, the CTS covers all cattle births, movements and deaths in Great Britain from 28 September 1998; all farmers must use the system for all bovine animals for the beef to be traceable and hence the CTS data are highly complete (it is essentially a census) except for instances where animals may be in transit or at vets or market on specific days. The CTS does not cover Northern Ireland which has a separate tracing system in place; the Inventory Agency obtains equivalent cattle data from Northern Ireland government.
The introduction and use of CTS datasets was phased in across GB, with some data discontinuity evident when compared against JAS/C data and when new breeds / categories are identified and reported over time. Overlap periods were used per DA to QA the change -over from using JAS/C to CTS data, including 2004-6 for Wales, 2005-6 for England and 2011 for Scotland.
There is ongoing work in the UK to develop a more holistic Livestock Information Programme 21 (LIP) via a partnership between industry and government to develop a digital traceability system for a wider scope of livestock, to include sheep, cattle, pigs, goats and deer. The primary reason for the system development is to facilitate rapid and effective disease prevention and management but the Inventory Agency is in dialogue with the team of developers to ensure that inventory data requirements may be designe d into the LIP.
LIP (or equivalent) systems are planned for each constituent country of the UK and over time this is expected to replace the CTS 20 21

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 845 as an inventory data source, and also augment the data provision for inventory methods for other The British Survey of Fertilise Practice (BSFP) - description The British Survey of Fertiliser Practice 22 (BSFP) is a nationally representative interview survey based on a random stratified sample of farms from mainland Britain and collects evidence on all aspects of fertiliser management. The survey takes place in spring and is published in summer each year, reporting on the previous agricultural year.
The survey enables the calculation of annual application rates of fertilisers to agricultural crops and grassland by crop / grass type and by fertiliser type, using the information from the representative, stratified farm -level responses, scaled up to estimate the total fertiliser applied. The survey is funded by Defra and the Scottish Government, and the annual database of responses is provided to the Inventory Agency around mid-July each year to enable direct query of the raw data for the purposes of the national inventory compilation.
The survey outputs contain information on trends in the use and application rates of nitrogen, phosphate, potash, sulphur, organic manures and lime onto crop and grassland. The survey seeks to identify the cropping patterns that influence fertiliser rates and dressing covers, including for key crops (e.g.
winter wheat, winter barley, oil seed rape, other tillage crops). The BSFP has been run since the 1940s across England and Wales, with amalgamated data for GB presented since 1992, as well as individual data for Scotland, England and Wales.
The method is described in detail within annual reports but can be summarised • A sample of holdings are surveyed to reduce the reporting burden on the industry and to manage resources efficiently. In 2021 the target sample size was 1,500 farms and the actual sample size was 1,310; this is regarded as a statistically representative sample size • The survey sample is selected from the population of agricultural holdings that report to the JAS/C, with survey samples selected from either Scotland or from England & Wales.
Holdings with <20ha allotted to crops and grass in total are excluded from the BSFP • In England & Wales, farms are classified across three cropping, livestock, horticulture, and then four size groups. Scotland farms are classified similarly but across two mainly cropping and mainly livestock.
Robust farm type “Other” are excluded. • The survey design is such that the number of farms sampled per farm type and size combination (strata) is in proportion to the latest JAS/C data on total area of crops and grass, except for E&W horticultural farms which are sampled at a higher rate to ensure that a robust estimate can be made across this more heterogeneous farm type.
• To assure survey response rates and reduce year to year variability, a core of respondents completes the survey annually; for example in 2021, 74% of the panel had responded the • This sample design is constructed primarily to collect data on manufactured fertiliser usage, whilst it may not fully represent farms using organic manures; therefore the survey responses are considered accordingly by the Inventory Agency, taking note of the sample 22

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 846 Other Input Datasets used for the UK Agriculture Inventory In addition to the three core, cross -cutting annual datasets outlined above, the UK Inventory Agency requests a host of other data for use within the inventory source category methods annually. The most critical of these additional datasets are summarised Data Provider Data / information Defra Monthly UK Statistics on Cattle, Sheep and Pig Slaughter and Meat Production UK Home Fed Meat Production, Trade and Supplies Sheep and Lamb Dressed Carcase Weights - England Slaughterhouses Only Agriculture in the United Kingdom, Chapter 7 - Crop Area and Production Cereal and Oilseed Area, Yield and Production UK Poultry Slaughter Weights and Poultry Meat Production - Monthly Data UK Chick and Poultry Placings - Monthly Data England Farm Type and England Holding Locations - Holding Level Datasets Horticulture Statistics Dataset - Area and Production England and UK, ideally also the GB/NI split of milk yield from dairy cattle DAERA Northern Ireland Farm Business Survey DAERA-NI Fertiliser Delivery Statistics Northern Ireland June Agricultural Survey - Holding Level Dataset Northern Ireland June Agricultural Survey - National summary data NI dairy and beef cow breeds percentages Northern Ireland Lamb and Ewe Carcase Weights Scotland June Agricultural Survey - Holding Level Dataset Scotland June Agricultural Survey - National summary data Scotland milk yield data; Economic Report on Scottish Scotland Slaughtered Sheep Number and Weight Wales June Agricultural Survey - Holding Level Dataset Wales June Agricultural Survey - National summary data AHDB Great Britain Animal Feed Production Performance Pork indoor breeding herd, Pork outdoor breeding herd, Pork rearing 7-35kg herd, Pork finishing 35-110kg herd.
Hybu Cig Cymru Wales Total Sheep Carcase Weights ESCAA Certified Seed Production in the United Kingdom IFA Stat IFA Stat Fertiliser Supply and Consumption Cattle breed performance statistics SRUC Estimated liveweight of mature cows (from slaughter weight data) UKCEH Anaerobic Digestion data (agriculture sector only); Mineralisation data Ricardo Sewage sludge data; Poultry litter incineration data The above datasets are used by sector experts in the annual updates to inventory methods, as described within the main chapter of the National Inventory Report. In addition to the above annual routine data requests, the UK agricultural methods also rely on periodic data inputs / updates, which may be the result of interstitial sector surveys or one -off research by academics or other organisations.
These datasets

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 847 • Holding location information / numbers, for • UK farming structural analysis, for le/1106562/AUK_Evidence_Pack_2021_Sept22.pdf • UK fertiliser sales, import, export data, including industry publications such • Detailed surveys or research projects on e.g. housing, management practices • Information / research regarding the uptake and impact of mitigation actions • The findings of literature searches and / or stakeholder consultations that inform of new research or data on EFs, assumptions.
A 3.3.7.13 Mitigation Actions per Source Category Within the source category methods across all sectors there is scope for consideration of the impacts of mitigation measures, which may impact upon the source emissions of ammonia (NH3) and/or GHGs. The inventory model code h as been developed such that method algorithms can call upon parameters ( where available) that indicate the level of uptake and the impacts of the mitigation measure per pollutant at a detailed level of resolution (e.g.
per DA, per crop/livestock type, per stage – e.g. housing, storage, yarding, spreading).
This ensures that the inventory model can reflect the impacts of mitigation uptake, where it occurs, subject to data availability below), each is potentially applicable to a number of sectors and sources with variations in cost and effect, resulting in around 200 measure variants across the inventory. The list of variants covers all those currently reflected in the Agricultural Ammonia and Greenhouse Gas Inventory (AAGHGI), and some which are applicable only to future projection scenarios but is not Measure uptake values are year specific, and often region-specific (e.g.
per DA, for GB only, for NI only) as they are based on a range of data sources, typically the annual surveys noted above (i.e. the BSFP, the JAS/C) or from more targeted surveys and studies, such as the Farm Business Surveys that are conducted around the UK, capturing evidence on cu rrent farm management practices.
Each mitigation measure is associated with a target Sector, Stage, Country, Farm Type, Item, Source and Location and then with a reduction factor applicable to the unmitigated pollutant (e.g. NH3, N2O, CH4) emission factor.
For the purposes of policy scenario analysis, each measure is also considered on a cost basis (per stock place, per occupied stock place, per mass of manure, per mass of nitrogen fertiliser), a cost value comprising capital and operating components and a list of mutually exclusive mitigation measures for the target Stage, Location and Source. Pollutant emission reduction factors are primarily based on empirical evidence, largely from experimental work conducted in the United Kingdom but also taking accoun t of relevant international studies and with reference to international literature such as the UNECE Task Force for Reactive Nitrogen Ammonia Abatement Guidance Document (Bittman et al., 2014).
details of all these parameters, references and dependencies can be provided upon request. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 848 Where dietary CP is in excess to requirement, reductions in intake will be reflected in reductions in This is a scenario measure, not implemented in the historical inventory time series.
Where soil conditions allow, longer grazing season means cattle urine returns will infiltrate the soil and this has a lower NH 3 EF compared to urine deposited on concrete and managed as manure. This is a scenario measure, not implemented in the historic inventory time series.
Applies to outdoor dairy cattle concrete collecting yards where washing rather than just scraping reduces excreta from surfaces, reducing emissions. ~73% GB; ~100% NI uptake in recent years More frequent removal of excreta from dairy cow cubicle floors to the slurry store, (e.g.
twice a day) will reduce the source from which emissions are occurring. Implementation assumed as zero to date.
Grooved concrete floor plus “toothed” scrapers means quicker removal of urine from the floor surface to slurry storage, reducing the emissions potential from the Implementation assumed as zero to date. Rubber slat mats fitted to concrete slatted floor.
The hard-wearing curved smooth surface promotes urine and dung movement to the below -slat storage pit. Automatic scraper units remove manure from surfaces.
Implementation assumed as zero to date. Acidification of the below -slat slurry through the automated addition of acid (normally sulphuric) to achieve a slurry pH of below 6.
Implementation assumed as zero to date. Modified floor and pit design result in lower emissions from the floor and below -slat slurry storage compared with conventional fully slatted systems for weaner a nd finishing pig slatted floor housing.
2007 onwards ✓; now assumed BAT on EPR - permitted farms. Assumed 33% uptake across UK for weaner and finishing pig housing in recent years Replace longer -term below -slat slurry storage with more frequent (twice per week), complete removal of slurry to an outside store using a vacuum removal

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 849 2007 onward s ✓; now assumed BAT on EPR - permitted farms.
Assumed 22% uptake across UK in sow & gilt housing, 0% in weaner & finisher housing. Acidified moisture air scrubbers fitted to the outlets of mechanically ventilated pig or poultry housing to remove almost all ammonia from exhaust air.
Implementation assumed as zero to date. Drying systems installed in laying hen houses reduce the moisture content of the layer manure collected on below-cage belts -> delays hydrolysis of the excreted uric acid in the manure, lowering NH3 emissions.
Implementation assumed as zero to date for GB; NI data indicates ~7% up take in recent years, started Drying litter using above -floor fans, heat exchanger units, below -floor systems reduce the poultry litter moisture content -> delays hydrolysis of the excreted uric acid in the manure, lowering NH3 emissions. Assumed as BAT on EPR-permitted farms for housing for growing pullets, breeding birds, broilers, turkeys, ducks, geese and all other poultry except for laying hens.
Assumed 72% growing pullets, breeding birds, broilers; 35% turkeys, ducks and geese, Allow natural crust to develop on stored slurry surfaces, (fibre, bedding material in the slurry); retain the crust to create a physical barrier between the ammoniacal N in the slurry and the air above the crust, reducing ammonia emissions for above ground slurry 80% of dairy and beef cattle slurry storage is assumed to develop a natural crust; all years, all UK. Fixed cover, with vents, fitted to slurry tanks to reduce ammonia emissions.
Reduces airflow across the slurry surface; higher ammonia concentration builds up in the headspace inhibits emission from the slurry surface. Implementation assumed as 0% to date for cattle slurry tanks in GB, 3% uptake in Northern Ireland since 2010; 2007 onwards ✓ now assumed BAT on EPR-permitted pig farms.
Assumed 24% uptake across UK in recent Floating covers of permeable or impermeab le materials that reduce ammonia emissions by restricting contact between the slurry surface and the Implementation assumed as zero to date for cattle slurry tanks; 2005 onwards ✓ now assumed BAT on

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 850 EPR-permitted pig farm slurry lagoons. Assumed 24% uptake across UK in recent years.
A sheeting cover provides a physical barrier preventing transfer of ammonia from the manure heap to the air. Implementation assumed as zero to date across all Slurry applied to land in narrow bands via hoses trailing from the slurry applicator boom to just above the soil surface.
The emitting slu rry surface area is greatly reduced compared with surface broadcast application. Ideal for arable crops; placement of slurry below the crop canopy reduces air flow reducing emissions more.
Assumed uptake by 2021: 0% cattle or pig slurry to grassland; 15% cattle slurry to cropland in GB, 25% in Northern Ireland; 28% pig slurry to cropland; 77% cattle or pig digestate to grassland or cropland. Slurry applied to land in narrow bands via hoses with adapted ends (‘shoes’) trailing from the slurry applicator boom directly to the soil surface, below the grass canopy.
The emitting slurry surface area is greatly reduced compared with surface broadcast application and further protected by the grass canopy which reduces air flow at the emitting surface. Assumed uptake by 2021: 15% cattle in GB, 10% in Northern Ireland, 28% pig slurry to grassland; 0% cattle or pig digestate to grassland.
Slurry or digestate applied to soil in shallow surface slots, greatly reducing the emitting surface area of the slurry i n comparison with surface broadcast application. Ammonium-N in slurry or digestate placed directly into the soil will be fixed on to clay particles, further reducing the potential for ammonia emission.
Assumed uptake by 2021: 7% cattle slurry to grassland or cropland in GB, 0% uptake in Northern Ireland; 23% cattle digestate to grassland or cropland; 23% pig slurry or digestate to grassland or cropland. (BSFP) Slurry or digestate applied to soil behind tines which ~eliminates the emitting surface area of the slurry in comparison with surface broadcast application.
Ammonium-N in slurry or digestate placed directly into the soil will be fixed on to clay particles, further reducing the potential for ammonia emission. Assumed uptake by 2021: 2% cattle slurry to grassland or cropland in GB, 1% in Northern Ireland; 0% cattle or pig digestate to cropland.
(BSFP) Surface applied manure is incorporated into the soil by ploughing or by disc or tine cultivation soon after application. The shorter the delay between application

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 851 and incorporation, the greater the emission reduction.
The inventory models two 4h, 24h delay. Assumed uptake by 2021: 2% cattle slurry or FYM disc 4hr; 3% c attle slurry disc 24hr; 5% cattle FYM disc 24hr; 3% pig slurry or FYM disc 4 hr; 2% pig slurry disc Slurry is acidified during the spreading operation through the use of a specially adapted tanker which adds acid to the slurry to reduce the slurry pH to < 6.
Implementation assumed as zero to date. Apply a product with urease inhibitor incorporated with the urea to delay the hydrolysis of urea to ammonium carbonate, giving more time for the fertiliser to enter the soil matrix, reducing potential for ammonia emissions.
Assumed uptake by 2021: 16% urea to cropland; 10% UAN to cropland; 17% urea to grassland; 6% UAN to Incorporation of fertiliser into soil by direct closed -slot injection or by cultivation. Mixing with the soil matrix greatly reduces potential ammonia emissions.
Applicable to tilled land prior to crop establishment. Assumed uptake by 2021: 0% all to grassland; 5% urea to cropland; 3% AN, UAN to cropland; 9% CAN to cropland; 36% AS to cropland; 34% Other to cropland.
Selective breeding for feed conversion efficiency improvements to reduce N excretion per animal. This is a scenario measure, not implemented in the historical inventory time series.
Planting of a cover crop enhances over-winter nitrogen plant uptake and reduces nitrate leaching, and hence indirect nitrous oxide from leached nitrate. Assumed uptake by 2021 is per DA 4% for England and Scotland, 3% for Northern Ireland and 16% for Each of the three key annual surveys provide input data for multiple source categories within the UK inventory; all require pre -processing into a common format of activity data tables, including ✓ allocate survey data into agriculture inventory source-specific activity data (e.g.
raw data on cattle numbers per breed, age, allocated to defined inventory livestock sub-categories; raw data on crop production allocated to defined inventory crop types); ✓ conduct quality checks between (i) detailed spatially -resolved survey data, and (ii) published annual statistical summary data at country (i.e. E, S, W, NI) level;

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 852 ✓ identify gaps and outliers, run range checks, time -series consistency, completeness and The data flow in the first steps of inventory compilation are summarised in the schematic below.
PROCESS Assignment to 10 x 10 km grid squares Across all of the data pre -processing, the various survey data are used to generate spatially resolved activity data estimates at the holding level, which are then aggregated at 10 x 10km grid square level, ready for use in the inventory calculations per source category. For holdings in England and Wales (10 x 10 km) emission model grid squares are assigned based on Easting and Northing of the holdings, as supplied in the JAS/C holding level data.
In rare instances, where the Easting and Northing of a holding is not associated to a valid grid square (i.e. associated with the correct country/region), the parish associated with each holding is used to assign a grid square, based on the centroid of the parish.
For Scottish holdings, grid squares are assigned using postcode information associated with each holding. To ensure consistency throughout the time se ries, additional pre-processing is needed to ensure accuracy, for example to cross -check postcode information against parish records in For Northern Ireland, grid squares are assigned in the same way as for England and Wales, with holding coordinates converted from Irish National Grid (TM75) projected to British National Grid PROCESS Robust Farm Type (RFT) Calculation The UK inventory model conducts calculations at the level data aggregated per Robust Farm Type (RFT) within ea ch 10 x 10 km grid square, for each source category.
The RFT allocation process is critical to the methodology as the UK model applies assumptions regarding the farm management practices and investment in technologies/fertiliser use etc according to the RF T type; for example, a mixed farm with a small number of cattle and some cropland for arable will

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 853 likely manage manure waste differently to a large farm that is predominantly beef or dairy and manage fertiliser use differently to a large arable farm. The RFT allocation enables the inventory to model farm management practices aligned to the relative economic importance of different The farm type of each holding is calculated using the method outlined in the EC typology handbook.
Farm type is based on the type of farming and economic size, determined on the basis of Standard Output (SO) coefficients and structural information (area of crops and number of heads of livestock) of the various types of agricultural production. Ten robust farm types (RFT) are applied at Devolved Administration level (England, Wales, Scotland and Northern Ireland): (1) cereals; (2) general cropping; (3) horticulture; (4) specialist pig; (5) specialist poultry; (6) dairy; (7) Grazing livestock (LFA); (8) Grazing livestock (Lowland); (9) mixed; (10) other.
Although the Robust Farm Types of (most) holdings are calculated as part of the JAS/C (by the relevant statistical authorities), methodologies and coefficients used to estimate Standard Outputs (SO) have changed over time and hence the Inventory Agency has produced a consistent time series, using a common set of coefficients and a consistent methodology. To preserve the contribution each census item makes to the Standard Output of a holding, unscaled versions of livestock (dairy, beef, sheep, pig, poultry and horses, goats and deer), grassland and arable sector tables from the agricultural census are used, together with cattle data from CTS.
Standard Output coefficients (for the year 2010) are applied to each agric ultural census item (excluding deer) to assign a farm type. For specific years, some horticultural items in the holding level data must be disaggregated further into flowers and nursery stock, in order to apply the output coefficients consistently across all years in the time series.
The June Agricultural Survey / Census (JAS/C) – pre-processing As part of the annual inventory process, JAS/C data are acquired at the holding level from the country statistics authorities in the UK, i.e. Defra (England), the Scottish Government (Scotland), Welsh Government (Wales) and DAERA (Northern Ireland).
Category totals are then cross - checked with national summary statistics. As a next step, the holding level data for the different countries are then aggre gated to the common set of emission source categories used by the agricultural emission inventory model, to ensure compatibility between the different countries’ systems and consistency over time.
All data are then cross-checked against the national totals. QC Adjustments for missing data The original JAS/C holding level data received from the relevant government agencies contained a number of data gaps over the years.
The majority of these data gaps are associated with the minor livestock sector (i.e. horses, farmed deer, goats), with some livestock categories not being recorded at the start of the time series (1990 – 1993).
Some poultry categories were also missing from the time series, for example with no category for Turkeys in Scotland 1992 – 2015 and no poultry categories at all being present in the Welsh dataset for 1996. In all instances, these gaps were addressed by using IPCC good practice gap -filling techniques, such as interpolation or extrapolation of data from adjacent years, with the processes documented in detail.
Spatial information used to assign each holding to a 10 by 10 km grid square (as used by the agricultural inventory model) were missing for some holdings; these have been imputed based on other years, where available. Where thi s was not possible, alternative locational information such as parish centroids was used with these assumptions documented by the inventory team.
The pre-processing of the JAS/C is summarised in the schematic below. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 854 QC Producing consistent cattle categories between CTS and JAS/C To achieve a consistent dataset of cattle population data across the time series (i.e.
pre and post CTS, which began in 2005), some JAS/C categories needed to be split into sub -categories and re-aggregated for the emission model database. In particular, there is ambiguity for young cattle which are not explicitly associated to the beef or dairy sector in the JAS/C holding level data.
The methods for deriving the required cattle categories are detailed In the pre-CTS era (1990 – 2005, and all years to present for NI), census items frequently do not distinguish beef or dairy sector allocation, especi ally for young animals. In such circumstances, these ambiguous cattle categories were separated based on other cattle categories present on each holding, as detailed • All ambiguous cattle are assigned to the dairy herd on holdings that contain only female breeding cattle from the dairy sector and NONE from the beef sector.
• All ambiguous cattle are assigned to the beef herd on holdings that do not contain any breeding females OR those containing only female breeding cattle from the beef sector but NONE from the dairy sector. • Ambiguous cattle are split between the beef and dairy herd on holdings that contain both beef AND dairy breeding females and are split using the ratio of beef/dairy breeding The methodology above was applied for all female calves (<1 year old), and for the category “female replacements >2 years”, which was present in the English dataset for the years 1990 – The Cattle Tracing System (CTS) – pre-processing The pre-processing of the CTS data is a computationally intensive process as the raw database from APHA is typically ~60GB in size for one year’s worth of data; the data are provided to the Inventory Agency on encrypted hard disks and the data processing is conducted by Cranfield University on a dedicated Solid State Drive (SSD) to improve computational performance and The pre-processing of the CTS is summarised in the schematic below.
Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 855 The pre -processing of the raw data files to generate useable AD tables with the correct UK inventory data labelling and ready for source-category calculations comprises the following rows are complete, and to check that none contain a naturally occurring delimiter; identification of new data fields and subsequent investigation and adaptation of code to handle them correctly. For example, new breeds must be identified and categorised for • SQLite database build : use of SQLite Administrator and DOS -prompt command line software to create the database, create the structure of the data tables, import the raw data into the appropriate data tables, index the data tables to enable faster querying, • Querying the SQLite database : SQLite3 scripted queries are used to extract monthly datasets of cattle and store them in another SQLite database; this proce ss identifies all cattle that are alive, identifies their location from latest on -movement records.
Data cleaning identifies exported or otherwise lost cattle; outputs are monthly cattle data per location, logged against locational Location ID, postcode, premises type and • Data cleaning and The next step is to take the text file lists of animals alive per month and map them onto counts of cattle categorised by gender, breed type, cattle role, and age group, split into separate beef and dairy output tables. Within this there are some special (further details are available within supplementary o remove cattle that are located where the County Parish Holding (CPH) is of the form 99-xxx-xxxx as these are export or other anomalies like lost or stolen; o re-assign cattle that are dual breeds or unknown to either dairy of beef; o encode the cattle into cattle roles, breed type, and age group; o relocate cattle that are located off-farm, such as at a showground, onto the set of CPHs locations that exist within the June Census/Survey dataset; and o rescale the extracted cattle numbers at DA level to achieve agreement with the published June Agricultural Census/Survey dataset.
The output files are formatted respectively, using column headings ready for downstream data transfer via ACCESS database and tables, for subsequent checking by sector experts and then

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 856 (i) in the derivation of Robust Farm Type allocations (combined wi th JAS/C data), and (ii) within source category method o “INVENTORY|YEAR|HOLDING|CATTLETYPE|CATTLEBREED|MONTH|NUMBER” o “INVENTORY|YEAR|HOLDING|CATTLETYPE|CATTLEBREED|MONTH|AGECODE| The British Survey of Fertilise Practice (BSFP) – pre-processing The BSFP database is provided to the Inventory Agency, with the information on fertiliser type, rates, applications and location / field all then processed into datasets that are ready for use within the crop and grassland sector methods. The BSFP is a stratified survey that is designed based on the previous year’s JAS/C; the Inventory Agency is able to take the JAS/C data from the latest inventory year (i.e.
data on grass and crop production from the same year as the BSFP dataset) and re-stratify the results of the BSFP, to improve accuracy. The pre-processing involves several steps and is pr imarily conducted using R code and database tables, with outputs then held in inventory-ready Activity Data tables.
The Inventory calculations are based on a disaggregation of manufactured fertiliser nitrogen between 6 Fertiliser Types, having differing ch emistry affecting their direct nitrous oxide and ammonia (NH 3) emission factors. On a per kilogram of nitrogen basis, direct nitrous oxide emissions are generally higher from the nitrate -based Fertiliser Types (Ammonium Nitrate (AN) and Calcium Ammonium Nitrate (CAN)) (Cowan, 2020), whilst direct ammonia (NH 3) emissions are higher from the urea -based Fertiliser Types (Urea (U) and Urea Ammonium Nitrate (UAN)) (Misselbrook et al., 2004).
Ammonia emissions from the Ammonium Sulphate (AS) and Di - ammonium Phosphate (DAP) are critically sensitive to soil pH (Powlson and Dawson, 2020). Data fields within the BSFP are linked to inventory data fields using Access queries, for example cropland as well as identifying land that is set aside.
e.g. BSFP entries for oats, spring oats and winter oats are assigned inventory crop codes 1, 2 and 3, but are all within inventory crop group #1 to enable data handling and aggregation wher e necessary to reflect the size of the survey dataset; similarly wheat, wheat – milling and wheat non-milling are inventory crop codes 10, 11 and 12, within the wider inventory crop type #10.
Estimates of manufactured fertiliser nitrogen is split across 6 fertiliser types; the BSFP fertiliser extract all data separately for the 6 types used in the AN, CAN, U, UAN, OSN, NCB (compounds and blends). This en ables individual BSFP records to be assigned to an inventory fertiliser category for subsequent calculations of fertiliser N content; further queries assign enumerators for urease inhibitors, nitrification inhibitors and fertiliser placed as well as validating the raw BSFP data by applying acceptance ranges for key parameters (e.g.
to remove locations with spuriously high nitrogen application rates). Analysis in Access to link details of BSFP database tables crop (e.g.
crop_id, crop, crop_group_1, tillage_or_grass) to field (field_id, crop_id, farm_id, field_size_sampled) enables calculation of interim data such as residue fate per field/location. Subsequent processing in R enables additional manipulations, e.g.
to account for the 1993 ban in the UK on burning of field residues. Further analysis in Access joins details of BSFP database tables fertiliser application (e.g. fertiliser application timing, recorded_fertiliser_rate, farm_id, nitrogen_rate, field_id) to field (field_id, crop_id, sown_area_sampled) in order to generate outputs in terms of total nitrogen rate across year, farm and field. Subsequent calculations, combining the data on total nitrogen rate with the sown area, generates data on the total N fertiliser applied per crop; data range checks are used to automatically QC the raw BSFP data, e.g. to exclude locations where the reported nitrogen rate is either zero or spuriously high. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 857 Using the latest JAS/C data in conjunction with the BSFP, an updated farm stratification is calculated using crop and grass sector groupings and the holding count survey weights. A 3.3.7.15 Compilation of Activity Data Tables per Source Category The next step in the inventory model data flow is for sector experts to compile the necessary activity data tables that the coded model then draws on to generate the emission estimates. Each source category method requires a set of data tables with clearly defined data fields populated for all years, countries, farm types and other sub -categories as appropriate. The sector leads draw upon the pre-processed sector datasets from the annual surveys described above. In many cases the task is merely to populate the tables for the latest year, check for any revisions to historic data, check for any new source -category combinations and run quality c hecks per DA, per farm type etc to ensure completeness and time series consistency, identifying and resolving any gaps or outliers. For some source sectors there is more analysis needed to review the latest year data and aggregate data or gap -fill, for example where the latest annual survey data for a specific activity may be missing or there are insufficient data records from the latest survey to regard the available data as representative. The methods applied per sector are not described in detail here; additional information can be provided upon request. Each source category method is described within a “sector recipe” developed during the process of establishing the new inventory system. The majority of this phase of data processing is performed in MS E xcel, although increasingly the data cleansing and gap- filling is being performed in code such as R scripts. The schematic below summarises the data processing steps to generate Activity Data tables and the Sector Database, ready for subsequent emission calculations.
Examples of the Activity Data tables completed per sector and source category are as o B1 Crop Year / Country / Cell / Farm type / LFA Status / Crop Type / Area o B2 Crop Holding Year / Country / Cell / Farm type / Holding Count o CT10 Crop Primary Year / Country / Month / Crop / Yield

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 858 o CT14 Residue Year / Country / Farm Type / Crop / Fate Type / % Residue Area o CT21 Fertiliser Year / Country / Farm Type / Crop / Fertilising Type / Fertiliser Rate o CT22 % Year / Country / Farm Type / Crop / Fertilising Type / % Fertilising o CT23 % Fertiliser Year / Country / Farm Type / Crop / Fertilising Type / % Fertiliser o CT24 Fertiliser Year / Country / Month / Crop / % Weight Month o CT25 Nitrification Inhibition Year / Country / Farm Type / Fertiliser Type / % o CT27 Urease Inhibitor Year / Country / Farm Type / Fertiliser Type / % Uptake o CT28 Urease Inhibition N2O / CH4 / Fertiliser Type / % Reduction o CT29 Cover Crop Year / Country / Farm Type / % Area Cover Crops o CT34 Fertiliser Placement Year / Country / Fertiliser Type / % Uptake o Sheep Year / Holding / Sheep Type / Count o Lamb Year / Country / Sheep System Type / Lamb Type / % Lamb Type o Ram Year / Country / Sheep System Type / Ram Type / % Ram Type o Ewe Year / Country / Sheep System Type / Ewe Type / % Ewe Type o Lamb Slaughter Year / Country / Sheep System Type / Lamb Type / Lamb Slaughter o Ewe Replacement Year / Country / Sheep System Type / % Annual Replacement o Ewe Lambing Year / Country / Sheep System Type / % Ewe Lambing Rate o Sheep (Latest Year): Year / Holding / Sheep Type / Country / Count o Sheep Compost Manure Year / Country / Farm Type / Compost Manure Uptake o Sheep Cover Manure Year / Country / Farm Type / Cover Manure Uptake o Sheep Winter Country / System Type / % Winter Housed o Sheep Diet Year / Country / System Type / Sheep type / Sheep Subtype / Diet o Sheep Country / System Type / Sheep Type / Sheep Subtype / % Sheep o Sheep System Country / LFA Status / Sheep System Type / % System o Sheep System by Sheep Country / LFA Status / Sheep type / System Type / % Sector leads manage the compilation and checking of these Activity Data tables, including (where appropriate) to check the AD against other sector experts across the UK Inventory Agency , • Total cropland and grassland land areas per DA applied within the agriculture inventory model are checked against the data used by the UKCEH team that compiles the Land Use, Land Use C hange and Forestry (LULUCF) source and sink emission and removal • Poultry litter fate data are checked with the Ricardo team that derives estimates of poultry • Sewage sludge applied to land estimates in the agriculture model are checked against the data available from water companies which are managed by the Ricardo waste sector Sector leads then share the final Activity Data tables to the team at ADAS that manages the central C# agriculture model. Further quality controls are then conducted by ADAS to ensure that all Activity Data tables have a consistent and complete number of data field entries, for

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 859 as per historic years; (ii) checks that new data combinations (e.g. new breeds, crops) are handled consistently and – where appropriate – are reported across the time series. Any issues identified through this central QC step are communicated back to the sector leads , resolved and documented, and the tables re-submitted. The finalised sectoral Activity Data tables are collated together with a Sector Database, ready for the subsequent stages of emission calculations to be performed. A 3.3.7.16 C# Inventory Model Calculations → Source Category reporting outputs inputs from the Physical Database ( soil, temperature, rainfall ), Base database ( geo-spatial referencing) and Sector database ( activity data tables per source category) to prepare an SQL run database. The individual sector recipe calculations are stored in code (C#); these calculations have been developed and tested through a series of projects during the 2010s to model the higher-Tier in ventory methods, consistent with IPCC guidelines (for GHGs) and EMEP -EEA guidelines (for AQ pollutants). The sector calculations are run to generate sector files, executing calculations per source, per year, per grid cell and then aggregating for quality checking (and reporting) purposes to DA level, per inventory source-activity; these outputs can then be aggregated for reporting to the required taxonomy, such as CRF tables (for GHGs), NFR tables (for AQ pollutants) or to UK national statistics and other (e.g. Net Zero Strategy) reporting formats. The individual sector recipes are not presented here; in all cases the code enacts the scientific method that is described within the National Inventory Report (for GHGs) and the Informative Inventory Report (for AQ pollutants, including ammonia (NH3)). The final stages of inventory compilation data flows are summarised in the schematic below. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 860 The C# model run outputs are circulated to each of the sector expert teams per source category and a round of further implementation quality checks are conducted, including, per source • Time series consistency checks, to identify any gaps or outliers and to check the reported level of activity and emissions in the latest year; • Completeness checks, i.e.
to ensure that emissions data are coming through for each component of the source category, e.g. for each crop type, l ivestock type / subtype, • Implied Emission Factor (IEF) checks; range checks and QC (where applicable) against the ranges presented in IPCC/EMEP -EEA guidebooks for priority pollutants such as methane, nitrous oxide and ammonia (NH3); • Recalculation checks, to identify and check or justify the reasons for changes in historic inventory emissions data; where there have been changes to the scientific methods, input data and /or assumptions in the methods, these are documented by the sector l eads for onward reporting within the NIR, IIR and other outputs (e.g. DA inventory reports). Once the inventory data per source category-pollutant are checked and signed off by the sector expert leads, the source category estimates are compiled into summary files for onward reporting within the UK inventory system. The inventory data are subsequently used for national and international reporting outputs and are also used as the starting point for policy-focused analysis and tools; for example, the inventory data are used to underpin GHG and air quality pollutant projections and are used within UK Government analytical mechanisms such as regional -scale AQ pollutant dispersion modelling and the Defra mitigation policy -focused Scenario Modelling A 3.3.7.17 Output Viewer and Scheduler files Whilst the fully -detailed outputs at high resolution (per year, per DA, per source, per livestock type, per RFT etc.) are held in sector output databases, these files are very large in size and not readily useable by invento ry data users, e.g. within Government statistics and policy teams. Therefore, the UK agriculture inventory data are synthesised into simpler summary files, typically by aggregating outputs per pollutant per source-category and per activity, aligned to the various required reporting taxonomies, e.g. IPCC source categories for GHGs, NFR categories for ammonia (NH 3) and other AQ pollutants, Net Zero Strategy categories for UK national GHG statistical outputs and so on. To facilitate access to the dataset, more user-friendly tools have been developed to allow policy leads to explore the data via an Output Viewer, whilst sector leads typically conduct their quality controls on tabulated data within Scheduler files. The Scheduler is a macro-enabled set of MS Excel workbooks that summarise the timeseries of key outputs from the Inventory. It uses as input a set of template reports, which are generated from the output files from the model runs, by the ‘Output Viewer’ tool and is set up so that it a) Converts units, e.g. from kg N2O-N to kt N2O b) Summarises information by NAEI CRF and NFR reporting codes; c) Calculates ‘implied’ emission factors (IEFs) to help check the time series of source emission calculations and identify outliers; d) Summarises both the Activity Data and Emissions. It’s a tool that re-formats the model output into a ‘standardised’ set of inputs ready for summary and upload into the National Atmospheric Emissions Inventory (NAEI) database. The NAEI is the central database, managed by Ricardo as the DESNZ-funded Inventory Agency, that holds AD and EFs from all sectors and source categories (i.e. across Energy, IPPU, Agriculture, LULUCF,

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 861 Waste and Other) and is the central repository of all inventory data required for UK and sub - national reporting outputs for GHGs and AQ pollutants. A 3.3.7.18 UK Agriculture Inventory Model QA/QC System and Approach The UNFCCC Reporting Guidelines for Annex 1 country submissions of national GHG inventories set out the expectations for the transparency of reporting of national models used in higher-tier methods. Further, in recent UNFCCC expert reviews of the UK inventory the review teams have encouraged the UK to present more information regarding model quality assurance; this section summarises how the UK is addressing these requirements. The guidelines23 note that Annex 1 parties shall include the following in the “Descriptions, references and sources of information for the specific methodologies, including higher-tier methods and models, assumptions, EFs and AD, as well as the rationale for their selection. For tier 3 models, additional information for improving transparency.” The UK inventory submission, within the NIR chapter for Agriculture, presents all of the necessary information to cover the above requirement, i.e. regarding data sources, assumptions, methods, algorithms etc. This is evidenced by the fact that UNFCCC expert review team findings relating to the model QA documentation are noted in the recent Annual Review Reports as “not an issue” and any suggest ed improvements to model documentation are cited as an “ encouragement” rather than a more formal (i.e. mandatory requirement) “recommendation”. That said, the UK NIR chapter will continue to be revised and improved, as within the latest 2023 submission based on an extensive review of several Annex I countries NIRs, where we have added numerous flow charts, graphs and further information to provide UK evidence regarding methods, EF selection etc, and also to illustrate the quality controls that are implemented across the compilation cycle. Footnote 12 of the UNFCCC Reporting Guidelines clarifies that parties should also report “The basis and type of model, application and adaptation of the model, main equations / processes, key assumptions, domain of application, how the model parameters were estimated, description of key inputs and outputs, details of calibration and model evaluation, uncertainty and sensitivity analysis, QA/QC procedures adopted and references to peer-reviewed literature.” In addition to these requirements driven by international reporting obligations, the UK Government (DESNZ, formerly DECC) QA Guidance for Models 24 sets out further modelling good practice guidance to help ensure that models are transparent and present information to ena ble users to assess the level of risk, for example regarding data inputs and assumptions applied. Regarding adherence to the UNFCCC Guidelines, the Agriculture chapter of the UK National Inventory Report presents the following ✓ Descriptions, re ferences and sources of information for the specific methodologies, including higher -tier methods and models, assumptions, EFs and AD, as well as the rationale for their selection. ≈ For tier 3 models, additional information for improving transparency.
✓ Main equations / processes, key assumptions, description of key inputs and outputs, The UK will continue to improve the information presented in the NIR, noting that there is scope for providing more detail regarding some of the underlying data and assumptions and the rationale for their selection; for example, within this annex, we now present more details regarding the 24

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 862 data applied within the UK methods for recent years regarding the uptake of mitigation practices in the UK inventory calculations. The NIR chapter includes an overview of the tier 3 models that are used, and we have now included additional graphs (e.g. to justify the country -specific EFs applied) and flow charts to illustrate the QC for some of the more complex models, e.g. the N-flow model used across the livestock MMS. This is in direct response to the ERT comments from the review of the 2022 submission. We welcome further reviewer feedback on where to focus future efforts. Further, this annex seeks to address the remainin g UNFCCC QA reporting requirements, ✓ For tier 3 models, additional information for improving transparency ✓ The basis and type of model, application and adaptation of the model ✓ how the model parameters were estimated ✓ details of calibration and model evaluation ✓ QA/QC procedures adopted and references to peer-reviewed literature This annex provides an overview of the model structure, software, processes and quality controls. It outlines the governance, roles and responsibilities for key stakeholders and provides an insight into the background to the development of the UK model, providing links to the more detailed individual studies conducted during the 2010s to develop the methods and country -specific methane and nitrous oxide emission factors. Over a period of ~5 years the UK model was developed, with the development of sector-specific throughout that process, unit testing of steps within each method were conducted. The sector experts implemented reperformance checks on the C# code by running equivalent calculations conducted in MS Excel. Through this process the UK model methods were verified to be correctly performing the intended calculations. Numerous academic papers were developed and published to support and evidence this process and the selection of UK-specific parameters. The UK will continue to explore how to efficiently present a concise but informative summary of some of these facets of the model assurance, other than merely to provide links and references. A 3.3.7.19 Annual Inventory Quality Assurance, Quality Controls and Checks This section provides an overview of the approach to QA/QC across the annual cycle of agriculture inventory compilation and reporting; for specific details per source category, see the relevant section of the National Inventory Report or Informative Inventory Report. Through the inventory steering groups (NISC, managed by DESNZ; AQISG managed by Defra) the agriculture inventory team consults with stakeholders (including UK and DA policy leads, statisticians and researchers) on the current methods, areas of uncertainty and potential for improvements to data sources, assumptions applied and emission estimation methods. Where potential inventory improvements are identified, the inventory team may opt to prepare draft documentation to outline the proposed data/method improvement and submit those proposals to peer reviewers, typically academic and/or agriculture sector experts from the UK or other countries with similar production systems. In the last 5 years the UK team has engaged in several peer or bilateral reviews with agriculture sector experts from countries including Ireland, The UK agriculture inventory team engages annually in UNFCCC Expert Review Team reviews, CLRTAP and NECR reviews as well as attending international workshops and conferences to

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 863 keep abreast of developments in other countries and identify poten tial improvements for the UK inventory system, data and models. Quality Control and Documentation The agriculture sector Quality Control (checking, documentation and archiving) occurs throughout the data gathering, compilation and reporting cycle. The key activities that are undertaken and documented to check the estimates Second person checking as standard. Throughout the compilation process, the team updates the inventory calculation, checking and reporting files via a compiler/author and then a second expert / checker to sign off the work. As we work across multiple organisations, we have developed data QA templates to accompany all data / file transfers, with a common approach to set out the compiler (name, date) and checker (name, date), data f ields to log origin data files / date accessed, and a documented series of quality checks that the compiler must complete and sign off, and then the checker also. In this way we have a complete audit trail of the data sources and QC conducted through the process. • Updated activity data tables are checked for accuracy, time series consistency and completeness at a high level of resolution (e.g. per DA, livestock type, age, grid cell) by a second person not involved in the initial data compilation. • Draft outputs from the model are checked for completeness and accuracy by the lead sector expert, after the model runs are completed by the central team of analysts. Checking of input data for scope, completeness, consistency with data for recent years and (where available) verification against other independent datasets. Compilers check the incoming data from data providers to assess whether the data are complete and consistent with data for recent years. Specific examples are presented earlier in this annex to illustrate the specific QC of Mass flows / balances. The draft outputs are checked to ensure that process inputs and outputs are balanced and understood. For example, for the livestock MMS we conduct a nitrogen flow assessment to check on the origin, transfers and fate of N across the system. Sector expert s check the N partitioning across different parts of the MMS systems, e.g. the fate of N across housing, storage and spreading end uses. Completeness checks.
The database is checked for completeness and consistency of entry across the different pollutants and gases, per DA, livestock / crop / fertiliser type etc. Recalculation checks. The latest inventory dataset is compared against the previous inventory submission. Any recalculations are documented by inventory compilers and signed off by checkers. Reasons for the recalculations are documented, e.g. method improvements, revisions to input data or assumptions. These recalculation notes are referenced within the inventory database to facilitate reporting and transparency of recalculations. The sector leads and QA manager check that all recalculations are documented and that updates expected from previous review findings and improvement projects are completed and transparently described within reports to DESNZ and Defra, and the NIR and/or IIR. Time series checks and benchmarking checks. The time series of emissions are checked for step changes, trends, and any outlier data (e.g. outlier EFs or peaks/dips in activity data trends). Any unusual features are checked and explained, with reasons for significant trends and outliers documented in the method sections of the NIR and/or IIR. Implied Emission Factors (IEFs) are checked against previous estimates and for key categories against defaults (from IPCC guidance) to identify any notable UK-specific EF outliers. Method implementation checks. A range of common checks are performed across inventory calculation models, such checking that units are correct for input parameters; checking for

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 864 either new emission estimates (e.g. due to new UK data or new methodological guidance or new EFs within the IPCC guidance) or for any missing emission sources compared to previous Reporting checks. Inventory submissions are checked to ensure correct allocation into the CRF categories. Emission totals at national an d sub-category level are checked against the “master” dataset derived from the UK inventory database outputs, to minimise risks of data transcription errors into reporting templates. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 865 The following section describes in detail the methodology used in the LULUCF sector described linkages between (i) 4A and 4G, (ii) 4B, (iii) 4C, (iv) 4D, (v) 4E (i) 4A Forest Land and 4G Harvested Wood product data flows

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Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 869 A 3.4.1 Carbon stock changes due to afforestation and forest A 3.4.1.1 The Forest carbon accounting model CARBINE Carbon uptake by the forests planted in the UK is calculated by a carbon accounting model, CARBINE, as gains and losses in pools of carbon in standing trees, litter and soil in conifer and broadleaf forests and in harvested wood products. Restocking is ass umed in all forests. The method is Tier 3, as defined by IPCC (2006). Section A 3.4.2 demonstrates how the use of this model complies with IPCC good practice criteria for the use Tier 3 models. CARBINE simulates forest C stock changes represented by tree biomass growth, mortali ty and subsequent loss. The CARBINE model is primarily dedicated to reproducing the UK forest The model as used for this inventory consists of three sub -models or ‘compartments’ which estimate carbon stocks in the forest biomass, soil, and harv ested wood products. The forest biomass carbon sub-model is further compartmentalised to represent fractions due to tree stems,

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 870 A 3.4.1.1.1 Carbon in forest biomass The main driving module of CARBINE consists of a set of computerised math ematical functions and algorithms describing the accumulation (and loss) of carbon in tree biomass of different forestry systems at the per-hectare scale. Different functions and algorithms are used to represent distinct forestry systems, defined in terms • Tree growth rate (yield class) The tree species and growth rates represented are based on yield models originally produced by the British Forestry Commission (Matthews et al., 2016a, 2016b). The tree species covered include examples for coniferous species of spruces, pines, firs, larches, cedars, cypresses and all the major temperate and boreal broadleaf tree species. Growth rates in terms of maximum mean annual increment of stem volume (‘Yield Class’) can be represented in the range from 2 m3 ha-1 yr-1 up to 30 m3 ha-1 yr-1. The CARBINE model uses standard estimates for wood density wood carbon content to derive stem biomass from the stem volume predictions simulated by the M1 model (Lavers and Moore, 1983; Jenkins et al., 2011; Matthews, 1993). Wood and bark density along with the carbon content differences are not taken into account. The density of bark is lower than that of wood (Aaron, 1970), but the carbon content is usually higher (Matthews, 1993), hence it is assumed that the two effects cancel out. The biomass and carbon in tree foliage, branches, and coarse and fine roots are derived from the results for the stem by applying expansion factors. Species -specific biomass expansion factors are applied for these calculations. The biomass of a component of interest is calculated by multiplying stem biomass by a corresponding expansion factor. The UK species -specific crown and root biomass expansion factors were derived from the report of Jenkins et al., (2011) report. Branch biomass is calculated by subtracting foliage biomass from crown biomass. The coarse root biomass expansion factor includes an allowance for stump material. Robust information on foliage expansion factors relevant to UK conditions were not available, hence these were obtained from scientific literature. The ratio of foliage to stem changes over time, but approaches an asymptote (Matthews et al., 1991; Matthews and Duckworth, 2005) .
However, the asymptote in general is more suited to older, larger trees and as such is considered not to be representative of typical forests under regular manag ement. It was decided to use a biomass expansion relationship for trees of approximately 20 cm diameter in order to better represent managed forests. It is likely that this will ultimately underestimate foliage biomass in smaller trees, and conversely over -estimate in older, larger trees. Finally, fine root biomass is calculated with a uniform expansion factor βr=0.02 from a Liski et al., (2002) study. The expansion factors are not sensitive to stand age, management regime or growth rate. This approach was adopted for the simplicity and ease of implementation on the large-scale simulations. The mass of carbon in a forest was calculated from biomass by multiplying by the fraction of carbon in wood (0.5 assumed). As an example, the values used for these parameters for Sitka UK forests (c. 30%); parameters for other tree species are given in Matthews et al. (2014). Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 871 uptake by planting of forests of Sitka spruce (P. Sitchensis), yield Time of maximum mean annual increment (years) 60 Age at first thinning (years) 25 Stemwood density (oven dried tonnes m-3) 0.33 % Stemwood conversion loss 10% % Branchwood left in forest 100% % Branchwood harvested for fuel 0% % non-fuel products from bark 70% % small roundwood (underbark) used as fuel 20% % Pallets and fencing from small roundwood (under bark) 20% % Paper from small roundwood (under bark) 35% % Particleboard etc. from small roundwood (under bark) 25% % Fuel from sawlogs (under bark) 30% % Pallets and fencing from sawlogs (under bark) 0% % Particleboard from sawlogs (under bark) 40% % Structural timber from sawlogs (under bark) 30% Foliage turnover rate (annual) 0.2 Branchwood turnover rate (annual) 0.04 Coarse Root Turnover rate (annual) 0.02 Fine Root turnover rate (annual) 0.8 Underbark/overbark ratio at 15cm Diameter at Breast Height (DBH) – varies with DBH 0.9 Ratio of thinned stem volume that is sawlog at 15cm DBH (varies with DBH) 0.05

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 872 A 3.4.1.1.2 Dead wood and litter CARBINE includes a sub-model for representing accumulation and loss of carbon in dead wood and litter. Inputs of litter are related to the standing biomass of trees and to rates of tree mortality. Levels of tree mortality are represented implicitly in the standard Forestry Commission growth models, and explicit estimates are included in models for stands subject to no thinning, where mortality levels are high. Root and branch wood volume associated with dead trees is estimated in the same way as for living stemwood, by reference to allometric relationships. Deadwood and litter are assumed to decay according to a first order process, with rate constants that are normally set to be consistent with boreal and temperate conditions but can be adjusted for Mediterranean and tropical conditions. The other sign ificant input of carbon to the dead wood and litter pool is due to harvesting operations (as part of either thinning or clear -felling). The carbon in roots of harvested trees is assumed to enter the litter pool. The harvesting of stem wood is assumed to involve a conversion loss equivalent to 10% of standing stem volume, which also enters the litter pool. It is difficult to make accurate assumptions about the fate of branch wood and foliage at time of harvesting. In many situations, this material will be le ft on -site to deteriorate and decay. Sometimes it is possible that branch wood remaining after clear -felling may be deliberately burned. There has also been an increasing interest in active harvesting of branch wood (or at least some proportion of it) to s upply biomass to the Energy sector. However, currently, such practice remains very limited. For this inventory the assumption has been made that no branch wood is harvested but is left to degrade and decay on site as part of the litter pool. The branch Ann ual Turnover Rate (ATR) was fixed at 4% in accordance to Canadian forest carbon accounting model CBM -CFS (Kurz et al., 2009) . Deciduous species foliage turnover is assumed to be 100% (Kurz et al., 2009; Ťupek et al., 2015 ). Conifer species foliage ATRs were obtained by referring to relevant scientific literature. If insufficient empirical literature and data was available, the species were mapped to an allometrically similar species. Coarse root annual turnover was assumed to be 2% as in the CBM-CFS (Kurz et al., 2009; Kurz and Beukema, 1996; Li et al., 2003) . Fine root ATRs were mapped from the available scientific literature and the UK specific datasets provided by Vanguelova (pers. com.). The UK ATRs for fine roots were derived from Kielder forest for Sitka spruce and Alice Holt forest for oak. Lastly, root exudate ATR was set to 160% of fine root dry biomass, the upper quartile of reported exudate mass from grassland was adopted (Jones et al., 2009), because of limited understanding about forest rhizodeposition. Aboveground shed litter, foliage and branches, are accumulated in a litter layer and after partial degradation passed to the Fermenting (F) layer. Residues that are left after thinning or felling can be set to enter a litter layer. If the crop is not a forest, it is assumed that the litter and F layers are zero. The litter layer decomposition is modelled using modified ForClim-D model version (Liski et al., 2002; Perruchoud et al., 1999). Belowground litter is not included in this simulation, while the annual transfer rates are applied to foliage (Cf) and branch (Cb) litter biomass. They are expressed as a proportion relocated annually. Branch and foliage litter transfer are set according to the model proposed by Liski et al. (2002).
The transferred biomass is pooled and degraded by a fixed constant of 0.5, which is the average of constants given in the Liski et al. (2002) study. The CARBINE Soil Carbon Accounting model (SCOTIA, formerly referred to as CARBINE SCA; with a litter decomposition model derived from the ForClim -D model (Perruchoud et al., 1999; Liski et al ., 2002). Above -ground turnover of material such as foliage, branches and dead

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 873 stemwood enters the litter pool, which is then broken down to F -material (Fermenting) as a function of temperature and rainfall, releasing CO2. Within the soil, a number of layers exist, each with its own set of texture (Sand, Silt, Clay) characteristics. Carbon from decayed litter, dead roots, and root exudates enters each layer and is assigned to four active pools; resistant plant material (RPM), readily decomposable plant material (DPM), biological material (BIO) and humic material (HUM). A proportion of organic carbon is also assumed to be inert, and unavailable for further activity. The active pools undergo decomposition and transference, releasing CO 2. Decomposition (aerobic and anaerobic) within each pool and layer is influenced by response functions to water saturation in the soil, temperature, pH, and the presence (or not) of plant cover on the so il surface. The availability of water within each layer, and the level of saturation are largely defined from soil texture following Saxton and Rawls (2006) coupled with inputs from rainfall, (or drainage) and removal of water through evapotranspiration. I n any soil layer, water above field capacity can drain to lower soil layers, complete with any dissolved organic carbon (DOC). The rates of potential decomposition of each carbon pool and the response functions follow ECOSSE (Smith et al., 2011). New carbon input to the soil arises from four • Recently dead root material (according to a rooting profile depth), • Transfer from the F-material arising from the decomposition of above-ground litter, • Secretions and exudates from the roots, • DOC; this carbon can become available to the biological pool and enter the ‘reactive Turnover rates for mortality of tree components (roots, foliage etc.) are species dependent and An improved version of the soil sub -model was implemented for the 1990 -2016 inventory. This included work on parameterisation of litter input from ground flora and other non-forest vegetation, assuming a decrease in the contribution of non-tree litter from 60% of the carbon input assumed in ECOSSE for pasture to zero contribution at canopy closure, after an initial year of clearance of A more comprehensive description of the soil sub-model will be described in a technical report. Additional pathways in which carbon is lost from organic soil include waterborne export of DOC and particulate organic carbon (POC) from drained organic so ils, which is related to land use disturbances (erosion, burning), and drainage ditches. The off -site CO 2 emissions from the decomposition of POC and DOC exported from drained organic soils are calculated separately using Tier 1 and 2 approaches (see Evans et al 2017 and Section A 3.4.7). Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 874

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 875 A 3.4.2 Alignment of CARBINE to the IPCC suggestions for “Use of Models in Good Practice National Greenhouse Gas The IPCC has published suggestions on the approach to implementing good practice in the use of models25 in national GHG inventories. These suggestions are provided in “Use of Models and Facility-Level Data in Greenhouse Gas Inventories. Report of the IPCC Expert Meeting on Use of Models and Measurements in GHG Inventories, 9-11 August 2010, Sydney, Australia”. Chapter 3 provides a bullet point list of the key elements of a model that can be used to guide the description provided by inventory compilers of the modelling approach they use. Providing this detail increases the transparency of the methodological description. used in CARBINE for each of the elements, or criteria, and provides references to where further Carbon change in the forests of the UK (meeting the UK definition of forest for inventory purposes) is calculated by a carbon accounting model, CARBINE, as the sum of gains and losses in pools of carbon in vegetation, litter and soil in conifer and broadleaf forests and in harvested wood products. Restocking is assumed in all forests. The method is Tier 3, as defined by IPCC (2006). • UK Annex 3.4.1. Carbon stock changes due to afforestation and forest management (4A); Section 6.2. CATEGORY 4A – FOREST LAND; Section • Matthews et al. (in prep). The CARBINE model. A technical description Chapter 2. Modelling purpose and scope 25 In the application of models in National Greenhouse Gas Inventories, critical issues are suitability, parameterization, calibration, evaluation, and uncertainty. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 876 The CARBINE model was first developed by the then Research Division of the Forestry Commission in 1988 (Thompson and Matthews, 1989), now Forest Research. It is built around the stand level M1 growth and yield model which is based on yield tables published in the early 80s (see Arcangeli and Matthews). The general purpose of the CARBINE model is to address questions about the carbon and GHG balances of forestry systems, and to inform the development of forest policy and practice, particularly regarding the goal of climate change mitigation. It was adapted for use specifically with the UK GHG inventory and first used in the GHG inventory submitted in 2014. The UK replaced the C-FLOW model with CARBINE because CARBINE has several advantages which allows it to more accurately estimate GHG emissions and removals. CARBINE can model a more diverse range of species and forest management practices, and model complex changes or trends in forest management over time.
It also addresses a key limitation of C-FLOW, which assumed that forests planted prior to 1921 were at carbon equilibrium. Matthews et al. (2014) gives an overview of the CARBINE model and a comparison of its use in the 1990-2012 LULUCF inventory with the C-Flow model previously used model to forest carbon stock changes. The growth conditions of forests represented by CARBINE are principally of relevance to UK forest conditions. The CARBINE model is not operating outside its originally intended • UK NIR. Annex 3.4.1. Carbon stock changes due to afforestation and forest management (4A); Section 6.2. CATEGORY 4A – FOREST LAND; 6.8 • Matthews et al. (in prep). The CARBINE model. A technical description. Chapter 2. Modelling purpose and scope • Thompson, D.A., Matthews, R.W., 1989. The storage of carbon in trees and timber. Forestry Commission Research Information Note 160. Forestry • Matthews, R., Malcolm, H., Buys, G., Henshall, P., Moxley, J., Morris, A. and Mackie, E. (2014) Changes to the representation of Forest Land and associated land-use changes in the 1990-2012 UK Greenhouse Gas Inventory. Forest Research and Centre for Ecology and Hydrology (DECC Contract GA0510, UKCEH Contract no. NEC0376) The CARBINE model is a complex model, with several sub models. These sub-models forest carbon sub-model, soil sub-model, SCOTIA, and wood products sub- model. The forest carbon and soil sub-models are used in the GHG inventory and the latest UK NIR provides a summary of CARBINE model. Forest Research are working on a report which documents the methods employed in the implementation of the CARBINE forest sector carbon accounting model. The report includes model equations, parameters, assumptions, verification and supporting scientific of the structure and components of CARBINE. • Matthews et al. (in prep). The CARBINE model. A technical description. Chapter 3. Representation of forest stands; to; Chapter 4. Representation of harvested wood products; and; Annex 1. Detailed soil carbon model • UK Annex 3.4.1. Carbon stock changes due to afforestation and forest

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 877 The CARBINE model is a complex model, and only a summary of the key assumptions can be given here. A key concept of the methodology underlying the CARBINE model is that net exchanges of carbon between forest pools (trees, deadwood, litter and soil) and related pools (deforestation, HWP) can be inferred from the changes in the carbon stocks of these individual pools. The section numbers in the description below refer to the sections in Matthews et al. The CARBINE model. A technical The main assumption is that all types of forests and management in the UK can be represented by the FC yield models – including by assuming that species not covered by the UK yield tables can be mapped to a species for which a model is available. Immediate restocking is assumed in all forests. 3.2. Stand stem biomass and carbon. The key assumption is that stem merchantable biomass can be calculated from stem volume from the yield tables using a species-specific stem density and a country-specific estimate of carbon content for all wood (50%) 3.3. Stand tree biomass and carbon. The key assumption is that the stem biomass can be converted to whole tree biomass using biomass expansion factors for branches, leaves, coarse roots and fine roots. Forest stand management is represented by four broad 1) No thinning and no felling (i.e. effectively no management for production); 2) Clear-felling on a specified rotation without thinning; 3) Thinning with clear-felling on a specified rotation; 4) ‘Continuous cover’ silviculture (i.e. woodland management with harvesting based on thinning only, that also aims to always maintain tree cover on the land). 3.5. Stand disturbance events. The key assumption for the inventory is that there will be no mortality beyond the normal senescence of part of tree and within stand competition as predicted by the yield model as calibrated on the permanent sample plots. Supplementary calculations are carried out for the purposes of reporting relevant GHG emissions as part of GHG inventories for disturbances from fires on Forest Land. 3.6. Tree harvesting in stands. The assumption is that the harvesting is as modelled in the yield tables and that only certain parts of the tree are removed – i.e. there is no whole-tree harvesting. 3.7. Losses of carbon from parts of living trees through senescence. The key assumptions are that deadwood and litter inputs in the form of losses of branches and foliage from living trees can be modelled by annual turnover rates and that these losses will be replaced (i.e. that the relationships between the carbon in the different compartments implied by the biomass expansion factors still holds). 3.8. Stand deadwood and litter accumulation. Carbon enters the deadwood and litter pools through several • Losses of biomass from the senescence of parts of growing trees (Section 3.7) • Mortality of trees as a result of stand competition (Sections 3.2 and 3.3) • Mortality of trees as a result of stand natural disturbance (Section 3.5) • Tree biomass discarded in the forest during harvesting operations (Section 3.6). Carbon is lost from the deadwood and litter pools through decomposition. 3.10. Soil carbon (including fermenting material). That the soil carbon and fermenting material can be represented using an “ECOSSE- style model”. This model, called SCOTIA, was developed to allow full representation of UK specific conditions, including both mineral and organic soils. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 878 4.1. Representation of Harvest Wood Products. While a more disaggregated description of HWP is available in CARBINE, for the purpose of the Convention reporting, wood products are grouped in a more limited set of semi-finished product categories consistent with the IPCC guidelines and modelled using first order decay functions and product half-lives specified as part of Tier 1 methods in the IPCC good practice guidance. The summary above of the key assumptions increases the transparency of the of the • Matthews et al. (in prep).
The CARBINE model. A technical description. Chapter 3. Representation of forest stands; to; Chapter 4. Representation of • UK Annex 3.4.1. Carbon stock changes due to afforestation and forest A version of CARBINE has been specifically developed for the UK GHG inventory. In principle, the M1 forest growth model that underlies CARBINE permits a very wide range of possible stand management regimes to be represented. However, when modelling UK forests at national scale, forest stand management are represented by four broad prescriptions types and a range of rotation length (see Annex 3 of the National Forestry • Matthews et al. (in prep). The CARBINE model. A technical description. Chapter 2. Modelling purpose and scope • UK National Forestry Accounting Plan, 2021 to 2025 (2019) Annex 3. Many of the parameters in CARBINE are based on literature reviews and the key parameters are described in the report describing CARBINE. • Matthews et al. (in prep). The CARBINE model. A technical description. Chapter 2. Modelling purpose and scope. • Lavers, G.M. and Moore, G.L. (1983) The strength properties of timber. Building Research Establishment Report CI/SFB I(J3). Building Research • Levy, P.E., Hale, S.E. and Nicoll, B.C. (2004) Biomass expansion factors and ratios for coniferous tree species in Great Britain. Forestry, 77,

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 879 CARBINE uses a wide range of input data – see above for a description of these. The main input data are, inter alia, 1) information on the growth of stem wood volume in different stands of trees from the M1 growth model; 2) standard estimates for wood management practices; 4) soil turnover rates; 5) decomposition rates of active soil pools to DOC, 6) occurrence of land use changes over time. All the data necessary for the reporting of forest land gains and losses in pools of carbon in standing trees, litter and soil in conifer and broadleaf forests and in harvested wood products and forest. Gains and losses in forest soils are also • Matthews et al. (in prep). The CARBINE model. A technical description. Chapter 3. Representation of forest stands; to; Chapter 4. Representation of harvested wood products; and; Annex 1. Detailed soil carbon model CARBINE (excluding soil carbon): In 2003, Robertson et al. undertook a study to evaluate the completeness and suitability of the C-FLOW and CARBINE models for estimating carbon stocks and potential stock changes in the forestry sector at the stand and national levels, and the reliability of underpinning data and parameter estimates used by C-FLOW and CARBINE. Based on the results considered, Robertson et al. concluded that, while there may be some issues to address with regard to some inaccuracies in predictions of tree carbon stocks made by both CARBINE and C-FLOW, such model predictions are reasonably accurate. Although the analysis of Robertson et al. can only be regarded as an initial investigation, the results indicated that the accuracy of predictions made by both models is well within short-term fluctuations observed for A more recent verification of the growth model M1 underpinning CARBINE started in 2017 has also confirmed that the growth model displayed good to reasonable consistency with growth trend data collected in sample plots. For some tree species, evidence of deviations in growth trends at older stand ages. This included Sitka spruce, but beyond the ages of conventional forest rotations. A programme to refine existing growth models is in progress. Significant work already done (new growth curves calibrated). The aspiration is to integrate the new growth models into CARBINE once fully tested. This is discussed in the report describing CARBINE. SCOTIA (soil sub-model in CARBINE): The work done to confirm the suitability of the soil carbon sub-model, SCOTIA, for use in the UK GHG inventory is explained in detail in a report in preparation. The work presents the evidence that the estimates of soil carbon stocks and stock changes produced by the SCOTIA model are consistent with available • Matthews et al. (in prep). The CARBINE model. A technical description. Section 8.2.5. Long-term trajectories • Matthews et al. (in prep). SCOTIA forest soil carbon Interim progress report on comparison of model estimates and measurements of soil carbon • Robertson et al . (2003). Evaluation of the C-FLOW and CARBINE carbon accounting models. Section 3 of UK Emissions by Sources and Removals by Sinks due to Land Use, Land Use Change and Forestry Activities (2003) ( CARBINE-carbon-models-Robertson-Ford- Robertson/22f909599387970eef6b61ff7057151f4b86d76e)

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 880 The QA/QC procedures used in the forest land inventory are summarised in this • Section 6.2.6. Category-Specific QA/QC and Verification (CATEGORY 4A – • Section 6.8.4. Category-Specific QA/QC and Verification (CATEGORY 4G – • Section 6.11. GENERAL COMMENTS ON QA/QC. There is a detailed QA/QC plan for the forest land inventory, which is described in Henshall (2018). Chapter 8 of the CARBINE description report presents some comparisons of parameters referred to in the CARBINE model with standard estimates for these parameters from scientific literature, where relevant, and with any published parameter estimates of particular relevance to UK conditions. The SCOTIA report provides a detailed comparison of the CARBINE SCOTIA soil carbon sub-model estimates and measurements of soil carbon stocks and fluxes. • Matthews et al. (in prep). The CARBINE model. A technical description. Chapter 8. Comparisons with standard parameters and estimates • Matthews et al. (in prep). SCOTIA forest soil carbon Interim progress report on comparison of model estimates and measurements of soil carbon • Henshall (2018). UK Greenhouse Gas Inventory LULUCF Sector Forest Land QA Plan. Paul Henshall. Forest Research

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 881 There are several papers describing the C-FLOW model which follows the same theory of combining the FC yield tables with biomass expansion factors and a soil and litter model. There are also non-journal publications that have been reviewed by peers.
Peer reviewed journal publications • Cannell, M.G.R., Dewar, R.C. (1995). The carbon sink provided by plantation forests and their products in Britain. Forestry 68, 35–48. • Dewar, R.C. (1990). A model of carbon storage in forests and forest products. Tree Physiol. 6, 417–28 • Dewar, R.C. (1991). Analytical model of carbon storage in the trees, soils, and wood products of managed forests. Tree Physiol. 8, 239–258 • Dewar, Roderick & Cannell, M. (1992). Carbon sequestration in the trees, products and soils of forest An analysis using UK examples. Tree physiology. 11. 49-71. 10.1093/treephys/11.1.49. • Matthews, R.W. (1996). The influence of carbon budget methodology on assessments of the impacts of forest management on the carbon balance, Apps, M.J., Price, D.T. (Eds. ), Forest Ecosystems, Forest Management and the Global Carbon Cycle. Springer-Verlag, Berlin, New York, pp. 233– • Matthews, R.W. (1994). Towards a methodology for the evaluation of the carbon budget of forests. Kanninen, M. (Ed. ), Carbon Balance of the World’s Forested Towards a Global Assessment. Proceedings of a Workshop Held by the Intergovernmental Panel on Climate Change AFOS, Joensuu, Finland, 11-15 May 1992. Painatuskeskus, Helsinki, pp. • Matthews, R.W. (1992). Forests and arable energy crops in can they stop global warming? Richards, G.E. (Ed. ), Fuel for Thought. • Matthews, R.W. (1991). Biomass production and carbon storage by British forests. Aldhous, J.R. (Ed. ), Wood for The Implications for Harvesting, Utilisation and Proceedings - 1991 Discussion Meeting. Institute of Chartered Foresters, Edinburgh, pp. 162-177. A 3.4.2.1 Forest activity management The activity data for forests comes from different data sources for the public and private forest estates. The public forest estate is as defined in Forestry Statistics and covers the woodland that used to be managed by the Forestry Commission (FC) in Great Britain and that managed by the Northern Ireland Forest Service (NIFS). The public forest estate in Great Britain is now managed by separate organisations in England (Forestry England (FE)), Scotland (Forestry and Land Scotland (FLS)) and Wales (Natural Resources Wales (NRW)). The private forest estate covers all other woodland, including areas of forest managed by local authorities and other public bodies. Each organisation maintains a Sub -Compartment DataBase (SCDB), containing information on location, size, species, growth rate and management of the forests. Information from the SCDB) was used to create a distribution of species and yield class (an indication of growth rate) for the public forest estate. For the private forest estate, information from the National Forest Inventory (NFI) survey of woodlands w as analysed to estimate yield class and species by age class and scaled to represent the whole private forest estate. Data from the Forestry Commission’s new planting and wood production statistics were used to assign the areas in an age class to individual years, either as areas restocked or areas newly planted. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 882 Management of forests is represented as one of four Clearfell with thinnings, clearfell without thinnings, managed but not clearfelled, and not used for timber production. For the clearfell forests restocking occurs after the rotation period. For non-clearfell productive woodlands it is assumed there is a 30-year overlap of restocking and non-restocked trees. The area of land felled each year was estimated from the wood production statistics separately for both public and private forests. The rotation periods for forests were estimated based on information on the intended management of the public estate. This analysis gave a target rotation period for each modelled species and yield class. The actual rotations historically applied to the forest estate are unknown and for the private forests the area of woodland used for timber production is also unknown. In order to match production, given the age class distribution of the forest, an algorithm was implemented to adjust the assumed rotations and the percentage of private sector woodland not used for timber production. This algorithm adjusts these assumptions in order to match the modelled wood production with the timber production statistics separately for the public and private forests. It was assumed that the forests would be felled evenly over a period +/ -7 years from the target rotation period. A comprehensive description of this algorithm will be presented in a separate technical report. Information on the management of privately owned forests that is used to inform the inventory estimates, as well as a description of how forest land AD (forest land remaining forest land; land converted to forest land) are derived, is included in the UK National Forest Accounting Plan, which A 3.4.2.2 Forestry activity historical and current afforestation rates Irrespective of species assumptions, the variation in CO 2 removals from 1990 to the present is determined by the afforestation rate in earlier decades, the effect this has on the age structure in the present forest estate, and hence the average growth rate. Afforestation is assumed to occur on ground that has not been wooded for many decades, based on the assumption that if it had previously been woodland it would be in the restocking statis tics rather than the new planting statistics as a result of the regulatory framework that applies to forestry in the UK. A comparison of historical forest census data and the historical annual planting rates has been undertaken. Forest censuses were taken in 1924, 1947, 1965, 1980 and the late 1990s. The latest census (National Forest Inventory - NFI) has only just been completed. The comparison of data sources showed that discrepancies in annual planting rates and inferred planting/establishment date (from woodland age in the forest census) are due to restocking of older (pre-1920) woodland areas and variations in the harvesting rotations. However, there is also evidence of shortened conifer rotations in some decades and transfer of woodland between broadleaved categories (e.g. between coppice and high forest).
It is difficult to incorporate non-standard management in older conifer forests and broadleaved forests into the Inventory because it is not known whether these forests are on their first rotation or subsequent rotations (which would affect carbon stock changes, particularly in soils). The area of afforestation in a given year is predicted based on applying the yearly distribution from the new planting and restocking statistics to the age class inventory. Age classes prior to the availability of new planting statistics are assigned evenly to individual years. For this inventory submission the assumption was made that we can estimate the area felled for recent years based on the timber production in the year of felling. It is assumed that woodland felled is immediately restocked. As we have an estimate of the area restocked for these years, the remainder of the area for each year was assumed to be restocking or natural

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 883 regeneration. For years prior to the timber production statistics (i.e. prior to 1976), an estimated ratio between restocking and afforestation was used based on the earliest data. For restocked woodland the forest area was assumed to have been restocked twice and had been managed in the same fashion and on the same rotation. The planting data used as input to the CARBINE model come from national planting statistics from 1921 to the present (provided by the Forestry Commission) for England, Scotland and Wales and from 1900 to the present (pro vided by the Northern Ireland Forest Service). For England, Scotland and Wales estimates of area of woodland by species, yield class and broad age class came from analysis of the NFI (for private woodland) and the SCDB (for FC/NRW woodland). The NFI provides woodland statistics for Great Britain, (England, Wales and Scotland), broken down by region. It comprises a digital woodland map based on comprehensive aerial photography and a field survey using 15,000 one -hectare sample squares. The digital map and fi eld survey cover all woodland areas down to 0.5 hectares. An initial digital woodland map was published in spring 2011. The NFI woodland field survey provides direct assessments of woodland growing stock including species composition, stand structure, tree age (distribution) productivity indices, numbers of trees, and diameter and height distribution. Standing biomass (and carbon) in trees, including above and below ground biomass, can be derived from these assessments using GB - specific conversion factors a nd allometric equations. A complete 5 -year cycle of ground survey has now been completed. NFI data do not allow the carbon stocks of deadwood or litter to be estimated. The NFI has been supplemented by an assessment of the area of small woods (woodland between 0.1 ha and 0.5 ha) to align with the minimum woodland area for UNFCCC reporting as set out in CMP.7 (Forestry Commission, 2017). The analysis of small woods area included no characterisation of the resource. Since there is currently no information on the age- distribution of the area of small woods, it was assumed to have established evenly between 1900 The NFI uses a lower integral open space threshold of 0.5 ha (as opposed to 1 ha), which requires a downward adjustment to areas. However, th e main differences in 2010 GB woodland cover between the NFI (2982 kha) and previous estimates (2757 kha, Forestry Statistics 2010) arise from identified errors in the previous woodland survey, particularly the under -estimate of woodland areas between 0.5 and 2 hectares. Estimates of woodland loss have been assessed, which affect the total estimated woodland area in the GHGI (but are not yet reflected in the national Forestry Statistics). We assumed that the NFI survey gives a distribution of all the private forest area for a base year of 2011, and the SCDB gives a distribution of all the public forest area for a base year of 2014. The main NFI survey includes areas of woodland >0.5 ha. An adjustment was made to the areas of woodland to account for woods be tween 0.1 ha and 0.5 ha. For England and Wales, the estimates are derived from a calibration of tree cover plotted in the National Tree Map (NTM) product across England and Wales26, using a comparison of manual photographic interpretation with the NTM product within a sample of 1 km square tiles. For Scotland, the estimates are derived from a direct evaluation of polygons in the map constructed for the Native Woodlands of Scotland Survey (NWSS)27, which mapped all woodland polygons in Scotland down to 0.1 hectares in size 26 27

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 884 by photographic interpretation. The areas of small woods used in this inventory were based on data published in 2017 by the Forestry Commission in the report “Tree cover outside woodland An algorithm was used to obtain the area of woodland afforested each year by removing the area of felling from the age class distribution. The species were then allocated to this “residual distribution’ by starting in the base year and allocating the shortest rotations first. The plantin g years for all restocked woodland are assigned by the algorithm to give two rotations of the same length as the assigned rotation and are thus notional. This approach was undertaken to “spin up” the model in terms of soil and litter in order to reach a st ate consistent with land that has been forest for a long period. This algorithm will be described in detail in the same technical report as the description of allocation of the management of forests. As part of implementation of wetland drainage and rewetting accounting, new maps of peat soils have been created for each of the DAs (see Section A 3.4.7.1). These maps were combined with a forest map to give areas of conifer and broadleaf forests planted on organic soils for each DA.
These total areas were assigned to individual afforestation years by adjusting the previously applied the distribution of organic soil areas and an analysis of recent grant -aided new planting on organic soils and implementation of policies against planting on organic soils in each of the records, information on forest age class distribution from NFI field assessments and interim are therefore significantly different to those reported as official planting statistics supported by grant-aid. The afforestation rates for each planting type in the UK have been calculated from the Kingdom since 1500 based on estimates of woodland area by age from the NFI and administrative records. 1911-1920 3.13 0.60 11.72 0.26 28 cover-outside-woodlands-are-like-today-8211-nfi-inventory-reports-and-woodland-map-reports/

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 885 1921-1930 3.12 0.61 13.61 0.36 1931-1940 3.82 0.66 14.70 0.52 1941-1950 8.21 1.90 18.06 0.80 1951-1960 13.31 3.73 19.34 1.06 1961-1970 20.64 6.75 21.29 1.30 1971-1980 28.10 10.69 14.76 0.97 1981-1990 20.63 7.62 17.11 0.97

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 886 A 3.4.2.3 Allocation of CARBINE outputs to UNFCCC inventory sub-categories The CARBINE model output was post-processed using the IPCC default 20-year transition period for Land converted to Forest to move into the Forest remaining Forest category. The area within the Land converted to Forest Land sub -category is split between cropland, pasture grassland, semi-natural grassland, sett lement and other areas. Pasture grassland and semi -natural grassland are combined for Grassland reporting in the CRF. This split is based on the relative proportions of historical land use change from these categories to forest. The proportions for each country change over time because the 20-year transition period has a different start date for each reporting year. The CARBINE model outputs take account of forest area loss through conversion to other land uses (deforestation). A 3.4.2.4 Nitrogen fertilization of forest land Nitrogen fertilization of forest land is assumed to occur only when absolutely necessary, i.e. new planting on ‘poor’ soils (mining spoil, impoverished brown field sites, or upland organic soils). In terms of the inventory, this means that N ferti lisation is assumed for Settlement converted to Forest land and Grassland converted to Forest Land on organic soils. The areas of new planting A 3.4.3) for 4.A.2. Land converted to Forest land. Where fertilisation occurs, an application rate of 150 kg N ha -1 is assumed based on Forestry Commission fertilisation guidelines (Taylor, 1991). The guidelines recommend applying fertiliser on a three-year cycle until canopy closure (at approximately 10 years), but this is thought to be rather high (Skiba 2007) and unlikely to occur in reality, so two applications are adopted as a compromise. These applications occur in year 1 and year 4 after planting. The N 2O emission factor for applied nitrogen fertiliser is the default value of 1% used in the IPCC 2006 Guidelines. Emissions of N 2O from N fertilisation of forests are estimat ed using a Tier 1 methodology and IPCC default emission factors. The emissions have fallen since 1990 due to reduced rates of new forest planting. A GWP of 298 for N 2O is used. Indirect emissions of N 2O from atmospheric deposition also arise from this activity and are reported under Sector 4. A 3.4.2.5 Non-CO2 emissions from drainage on forest soils Emissions from forest on drained soils are calculated for mineral and organic soils separately using a Tier 1 methodology. Emissions of CH 4 and N2O from organic soils are calculated using Tier 1 EFs (see Section A 3.4.7). Emissions of N2O from mineral soils are calculated using Tier 1 EFs (IPCC 2006) and information on the distribution of forest cover on different soil types (Yamulki et al. 2012), adjusted by the amount of forest planted since 1920. The area of forest on mineral soil is adjusted by splitting it between organo-mineral soils, free-draining mineral soils and

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 887 easily waterlogged mineral soils, which require artificial drainage (based on the current guidance and policy for forest operations and management). The proportion of min eral soils requiring artificial drainage 34% in England, 24% in Scotland, 3% in Wales, 68% in Northern Ireland and 26% in the UK as a whole. We assumed all forest on organo -mineral soils is cultivated prior to planting and therefore effectively drained. A 3.4.3 Land Use Change and Soils (4B, 4C, 4E) A 3.4.3.1 Estimation of land-use change using Bayesian data assimilation The tracking of land use and land -use change is fundamental to producing accurate and consistent greenhouse gas inventories for the LULUCF sector. In pr evious inventories the methodology for land use/land -use change modelling applied Approach 2 (non -spatial LUC matrices) using a combination of ‘snapshot’ Countryside Surveys, forest inventory and administrative statistics. However, this approach has proved to be insufficient for tracking annual land use change, which was particularly a consideration for the reporting requirements of the second commitment period of the Kyoto protocol. Firstly, the non-spatial matrix-based approach is insufficient for tracking annual land-use the matrices have no time dimension and are defined independently each year. There is therefore no possibility of representing a sequence of land-use on the same parcel of land (such as afforestation followed by deforestation, or crop- pasture rotations). Secondly, the data used to estimate these matrices in the UK are rather limited. The Countryside Surveys are only carried out approximately once per decade (and not since 2007), and whilst the geographical extent is very wide, the actual ground area surveyed is a small fraction of the total UK area. The afforestation/deforestation statistics from the Forestry Commission have good national coverage (excluding Northern Ireland) but do not contain any information on the spatial location or land use prior to afforestation or following deforestation.
BEIS (now DESNZ) commissioned a research project (Land-Use Tracking) to improve tracking of land use change for the LULUCF sector (reference TRN 2384/05/2020), with additional funding for inventory implementation. This built on the successful pilot project that used a Bayesian data assimilation approach to integrate available multiple land -use data sources into vectors, i.e., unique sequences of land use histories and their associated areas (Levy et al. 2018). In the pilot study, carbon emissions arising fr om land-use change were modelled using the vector output, demonstrating the feasibility of using this as a methodology for the LULUCF sector. Although the UK is well -provided with land-use datasets, UK land-use change occurs on small, fragmented areas and there is no single time-series with sufficient reliability to infer annual land- use change by difference for inventory reporting. Even with advances in satellite sensors, GIS and spatial data handling, the accuracy of change detection from EO-based products is generally too poor; the different EO products are inconsistent (with each other, and with themselves over time), irregular, and infrequent before 2000. Repeat ground -based surveys can detect change more reliably but lack a spatial element or informat ion on gross change (e.g., the annual June Agricultural Census) and survey coverage is often incomplete or at infrequent intervals. In the 1990-2020 submission the UK updated the methodology for estimating land use change using a Bayesian data assimilation approach. This constrains estimates of gross land-use change with national-scale census data, whilst retaining the detailed information available from other land- use data sources. A full description of the methodology is given in the project reports (Levy et al. 2020, 2021; Rowland et al. 2021) and a summary is given here. Development work on the matrices and the methodology is ongoing. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 888 A 3.4.3.1.1 Land-use change data sets A range of datasets containing relevant information on land use and land -use change in the UK were assessed for inclusion in the Bayesian assimilation • Countryside Survey (DAERA 2016, UKCEH 2020a): GB and NI field repeated surveys of stratified 1km sample grid squares giving land-use and land-use change; • Agricultural census data (DAERA 2021, Defra 2021, Scottish Government 2021, Welsh Government 2021): annual records of the total area in the main agricultural land uses; • Land Cover® Crops (UKCEH 2016): Based on the LCM parcel framework with additional crop information from satellite data • CORINE Land Cover (CORINE 2020): Survey of European land cover and land cover change from semi-automatic interpretation of high resolution satellite imagery; • Agricultural holdings this is the finest level of detail from the Agricultural census dataset with information on agricultural land areas at the farm level; • field-level register of agricultural subsidy claims under the EU Common Agricultural • crop classification of satellite imagery for England, with ground -truth data from agricultural subsidy inspectors; • Forestry Commission and Forest Service Northern Ireland forestry statistics on afforestation and deforestation currently used in the GHGI, forest maps from National Forest Inventory and National Forest Estate Sub -Compartment Database (Forestry • FAOStat MODIS data on artificial annual area of coverage of artificial surfa ces (urban) in the UK 2001 -2018 from satellite imagery . The decadal LUC matrices compiled for 1950 -1979 from the Monitoring Landscape Change (MLC) dataset for Great Britain (MLC 1986) were also used. The only data available for Northern Ireland pre -1990 are land use areas from The Agricultural Census and The Forest Service (Cruickshank and Tomlinson 2000). Some datasets were obtained under licence from the different national governments (IACS, agricultural holdings data) as they contain sensitive information. It was not possible to obtain permission for some data within the timescales of the research project (agricultural holdings for England, Wales and Northern Ireland, and IACS data for Scotland, Wales and Northern Ireland) but efforts are continuing to obtain, assess and integrate these datasets in the future. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 889 Northern Ireland, United Kingdom of Great Britain and Northern Ireland) Forest Land Cropland Grassland- Land Cover Map 1990, 2015,

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 890 Forest Land Cropland Grassland-

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 891 The agricultural holdings data for Scotland were supplied for 1990-2018 and provide spatial data at the postcode or parish level, rather than the exact location. IACS data use a very large number these and LULUCF classes is too large to display here but available on request. The IACS data require considerable processing and classification to ensure consistency and only the data for England 2004-2014 were included in the data assimilation, as issues with 2015 -2019 data had not been resolved within the timescale of the Land-Use Tracking project. Ordnance Survey Land Use Change Statistics and LUCAS (Land Use Cover Area Frame Survey) were also assessed but not included in the final data assimilation because of time constraints on A 3.4.4), with a sub -division of grassland between improved pasture and semi -natural rough grazing land. Land -use change information from repeated survey data and decadal land -use change matrices were transformed to annual time series based on interpolation between the A 3.4.3.1.2 Bayesian data assimilation methodology There are three data structures that provide information on land-use - the time series of areas of each land use, A - the transition matrix B denoting the areas which have changed from each land use to each of the other land uses, over a given time period - the 3-D spatio-temporal array U which denotes land use at each location in space and time, in a regular gridded format. The three data structures are inter-related by arithmetic equations (see Levy et al. 2018). These equations are used as a model to relate the different observational data via Bayesian data assimilation in a two-stage process. • Firstly, a Baye sian approach is used to estimate the parameters in B, given prior information and partial observations of U and A.
• Secondly, the posterior distribution of B and spatial and probabilistic information on the location of land-use change (W) is used to simulate posterior realisations of U. The maximum a posteriori probability (MAP, the mode of the posterior distribution) realisations represent the best estimate of land -use and land -use change, given the available data. The spread of possibl e realisations in the posterior distribution provides the uncertainty in our knowledge of the true land-use change. The output data structure U has the high temporal and spatial resolution (annual and 100 m) necessary for capturing small-scale land-use. The data cuboid U contains a lot of repetition and can be summarised efficiently as the set of unique vectors of land use over time, together with their associated areas. This provides an intermediate option for modelling the effects of land-use change in the GHG inventory, without requiring a fully spatially explicit model. The data assimilation approach produced transition matrix (the gross area changing annually between the different land uses from 1951 -2020), the gross gain in area of each land use, the gross loss of area for each land use, and the net change in area occupied by each land use, for each devolved administration (sections 7-10 in Levy et al. 2021). Example outputs are shown in

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 892 between land uses 1951-2020, showing the observations and posterior distribution of estimates. The grey shaded band is the 2.5-97.5 percentiles of the posterior distribution, with the MAP the black line within this. (Levy et al. 2021). Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 893 showing the observations and posterior distribution of estimates. The grey shaded band is the 2.5-97.5 percentiles of the posterior distribution, with the MAP the black line within this. (Levy et al. 2021). The thin coloured lines are the corrected observations, after accounting for systematic uncertainty, and interpolating. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 894 A 3.4.3.1.3 Uncertainty associated with the different datasets in the Bayesian assimilation There was a detailed quantification of uncertainty for data included in the assimilation (Levy et al. 2021). For every spatial data source, the following contribution to uncertainty were • False positive rate, Fp (over-estimation of land-use change that did not happen) • False negative rate, FN (under-estimation through missing true land-use change) o Sampling frequency (increasing uncertainty as the sampling frequency increases from annual to decadal intervals) o Sampling coverage (higher uncertainty outside the bounds of the data coverage). These contributions were included when estimating the likelihood of every land-use transition. For every observation, likelihood was Most spatial datasets had high false positives and random uncertainty, which were large in relation to the small signal of true land-use change in the UK. These uncertainties were corrected for in the data assimilation algorithm by correcting for false positives and down-weighting the more uncertain datasets. A preliminary assessment with only a subset of datasets to constrain the land- use change matrix produced fairly similar results. A 3.4.3.1.4 Land-use change results A 3.12, superimposed with the rates of land -use change used in the 1990-2019 inventory. The annual rates of land -use change in 2020 have also been used for 2021, pending further refinement of the methodology. As in previous years, the land -use change activity data uses Approach 2, but with annual input data instead of decadal. Pending further analysis, it has been assumed that gross changes to/from the 4F Other Land category correspond to noise, especially from the EO -derived datasets, and these transitions are not considered (as has been done previously). The UK c ontinues to use the rates of afforestation (whole time series) and deforestation (1990 onwards) from national forest statistics. However, additional information on the proportions of afforestation on cropland, grassland and settlements and information on p re- 1990 deforestation from the Land-Use Tracking project is used in the transitions. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 895 inventory LUC time series, based on Countryside Survey and historic datasets. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 896 Land use change on organic soils uses the information from Evans et al. (2017) (see Section A 3.4.7), but further analysis of land-use vectors on organic soils will be undertaken in the second stage of implementation of the Land-Use Tracking project. Work was also done to improve the accuracy of representation of crop -grass rotational management, using the idea of “life tables” or age-specific transition probabilities, from population CROME, LCM, and LCC, and these all show consistent patterns. Of these, IACS has much the longest time span of data, and is the dominant sourc e of information. Using this method, the observed frequencies of transitions from crop to grassland as a function of cropland age (and vice versa for grassland) are now reproduced in the land -use vectors, and thereby approximate the observed frequency of crop-grass rotations. Implementing this in the inventory requires re-writing the soils model to use land-use vector data rather than land-use matrices. Work is being done on this, with rigorous QA/QC, with the intention of implementing in later submissions. The full set of annual land use change matrices 1990-latest inventory year for the GBE, GBK and GBR submissions are provided in in Chapter 6 of the main NIR report, Section 6.1.1. The full set of annual matrices is used in the calculation of non-forest biomass carbon stock change. The full set is adjusted to take account of land use change on organic soils (see Section A 3.4.7) to produce a set of matrices for changes on non-organic soil only (which includes both mineral and organo-mineral soils) and these are used as input for the soils model described below.
A database of soil carbon density for all soils in the UK (Milne & Brown 1997, Cruickshank et al. 1998, Bradley et al. 2005) is used in conjunction with the land use change matrices. There are three soil surveys covering England and Wales, Scotland and Northern Ireland. The field data, soil classifications and laboratory methods for these surveys have been harmonized to reduce uncertainty in the final joint database. The depth of soil considered was also restricted to 1 m at maximum as part of this process. The dynamic model of carbon stock change requires the change in equilibrium carbon density from the initial to the final land use. The core equation describing changes in soil carbon with time for any land use transition Ct is carbon density at time t, C0 is the assumed equilibrium carbon dens ity initial land use, Cf is the assumed equilibrium carbon density after change to new land use and k is time Differentiating this equation gives the flux ft (emission or removal) per unit This equation gives, for any inventory year, the land use change effects from any specific year in the past. If AT is area in a particular land use transition in year T considered from 195 0 onwards then total carbon lost or gained in an inventory year, e.g. 1990, is given

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 897 This equation is used with k, AT and (Cf-C0) chosen by Monte Carlo methods within ranges set by prior knowledge, e.g. literature, soil carbon database, agricultural census, LUC matrices. The model calculates the change in equilibrium carbon density from the init ial to the final land - use for each land-use category as averages for Scotland, England, Wales and Northern Ireland. These are weighted by the area of land-use change occurring in each soil group to account for the actual carbon density where change has occ urred. In previous inventories four broad soil groups were used (organic, organo -mineral, mineral, unclassified). With the implementation for the first time of methodologies consistent with chapters 2 and 3 of the 2013 IPCC Wetlands supplement, we now have areas of land use change on organic soils and the emission factors for the associated carbon losses. This would lead to double -counting without the adjustment of the soil carbon model to exclude land use change on organic soils. The equilibrium soil carbon density and weighting is now based on the non-organic soil groups only. Mean soil carbon density change is calculated This is the weighted mean, for each country, of change in equilibrium soil carbon when land use changes, i = initial land use (Forestland, Grassland, Cropland, Settlements), j = new land use (Forestland, Grassland, Cropland, Settlements) , c = country (Scotland, England, N. Ireland & Wales), s = soil group (organo-mineral, mineral, unclassified) and Csijc is change in equilibrium soil carbon for a specific land use transition The land use data (1990 to 1998) is used in the weighting. The average change and range equilibrium non-organic soil carbon density (t ha-1) to 1 m deep for changes between different land types in England To Forestland Grassland Cropland Settlements Forestland 0 5 (±44%) 30 (±7%) 79 (±4%) Grassland -5 (±49%) 0 24 (±0%) 75 (±4%) Cropland -30 (±7%) -24 (±2%) 0 49 (±3%) Settlements -79 (±4%) -73 (±3%) -50 (±2%) 0

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 898 equilibrium non-organic soil carbon density (t ha-1) to 1 m deep for changes between different land types in Scotland Forestland Grassland Cropland Settlements Forestland 0 56 (±4%) 174 (±2%) 235 (±2%) Grassland -61 (±4%) 0 101 (±0%) 171(±0%) Cropland -180 (±1%) -101 (±0%) 0 62 (±4%) Settlements -242 (±0%) -168 (±1%) -54 (±14%) 0 equilibrium non-organic soil carbon density (t ha-1) to 1 m deep for changes between different land types in Wales Forestland Grassland Cropland Settlements Forestland 0 14 (±54%) 51 (±13%) 105 (±12%) Grassland -13 (±65%) 0 38 (±11%) 91 (±10%) Cropland -48 (±21%) -39 (±2%) 0 46 (±10% Settlements -104 (±12%) -89 (±6%) -55 (±6%) 0 equilibrium non-organic soil carbon density (t ha-1) to 1 m deep for changes between different land types in Northern Ireland Forestland Grassland Cropland Settlements Forestland 0 39 (±10%) 106 (±10%) 192 (±10%) Grassland -39 (±10%) 0 68 (±10%) 153 (±10%) Cropland -106 (±10%) -68 (±10%) 0 85 (±10%) Settlements -192 (±10%) -153 (±10%) -85 (±10%) 0 For transitions where carbon is lost e.g., transition from Grassland to Cropland, a ‘fast’ rate is applied, whilst a transition that gains carbon occurs much more slowly. A literature search for information on measured rates of changes of soil carbon due to land use was carried out and ranges of possible times for completion of different transitions were selected, in combination with

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 899 modelling and expert judgement (Milne and Brown, 1999; Ashman, et al, 2000, Salway et al, Scottish soils generally have a higher equilibrium carbon content. Forestland Grassland Cropland Settlement change in land use in England (E), Scotland (S), Wales (W) and Northern A Monte Carlo approach is used to vary the rate of change, the area activity data and the values for soil carbon equilibrium (under initial and final land use) for all countries in the UK. The model of change was run 1000 times using parameters selected from within the ranges described above. The mean carbon flux for each region resulting from this imposed random variation is reported as the estimate for the Inventory. An adjustment was made to these calculations for each country to remove increases in soil carbon due to afforestation, as the CARBINE model provides a better estimate of these fluxes in the Land Converted to Forest Land category.
Variations from year to year in the reported net emissions reflect the trend in land use change as described by the land- A 3.4.3.2.1 Change in soil carbon stock due to cropland management activities Change in soil carbon stocks due to cropland management activities is estimated using the methodology developed in Defra project SP1113 (Moxley et al , 2014a) wh ich reviewed UK relevant literature on the effects of cropland management practices on soil carbon stocks and attempted to model UK specific emission factors. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 900 Increases in inputs of fertiliser, manure and crop residues were found to increase soil carbon stocks of tillage land, but changes in the tillage regime from conventional tillage to reduced or zero tillage were found to have no significant effect in a UK context. This activity is only relevant for non-organic soils in the UK. Using this methodology, tillage crops are divided into Medium and Low residue groups based on the data on total crop biomass. Where land receives inputs of fertiliser or manure the inputs moved up a class (e.g., cropland producing a Low residue crop which receives manure is considered to receive Medium inputs, while land producing a Medium residue crop which received manure inputs is considered to receive High inputs). If crop residues are removed from land the input level drops. A decision tree for assessing the effect of cropland management on soil carbon stocks is For most cropland management activities there were insufficient UK field data to develop reliable Tier 2 stock change factors, and so Tier 1 factors have been used (for manure and residue inputs, and for differentiating perennial crops, annual crops and set -aside). These Tier 1 factors have been derived for soil carbon reference stocks of 0 -30 cm depth (as opposed to the 1 m depth used in the land use change calculations. The 0 -30 cm reference stocks for cropland soils are 70 tC/ha for England and Wales, 100 tC/ha for Northern Ireland and 120 tC/ha for Scotland. These values come from the same da tabase of soil carbon density used for the land use change modelling. However, for tillage reduction both a literature review and modelling work suggested that it did not have a significant effect on soil carbon stocks, and that the Tier 1 stock change factors over-estimated its effect under UK conditions. Therefore, a stock change factor of 1 has been used for tillage reduction. As changes in soil carbon stocks due to changes in cropland management are smaller than changes due to land use change the IPCC default transition time of 20 years is used. Data on the areas under the main crop types is obtained from the annual June Agricultural Surveys/Census carried out by each UK administration (Defra; Welsh Government; Scottish Government; DAERA) and the annual Defra report on areas of crops grown for bioenergy . Data on the areas of cropland receiving inputs of manure, fertiliser and crop residues is obtained from the annual British Survey of Fertiliser Practice (Defra). Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 901 activities on soil carbon stocks. A 3.4.3.2.2 Change in soil carbon stock due to grassland management activities Defra project SP1113 attempted to develop a methodology to allow reporting of changes in soil carbon stocks resulting from grassland management activities. There are reasonable data on the effects of management practices such as liming, reseeding and drainage on improved grassland

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 902 on mineral soils. However, there are few data on the effect of many management practices if applied to semi -natural grassland or those on organo -mineral or organic soils where there is a risk that more intensive management could increase carbon losses. As semi -natural grassland makes up a large proportion of grassland in the UK the lack of field data makes it impossible to reliably report changes in soil carbon stoc ks from grassland management activities. BEIS (now DESNZ) funded a research project to improve understanding of the effect of grassland management practices on soil carbon stock changes, which has not yet been published and work A 3.4.4 Changes in stocks of carbon in non-forest biomass due to management and land use change (4B2, 4C2, 4E2) A 3.4.4.1 Change in biomass carbon stock due to change in Cropland and Grassland Change in Cropland biomass carbon stocks was assessed based on agricult ural census data. Areas under different crop types were taken from annual agricultural census data and assigned on one of five annual crops, orchard crops, shrubby perennial crops, perennial grassland used for biomass fuel ( Miscanthus), short rotation coppice and set aside and fallow biomass carbon stocks. Areas of planting of Miscanthus and short rotation coppice are only available since 2007, when biomass crop incentives were any areas before 2007 were very small and for research purposes. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 903 carbon stock changes from Cropland Management Orchard Fruit Soft fruit, Hardy Orchard fruit Soft fruit Fallow , Fruit Ornamentals Fallow and set The areas under each aggregated crop type were multiplied by the biomass carbon stock of each from a literature review. (Moxley et al. 2014b), except for Short Rotation Coppice, where the values came from IPCC 2019 Refinement Tables 4.4 and 5.3. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 904 Annual 5 1.2 Assume no Below Ground 3.7 2.0 Assume no Below Ground Biomass carbon stock change was assumed to occur in the year in which the change in crop type was reported. Cropland biomass stock changes resulting from land use change to or from Cropland were subtracted from the changes due to change in cropland management, as they are accounted for under land use change.
Change in Grassland biomass carbon stocks was assessed based on Countryside Survey data, which is the most reliable source of change between different habitat types within a land category. Grassland was separated into shrub by, non -shrubby and unvegetated Grassland based on allocated to which Grassland type. biomass carbon stock changes from Grassland Management Shrubby Grassland Non-shrubby Grassland Unvegetated Grassland The areas under each aggregated Grassland type were multiplied by the biomass carbon stock

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 905 generated from literature reviews (Moxley et al. 2014b). Only biomass carbon stock changes resulting from change between shrubby and non -shrubby Grassland were considered, as changes to and from unvegetated littoral and supra-littoral sediments were considered unlikely. Non-shrubby Grassland 2.8 1.5 4.0 Shrubby Grassland. 10 3.6 0.53 Unmanaged hedge 175.3 476.6 0.3 Countryside Survey data are only collected on an approximately decadal basis. The annual stock change between survey years was estimated using linear interpolation. Biomass carbon stock change was assumed to occur in the year in which the change in Grassland type occurred. Grassland biomass stock changes resulting from land use change to or from Grassland were subtracted from the changes due to change in grassland management. Change in Grassland biomass carbon stocks due to change in hedge length are included in the estimate of change in Grassland biomass carbon stock using Countryside Survey data on hedge length and condition. Hedges were divided into managed hedges which are trimmed to prevent the growth of large trees and unmanaged hedges which do not received routine maintenance. Unmanaged hedges do not fall within the UK’s definition of Forest, but may contain isolated trees and may also have some gaps in them. The biomass carbon stocks of managed and unmanaged hedges are estimated as the median of UK -relevant values in published litera ture, based on a literature review commissioned by BEIS (Moxley et al. 2014b) supplemented with more recent data. Full details of these values and data sources are included the Grassland Management A 3.4.4.2 Change in biomass carbon stock due to land use change. Changes in stocks of carbon in biomass due to land use change are based on the all-soils area matrices (see previous section). The average biomass carbon density for Cropland, Grassland biomass densities of the different crop and grassland types in each country of the UK . For Settlements the biomass stocks from Milne and Brown (1997) and land cover data from the 2007 Land Cover Map was used to asses s the proportion of gardens, pasture -type grass (including sports pitches, golf courses and parks) and urban (built over) area within areas identified as

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 906 Mean C stock tC/ha Cropland Grassland Settlement Northern Ireland 5.14 2.93 2.64 for changes between different land types in England To Forestland Grassland Cropland Settlements (Transitions to and from Forestland are considered elsewhere) for changes between different land types in Scotland. To Forestland Grassland Cropland Settlements (Transitions to and from Forestland are considered elsewhere)

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 907 for changes between different land types in Wales. To Forestland Grassland Cropland Settlements (Transitions to and from Forestland are considered elsewhere) for changes between different land types in Northern Ireland. To Forestland Grassland Cropland Settlements (Transitions to and from Forestland are considered elsewhere) Living biomass carbon stocks and Dead Organic Matter (DOM) stocks on Forest Land are modelled using CARBINE and used to calculate changes in carbon stocks due to conversions to and from Forest Land. When land is deforested to another land use, it is assume d that all living biomass and DOM is either converted to Harvested Wood Products or burnt on site in the year in which deforestation takes place. Increase in biomass carbon and DOM stocks on afforested land is modelled in CARBINE. Full details of CARBINE modelling of carbon stocks on Forest Land are A 3.4.5 Carbon stock changes and biomass burning emissions due to Deforestation (4B, 4C, 4E, 4G) Deforestation is an activity that cuts across LULUCF categories, affecting net emissions and removals in all the land use categories. The process of land use change affects carbon stock changes in biomass and soil, and the woody material lef t after felling either moves into the harvested wood products pool or is assumed to be burnt on-site, resulting in immediate biomass Levy and Milne (2004) discuss methods for estimating deforestation since 1990 using a number of data sources. Their approach of combining Forestry Commission felling licence data for rural

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 908 areas with Ordnance Survey data for non -rural areas was expanded to include new sources of information and to improve coverage of all countries in the UK. Deforestation before 1990 (which contributes to soil carbon stock change from historical land use change) is estimated from the land use change matrices described in Section A 3.4.3. A 3.4.5.1 Types of deforestation activity in the UK In Great Britain, some activities that involve tree felling require permission from the Forestry Commission (FC), in the form of a felling licence, or a felling application within the various forestry grant schemes. There is a presumption that the felled areas will be restocked with trees, usually by replanting but sometimes by natural regeneration. However, some licences are granted without the requirement to restock – so-called unconditional felling licences. A felling licence is required unless special conditions are met29. Felling for urban development, with no requirement to restock, can be allowed under planning permission but only local planning authorities hold documentation for this. Since 2006, remotely sensed data used in the NFI has included this change, but prior to this, the need for collation of data from local authorities makes estimating the national total difficult.
However, in England, the Ordnance Survey (the national mapping agency) makes an annual assessment of land use change from the data it collects for map updating and provides this assessment to the Department for Levelling Up, Housing and Communities (DLUHC)30. D LUHC provides a n extract of this dataset, listing annual land use change from Forest to developed land uses (1990 -2008 in the latest submission). This dataset comes from a continuous rolling survey programme, both on the ground and from aerial photography. The changes reported each year may have actually occurred in any of the preceding 1 -5 years. The survey frequency varies among areas, and can be up to 10 years for moorland/mountain areas. Consequently, for pre-2006 deforestation to Settlement a five-year moving average is applied to the data to smooth out the between -year variation appropriately, to give a suitable estimate with annual resolution. A 3.4.5.2 Compilation of activity datasets Pre-1990 conversion of forest to other land categories is estimated using the land -use change matrices produced from the land-use data assimilation methodology (Section A 3.4.3) For 1990-1999 the deforestation activity dataset is compiled from the felling licence and DLUHC datasets as far as possible, using Countryside Survey (CS) data to fill gaps in the time series, to estimate deforestation in Northern Ireland (for which no direct data are available) and to estimate the conversion to different land use categories. The DLUHC data are used to estimate the area of Forest Land converted to Settlement (4.E.2.1). The unconditional felling licence data are used to estimate the area of Forest Land converted to Cropland (4.B.2.1) and of Forest Land converted to Grassland (4.C.2.1). Only England has any post -1990 forest to cropland the estimated areas in Scotland, Wales and Northern Ireland are so small that they are thought to be due to survey classification error rather than genuine land use change. The land-use change matrices from the data assimilation are used to estimate the relative split of 29 30

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 909 3.4.21 shows the Corrected Forest conversion rates. A correction ratio is used to adjust the estimated deforestation areas, as the Countryside Survey is known to over-estimate deforestation as described in the section above. There are no non-CS data for Northern Ireland so the correction ratios for England or Wales are used, depending on availability. The annual area of forest converted to other land uses is removed from the area of 4A1 Forest Land remaining Forest Land to maintain consistency in the land area matrix. 5.600 4.418 1.099 0.171 0.61 0.86 0.72 3.081 0.608 0.418 0.086 0.33 0.14 0.28 0.545 0.097 0.019 0.008 0.06 0.00 0.00 2.656 10.327 0.120 0.209 0.86 0.98 0.42 0.277 0.186 0.162 0.102 0.09 0.02 0.58 0.141 0.006 0.001 0.001 0.05 0.00 0.00

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 910 2% a - - 0.159 0.088c 0.026 c 0.005 c Land 28% b - - 0.390 0.145 c 0.052 c 0.027 c 20% a 2% a 15% a 0.602 0.262 0.041 0.045d Land 28% b - - 0.296 0.224 c 0.133 c 0.048 c a Unconditional felling licence data used for correction b Land Use Change Statistics used for correction c England correction ratio used For 2000 onward, the area and subsequent land-use of deforestation were estimated based on a • observations on forest loss by the NFI (internal Forestry Commission analysis) by IPCC category. This inventory includes an analysis of deforestation from 2006 to the current inventory year based on a new analysis of woodland maps (Forestry Commission, 2016); • unconditional felling licences granted (assumed all converted to Grassland); • analysis of the FC Sub -Compartment Database for restoration of Forest land to open habitats (conversion to Grassland or Wetland); and • conversion to non-forest on private sector forest covered by long-term forest plans rather than felling licences (internal Forestry Commission report, assumed all converted to The revision in deforestation was only d one from 2000 onwards, partly because there were no suitable data on which to base adjustments for 1990-1999, but also because a number of policy developments came into play in 2000 or shortly beforehand, which affected deforestation to restore open habitats or develop wind-farms. These include the introduction of the UK’s climate change policy (2000), and the diversification in relevant forest policies in England, Scotland and

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 911 Wales following the devolution of forest policy to countries in the late 1990s ( Matthews et al. 2014). The deforestation information used in this inventory is based on the assumptions used in the previous inventory, updated with the latest available information from the Forestry Commission on deforestation for recent years and to incl ude estimates of areas deforested to allow rewetting to take place. Soil carbon stock changes associated with deforestation are estimated using the dynamic soil carbon model described in Section A 3.4.3. When deforestation occurs, it is assumed that 60% of the standing biomass is removed as timber products and the remainder is burnt in the UNFCCC inventory. Country-specific forest biomass densities for living and dead organic matter from CARBINE are used. These densities change over time in relation to the forest age and species structure. Biomass losses are reported in the relevant carbon stock change tables, assuming a carbon fraction of 0.5 on a dry weight basis. The carbon removed as timber is reported as Harvested Wood Products (HWP) in 4G (described in Section 6.8). Direct and indirect greenhouse gas emissions from associated biomass burning is estimated using the Tier 1 methodology described in the IPCC 2006 guidelines (IPCC 1997 a, b, c) and the LULUCF.
Only immediate losses are considered because sites are normally completely cleared, A 3.4.6 Biomass Burning – Forest and Non-Forest Wildfires (4A, 4B, Data on Forest wildfires prior to 2009 come from the Forestry Commission and the Forest Service In 2009 the Fire and Rescue Service (FRS) bega n recording wildfires in England, Scotland and Wales on a new Incidence and Reporting Systems (IRS) which includes wildfires on all land use categories. The IRS database contains 30 attributes for each fire to which a fire appliance was called, including date, spatial location, property type description (e.g. heathland and moorland, standing crop) and an estimate of the area burnt. This dataset is available from 1 st April 2009. The original dataset had >126,000 fire records but 99% of these fires were less than 1 ha in size. The IRS database is manually completed by fire service personnel and its use requires some subjective judgement. This is likely to lead to non -systematic differences in the accuracy and precision of the data. The accuracy of the location s is variable, but an assessment of a number of the larger fires suggests that the land cover type attribute is reliable. The accuracy of the FRS burnt area estimates could not be validated using aerial photography as the available imagery was not recent enough, so Landsat images were used to validate the FRS data. However, it was still difficult to find cloud-free, pre- and post-fire images for fires in 2009. In addition, Landsat has some data loss. There are issues with re -ignited fires or additional fires in the same area being logged in the database as separate events. Overall, the uncertainty associated with this dataset is high but should be re -assessed once a longer time series is available. A scoping study is underway in the DESNZ -funded GHG Inventory Improvement programme to assess available wildfire earth observation products. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 912 To provide data on non -Forest wildfires prior to 20 09, thermal anomaly data fr om the NASA - operated MODerate Resolution Imaging Spectroradiometer (MODIS) were obtained from the Fire Information Resource Management System (FIRMS) and allocated to land uses using the proportions of fire on each land use type from the Fire and Rescue Se rvice IRS data. The correlation between MODIS data and IRS data breaks down below 25 ha, so for consistency a 25 ha threshold was set for reporting wildfires logged on the IRS. Thermal anomalies usually represent active fires, but may also detect industria l heat sources, although these are typically masked out by the thermal anomaly processing chain. The IRS dataset records 89 fires > 25 ha occurring in 2010. The FIRMS dataset records 335 fire detections for the same period, however, the FIRMS detections may contain multiple detections for a single fire event and the FIRMS detections are for a single 1 km pixel, and do not have a straightforward conversion to burnt area. Searching the IRS and FIRMS data sets for temporally and spatially coincident events, using a 2 km buffer around the IRS data, suggests that 22 fires were recorded by both the IRS and FIRMS systems. There are wide discrepancies between the two datasets, reflecting their different natures. The IRS data set records fires where a fire service response was required, so does not record controlled burning, unless the fire gets out of control. The FIRMS dataset, however, responds to anomalous heat signatures, so records controlled and uncontrolled fires. However, in the UK controlled burning, which is primarily carried out for heath management, is only permitted between October and mid-April to reduce the risk of these burns running out of control (Natural England, 201431; Scottish Government, 201132). As the FIRMS thermal anomaly data is only collect ed between March and August it will not detect most fires from controlled burning. FIRMS is only able to detect fires under cloud -free or light cloud conditions and is also only able to detect fires alight at the time of the satellite overpass. The FIRMS data are more likely to detect larger fires than smaller ones, probably due to the stronger heat signature and the longer burn time that larger fires tend to exhibit. The IRS and FIRMS thermal anomalies give a very different perspective on the extent, timing and duration of fire events in the UK. However, the datasets did show correlation (R 2 = 70-81%) for fires larger than 25 ha, which enabled an empirical relationship to be derived to extend the burnt area record back to 2001. A burnt area threshold of 25 hectares was used to extract a subset of the IRS this captured 75% of the IRS wildfire-burnt area in England, 86% in Scotland As more IRS data become available confidence should increase in the relationship between fires detected by FIRMS and fires logged in the IRS. This may allow FIRMS data to be extrapolated to fires covering less than 25 ha the inventory in future (although annual FIRMS data is no longer collected as of 2020) . However to extend this to small fires there would need to be reasonable confidence that the ratio of large to small fires used was valid , and also some investigation of whether the distribution of small fires across land use classes was the same as that of larger fires. It was assumed that all fires in the IRS database were even if they started as controlled burning, because the need for a fire appliance call -out indicates that they are no longer under 31 32

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 913 3.4.22).
There is a very small area of wildfires that occur on Settlement types, and these are included in the Grassland category as the IRS land type classification suggests that they occur on grassy areas within Settlements and there is no t a separate reporting field for wildfires in Forest Cropland Grassland Settlement conifers/softwood Straw/stubble burning Heathland or moorland Domestic garden broadleaf/hardwood Stacked/baled crop Grassland, pasture, Nurseries, market garden Scrub land Roadside vegetation Standing crop Tree scrub Railway trackside A time series of wildfire-burnt areas for each non-forest land use type was constructed for 1990- the IRS burnt areas were used for 2009 to the current inventory year and the burnt area estimated from thermal anomalies from 2000 to 2008. For 1990-2000 the average annual burnt area 2001- In Northern Ireland, where no IRS data were available, it was assumed that the heathland and grassland burning rates were in the s ame proportions as the Scottish burning rates, using the area of heathland and grassland from the 2007 Northern Ireland Countryside Survey. Estimates of the forest area burnt in wildfires 1990 -2004 are published in different locations (FAO/ECE 2002; FAO 2005) but all originate from either the Forestry Commission (Great Britain) or the Forest Service (Northern Ireland). There is a gap in the time series 2005 -2010 for Great Britain but areas of forest wildfires are reported annually for Northern Ireland. The gap was filled using the annual average areas burnt 1995-2005. These areas refer only to fire damage in state forests; no information is collected on fire damage in privately owned forests. The proportion of private-owned forest that was burnt each year wa s assumed to be the same as the percentage of the state forest that was burnt each year. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 914 (thermal anomalies were filtered to exclude those recorded over years such as 2003. The FIRMS data used only includes thermal anomalies for March – August for each year, as these are the months where the IRS database recorded fires greater than 25 ha. Some FIRMS thermal anomalies were recorded outside these months due to FIRMS detecting both controlled burns and some fires less than 25 ha in size which are not included in the IRS data. As of the 1990-2020 inventory annual FIRMS data is no longer collected as only 2001-2010

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 915 A 3.4.6.2 Estimation of emissions The IPCC Tier 1 method is used for estimating emissions of CO 2 and non -CO2 gases from wildfires (IPCC 2006). The Calluna heath fuel biomass consumption factor and grassland emission factors are used for heathland and moorland fires, the agricultural residues EFs for cropland and the “savannah and grassland” EFs for other grassland and settlements. Country-specific biomass and Dead Organic Matter densities from the CARBINE model are used for estimating fuel consumption in forest fires (as discussed in the deforestation methodology section) and the ‘extra tropical forest’ EFs in the 2006 Guidelines. In line with the default value in the IPCC 2006 Guidelines for AFOLU it is assumed that 45% of the biomass is consumed in a wildfire in an unfelled temperate forest. Emissions from all wildfires are reported under the ‘Land r emaining Land’ categories (i.e. 4A1, 4B1 and 4C1) and IE reporting under 4A2, 4B2 and 4C2. A 3.4.6.3 Discussion of controlled burning in the Grassland category The UK does not report emissions from controlled burning on grassland, leading to a recommendation by the 2017 UNFCCC review team. Controlled burning in the UK context occurs in very small (<1 ha) scattered patches, and is undertaken to enhance vegetation productivity and forage quality, and to promote new growth to improve grazing for game species and livesto ck. Only controlled burning that spreads out of control would be captured in the wildfire reporting A literature review of controlled burning in the UK and its Overseas Territories and Crown Dependencies was undertaken in 2018, to identify any new information and estimate GHG emissions if possible. This literature review was presented to the LULUCF Scientific Steering Committee and was shared with the 2021 UNFCCC review team. Its main findings are

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 916 • The evidence for the overall im pact of managed burning on moorland habitats in the UK is mixed with regard to the longer term GHG balance beyond the immediate combustion • Managed burning following the good practice guidelines should have a minimal impact on soils, with no r elease of GHGs. The longer -term impact on soil carbon stocks is contentious and evidence is not sufficient to produce a simple estimate of GHG emissions or removals from soil due to managed burning. • Reasonable evidence for burning rates on upland moorland is available for England, but much less is available for Scotland, Wales and Northern Ireland , and it is not possible to construct a robust time series for the UK. • Rates of burning across all moorland for each administration are low (less than the optimal rotation length for heather regeneration of 8 -25 years), and the estimated GHG emissions/removals from biomass burning and regrowth are also low (see below). 3.4.23) was made using the areas of heather moorland for each country in the Land Cover Map 2015 (a subset of the area in the eastern regions is used for Scotland). An annual burn rate of 1% was used for Scotland, Wales and Northern Ireland and 3.88% for England. These burn rates are on the higher end of the burn rates identified by sources in the literature review. Both the emissions from burning and the carbon stock changes due to regrowth of vegetation are included in the GHG emission calculations.
The IPCC 2006 Tier 1 fuel consumption, combustion factor and above -ground biomass for Calluna heathland have been used, with the Tier 1 emission factors for Savannah and grassland for CO2, CO, CH4, N2O and NOx. England 6747 88.86 0.13 0.01 3.58 0.21 95.479 Scotland 4103 54.04 0.08 0.01 2.18 0.13 58.060 Wales 428 5.64 0.01 0.00 0.23 0.01 6.056 Ireland 262 3.45 0.00 0.00 0.14 0.01 3.707 If the burning rate is fixed over time, the CO2 emissions are largely compensated by the following regrowth, The regrowth of vegetation is assumed to increase linearly over an eight-year cycle, as by eight years burn scars become unidentifiable on aerial photography (this is not relevant if we are only able to assess an average rate of burning but will become so if there is variation in

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 917 A 3.4.7 Emissions from organic soils (4A, 4B, 4C, 4D, 4E, 4(II)) In a natural state, peatlands are important long-term sinks for carbon. However, drainage of peat can drastically alter the carbon balance in these systems, shifting them from a net sink to a net source of carbon. Peatlands comprise a high proportion of the total land area in the UK (~12%). Of these peatlands, around 76% are in a modified state and no longer functioning as a carbon (C) sink. This ranges from relatively minor changes in vegetation composition and hydrology and GHG emissions, through to deep drainage and replacement of the wetla nd vegetation for agriculture, forestry, and peat extraction practices that result in large sources of anthropogenic GHG emissions. The remaining 22% of peatlands in the UK are classified as near-natural bog or fen with suitable conditions for C sequestrat ion, and 4% of UK peatlands have undergone restoration actions to restore normal peatland hydrology and biogeochemical functioning. Prior to the 1990 -2019 inventory, the UK’s LULUCF inventory reported limited emissions from peatlands, namely direct CO2 and N2O emissions arising from domestic and industrial extraction of peat, reservoir creation, conifer plantations on organic soils, and lowland drainage of cropland and agricultural grassland. Following the publication of the 2013 Supplement to the 2006 IPCC Guidelines, which provides methodology and default emission factors to allow calculation of GHG emissions and removals for a wider range of drained and rewetted peatlands (IPCC 2014), the UK elected to report Wetland Drainage and Rewetting (WDR) for the second commitment period of the Kyoto Protocol (KP). A BEIS -funded (now DESNZ) study on the Implementation of an Emission Inventory for UK Peatlands (Evans et. al. 2017) was undertaken to provide activity data and a UK-specific Tier 2 emissions reporting approach for UK peatlands. This section summarizes the main results used to estimate emissions from organic soils in the LULUCF inventory and full details are available in Evans et al. (2017) on the NAEI website. A 3.4.7.1 Areas of organic soils Peatland soils occur in all LULUCF land categories in the UK apart from 4F Other Land. Peatland condition categories comprised near -natural bog and fen, semi -natural peatlands affected by human activity (such as drainage, controlled burning and livestock grazing), cropland, ex tensive and intensive grassland, woodland, domestic and industrial peat extraction areas and active peatland restoration (rewetted) areas of bog and fen. This classification encompasses peat condition categories in the UK that were sufficiently well mapped to derive emission factors peat extent and land use maps, including key assumptions in the assignment of peat areas to condition categories, are given in Evans et al. (2017). Updates to activity data since the publication of the Evans et al (2017) peatlands implementation report include : the addition of a Settlement peat condition category; amendment to the Eroded Bog category to include only the actively eroding (bare peat) component of the landscape, with areas of not actively eroding bog captured under Modified Bog areas; and separate Rewetted categories for rewetted semi-natural Modified Bog, and more intensively modified peatlands (e.g. Forest, Cropland, Intensive and have all been altered by human interventions and practices to some extent, with natural peatlands managed for their high conservation value (e.g. SSSIs, SACs). Thus, emissions and removals from all managed UK peatlands are reported “regardless of whether they are anthr opogenic or non-anthropogenic” (IPCC 2014, Section 1.3, Chapter 1). Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 918 peatlands, amended from Evans et al (2017) Grey cells represent higher-level categories encompassing two or more sub-categories. Note that there are separate rewetted bog categories for transitions from heavily modified peatlands (Forest Land, Cropland, Grassland, Extraction, Eroding bog), and Semi-natural peatlands (Heather dominated and Grass dominated bogs). Changes in peat condition were associated with restoration (rewetting) of peatlands, which has largely occurred in the UK since 2000, changes in peat extraction, and forestry. Most large-scale peatland drainage occurred prior to 1990, however some new drainage has occurred due to wind farm or settlement developments, but to date it has not been possible to acquire data to report these effects. Similarly, land-use transitions between cropland and grassland, or change between intensive and extensive grassland on organic soils could not be reliably quantified due to an absence of spatially explicit data. Changes in peat extraction site area were generated from Google Earth satellite imagery and information on site operations data from Growing Media Association and applied to the baseline peat extraction areas (Evans et al. 2017) to give a time series of peat extraction area (see section 3.4.8). In contrast to the IPCC Tier 1 assumptions, fuel peat extraction was assumed to occur on nutrient-poor bog peat, and horticultural peat extraction on nutrient -rich fen peat, typical of UK practice (see Evans et al. 2017 for more detail). Evans et al. (2017) reported spatial datasets of peatland restoration from 2000 to 201 3. It was assumed that no rewetting activity took place before 2000 (other than peat extraction sites).
An average rate of restoration was applied 2000 – 2013 due to limited temporal information. This annual rate has been extrapolated to the latest inventory year for England, Wales, and Northern Ireland, which is likely an underestimate given that funding for peatland restoration in the UK has increased in recent years. Efforts are underway to provide a reporting mechanism for recent rewetting activities. An annual timeseries of peatland restoration in Scotland 2013- latest inventory year was provided by Peatland Action, NatureScot. Estimates of changes in area of each peat condition due to rewetting between 1990 and the latest inventory year are shown by UK

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 919 inventory, following a DESNZ (formerly BEIS) improvement project to edit and check the geometry of the maps and clarify activity data and the assumptions used to create the maps in Evans et al. (2017). Notably, this has included revision of the assumed planting on organic soils to adjust for deforestation (see Section 6.2.4), so that the total reported forest land on organic soils matches the estimates from the peat maps. The restoration data for intensive grassland was also revised to correct an over -estimate of intensive grassland to rewetted fen restoration in south -west England as identified in the Peatland Compendium dataset. Updated restoration data were administration in 1990 and 2021. Country England Scotland Wales Northern Ireland UK Total Peat category Deep peat Wasted peat All All All All All All All All Year 1990 2021 1990 2021 1990 2021 1990 2021 1990 2021 1990 2021 Forest, D 48.38 50.62 13.15 13.86 332.75 359.61 9.51 9.36 27.98 31.61 431.76 465.06 Cropland, D 53.74 48.28 132.18 132.18 8.18 5.15 0.10 0.10 3.15 3.15 197.35 188.86 Bog (bare peat), D 1.13 0.69 0.00 0.00 11.27 8.33 0.00 0.00 0.26 0.23 12.66 9.25 Bog (bare peat), UD 6.66 6.53 0.00 0.00 29.72 28.65 0.03 0.03 0.69 0.65 37.09 35.86 (H.&G.dom), D 69.13 32.73 0.00 0.00 252.31 213.94 3.19 1.52 12.41 10.88 337.05 259.08 (H.&G.dom), UD 160.48 157.52 1.95 1.91 665.19 641.24 35.39 35.12 32.36 30.48 895.36 866.27 fen), D 4.01 0.19 0.53 0.52 31.39 32.34 8.98 1.81 4.88 3.87 49.78 38.73 Dl 6.93 1.31 0.00 0.00 2.88 2.43 0.00 0.00 0.50 0.68 10.31 4.43 Settlement, D 4.75 4.73 5.44 5.40 4.26 6.48 0.18 0.22 1.62 1.51 16.25 18.35 Rewetted Bog, UD 0.08 4.13 0.00 0.00 0.00 16.74 0.00 5.15 0.36 4.99 0.44 31.01 Rewetted Fen, UD 0.88 19.25 0.00 0.00 0.00 2.04 0.00 2.61 0.03 0.70 0.91 24.61 Total 487.22 487.22 191.64 191.64 1,951.73 1,951.73 90.16 90.16 243.96 243.96 2,964.71 2,964.71 D= Drained, UD = Undrained, H.&G.dom = Heather and grass dominated land-use change, drainage and rewetting between 1990 and 2021 Scotland Wales Northern Forest 2.242 0.708 26.866 -0.148 3.634 33.302

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 920 Scotland Wales Northern Cropland -5.457 0.000 -3.033 0.000 0.000 -8.489 peat) -0.561 0.000 -4.009 -0.002 -0.072 -4.644 dominated) -39.346 -0.036 -62.318 -1.940 -3.419 -107.059 bog + fen) -3.814 -0.010 0.948 -7.169 -1.009 -11.055 Intensive Grassland -5.371 -0.628 -8.036 -0.179 -1.043 -15.256 Near Natural Bog 0.000 0.000 0.000 0.000 0.000 0.000 Near Natural Fen 0.000 0.000 0.000 0.000 0.000 0.000 Extracted Domestic -0.011 0.000 -0.268 0.000 -4.305 -4.583 Extracted Industrial -5.615 0.000 -0.447 0.000 0.187 -5.875 Settlement -0.016 -0.035 2.226 0.035 -0.108 2.103 Change in drained area -57.948 0.000 -48.071 -9.402 -6.136 -121.557 natural) Bog 35.527 0.000 29.287 1.644 0.829 67.287 Rewetted Bog 4.050 0.000 16.740 5.147 4.633 30.569 Rewetted Fen 18.371 0.000 2.044 2.611 0.674 23.700 Change in rewetted area 57.948 0.000 48.071 9.402 6.136 121.557 The peat condition categories are assigned to the LULUCF land categories with the majority area • 4C Eroding Modified Bog, Modified Bog, Extensive Grassland, Intensive Grassland, Rewetted Modified Bog (from Modified Bog), Rewetted Bog or Fen (from Eroding Modified Bog, Intensive and Extensive Grassland); • 4D Near Natural Bog/Fen, Extracted Domestic, Extracted Industrial, Rewetted Bog or Fen (from Forest Land, Cropland, Extracted Domestic, Extracted Industrial and

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 921 A large area of the cropland organic soil area in England is classified as shallow , wasted former deep peat that has been partly lost through agricultural activity. GHG emissions from wasted peatlands are not well quantified so it has previously been assumed that wasted peat soils continue to emit at the same rate as deep peat, ma king these emissions estimates particularly uncertain. As such, the EFs for wasted peat are under review, with a BEIS-funded (now DESNZ) research project underway to measure GHG emissions from agriculturally managed wasted peat in England. Flux tower measurements from this study have been used to produce a new Tier 2 et al., 2022b). At present, separate EFs for grassland on wasted peat cannot be derived due to lack of data, and measurements are ongoing for N2O. Emission factors for organic soils Tier 2 emission factors for the UK -relevant peat condition categories were initially developed by Evans et al. (2017). The EF literature review and meta-analysis was updated in 2019, and again in 2022 (see Evans et al. 2022a) to include recent GHG flux measurement publications and least four different primary study locations were considered reliable enough to replace Tier 1 3.4.26 the Tier 2 EF for that category was not reliable enough to replace the Tier 1 value (for instance, this means a Tier 1 CO2 EF is still used for Domestic Peat Extraction). A continued Tier 3 approach for forestry carbon stock changes and fluxes on organic soils using the CARBINE

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 922 model has been used, with updates to the areas of organic soils used in the model, documented in Section A 3.4.1. Other GHGs from forested peat (CO 2 from dissolved organic carbon (DOC) 3.4.26.
A Tier 2 approach was used for most peatland categories for Direct CO2, Direct CH4, and Direct N2O. Limited studies were available for CH 4 from ditches, CO2 from DOC, and CO2 from POC, thus a Tier 1 approach was adopted until more UK -specific flux data are available. Comparisons of the direct emission factors adopted for each UK peat condition category are given al. (2017) i nclude EFs for Settlement, which uses the closest national condition category of drained organic soils assuming 50% garden (heather/shrub) using the EFs for Modified Bog, and 50% impermeable land (no emissions); amendment to the Eroded Modified Bog EF to represent emissions from actively Eroding Modified Bog (bare peat) only, with emissions from the not actively eroding bog captured by the EFs for Modified Bog; and an additional Rewetted EF for Rewetted Modified (Semi-natural Bog), described further in Sec tion 3.4.6.3, which employs the CO2-C ha-1 y-1, kg CH4 ha-1 yr-1, and kg N2O-N ha-1 yr-1, respectively (updated from Evans et al (2017) and Brown et. al. 2020, 2021). Total fluxes are shown in tCO2e ha-1 yr-1 using AR5 GWP with no feedbacks (CH4= 28, N2O = 265).A positive EF indicates net GHG emission, and a negative EF indicates net GHG removal. Note that the EFs for Direct CH4 include a correction for CH4 lost in ditches (1-fraction of ditches in the applicable (i.e. drained status). Combined standard errors are shown for 0.34c 0.31a 0.07a 2.44a 5.43a 1.48b 40 cm) Drained 7.38b 0.31a 0.14a 1.86b 58.25a 16.28b 37.17 (±8.18) 40 cm) Drained 4.36b 0.31a 0.14a 1.86b 58.25a 16.28b 26.10 (±2.67) Drained 1.48b 0.31a 2.80a 40.59b 27.10a 0.30a 18.86 (± 1.07) Undrained 1.48b 0.19a 2.80a 42.72b 0a 0.30a 17.72 (±1.00) Drained 0.01b 0.31a 0.07a 60.21b 5.43a 0.13b 3.32 (±0.73) Undrained 0.01b 0.19a 0a 61.75b 0a 0.13b 2.51 (±0.73) Drained 3.21b 0.31a 0.14a 34.11b 26.35a 1.82b 15.88 (±3.38) Grassland Drained 4.06b 0.31a 0.14a 27.58b 58.25a 7.39b 22.00 (±2.08) Rewetted Bog Rewetted -0.16b 0.24a 0a 111.11b 0a 0.03b 3.42 (±1.07)

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 923 Rewetted Fen Rewetted -0.19b 0.24a 0a 111.44b 0a 0a 3.31 (±4.61) (Semi-natural) Bog Rewetted -0.97b 0.19a 0a 113.07b 0a 0a 0.32 (±1.17) Near Natural Bog Undrained -0.97b 0.19a 0a 113.07b 0a 0a 0.32 (±1.17) Near Natural Fen Undrained -1.38b 0.19a 0a 143.25b 0a 0a -0.36 (±1.98) Domestic Drained 2.80a 0.31a 1.48a 40.59b 27.10a 0.30a 15.18 (±2.94) Extracted Industrial Drained 1.48b 0.31a 2.80a 40.59b 27.10a 0.30a 18.86 (±1.07) Settlement Drained 0.005b 0.16a 0.04a 30.10b 2.71a 0.07b 1.66 (±0.73) a Tier 1 default EF (IPCC 2014) b Tier 2 EF (updated literature analysis in 2022 incorporating data from Evans et al. 2017; published in c Tier 3 Forest Research CARBINE model implied EF for 1990 to 2021. The decreasing trend is due to an increase in age of forests on organic soils due to decreasing afforestation on organic soils. updated from Evans et al (2017) ± standard error. Note that an EF for Forest is not shown as the Tier 3 method employed results in a range of EFs for forests on organic soils, which is due to changes in the age of forests and differences in afforestation over time. (All fluxes are shown in tCO2-C ha-1 yr-1. Note that a positive EF indicates net GHG emission, and a negative EF indicates net GHG removal). Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 924 updated from Evans et al (2017) ± standard error. Note that the EFs for Direct CH4 include a correction for CH4 lost in ditches (1-fraction of (2014), where applicable (i.e. drained status). (All fluxes are shown in Kg updated from Evans et al (2017) ± standard error. (All fluxes are shown

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 925 Off-site CO2 emissions from fluvial DOC exported from drained areas were estimated following 2014 IPCC Tier 1 methodology which incorporated a large body of UK data. A Tier 1 flux for POC exported from bare peat surfaces of 4 t C ha-1 yr-1 is given in Appendix 2a.1 of the IPCC Wetlands Supplement, based on studies on UK blanket bogs (Goulsbra et al., 2013). Using Equation 2.A1 in the Wetlands Supplement and a conversion factor for the proportion of POC mineralised to CO2 of 70% (Evans et al., 2016), gives an emission factor of 2.8 t C ha-1 yr-1 (10.7 t CO2e ha-1 yr-1) for bare peat surfaces (i.e. Eroding Modified Bog, and Industrial Peat Extraction), but with a high uncertainty. For other peatland condition categories, the POC EF for bare peat surfaces is multiplied by the fraction of exposed peat in the landscape using default drainage ditch fractions drainage ditches is equivalent to exposed bare peat. Indirect N 2O emissions associ ated with nitrate leaching from organic soils were not incorporated at this time due to caution given in the 2006 and 2014 IPCC guidance against double -counting of emissions from fertilisation. A BEIS - funded (now DESNZ) research project to measure emissions from wasted peat under intensive agriculture will help to assess indirect emissions from N2O from organic soils. Findings from this study are expected in 2023 (see Evans et al. 2022b). A 3.4.7.2 Emissions from drainage and rewetting (4(II)) Large areas of UK peatlands, predominantly semi-natural heather- and grass-dominated bog, are modified by grazing and burning-management practices but remain undrained.
As these habitats for modified undrained peatlands, Tier 2 emissions factors were developed to capture undrained Separate emissions factors were developed for rewetted semi -natural, heather and grass dominated bog (rewetted total EF = 0.32 ± 1.17 tCO2e ha-1 yr-1), and more intensively modified peatlands, which includes forest, cropland, intensive/extensive grassland, extracted, and eroding modified bog, (total EFs of 3.42 ± 1.07 and 3.31 ± 4.61 tCO2e ha-1 yr-1 for rewetted bog and fen, respectively) to take account of the different scale of damage to these lands, and the greater ease and effectiveness of rewetting of habitats that already ha ve semi-natural vegetation and are 3.4.26 and reported under the Agriculture sector category of 3.D- Agricultural Soils, as these agricultural land types are considered to be highly cultivated and this is consistent with the approach taken in the Agriculture sector. N2O emissions from unintensive grassland on organic soils are reported under 4(II)/4.H to ensure completeness, as it is not possible to report these emissions in the 4(II)/4.C rows in the CRF tables. The uncertainties associated with the areas and emission factors from organic soils were assessed as part of the Evans et al. (2017) research and subsequent updates. The uncertainties associated with areas were not quantifiable (as there were limited data sources available) so expert judgement was used to assign estimated uncertainties to peatland condition • Low uncertainty (10%): rewetted Grassland, rewetted and near-natural Wetland;

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 926 • Medium uncertainty (30%): drained Cropland >40cm depth, drained intensive Grassland, industrial peat extraction on Wetland, Settlement; • Medium uncertainty (42%): drained and undrained modified bog and eroding modified bog • High uncertainty (60%): all condition classes on wasted peat, drained extensive grassland, domestic peat extraction on Wetland. Uncertainties for the Tier 2 emission factors were calculated from the 95% confidence interval for CO2, CH4 and N2O from the 2022 emission factor meta -analysis(see Evans et al. 2022 a) and taken from the IPCC 2013 guidance for Tier 1 factors. The combined uncertainties for organic soils by LULUCF category are included in Section A 3.4.12 The uncertainties for forest on organic soil are calculated as part of the overall Forest uncertainty, as this uses a Tier 3 model rather than an emission factor approach. Settlements on organic soils were not included in the Evans et al. 2017 research, but their uncertainty is assumed to be Medium/High based on expert judgement. Emissions of N2O due to disturbance associated with land use conversion and land management All land use conversions or land management changes that result in a loss of soil carbon, leading to N mineralization and N 2O emissions, are reported. Direct emissions from soils, and indirect emissions from nitrogen leached from soil and subsequently oxidised are included in the inventory. The UK now includes emissions resulting from the land use 4A2 Land converted to Forest Land, 4B2 Forest to Cropland and Grassland to Cropland, 4C1 Grassland remaining Grassland, 4C2 Forest to Grassland, 4E1 Settlement remaining Settlement and 4E2 Land converted to Settlement. Emissions of N2O from 4C1 and 4E1 arise from land use change over 20 years before the inventory reporting year where loss of soil organic matter is still ongoing. Emissions of N2O from 4B1 Cropland remaining Cropland (resulting from land use change over 20 years before the inventory year) are calculated in th e same way by the LULUCF inventory team but are included in the Agriculture sector (category 3D1). The Tier 1 methodology described in the IPCC 2006 Guidelines is used. The activity data are the areas and soil carbon stock changes reported in the relevant categories in 4A2, 4B, 4C and 4E. Some ratios for UK soil/vegetation combinations are published in the Countryside Survey (with values of 11.7 to 13.4) but only for the top 15 cm of soil. However, the soil carbon stock changes reported in the inventory are from the top 1 m of soil, so these ratios were not felt to be applicable. Therefore, the IPCC default ratio of 15 is used for estimating mineralised N. The emission factor of 1% in the 2006 Guidelines was used to estimate N 2O emissions from mineralised N. Indirect N2O emissions from mineralisation are also estimated from carbon stock change using Tier 1 methodology. A 3.4.8 On-site and off-site emissions from peat extraction (4D) On-site emissions of CO2 and N2O from peat extraction activities (for energy and horticultural use) and off-site emissions of CO2 from the decomposition of horticultural peat are reported in category Available data sets on peat extraction vary between Northern Ireland and for Great Britain (England, Scotland and Wales). From 2002 onwards Google Earth imagery has been used to estimate the area of peat extraction from sites listed in the Directory of Mines and Quarries and

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 927 the BritPits online database 33, supplemented with recent ground -truthed site informatio n from Growing Media Association. Prior to the 2002 no Google Earth images are available, and peat of activity data used to estimate emissions from peat extraction. Google Earth Land use images to England, Scotland, Wales 1990/91 One off Tomlinson (2010) Estimate of peat Northern Ireland 1990 - 1991 One off England, Scotland, Wales 1947 - date Annual Northern Ireland 1990 - 1991 One off GMA (2021) Volume of horticultural England, Scotland, Wales 2011 - 2021 Variable 33

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 928 The areas of peat extractions sites listed in the BritPits database were assessed using Google Earth. Polygons were drawn around site boundaries and the area covered by the polygons was calculated in Google Earth. Change over time at individual sites was tracked to give an estimate of the extent of conversion to and from extraction sites. This method is repeated annually to incorporate changes in extraction site areas in new Google Earth images.
It is assumed that extraction areas continue to produce emissions while there is visible evidence of exposed peat soil from Google Earth satellite imagery, and do not convert back to functioning peatlands without restoration intervention . An extraction site is considered to have ceased emissions when there is visibl e evidence of the re -establishment of vegetation cover on the satellite imagery and evidence of rewetting (ditch blocking) from online documentation of the restoration works and communication with site managers. Additional information on active peat extraction areas and site history are provided by UK Growing Media Association. as published in the annual Mineral Extraction in Great Britain report, formerly known as the Minerals Raised Inquiry (ONS). This gives a breakdown for horticultural and other uses of peat, which are assumed to be fuel, for English regions and for Scotland. No peat extraction is reported in Wales. Annual production is highly variable because extraction methods depend on suitable summer weather for drying peat. Data are available from 1990-2014. For the period 2015-2021, UK sales volumes of horticultur al peat are derived from the Growing Media Association report Minerals Raised Inquiry/Mineral Extraction in Great Britain reports, and Year England Horticultural England Fuel Scotland 1990 1,116,940 2,727 293,170 93,163 1991 1,202,000 2,000 241,000 115,000 1992 1,079,000 4,000 332,000 91,000 1993 1,069,820 2,180 306,511 73,489 1994 1,375,000 1,000 498,000 108,000 1995 1,578,000 2,000 657,000 44,000 1996 1,313,000 2,000 517,000 53,000 1997 1,227,000 2,000 332,000 59,000 1999 1,224,000 0 392,000 37,000 2000 1,258,000 1,000 336,000 31,000 2001 1,459,000 1,000 325,000 30,000 2002 856,000 1,000 107,000 10,000

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 929 Year England Horticultural England Fuel Scotland 2003 1,227,000 1,000 741,000 38,000 2004 902,000 1,000 338,000 21,000 2005 927,000 1,000 550,716 26,284 2006 856,000 1,000 712,000 24,000 2010 456,000 1,000 527,000 21,000 2015 277,694 0 390,953 22,528** 2016 247,080* 0 347,341* 22,528** 2017 216,465* 0 303,729* 22,528** 2018 185,851 0 260,117 22,528** 2019 134,322 0 243,671 22,528** 2020 253,711 0 282,162 22,528** 2021 69,422 0 272,918 22,528** *Linear interpolation between 2015-2018 used in absence of measured horticultural peat volume data. ** Actual data for fuel peat are not available 2015-2021. Volumes are 2005-2014 average except for Fuel peat in England which is assumed to be zero as there are no longer any sites licenced for this activity. A 3.4.8.2 Estimation of emissions Tier 1 and Tier 2 emission factors are used to estimate on-site emissions for CO2, CH4 and N2O A value of 0.0641 tonnes C m -3 is used for Great Britain to estimate emissions from extracted horticultural peat volumes based on previous work (Thomson et al, 2011). Tomlinson (2010) gives production estimates of horticultural peat production for Northern Ireland for 1990/91 and 2007/2008. These have been interpolated to produce a time series. The total emission from horticultural peat production i s the sum of emissions from vacuum harvesting production, sod extraction production and mechanical extraction production. Emissions from vacuum harvesting production = area * annual depth of extraction * carbon fraction by volume

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 930 Annual depth of extraction by vacuum harvesting, m/ha = 0.1 Carbon fraction of air-dry peat by volume, tonnes C/m3 air-dry peat = 0.0641 Emissions from sod extraction production = area * sod extraction rate * % dry matter for sods * mean % C Sod extraction rate, tonnes/ha/yr = 200 Sod extraction, mean % dry matter = 35% Emissions from mechanical extraction production = area * extraction rate * % dry matter for mechanical extraction * mean % C The mechanical extraction rate was estimated to be 206.45 tonnes/ha in 1990/91 and 243.06 tonnes/ha in 2007/08 (Tomlinson, 2010). Mechanical extraction, mean % dry matter = 67% Carbon stock changes on land converted to Flooded Land (reservoirs) are included in the inventory, based on the IPCC 2006 Guidance. Data on all reservoirs over 1 km 2 were compiled but only reservoirs established since 1990 were reported (areas of inland water under 1 km2 are reported under 4D Wetlands remaining Wetlands). Activity data were compiled for England and Wales from the Public Register of Large Raised Reservoirs provided by the Environment Agency, which listed location, surface area and year built. Activity data for Scotland were compiled from the SEPA Water Body Classification data base (of water bodies > 0.5 km 2) and the associated Water Body data sheets. Additional information on the year of building was obtained • the Gazetteer for Scotland • hydro-electric power generators and • local authorities . It was established through discussion with local experts that no new large reservoirs had been built in Northern Ireland since the 1950s. Only five large reservoirs have been established in the UK since 1990, three in England and one each in Scotland and Wales (another five in England are sacrificial floodplains and do not fit the criteria of permanent conversion to Flooded Land). These cover a total of 1.995 kha. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 931 The location of each reservoir was examined using the geographic information portal. All reservoirs were in upland locations and were assumed to be Grassland prior to their conversion to Flooded Land. (Any forest removed as part of the land conversion will have been captured under the deforestation activity methodology). A Tier 1 methodology was followed, so carbon stock changes in living biomass stock in the y ear of flooding were estimated, but not carbon stock change in soils. The UK biomass carbon stock of shrubby grassland (10 t C/ha) was used for consistency with other parts of the LULUCF inventory. A 3.4.10 Harvested Wood Products (4G) The activity data used for calculating this activity are the annual forest planting rates. CARBINE then applies a forest management regime as given in input to the model.
For a given forest stand, carbon enters the HWP pool when thinning is undertaken and when harvesting takes place. Depending on the species, first thinning occurs approximately 20 years after planting. During wood extraction, conversion losses are assumed to be left as on-site harvest residue and enter the litter pool. The allocation of carbon to wood product categories is estimated by allocating the merchantable stem carbon to woodfuel, paper, wood-based panels and sawnwood, based on yearly proportions derived from reported forestry statistics . The CARBINE model uses standard estimates for oven-dried wood density to derive biomass from the harvested volume (Lavers and Moore, 1983; Jenkins et al., 2011). Carbon content of all oven-dried wood is assumed to be 50% (Matthews, 1993). CARBINE assumes the wood of a tree species all has the same oven -dried wood density and carbon content, irrespective of which semi-finished wood product categories it The proportions of wood produced which are allocated to different product categories are based on proportions derived from FAO data 34 (prior to 1994) and forestry commission data 35 (after 1993) on production of semi-finished wood products. A carbon retention curve is used to estimate product decay and return of carbon to the atmosphere. Each wood product category has its own carbon retention curve using the default half -lives in the IPCC 2013 Revised Supplementary Methods and Good Practice Guidance, taking into account the decay rate of wood products and the service life as influenced by socio -economic factors. The half -lives 35 years for sawn wood; 25 years for wood panels; 2 years for paper. Timber used as woodfuel is assumed to In implementing the 2006 IPCC guidelines for HWP the UK has elected to report using the production approach B2, which requires disaggregation of HWP into those produced and consumed domestically and those produced and e xported. In the annual Forestry Statistics publication, there is data on the apparent consumption of wood products in the UK. A consistent dataset is available at the product level (i.e., sawnwood, wood panels and paper & paperboard) for 2002 onwards. The ten-year average of 2002-2011 was calculated for each product type and those values were used for the years 1990-2001. This dataset was used to assign the HWP output from the CARBINE model into either consumed domestically or exported. 34 35

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 932 A 3.4.11 Methods for the Overseas Territories (OTs) and Crown The UK LULUCF inventory includes direct GHG emissions from UK Crown Dependencies (CDs) and Overseas Territories (OTs) which have joined, or are likely to join, the UK’s instruments of ratification to the UNFCCC. Currently, these Guernsey, Jersey, the Isle of Man, the Falkland Islands, the Cayman Islands, Bermuda, and Gibraltar. A web search of statistical publications is undertaken for any updates in datasets for every inventory compilation cycle . This work builds on an MSc project to calculate LULUCF net emissions/removals for the OTs and CDs undertaken during 2007 (Ruddock 2007). Gibraltar has a very small land area (6 km2) with no agricultural land. The only area of woodland (dense Mediterrane an scrub) occurs within the Upper Rock Nature Reserve/park, and is not managed for production 36. The whole land area of Gibraltar is categorised as Settlement remaining Settlement and has not undergone any land use change since 1970. It is therefore estimated that there are no net LULUCF emissions from this territory. An assessment of flooded land was undertaken for the Overseas Territories and Crown Dependencies. No flooded land areas exceed the area threshold of 1 km2 used for the UK, so the area of Flooded Land remaining Flooded Land has been included with the Inland Water area in the Wetlands remaining Wetlands category. Peat organic soils occur in the Falkland Islands and Isle of Man but not in the other Overseas Territories and Crown Dependencies. The reporting of drainage and rewetting of organic soils on the Isle of Man uses the same methodology (e.g. peatland classification and EFs) for the UK given in section A 3.4.7 above, with further details given in Evans et al. (2017). The organic soil area reported for the Isle of Man uses the British Geological Survey 1:50,000 superficial geology map, which employs a peat depth threshold of 1 m, and thus reports a small area (475 ha) all Wildlife Trust survey is currently underway to map peat extent >40 cm, which will be used to complement the area estimates provided below. Data on land use change on organic soils is currently unavailable for reporting in the inventory. Peatland restoration activities are likely to increase in the near future; however, Isle of Man are a few years away from reporting outcomes (Shaun Gelling Isle of Man Government Agriculture and Lands Directorate, pers. comm., 36

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 933 Drained Eroding Modified Bog (bare peat) 1 1 Undrained Eroding Modified Bog (bare peat) 0 0 Drained Modified Bog (Heather + Grass dominated) 0 0 Undrained Modified Bog (Heather + Grass dominated) 13 13 Extensive Grassland (combined bog + fen) 99 99 Rewetted Modified (Semi-natural) Bog 0 0 Information on the distribution and emissions from peat condition classes in the Falkland Islands is incomplete, but is being addressed by ongoing scientific research (see Chapter 6). Estimates of emissions from peat extraction for domestic fuel use were i ncluded for the first time in this submission with the incorporation of a new data source (Falkland Islands 2016 Census report). Information from the Falkland household census (repeated every 5 -6 years from 1991) on the number of households using peat as a fuel was combined with expert local knowledge on peat use per household to produce an activity data time series using interpolation between census A small area of deforestation occurs in Guernsey, obtained from habitat surveys in 1999 , 2010 and 2018.