A five-stage protocol for systematic measuring and monitoring soil carbon and greenhouse gas fluxes in complex estates

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Barneze, David A. C. Manning This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5677695/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jul, 2025 Read the published version in Plant and Soil → Version 1 posted 6 You are reading this latest preprint version Abstract Background and Aims Plant-soil interactions are critical in governing soil carbon (C) stocks and greenhouse gas (GHG) fluxes, but they vary significantly across land uses, soil types, and soil management practices. Finding potential intervention that could enhance soil C and GHG fluxes relies on reliable baseline data that capture these variations. Complex estates, characterised by such heterogeneous conditions, require standardised protocols to ensure reproducibility and comparability across sites. Methods This study introduces a five-stage protocol for systematically measuring and monitoring soil C stocks (including organic and inorganic forms) and GHG fluxes. The protocol is designed for "Time-Zero" (T = 0) baseline assessments and the strategic selection of monitoring sites for long-term soil sampling and GHG flux measurements. The approach was tested at RAF Leeming (Yorkshire, UK), a estate with varied land uses, soil types, and management practices. Results The protocol provides a rigorous, reproducible and adaptable framework for obtaining robust baseline data. It facilitates the quantification of soil C and GHG fluxes, while it can guide site-specific interventions, ensuring that aspects such as plant and soil interactions are considered for comparability purposes. Its design is scalable, with applications extending to urban areas, military installations, airports, and other managed estates. Conclusions The proposed protocol enables standardised, transparent soil C and GHG monitoring to meet internationally accepted standards. We advocate for its broad implementation across estates with varying land uses and soil characteristics to support sustainable soil management and climate mitigation efforts. baseline carbon cycle ecosystems land management reporting verification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The increase of greenhouse gas (GHG) concentrations in the atmosphere since the Industrial Revolution has brought about global climate change concerns. According to the Intergovernmental Panel on Climate Change (IPCC), the Earth’s surface temperature has already increased by 1.1°C since pre-industrial times. This number is expected to increase to 1.5°C by the end of this century and to 2°C or more by 2100, if the current trend of GHG fluxes continues (IPCC, 2021 ). This increase in GHG concentration in the atmosphere can cause sea level rise, extreme weather events, loss of biodiversity, ocean acidification, among others (IPCC, 2014 ). To mitigate the effects of GHG fluxes, the Paris Agreement was signed in 2015 by 195 countries, which aims to keep the global average temperature increase to “well below” 2°C above pre-industrial levels and to pursue efforts to limit it to 1.5°C (UNFCCC, 2015). To achieve the goals of the Paris Agreement, countries have committed to reducing their GHG fluxes and implementing adaptation strategies to lessen the extent and impact of climate change. However, recent research has shown that GHG prevention and reductions (i.e. mitigation) and adaptations alone will not be enough, also requiring efforts to promote carbon (C) removals (also known as negative emission) from the atmosphere (Anderson & Peters, 2016 ). Arguably, soil could play an important role in the C capture goal as it has the largest dynamic reservoir of C on Earth, with figures suggesting a capacity of 2500 Pg, i.e. billion tonnes = 10 15 g (Batjes, 1996 ; Lal, 2004 ; Moinet et al., 2023 ). The absolute quantity of C held within a soil (i.e. the soil C stock) consists of two major components: soil inorganic C (SIC) and soil organic C (SOC). Soil inorganic C, the smaller portion of C on soils (approx. 950 Pg), is represented mainly by carbonates derived from pedogenic processes as well as geologic or soil parent material sources while soil organic C, the most abundant terrestrial C pool (approx. 1550 Pg), comprises soil organic matter (SOM) components (Trumper et al., 2009 ). According to (Lal, 2018 ), the potential for soils to sequester atmospheric C globally is between 1.4 and 3.4 Pg C year − 1 . As a practical example, and only considering SIC, in urban soils (Technosols), the presence of materials derived from demolition leads to the potentially rapid formation of pedogenic carbonates. Washbourne et al. ( 2015 ) measured the accumulation of calcium carbonate in an urban soil equivalent to the removal of 85 t CO 2 per hectare annually for a 12-ha city-centre site, confirming a pedogenic origin through analysis of C and O stable isotopes as well as 14 C evidence for the presence within the carbonate minerals of modern carbon. In addition to C capture and potential climate regulation, there are many other ecosystem services inherent to soils, including food security, and the provision of fibre and fuel, among others, which highlight that soils will not only play a key role in the climate system but also in essential components for maintaining humanity. Immediate actions are required across all sectors, including, but not limited to, energy, transport, agriculture, industry, and the military (IPCC, 2022 ). For military operations, particularly aviation, the implications of reducing reliance on fossil fuels are particularly serious (NATO, 2021 ). Currently, in the UK, it is estimated that the Ministry of Defence (MoD) contributes to around 50% of all government departmental emissions (TEAM Defence, 2020 ), highlighting the need for the military sector to play a key role in decarbonisation (Rajaeifar et al., 2022 ). Additionally, since the MoD is one of the largest landowners in the country, with an estate (433,000 ha) nearly equal to 2% of the UK’s land mass (National statistics, 2022 ), the opportunity to manage and enhance C capture in soils is a strategy not yet explored by the defence sector. Despite soil’s large C storage capacity, factors such as land use, agricultural systems, and management practices influence soil and plant interactions, leading soils to act either as a sink or source of C, with substantial variations in magnitude and rate (Lal, 2004 ; Smith et al., 2007 , 2008 ). Hence, it is critical to take these, as well as other aspects (such as soil characteristics, soil type, vegetation, topography, climate, among other soil-forming factors and processes) into consideration when planning a soil sampling campaign for baseline measurement and/or monitoring purposes that is reliable and sound for measuring and monitoring soil C and GHGs (Smith et al., 2008 ; Minasny et al., 2017 ; Lal et al., 2018 ; Batjes, 2019 ). However, a standardised protocol for baseline measuring and monitoring SOC/SIC changes and GHG fluxes is still lacking. For single land management practices (such as farming or forestry), there have been notable advancements in the formulation of guidelines for measuring and monitoring, reporting, and verification (MRV) of SOC/SIC baseline and changes, as well as GHG fluxes (FAO, 2020 ; World Bank, 2021 ; puro earth, 2022 ; VERRA, 2023 ). However, these advancements have primarily centred on the field level, with occasional attention extended to the farm level or even national level. There is, therefore, still a need to elucidate strategies for soil sampling and GHG measurements for estates that combine different land uses, soil types, and soil management practices, and which span over large areas. This is particularly challenging as it must also be cost-effective and easily understood, as well as simple and broadly applicable in practice. The standardisation of strategies for baseline measuring and monitoring SOC/SIC and GHG fluxes is critical as it will provide the basis for where soil samples and GHG measurements must be undertaken. The overall aim of this study is to establish a standardised protocol for baseline measuring and monitoring soil C (accounting for both SOC and SIC) and soil GHG fluxes in estates with different land uses, soil types, and soil management practices. The five-stage protocol has been designed to offer a unified approach that is cost-effective, repeatable, and easy-to-use across any sector, allowing SOC and SIC, as well as soil GHG fluxes, to be rigorously and systematically measured and monitored. Material and Methods While this five-stage protocol represents a unique approach to baseline measuring and monitoring SOC/SIC and soil GHG fluxes, it is important to highlight that this also encompasses elements of a series of international protocols previously published by different public and private institutions (including, but not limited to: (Alberta Government, 2012 ; Australian Government, 2018 ; Gold Standard, 2019 ; USDA-NRCS-CSU, 2019 ; FAO, 2020 ; World Bank, 2021 ; puro earth, 2022 ; VERRA, 2023 )). The guidelines were deliberately designed to be rigorous and systematic, but elements of simplicity, repeatability, and feasibility were thoroughly considered. In this sense, it is expected that it can be applied by any individual with basic computer knowledge and skills, who wishes to assess soil C stocks and soil GHG fluxes in an estate with different land uses, soil types, and soil management practices. Although the stages described below have been developed and deployed at a military base (RAF Leeming, Yorkshire, UK; 54.2927° N, 1.5317° W) it is expected that they could also be adopted at any other location. Planning and developing a soil sampling design for measuring soil C stocks at T = 0 This protocol recommends the use of the SCORPAN framework (McBratney et al., 2003 ) as a basis for the compilation of relevant data/information, hereafter referred to as covariates, for designing the soil sampling programme. The SCORPAN framework is a concept that highlights that soil formation and/or properties are highly dependent on their position in the landscape, i.e. affected by several environmental factors (including plant and soils interactions), which also apply to SOC/SIC storage, and thus potential C capture. As such, most of the elements/covariates needed for planning and developing a soil sampling design are primarily based on the SCORPAN function (Eq. 1): S = 𝑓(s,c,o,r,p,a,n) Eq. 1) where S is soil classes or attributes to be focussed, “s” refers to the soil (other or previously measured properties of the soil at a point), “ c ” is climatic properties of the environment at a point, “ o ” refers to organisms, including land cover and natural vegetation or fauna or human activity (plant and soil interactions), “ r ” is the relief, topography, landscape attributes, “ p ” is the parent material/lithology, “ a ” refers to the age, i.e. the time factor and finally, “ n ” is the spatial or geographic position. Stage 1 – Defining overall boundary The first step is to identify, delineate, and map the spatial boundaries of the target estate, which relates to the “o” in the SCORPAN function. This can be done by consulting the landowner(s) and requesting a simple drawing of the estate boundaries using for example Google Earth maps (“Google Earth Pro,” 2023) or any other mapping platform. Alternatively, other methods rather than satellite images and tools can be used e.g. land records or hard copy maps. At the end of this stage, the output should be a geospatial map/satellite image with the total spatial boundary of the target estate. Figure 1 shows an example of the spatial boundary for the RAF Leeming base, taken from ArcGIS (Environmental Systems Research Institute, Inc., Redlands, CA, USA) (Esri, 2023 ). Stage 2 – Target estate stratification : Still considering the “o” in the SCORPAN function, it is also important to identify and delineate current different land uses within the total area (i.e. high-level stratification of the target estate into discrete units). Examples include: farmland, paved areas (including runways in this case), urban/recreation, native vegetation, etc. If within one of these (or other) land uses, there is a different management system these should be considered as two different target intervention areas for soil sampling, i.e. management zones. Examples at this location of the same land use but different management systems, include but are not limited to the following: a farm that is partly conventionally managed and partly organically managed, land designed solely for pasture, or for crops, or for woodland, or native vegetation (or other distinctive management systems), a recreation area solely designed for gardening, or recreation, or football/sport pitch, etc. The easiest way of finding out such information is by discussing it with landowners and/or tenants, but some tools such as DIGIMAP (Digimap, 2023 ) (only for UK-located target estates) can also be used to gather such information. For the RAF Leeming base, we have used both approaches, i.e., we talked to landowners and tenants, as well as using DIGIMAP for gathering land use and management system information. At the end of this stage, the product should be a geospatial map/satellite image of the target estate that includes stratification (i.e. units) concerning different land uses and management systems. Figure 2 shows an example of the stratification of RAF Leeming base considering differences in current land uses and management systems. Stage 3 – Collecting covariates Once the total boundary, land uses, and management systems/zones are delineated, it is important to gather covariates related to potential material differences within the target estate, as well as in each identified unit. This step relates to “s” , “c” , “r” , “p” , and “a” in the SCORPAN function, and therefore must be thoroughly considered. Material differences include potential discrepancies in previously measured soil properties within the target estate that might affect SOC/SIC and soil GHG fluxes (e.g. nutrient content, soil bulk density, texture, pH, SOM, microbial abundance/diversity, etc.), soil type and underlying geology, land use history, landform, and climate (depending on the size of the target estate). In this protocol, we particularly highlight the use of the following covariates: previously measured soil properties (particularly, texture, pH and SOM), soil type (clay, sandy, silty, peaty), past/historic land uses and management systems (ideally within the last 10 years), landform, and climate. For landforms, the use of elevation data is highlighted. Elevation data can be used to derive several topography/terrain covariates, including slope (degrees), flow direction, flow accumulation, basin, aspect, curvature, hillshade as well as some computed indexes such as the Topographic Wetness Index and the Topographic Position Index (TWI and TPI, respectively). Functions on how to calculate TWI, TPI and all other aforementioned landform covariates are available in Moore et al. ( 1993 ) and ArcGIS (Environmental Systems Research Institute, Inc., Redlands, CA, USA) (Esri, 2023 ). Other mapping and analysing tools are also able and can be used to perform such analysis and derive the recommended landform covariates (e.g. QGIS, Maptitude, Python, R studio, etc.). There are no restrictions on what mapping and analysing tool to use in this step but specific knowledge of how to operate such software is required. For the RAF Leeming base, soil type, past land uses, landform, and climate covariates were all collected using Digimap and/or derived from them by using geostatistical approaches on ArcGIS. If the target area is outside the UK and/or Digimap is not available, we recommend talking to the landowner/tenant(s) of the target estate to collect as much material information as possible from them. If the data is still not available or limited, some of it can be obtained from global data sources, but local data is always preferred. Table 1 provides global databases and web links that can be used at this stage. Table 1 Global databases available for spatial information (adapted from FAO, 2020 ) Type Source Web address Resolution Range of datasets including historic, geology, marine, environmental, elevation across the UK Digimap https://digimap.edina.ac.uk/ Many Range datasets including historic, geology, marine, environmental, across the UK Magic https://magic.defra.gov.uk/ Many Monthly climatic data CRU – Climate Research Unit, University of East Anglia https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/cruts.1905011326.v4.03/ 50 km x 50 km National and regional climate for the UK. Meteorological Office for climate averages www.metoffice.gov.uk/weather/uk/climate.html Local Geology across the UK British Geological Survey http://www.bgs.ac.uk/discoveringGeology/geologyOfBritain/viewer.html Variable depending on location SOC stocks 0–30 cm GSOC Map - FAO-ITPS http://54.229.242.119/GSOCmap/ 1 x 1 km SOC stocks and SOC concentration; profiles International Soil Carbon Network https://iscn.fluxdata.org/ Different resolutions Soil texture 0–30 cm ISRIC Soil Grids https://soilgrids.org and at global level from https://data.isric.org/ )): 250 x 250 m 500 x 500 m 1 x 1 km Soil types for England and Wales Landis Soilscapes viewer https://www.landis.org.uk/soilscapes/ 1 x 1 km 5 x 5 km NDVI- Historic images (2001–2020) every 16 days MODIS - MOD13A2 datasets https://lpdaac.usgs.gov/products/mod13a2v006/ 1 x 1km Land Cover – Land Use MODIS Land Cover Dynamics MCD12Q2 https://modis.gsfc.nasa.gov/data/dataprod/mod12.php 500 x 500m 1 x 1 km Land Cover – Land Use European Space Agency (ESA) Climate Change Initiative (CCI)- Copernicus Climate Change Service (C3S) https://www.esa-landcover-cci.org/ 300 x 300m Land Cover – Land Use IMAGE Integrated Model to Assess the Global Environment. PBL Netherlands Environmental Assessment Agency https://models.pbl.nl/image/index.php/Land_cover_and_land_use 10 x 10 km Land Cover – Land Use FAO. Global Land Cover SHARE http://www.fao.org/land-water/land/land-governance/landresources-planning-toolbox/category/details/en/c/1036355/ ~ 25 x 25 km Land Cover – Land Use USGS Global Land Survey https://lta.cr.usgs.gov/GLS 30 x 30m Land Cover – Land Use CORINE land cover (Europe only) https://land.copernicus.eu/paneuropean/corine-land-cover 100 x 100 m At the end of this stage, the output should be one or more geospatial map/satellite images of each unit and/or from the whole target estate with material differences that might affect soil C storage and soil GHG fluxes. Examples are given for the RAF Leeming base in supporting information figures A1-A25. Table 2 shows how such covariates are related to the SCORPAN framework and their description. Table 2 Covariates collected and their relationship with SCORPAN framework and description. Covariate Scorpan Factor Description Elevation R The height of a location above the Earth’s sea level Slope R The inclination of the land surface from the horizontal Flow Direction R Direction of water flow in a given cell based on its steepest descent drop Flow Accumulation R Accumulated flow determined by accumulating the weight for all cells that flow into each downslope cell Basin N Connected cells belonging to the same drainage basin defined by the flow direction Aspect R, N The direction in which a land surface slope face Curvature R The shape or curvature of the slope i.e. concave or convex Hillshade C Representation of the surface considering the sun position for shading Topographic Wetness Index (TWI) C, R The relative wetness within moist catchments, but is more commonly used as a measure of position on the slope with larger values indicating a lower slope position Topographic Position Index (TPI) R Topographic position classification identifying upper, middle and lower parts of the landscape Agricultural Systems O Organic system in accordance with the Soil Association Organic Standards or Conventional system (UK best practices recommendations) Land uses O Runways, Urban, Native vegetation, farmland, recreation, etc. Stage 4 – Division into discrete parts and subparts This step refers to further stratification of the units (as designed in Stage 2 ) into discrete parts and subparts, which will be the target sampling areas, based on material differences found in Stage 3. Unfortunately, there is no set-in-stone procedure to be followed in this phase as it will depend on the availability, as well as the amount, of data gathered in the previous steps. However, it is recommended to carry out an in-depth evaluation of the scope of the project (i.e. which land uses, soil types, and soil management practices are present and what covariates are available to assess them), in order to get better insights on priorities for the target estate. In addition, it is highly advisable to carefully study all the maps, as well as other relevant information available to design the best strategy for soil sampling at the target estate. The following approach, developed for the RAF Leeming base, is particularly recommended for target estates that present these three elements, i.e. different land uses, soil types, and soil management practices. First, choose one of the material differences found in Stage 3 as a basis for division of the units into discrete parts. We highly recommend, if available, using soil type and/or soil texture maps, as they are closely related to high/low soil C potential. Divide each unit (i.e., each land use/management system/historic use, delineated in Stage 2 ) into corresponding soil type parts (or use other information that can characterise the unit’s high-level variability). Figure 3 shows an example of how the runway unit for the RAF Leeming base was divided into discrete parts based on soil type. Subsequently, divide each part of the unit (in this example based on soil type) into four further subparts (or more if needed) based on another covariate collected with a fine resolution variability (e.g. long-term average normalised difference vegetation index, soil electrical conductivity, yield maps, elevation, etc.). Any covariate with a fine-resolution variability can be used. However, if available, this protocol recommends elevation as the covariate to be used in this step as this is known to have a close relationship with spatially implicit soil-factors (Behrens et al., 2010 ). If elevation data is unavailable, the protocol recommends, preferably, utilising a relevant covariate with a high-resolution that may contribute to the variability within the unit’s part. For the RAF Leeming base, elevation data was available at a 5 m resolution for the entire area (Fig. A14), and it was selected as the covariate for subdividing the unit’s part into subparts (Fig. 4 ). If high-resolution data/information is not available, it is recommended to subdivide each unit part into a minimum of four subparts, ensuring they are equal/similar in terms of area and as relatively homogenous as possible (Fig. 5 ). The primary objective of this stage is to ascertain that the sampling points (which will be designed in the next stage) exhibit greater homogeneity within the specific subunit than those across the entire estate. At the end of this stage, the output should consist of geospatial maps/satellite images of each unit (as designed in Stage 2 ) further stratified into discrete parts according to the chosen material differences. This stratification should consider both high-level and high-resolution covariates, using soil type as a high-level covariate and elevation as a high-resolution covariate, for example (Figs. 4 and 5 ). It is important to highlight that this step must be repeated for all units identified in Stage 2 of this protocol. Stage 5 – Designing sampling points In this step, the target sampling points are chosen. The protocol recommends a minimum of three (for statistical purposes) random locations within each one of the subparts designed at the end of Stage 4 for the extraction of soil cores. However, the larger the area and the expected or know variability within the subpart’s unit, the more samples must be taken within that subpart. The use of statistical software (R, JMP, Minitab, SPSS, etc.) for the selection of the random sampling locations is highly advised. While the sampling locations should be randomly assigned, it is important to ensure that they adhere to the following constraints - Locate each sampling point at least 50 m away from each other within the subpart, - Avoid locating the sampling point near the field border (> 20 m from a field boundary), - If known, try to avoid locations that are likely to be disproportionately affected by compaction from either machinery and/or animal activity and/or chemical or other types of disposals or spillage. In addition, it is important to arbitrarily allocate sampling points in likely high and low soil C potential locations, in case it is not randomly allocated. This can be done by using the other covariate maps gathered in the previous stages (e.g. slope, flow direction, flow accumulation, basin, aspect, curvature, hillshade, TWI, TPI, or others), or high-level global maps, for example, the FAO-GSOC map (Global Soil Organic Carbon map; available at http://54.229.242.119/GSOCmap/ ), which gives a rough estimation of the current soil C stock (t C ha − 1 at 30 cm) expected at the location. Please note that the estimated soil C stock will not always be in line with the measurement of the lab. There are several reasons for this, but mainly because soil C is difficult to measure and sometimes it can vary up to 50%, secondly, the spatial resolution used on the FAO-GSOC map and the resolution of the target sample areas within the estate might differ substantially. As a general rule of thumb, the more parts the unit is divided into and the greater the number of sampling points within each one of the subparts, the better the capacity to reliably measure soil C stocks baseline, as well as to detect changes in soil C storage and GHG fluxes over time. A power analysis can be used to calculate the ideal number of sampling points (Eq. 2 and Eq. 3), but this should be optional, and used when not much data/information is available for the target area. \(\:MDD\:\ge\:\:\frac{S}{\sqrt{n}}\:\times\:\:\left({t}_{\alpha\:,u\:}+\:{t}_{\beta\:,u\:}\right)\) Eq. 2 \(\:n\:\ge\:\:{\left(\frac{S\:\times\:\:\left({t}_{\alpha\:\:}+\:{t}_{\beta\:\:}\right)}{MDD}\right)}^{2}\) Eq. 3 where MDD is the minimum detectable difference, “S” is the standard deviation of the difference in SOC stocks between t 0 and t 1 , “n” is the number of samples, “t α ” refers to two-sided critical value of the t-distribution at a given significance level (α) frequently taken as 0.05 (5 percent), “t β ” is the one-sided quartile of the t-distribution corresponding to a probability of type II error β (e.g., 90 percent). After randomly (and arbitrarily for some specific points, if needed) choosing the sampling point locations, if a deep soil sampling is going to be carried out, it is necessary to take into account information on underground services (water pipes, gas pipes, electrical wires, fibre optics, cables, etc) and then manually adjust any sampling point that might be in a “restricted” location of this type. The final target locations (coordinates, latitude, and longitude) are then recorded (Fig. 6 ). For a full inventory of soil C, this protocol strongly recommends soil cores to be taken at both top- and sub-soil layers, at the following distinctive soil depths: 0-0.10, 0.10–0.20, 0.20–0.30, 0.30–0.60 and 0.60–0.90 m depths, and using equipment that allow for soil bulk density determination (e.g. a cylinder of a known volume) and/or soil mass. Adapting the soil sampling design for monitoring soil C stocks and soil GHG fluxes It is acknowledged that it might not be practical, or even feasible, to monitor soil C stocks (i.e. conduct another round of soil sampling after a few years post-adoption of interventions) or soil GHG fluxes in all the locations designed in Planning and developing a soil sampling design for measuring soil C stocks at T = 0 . Therefore, it is recommended to adapt the approach that has been designed for T = 0 (i.e. baseline) measurement, to obtain a smaller (but still robust) dataset for monitoring purposes. This should follow the same principles described in the previous section, i.e., a stratified strategy with random locations within each unit’s subparts (Maillard et al., 2017 ), which will help to avoid user bias and uncertainties. In this sense, the following strategy is advised. First, take the final soil sampling designed map, developed in the previous section for soil C baseline, which contains all soil sampling locations. Secondly, for each one of the four subparts within the same soil type in each intervention area ( Stage 4 in Planning and developing a soil sampling design for measuring soil C stocks at T = 0 ), select at least one of the sampling locations used for the baseline so that a minimum of four points are selected per soil type of each land use/management system. Attempt to select points that will cover the variation of the covariates used (in the example of the RAF Leeming base, elevation) within the soil type. Additional points should be included in case the range of the used covariate (e.g. elevation) is not covered with only four samples. Note that for monitoring purposes (i.e. re-sampling and GHG flux measurements), the same sampling locations originally designed for soil C baseline measurement will be used, albeit with a reduced dataset. This strategy should deliver a small dataset for monitoring purposes that still has at least four repetitions per soil type within each land use/management system. The same approach should be applied in consecutive monitoring surveys with the selection of the points rotating to avoid fraudulent practices. Alternatively, for monitoring soil C stocks only, the same sampling design used for soil C baseline measurement (described in stages 1–5, i.e. considering all sampling locations) can be deployed. In this case, after analyses, samples in the same unit’s parts with identified small variations in soil C can be pooled to reduce the costs of laboratory analyses. This strategy should be particularly considered in very large target estates and/or regional and/or national scale soil C monitoring. Results The implementation of the proposed five-stage protocol effectively delineated and stratified sampling points, ensuring representativeness across heterogeneous and complex estates. The protocol, deployed at RAF Leeming, UK (54.2927° N, 1.5317° W), yielded a robust strategy for establishing baseline measurements of soil C (SOC and SIC) stocks and GHG fluxes (Fig. 6 ). In total, 165 soil sampling point was identified across the approximately 500-hectare RAF Leeming estate, equating to one sampling point for every three hectares within the target part of the unit. These sampling points were distributed across diverse land uses, soil types, and management practices, encompassing areas such as farmland, recreational zones, runways, and native vegetation (Fig. 6 ). The stratification approach was guided by the SCORPAN framework, which integrates soil, climate, organismal, relief, parent material, age, and geographic position variables to account for landscape heterogeneity. The stratification process revealed clear differences in soil properties and potential C storage and GHG fluxes across the estate. Covariates, including soil type, historic land use, and elevation data, were critical in identifying material differences and stratifying the estate into discrete units (Fig. 3 and Figs. A1 to A25). High-resolution topographic data, particularly elevation, was used to further subdivide units into smaller subparts, ensuring that sampling points reflected spatially explicit variability within the units (Fig. 4 ). The approach provided a quasi-random stratified soil sampling design with individual samples across units (Fig. 6 ), providing a good covering of the target estate while ensuring no bias by the introduction of the random aspect of it. The random component is essential to mitigate user bias while systematically addressing variability across key covariates such as soil type, texture, and management history. Figures A1 to A25 illustrate the estate’s spatial heterogeneity, including variations in elevation, soil characteristics, and land use, which were captured effectively by the proposed protocol. Lastly, by using a stratified approach with random sampling locations, the design ensured unbiased measurements that are statistically valid under a 90% confidence level. The inclusion of multiple soil depths, (0-0.10 m, 0.10–0.20 m, 0.20–0.30 m, 0.30–0.60 m, and 0.60–0.90 m), optional but highly recommended, can provide comprehensive data on soil C distribution across profiles, further enhancing the reliability of baseline assessments. The systematic yet flexible nature of the protocol allowed for its adaptation to estates with varying levels of data availability and heterogeneity. The deployment at RAF Leeming demonstrated the protocol’s scalability and applicability to other locations with similar or different land-use characteristics. Discussion The potential for C capture in soils and the concurrent reduction of GHG fluxes is widely acknowledged within the scientific community (Batjes, 1996 ; Lal, 2004 ). This consensus extends well beyond the primary goal of climate change mitigation and encompasses a range of vital ecosystem services (Moinet et al., 2023 ). However, despite its undisputable importance, no scientific consensus exists when it comes to measuring and monitoring soil C (accounting for both SOC and SIC) and soil GHG fluxes, particularly in estates with different land uses, soil types, and soil management practices (Smith et al., 2020 ). Here we outlined a comprehensive five-stage protocol for encouraging the standardisation of the soil C baseline measurement and subsequently monitoring C changes as well as GHG fluxes. We strongly advocate that this is of ultimate importance as it will steer the process towards unbiased, reliable, as well as comparable, results. Alongside skilled workers and expensive pieces of equipment, the number of samples needed (i.e. sample size) is often deemed a challenging aspect for high-accuracy soil C results (Izaurralde et al., 2013 ). As pointed out by Lohr, ( 2010 ), the determination of the sample size entails a balance between augmenting accuracy and managing the associated costs and complexities. This becomes even more important when it comes to the spatially dependent nature of soil C (Lawrence et al., 2020 ). The results obtained here (i.e. the sampling approach) show that it is possible to have a reasonable sample size even in large estates with mixed land uses, soil types and/or soil management practices. In this study, a soil sampling point was allocated for every approximately 3 hectares of the target part of the unit (165 sampling points for approximately 500 hectares in total) (Fig. 6 ). Yet, it was able to cover the range of soil types, elevation levels, and variation on other covariates, found across all land uses and management practices in the entire base (Figs. A1-A25). For the sake of comparison, the MRV protocol outlined by FAO, ( 2020 ) for agricultural landscapes, recommends collecting a composite sample every 10 hectares within target areas exceeding 50 hectares in size. The quasi-random stratified soil sampling design with individual samples across units, is acknowledged as a robust way of identifying even small variations across the target site (Carter & Gregorich, 2007 ). Besides that, it should attend statistical requirements for 10% uncertainty under a 90% confidence level (Oldeman, 1992 ), providing a good covering of the target estate while ensuring no bias by the introduction of the random aspect of it. Commonly, guidelines suggest the use of systematic sampling using transects or a grid approach with composite samples sent for analysis. However, depending on the size of the target area and its heterogeneity (especially if it is an estate with different land uses, soil types and soil management practices), such an approach might lead to skewed results (i.e. under- or overestimation) (Lawrence et al., 2020 ) with a potential increase in uncertainties and scepticism particularly with soil C programs that carry out/suggest such an approach. Based on the maps generated by our approach alone (Figs. 2 and 3 and Figs. A1-A25), it is already possible to predict a potential large variation in soil C across this estate, which could lead to misleading results when using different methods than the one suggested here for soil sampling and measurement of the soil C baseline. It is evident that the larger the sample size the smaller the errors and uncertainties might be. However, another frequently emphasised challenge pertains to the selection of sampling points, i.e. the actual locations where soil samples should be taken. For soil C measurement, the selection of sampling points should be accurate and precise, which will ensure its faithful assessment of the actual C stocks and minimal error intervals in the estimation, respectively (World Bank, 2021 ). If the selection of sampling points is not well designed (importantly: this is dependent on the soil variable of interest) it can result in either inaccurate and imprecise, inaccurate but precise, or accurate but imprecise estimation, which could lead to bias or systematic error and the presence of random errors (Pearson et al., 2007 ). In this present protocol, we highlight that having prior knowledge concerning variates that affect soil C variability, as suggested in the example of RAF Leeming base, is critical for meeting accuracy and precision while reducing sample size to the bare minimum. The rationale behind it is that many topographic/terrain attributes, particularly those derived from elevation (e.g., slope, curvature, water flow, TWI, etc., Figs. A15-A22), will play a key role when it comes to soil C storage (organic and inorganic), as these factors can directly affect plant and soil interactions that govern the quantity and quality of SOM inputs, decomposition rates under uncultivated soils (Minasny et al., 2013 ), as well as mineral dissolution and fine-scale temperatures (puro earth, 2022 ). Combining it with land uses (current and historic), soil map units, and management zones (as suggested in Stage 3 of this protocol) for stratification and sampling design, is expected to address potential limitations such as enhanced quantification compared to a simple random sampling approach (Mueller et al., 2001 ) and potentially inconsistent temporal results (Franzen et al., 1998 ). It is also pertinent to note that many chemical and biological factors can affect soil C storage (Vicca et al., 2022 ). In this context, the current protocol is anticipated to encompass the capacity to account for variations in spatial distribution linked to these factors. Selection of sampling points without prior knowledge will always lead to bias (even if it is carried out randomly) and, therefore, should be avoided at all costs, especially in estates with different land uses, soil types and soil management practices. Current knowledge and techniques being performed for the purpose of measuring and monitoring soil C are still too vague. For instance, in the Puro protocol (puro earth, 2022 ), there is a reference to the requirement for in-field soil C measurements by participants engaged in C crediting programs (related to C inorganic forms). However, there was no explicit stipulation regarding sampling design for both baseline and/or monitoring assessments. Similarly, in the VERRA methodology designed for SOC credits (VERRA, 2023 ), the need for direct measuring at t = 0 (i.e. baseline) and subsequent monitoring at intervals (e.g. approximately every 5 years) is acknowledged, but no explicit method is provided in this regard. The methodology, however, stresses the need for procedures to be unbiased and representative citing, for instance, the use (or adaptation) of published handbooks/protocols such as the ones provided in the FAO Soils Portal (FAO, 2020 ); and/or the ISO standards on soil sampling (ISO, 2018 ); and/or the IPCC Good Practice Guidance LULUCF (IPCC, 2003 ). While those methodologies offer valuable guidance on how, what, and why soil sampling should be conducted, they are often either more generic or focussed only on agricultural landscapes, rather than being tailored specifically for soil C assessments in estates with different land uses, soil types, and soil management practices. Furthermore, the potential for methodological adaptation may introduce biases into the results, as previously discussed. Similarly, recent research has introduced intriguing methodologies for measuring and monitoring soil C storage, but, again, predominantly concentrating on agricultural lands and most importantly not considering SIC (de Gruijter et al., 2016 , Manning et al., 2024 ). The rather simple rationale outlined in this five-stage protocol could improve our current knowledge and techniques, particularly those proposed in methodologies such as FAO, ( 2020 ) and World Bank, ( 2021 ), without bringing an extra layer of complexity. In addition, whilst this protocol aims to standardise the measurement and monitoring of soil C (SOC and SIC) and GHG fluxes, we underscore that the same samples could potentially be used for the measurement of other soil variables. Ultimately, we highlight the significance of understanding spatial heterogeneity, particularly with regard to plant and soil interactions and soil physicochemical characteristics, which has already been heavily emphasised in the advancement of sustainable strategies for agricultural crop cultivation (AbdelRahman & Arafat, 2020 ). Therefore, it is imperative to consider this aspect when measuring/assessing soil C and/or GHG fluxes. We also advocate for an ongoing enhancement of this protocol through collaboration with fellow researchers, institutions, organisations, and practitioners (e.g. farmers, technicians, and soil analysis laboratories) who are actively engaged in soil C programs. Conclusions We present a five-stage protocol for the systematic selection of sample locations in complex estates for surveys of factors such as soil C where a robust baseline is needed to underpin monitoring strategies designed to determine the effects of soil management interventions. After defining the boundary of the estate ( Stage 1 ), the land is stratified into different land use types ( Stage 2 ). Stage 3 considers a wide range of covariates, including previously measured soil properties (particularly, texture, pH and SOM), soil type (clay, sandy, silty, peaty), past/historic land uses and management systems that affect plant and soil interactions, as well as landform, and climate. Elevation data define topography/terrain covariates, including slope (degrees), flow direction, flow accumulation, basin, aspect, curvature, hillshade and indexes such as the Topographic Wetness Index and the Topographic Position Index. At this point, several geographic information system layers have been produced to inform Stage 4 , in which the domains identified in Stage 3 are subdivided to give areas within which sample locations can be regarded as having shared characteristics. Stage 5 allocates sampling locations according to a required statistical design, whilst avoiding domain boundaries, known buried services and other artificial constraints. In the case study reported here, within an area of 500 ha overall, the systematic five-stage approach led to designation of 165 sampling locations that represent variability in land use type and natural conditions. Following this protocol, a robust strategy for soil C stock baseline measuring and monitoring (SOC and SIC) and GHG fluxes can be set into motion and spatial-temporal variations accurately assessed, especially when interventions are deployed. Whilst we encourage the use of such a protocol, it is crucial to underscore that it should remain a dynamic and evolving framework. Inputs from fellow researchers, institutions, entities, and practitioners (including, farmers, technicians, and soil analysis laboratories), must be actively incorporated into its development. 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No specific data was generated in this study but any specific aspect such the maps or any other that support the findings of this study are mostly available in both in the main body of the paper and in the supplementary material of this article. These can also be provided on request from the corresponding author Supplementary Files ZanietalPlantandSoilSupplementaryInformationJan25.docx Cite Share Download PDF Status: Published Journal Publication published 25 Jul, 2025 Read the published version in Plant and Soil → Version 1 posted Editorial decision: Major revisions 04 Apr, 2025 Reviewers agreed at journal 13 Feb, 2025 Reviewers invited by journal 07 Jan, 2025 Editor invited by journal 06 Jan, 2025 Editor assigned by journal 06 Jan, 2025 First submitted to journal 03 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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3","display":"","copyAsset":false,"role":"figure","size":380544,"visible":true,"origin":"","legend":"\u003cp\u003eDivision of the runway unit into three discrete parts according to correspondents’ soil types at the RAF Leeming base.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5677695/v1/6bf0bbf5680ae88956b57849.jpeg"},{"id":73416505,"identity":"da8a94be-d2b3-4611-bc77-759d7b615503","added_by":"auto","created_at":"2025-01-09 17:13:02","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":400942,"visible":true,"origin":"","legend":"\u003cp\u003eDivision of the runway unit, soil part 1 (only), into four subparts based on observed variation in the elevation at the RAF Leeming base.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5677695/v1/37416d49ce74db6c0931db8a.jpeg"},{"id":73416504,"identity":"0839909f-c4c3-465a-bc63-0ddfec2a5879","added_by":"auto","created_at":"2025-01-09 17:13:01","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":241442,"visible":true,"origin":"","legend":"\u003cp\u003eDivision of the runway unit, soil part 1 (only), into four subparts with no high-resolution data available.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5677695/v1/75738a5f40b5092996944b7d.jpeg"},{"id":73415499,"identity":"6b6cc6b0-d85f-4066-8ff9-6011c2afa30e","added_by":"auto","created_at":"2025-01-09 16:57:01","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":914182,"visible":true,"origin":"","legend":"\u003cp\u003eFinal target soil sampling locations applying the guidelines proposed in this report across the RAF Leeming base.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5677695/v1/e34fb27b066c4bfd3c8a0122.jpeg"},{"id":87756842,"identity":"c4449902-a5b3-469d-ad58-dba7030e6024","added_by":"auto","created_at":"2025-07-28 16:09:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4370930,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5677695/v1/ed712af4-e405-429c-9017-ea3a1602a7f9.pdf"},{"id":73415509,"identity":"95aa160d-518d-4568-8ed3-615a090e7548","added_by":"auto","created_at":"2025-01-09 16:57:02","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11625936,"visible":true,"origin":"","legend":"","description":"","filename":"ZanietalPlantandSoilSupplementaryInformationJan25.docx","url":"https://assets-eu.researchsquare.com/files/rs-5677695/v1/9b01041b1fe1da85e13b7c42.docx"}],"financialInterests":"","formattedTitle":"A five-stage protocol for systematic measuring and monitoring soil carbon and greenhouse gas fluxes in complex estates","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increase of greenhouse gas (GHG) concentrations in the atmosphere since the Industrial Revolution has brought about global climate change concerns. According to the Intergovernmental Panel on Climate Change (IPCC), the Earth\u0026rsquo;s surface temperature has already increased by 1.1\u0026deg;C since pre-industrial times. This number is expected to increase to 1.5\u0026deg;C by the end of this century and to 2\u0026deg;C or more by 2100, if the current trend of GHG fluxes continues (IPCC, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This increase in GHG concentration in the atmosphere can cause sea level rise, extreme weather events, loss of biodiversity, ocean acidification, among others (IPCC, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo mitigate the effects of GHG fluxes, the Paris Agreement was signed in 2015 by 195 countries, which aims to keep the global average temperature increase to \u0026ldquo;well below\u0026rdquo; 2\u0026deg;C above pre-industrial levels and to pursue efforts to limit it to 1.5\u0026deg;C (UNFCCC, 2015). To achieve the goals of the Paris Agreement, countries have committed to reducing their GHG fluxes and implementing adaptation strategies to lessen the extent and impact of climate change. However, recent research has shown that GHG prevention and reductions (i.e. mitigation) and adaptations alone will not be enough, also requiring efforts to promote carbon (C) removals (also known as negative emission) from the atmosphere (Anderson \u0026amp; Peters, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eArguably, soil could play an important role in the C capture goal as it has the largest dynamic reservoir of C on Earth, with figures suggesting a capacity of 2500 Pg, i.e. billion tonnes\u0026thinsp;=\u0026thinsp;10\u003csup\u003e15\u003c/sup\u003e g (Batjes, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Lal, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Moinet et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The absolute quantity of C held within a soil (i.e. the soil C stock) consists of two major components: soil inorganic C (SIC) and soil organic C (SOC). Soil inorganic C, the smaller portion of C on soils (approx. 950 Pg), is represented mainly by carbonates derived from pedogenic processes as well as geologic or soil parent material sources while soil organic C, the most abundant terrestrial C pool (approx. 1550 Pg), comprises soil organic matter (SOM) components (Trumper et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). According to (Lal, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the potential for soils to sequester atmospheric C globally is between 1.4 and 3.4 Pg C year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. As a practical example, and only considering SIC, in urban soils (Technosols), the presence of materials derived from demolition leads to the potentially rapid formation of pedogenic carbonates. Washbourne et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) measured the accumulation of calcium carbonate in an urban soil equivalent to the removal of 85 t CO\u003csub\u003e2\u003c/sub\u003e per hectare annually for a 12-ha city-centre site, confirming a pedogenic origin through analysis of C and O stable isotopes as well as \u003csup\u003e14\u003c/sup\u003eC evidence for the presence within the carbonate minerals of modern carbon. In addition to C capture and potential climate regulation, there are many other ecosystem services inherent to soils, including food security, and the provision of fibre and fuel, among others, which highlight that soils will not only play a key role in the climate system but also in essential components for maintaining humanity.\u003c/p\u003e \u003cp\u003eImmediate actions are required across all sectors, including, but not limited to, energy, transport, agriculture, industry, and the military (IPCC, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For military operations, particularly aviation, the implications of reducing reliance on fossil fuels are particularly serious (NATO, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Currently, in the UK, it is estimated that the Ministry of Defence (MoD) contributes to around 50% of all government departmental emissions (TEAM Defence, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), highlighting the need for the military sector to play a key role in decarbonisation (Rajaeifar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, since the MoD is one of the largest landowners in the country, with an estate (433,000 ha) nearly equal to 2% of the UK\u0026rsquo;s land mass (National statistics, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the opportunity to manage and enhance C capture in soils is a strategy not yet explored by the defence sector.\u003c/p\u003e \u003cp\u003eDespite soil\u0026rsquo;s large C storage capacity, factors such as land use, agricultural systems, and management practices influence soil and plant interactions, leading soils to act either as a sink or source of C, with substantial variations in magnitude and rate (Lal, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Hence, it is critical to take these, as well as other aspects (such as soil characteristics, soil type, vegetation, topography, climate, among other soil-forming factors and processes) into consideration when planning a soil sampling campaign for baseline measurement and/or monitoring purposes that is reliable and sound for measuring and monitoring soil C and GHGs (Smith et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Minasny et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lal et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Batjes, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, a standardised protocol for baseline measuring and monitoring SOC/SIC changes and GHG fluxes is still lacking.\u003c/p\u003e \u003cp\u003eFor single land management practices (such as farming or forestry), there have been notable advancements in the formulation of guidelines for measuring and monitoring, reporting, and verification (MRV) of SOC/SIC baseline and changes, as well as GHG fluxes (FAO, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; puro earth, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; VERRA, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, these advancements have primarily centred on the field level, with occasional attention extended to the farm level or even national level. There is, therefore, still a need to elucidate strategies for soil sampling and GHG measurements for estates that combine different land uses, soil types, and soil management practices, and which span over large areas. This is particularly challenging as it must also be cost-effective and easily understood, as well as simple and broadly applicable in practice. The standardisation of strategies for baseline measuring and monitoring SOC/SIC and GHG fluxes is critical as it will provide the basis for where soil samples and GHG measurements must be undertaken.\u003c/p\u003e \u003cp\u003eThe overall aim of this study is to establish a standardised protocol for baseline measuring and monitoring soil C (accounting for both SOC and SIC) and soil GHG fluxes in estates with different land uses, soil types, and soil management practices. The five-stage protocol has been designed to offer a unified approach that is cost-effective, repeatable, and easy-to-use across any sector, allowing SOC and SIC, as well as soil GHG fluxes, to be rigorously and systematically measured and monitored.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eWhile this five-stage protocol represents a unique approach to baseline measuring and monitoring SOC/SIC and soil GHG fluxes, it is important to highlight that this also encompasses elements of a series of international protocols previously published by different public and private institutions (including, but not limited to: (Alberta Government, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Australian Government, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gold Standard, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; USDA-NRCS-CSU, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; FAO, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; puro earth, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; VERRA, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eThe guidelines were deliberately designed to be rigorous and systematic, but elements of simplicity, repeatability, and feasibility were thoroughly considered. In this sense, it is expected that it can be applied by any individual with basic computer knowledge and skills, who wishes to assess soil C stocks and soil GHG fluxes in an estate with different land uses, soil types, and soil management practices.\u003c/p\u003e \u003cp\u003eAlthough the stages described below have been developed and deployed at a military base (RAF Leeming, Yorkshire, UK; 54.2927\u0026deg; N, 1.5317\u0026deg; W) it is expected that they could also be adopted at any other location.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlanning and developing a soil sampling design for measuring soil C stocks at T\u0026thinsp;=\u0026thinsp;0\u003c/h2\u003e \u003cp\u003eThis protocol recommends the use of the SCORPAN framework (McBratney et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) as a basis for the compilation of relevant data/information, hereafter referred to as covariates, for designing the soil sampling programme. The SCORPAN framework is a concept that highlights that soil formation and/or properties are highly dependent on their position in the landscape, i.e. affected by several environmental factors (including plant and soils interactions), which also apply to SOC/SIC storage, and thus potential C capture. As such, most of the elements/covariates needed for planning and developing a soil sampling design are primarily based on the SCORPAN function (Eq.\u0026nbsp;1):\u003c/p\u003e \u003cp\u003eS = \u0026#119891;(s,c,o,r,p,a,n) Eq.\u0026nbsp;1)\u003c/p\u003e \u003cp\u003ewhere S is soil classes or attributes to be focussed, \u0026ldquo;s\u0026rdquo; refers to the soil (other or previously measured properties of the soil at a point), \u0026ldquo;\u003cem\u003ec\u003c/em\u003e\u0026rdquo; is climatic properties of the environment at a point, \u0026ldquo;\u003cem\u003eo\u003c/em\u003e\u0026rdquo; refers to organisms, including land cover and natural vegetation or fauna or human activity (plant and soil interactions), \u0026ldquo;\u003cem\u003er\u003c/em\u003e\u0026rdquo; is the relief, topography, landscape attributes, \u0026ldquo;\u003cem\u003ep\u003c/em\u003e\u0026rdquo; is the parent material/lithology, \u0026ldquo;\u003cem\u003ea\u003c/em\u003e\u0026rdquo; refers to the age, i.e. the time factor and finally, \u0026ldquo;\u003cem\u003en\u003c/em\u003e\u0026rdquo; is the spatial or geographic position.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStage 1 \u0026ndash; Defining overall boundary\u003c/strong\u003e \u003cp\u003eThe first step is to identify, delineate, and map the spatial boundaries of the target estate, which relates to the \u003cem\u003e\u0026ldquo;o\u0026rdquo;\u003c/em\u003e in the SCORPAN function. This can be done by consulting the landowner(s) and requesting a simple drawing of the estate boundaries using for example Google Earth maps (\u0026ldquo;Google Earth Pro,\u0026rdquo; 2023) or any other mapping platform. Alternatively, other methods rather than satellite images and tools can be used e.g. land records or hard copy maps.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAt the end of this stage, the output should be a geospatial map/satellite image with the total spatial boundary of the target estate. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows an example of the spatial boundary for the RAF Leeming base, taken from ArcGIS (Environmental Systems Research Institute, Inc., Redlands, CA, USA) (Esri, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eStage 2 \u0026ndash; Target estate stratification\u003c/em\u003e: Still considering the \u003cem\u003e\u0026ldquo;o\u0026rdquo;\u003c/em\u003e in the SCORPAN function, it is also important to identify and delineate current different land uses within the total area (i.e. high-level stratification of the target estate into discrete units). Examples include: farmland, paved areas (including runways in this case), urban/recreation, native vegetation, etc. If within one of these (or other) land uses, there is a different management system these should be considered as two different target intervention areas for soil sampling, i.e. management zones. Examples at this location of the same land use but different management systems, include but are not limited to the following: a farm that is partly conventionally managed and partly organically managed, land designed solely for pasture, or for crops, or for woodland, or native vegetation (or other distinctive management systems), a recreation area solely designed for gardening, or recreation, or football/sport pitch, etc. The easiest way of finding out such information is by discussing it with landowners and/or tenants, but some tools such as DIGIMAP (Digimap, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (only for UK-located target estates) can also be used to gather such information. For the RAF Leeming base, we have used both approaches, i.e., we talked to landowners and tenants, as well as using DIGIMAP for gathering land use and management system information.\u003c/p\u003e \u003cp\u003eAt the end of this stage, the product should be a geospatial map/satellite image of the target estate that includes stratification (i.e. units) concerning different land uses and management systems. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows an example of the stratification of RAF Leeming base considering differences in current land uses and management systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStage 3 \u0026ndash; Collecting covariates\u003c/strong\u003e \u003cp\u003eOnce the total boundary, land uses, and management systems/zones are delineated, it is important to gather covariates related to potential material differences within the target estate, as well as in each identified unit. This step relates to \u003cem\u003e\u0026ldquo;s\u0026rdquo;\u003c/em\u003e, \u003cem\u003e\u0026ldquo;c\u0026rdquo;\u003c/em\u003e, \u003cem\u003e\u0026ldquo;r\u0026rdquo;\u003c/em\u003e, \u003cem\u003e\u0026ldquo;p\u0026rdquo;\u003c/em\u003e, and \u003cem\u003e\u0026ldquo;a\u0026rdquo;\u003c/em\u003e in the SCORPAN function, and therefore must be thoroughly considered.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eMaterial differences include potential discrepancies in previously measured soil properties within the target estate that might affect SOC/SIC and soil GHG fluxes (e.g. nutrient content, soil bulk density, texture, pH, SOM, microbial abundance/diversity, etc.), soil type and underlying geology, land use history, landform, and climate (depending on the size of the target estate). In this protocol, we particularly highlight the use of the following covariates: previously measured soil properties (particularly, texture, pH and SOM), soil type (clay, sandy, silty, peaty), past/historic land uses and management systems (ideally within the last 10 years), landform, and climate.\u003c/p\u003e \u003cp\u003eFor landforms, the use of elevation data is highlighted. Elevation data can be used to derive several topography/terrain covariates, including slope (degrees), flow direction, flow accumulation, basin, aspect, curvature, hillshade as well as some computed indexes such as the Topographic Wetness Index and the Topographic Position Index (TWI and TPI, respectively). Functions on how to calculate TWI, TPI and all other aforementioned landform covariates are available in Moore et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and ArcGIS (Environmental Systems Research Institute, Inc., Redlands, CA, USA) (Esri, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Other mapping and analysing tools are also able and can be used to perform such analysis and derive the recommended landform covariates (e.g. QGIS, Maptitude, Python, R studio, etc.). There are no restrictions on what mapping and analysing tool to use in this step but specific knowledge of how to operate such software is required. For the RAF Leeming base, soil type, past land uses, landform, and climate covariates were all collected using Digimap and/or derived from them by using geostatistical approaches on ArcGIS. If the target area is outside the UK and/or Digimap is not available, we recommend talking to the landowner/tenant(s) of the target estate to collect as much material information as possible from them. If the data is still not available or limited, some of it can be obtained from global data sources, but local data is always preferred. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides global databases and web links that can be used at this stage.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGlobal databases available for spatial information\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003e(adapted from FAO, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeb address\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange of datasets including historic, geology, marine, environmental, elevation across the UK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigimap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://digimap.edina.ac.uk/\u003c/span\u003e\u003cspan address=\"https://digimap.edina.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMany\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange datasets including historic, geology, marine, environmental, across the UK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMagic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://magic.defra.gov.uk/\u003c/span\u003e\u003cspan address=\"https://magic.defra.gov.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMany\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly climatic data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRU \u0026ndash; Climate Research Unit,\u003c/p\u003e \u003cp\u003eUniversity of East Anglia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/cruts.1905011326.v4.03/\u003c/span\u003e\u003cspan address=\"https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/cruts.1905011326.v4.03/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 km x 50 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational and regional climate for the UK.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeteorological Office for climate averages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://digimap.edina.ac.uk/\" target=\"_blank\"\u003ewww.metoffice.gov.uk/weather/uk/climate.html\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.metoffice.gov.uk/weather/uk/climate.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeology across the UK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBritish Geological Survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bgs.ac.uk/discoveringGeology/geologyOfBritain/viewer.html\u003c/span\u003e\u003cspan address=\"http://www.bgs.ac.uk/discoveringGeology/geologyOfBritain/viewer.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable depending on location\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOC stocks 0\u0026ndash;30 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSOC Map - FAO-ITPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://54.229.242.119/GSOCmap/\u003c/span\u003e\u003cspan address=\"http://54.229.242.119/GSOCmap/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 x 1 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOC stocks and SOC\u003c/p\u003e \u003cp\u003econcentration; profiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternational Soil Carbon\u003c/p\u003e \u003cp\u003eNetwork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iscn.fluxdata.org/\u003c/span\u003e\u003cspan address=\"https://iscn.fluxdata.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifferent\u003c/p\u003e \u003cp\u003eresolutions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil texture 0\u0026ndash;30 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISRIC Soil Grids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://soilgrids.org\u003c/span\u003e\u003cspan address=\"https://soilgrids.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and at global level from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.isric.org/\u003c/span\u003e\u003cspan address=\"https://data.isric.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250 x 250 m\u003c/p\u003e \u003cp\u003e500 x 500 m\u003c/p\u003e \u003cp\u003e1 x 1 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil types for England and Wales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandis Soilscapes viewer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.landis.org.uk/soilscapes/\u003c/span\u003e\u003cspan address=\"https://www.landis.org.uk/soilscapes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 x 1 km\u003c/p\u003e \u003cp\u003e5 x 5 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI- Historic images\u003c/p\u003e \u003cp\u003e(2001\u0026ndash;2020) every 16 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMODIS - MOD13A2 datasets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lpdaac.usgs.gov/products/mod13a2v006/\u003c/span\u003e\u003cspan address=\"https://lpdaac.usgs.gov/products/mod13a2v006/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 x 1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Cover \u0026ndash; Land Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMODIS\u003c/p\u003e \u003cp\u003eLand Cover Dynamics\u003c/p\u003e \u003cp\u003eMCD12Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://modis.gsfc.nasa.gov/data/dataprod/mod12.php\u003c/span\u003e\u003cspan address=\"https://modis.gsfc.nasa.gov/data/dataprod/mod12.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500 x 500m\u003c/p\u003e \u003cp\u003e1 x 1 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Cover \u0026ndash; Land Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean Space Agency (ESA)\u003c/p\u003e \u003cp\u003eClimate Change Initiative\u003c/p\u003e \u003cp\u003e(CCI)- Copernicus Climate\u003c/p\u003e \u003cp\u003eChange Service (C3S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.esa-landcover-cci.org/\u003c/span\u003e\u003cspan address=\"https://www.esa-landcover-cci.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300 x 300m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Cover \u0026ndash; Land Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIMAGE Integrated Model\u003c/p\u003e \u003cp\u003eto Assess the Global\u003c/p\u003e \u003cp\u003eEnvironment.\u003c/p\u003e \u003cp\u003ePBL Netherlands\u003c/p\u003e \u003cp\u003eEnvironmental Assessment\u003c/p\u003e \u003cp\u003eAgency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://models.pbl.nl/image/index.php/Land_cover_and_land_use\u003c/span\u003e\u003cspan address=\"https://models.pbl.nl/image/index.php/Land_cover_and_land_use\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 x 10 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Cover \u0026ndash; Land Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAO. Global Land Cover\u003c/p\u003e \u003cp\u003eSHARE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/land-water/land/land-governance/landresources-planning-toolbox/category/details/en/c/1036355/\u003c/span\u003e\u003cspan address=\"http://www.fao.org/land-water/land/land-governance/landresources-planning-toolbox/category/details/en/c/1036355/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;25 x 25 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Cover \u0026ndash; Land Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSGS Global Land Survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lta.cr.usgs.gov/GLS\u003c/span\u003e\u003cspan address=\"https://lta.cr.usgs.gov/GLS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 x 30m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Cover \u0026ndash; Land Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCORINE land cover (Europe\u003c/p\u003e \u003cp\u003eonly)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://land.copernicus.eu/paneuropean/corine-land-cover\u003c/span\u003e\u003cspan address=\"https://land.copernicus.eu/paneuropean/corine-land-cover\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 x 100 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt the end of this stage, the output should be one or more geospatial map/satellite images of each unit and/or from the whole target estate with material differences that might affect soil C storage and soil GHG fluxes. Examples are given for the RAF Leeming base in supporting information figures A1-A25. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows how such covariates are related to the SCORPAN framework and their description.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCovariates collected and their relationship with SCORPAN framework and description.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScorpan Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe height of a location above the Earth\u0026rsquo;s sea level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe inclination of the land surface from the horizontal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlow Direction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirection of water flow in a given cell based on its steepest descent drop\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlow Accumulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccumulated flow determined by accumulating the weight for all cells that flow into each downslope cell\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConnected cells belonging to the same drainage basin defined by the flow direction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe direction in which a land surface slope face\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurvature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe shape or curvature of the slope i.e. concave or convex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHillshade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepresentation of the surface considering the sun position for shading\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopographic Wetness Index (TWI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC, R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe relative wetness within moist catchments, but is more commonly used as a measure of position on the slope with larger values indicating a lower slope position\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopographic Position Index (TPI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTopographic position classification identifying upper, middle and lower parts of the landscape\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrganic system in accordance with the Soil Association Organic Standards or Conventional system (UK best practices recommendations)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand uses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRunways, Urban, Native vegetation, farmland, recreation, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStage 4 \u0026ndash; Division into discrete parts and subparts\u003c/strong\u003e \u003cp\u003eThis step refers to further stratification of the units (as designed in \u003cem\u003eStage 2\u003c/em\u003e) into discrete parts and subparts, which will be the target sampling areas, based on material differences found in \u003cem\u003eStage 3.\u003c/em\u003e Unfortunately, there is no set-in-stone procedure to be followed in this phase as it will depend on the availability, as well as the amount, of data gathered in the previous steps. However, it is recommended to carry out an in-depth evaluation of the scope of the project (i.e. which land uses, soil types, and soil management practices are present and what covariates are available to assess them), in order to get better insights on priorities for the target estate. In addition, it is highly advisable to carefully study all the maps, as well as other relevant information available to design the best strategy for soil sampling at the target estate. The following approach, developed for the RAF Leeming base, is particularly recommended for target estates that present these three elements, i.e. different land uses, soil types, and soil management practices.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFirst, choose one of the material differences found in \u003cem\u003eStage 3\u003c/em\u003e as a basis for division of the units into discrete parts. We highly recommend, if available, using soil type and/or soil texture maps, as they are closely related to high/low soil C potential. Divide each unit (i.e., each land use/management system/historic use, delineated in \u003cem\u003eStage 2\u003c/em\u003e) into corresponding soil type parts (or use other information that can characterise the unit\u0026rsquo;s high-level variability). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows an example of how the runway unit for the RAF Leeming base was divided into discrete parts based on soil type.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, divide each part of the unit (in this example based on soil type) into four further subparts (or more if needed) based on another covariate collected with a fine resolution variability (e.g. long-term average normalised difference vegetation index, soil electrical conductivity, yield maps, elevation, etc.). Any covariate with a fine-resolution variability can be used. However, if available, this protocol recommends elevation as the covariate to be used in this step as this is known to have a close relationship with spatially implicit soil-factors (Behrens et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). If elevation data is unavailable, the protocol recommends, preferably, utilising a relevant covariate with a high-resolution that may contribute to the variability within the unit\u0026rsquo;s part. For the RAF Leeming base, elevation data was available at a 5 m resolution for the entire area (Fig. A14), and it was selected as the covariate for subdividing the unit\u0026rsquo;s part into subparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). If high-resolution data/information is not available, it is recommended to subdivide each unit part into a minimum of four subparts, ensuring they are equal/similar in terms of area and as relatively homogenous as possible (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The primary objective of this stage is to ascertain that the sampling points (which will be designed in the next stage) exhibit greater homogeneity within the specific subunit than those across the entire estate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the end of this stage, the output should consist of geospatial maps/satellite images of each unit (as designed in \u003cem\u003eStage 2\u003c/em\u003e) further stratified into discrete parts according to the chosen material differences. This stratification should consider both high-level and high-resolution covariates, using soil type as a high-level covariate and elevation as a high-resolution covariate, for example (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It is important to highlight that this step must be repeated for all units identified in \u003cem\u003eStage 2\u003c/em\u003e of this protocol.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStage 5 \u0026ndash; Designing sampling points\u003c/strong\u003e \u003cp\u003eIn this step, the target sampling points are chosen. The protocol recommends a minimum of three (for statistical purposes) random locations within each one of the subparts designed at the end of \u003cem\u003eStage 4\u003c/em\u003e for the extraction of soil cores. However, the larger the area and the expected or know variability within the subpart\u0026rsquo;s unit, the more samples must be taken within that subpart. The use of statistical software (R, JMP, Minitab, SPSS, etc.) for the selection of the random sampling locations is highly advised. While the sampling locations should be randomly assigned, it is important to ensure that they adhere to the following constraints\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e- Locate each sampling point at least 50 m away from each other within the subpart,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e- Avoid locating the sampling point near the field border (\u0026gt;\u0026thinsp;20 m from a field boundary),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e- If known, try to avoid locations that are likely to be disproportionately affected by compaction from either machinery and/or animal activity and/or chemical or other types of disposals or spillage.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn addition, it is important to arbitrarily allocate sampling points in likely high and low soil C potential locations, in case it is not randomly allocated. This can be done by using the other covariate maps gathered in the previous stages (e.g. slope, flow direction, flow accumulation, basin, aspect, curvature, hillshade, TWI, TPI, or others), or high-level global maps, for example, the FAO-GSOC map (Global Soil Organic Carbon map; available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://54.229.242.119/GSOCmap/\u003c/span\u003e\u003cspan address=\"http://54.229.242.119/GSOCmap/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which gives a rough estimation of the current soil C stock (t C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 30 cm) expected at the location. Please note that the estimated soil C stock will not always be in line with the measurement of the lab. There are several reasons for this, but mainly because soil C is difficult to measure and sometimes it can vary up to 50%, secondly, the spatial resolution used on the FAO-GSOC map and the resolution of the target sample areas within the estate might differ substantially.\u003c/p\u003e \u003cp\u003eAs a general rule of thumb, the more parts the unit is divided into and the greater the number of sampling points within each one of the subparts, the better the capacity to reliably measure soil C stocks baseline, as well as to detect changes in soil C storage and GHG fluxes over time. A power analysis can be used to calculate the ideal number of sampling points (Eq.\u0026nbsp;2 and Eq.\u0026nbsp;3), but this should be optional, and used when not much data/information is available for the target area.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:MDD\\:\\ge\\:\\:\\frac{S}{\\sqrt{n}}\\:\\times\\:\\:\\left({t}_{\\alpha\\:,u\\:}+\\:{t}_{\\beta\\:,u\\:}\\right)\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:n\\:\\ge\\:\\:{\\left(\\frac{S\\:\\times\\:\\:\\left({t}_{\\alpha\\:\\:}+\\:{t}_{\\beta\\:\\:}\\right)}{MDD}\\right)}^{2}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;3\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eMDD\u003c/em\u003e is the minimum detectable difference, \u003cem\u003e\u0026ldquo;S\u0026rdquo;\u003c/em\u003e is the standard deviation of the difference in SOC stocks between \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003e\u0026ldquo;n\u0026rdquo;\u003c/em\u003e is the number of samples, \u003cem\u003e\u0026ldquo;t\u003c/em\u003e\u003csub\u003e\u003cem\u003eα\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026rdquo;\u003c/em\u003e refers to two-sided critical value of the t-distribution at a given significance level (α) frequently taken as 0.05 (5 percent), \u003cem\u003e\u0026ldquo;t\u003c/em\u003e\u003csub\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026rdquo;\u003c/em\u003e is the one-sided quartile of the t-distribution corresponding to a probability of type II error β (e.g., 90 percent).\u003c/p\u003e \u003cp\u003eAfter randomly (and arbitrarily for some specific points, if needed) choosing the sampling point locations, if a deep soil sampling is going to be carried out, it is necessary to take into account information on underground services (water pipes, gas pipes, electrical wires, fibre optics, cables, etc) and then manually adjust any sampling point that might be in a \u0026ldquo;restricted\u0026rdquo; location of this type. The final target locations (coordinates, latitude, and longitude) are then recorded (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor a full inventory of soil C, this protocol strongly recommends soil cores to be taken at both top- and sub-soil layers, at the following distinctive soil depths: 0-0.10, 0.10\u0026ndash;0.20, 0.20\u0026ndash;0.30, 0.30\u0026ndash;0.60 and 0.60\u0026ndash;0.90 m depths, and using equipment that allow for soil bulk density determination (e.g. a cylinder of a known volume) and/or soil mass.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAdapting the soil sampling design for monitoring soil C stocks and soil GHG fluxes\u003c/h3\u003e\n\u003cp\u003eIt is acknowledged that it might not be practical, or even feasible, to monitor soil C stocks (i.e. conduct another round of soil sampling after a few years post-adoption of interventions) or soil GHG fluxes in all the locations designed in \u003cem\u003ePlanning and developing a soil sampling design for measuring soil C stocks at T\u0026thinsp;=\u0026thinsp;0\u003c/em\u003e. Therefore, it is recommended to adapt the approach that has been designed for T\u0026thinsp;=\u0026thinsp;0 (i.e. baseline) measurement, to obtain a smaller (but still robust) dataset for monitoring purposes. This should follow the same principles described in the previous section, i.e., a stratified strategy with random locations within each unit\u0026rsquo;s subparts (Maillard et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which will help to avoid user bias and uncertainties. In this sense, the following strategy is advised.\u003c/p\u003e \u003cp\u003eFirst, take the final soil sampling designed map, developed in the previous section for soil C baseline, which contains all soil sampling locations. Secondly, for each one of the four subparts within the same soil type in each intervention area (\u003cem\u003eStage 4\u003c/em\u003e in \u003cem\u003ePlanning and developing a soil sampling design for measuring soil C stocks at T\u0026thinsp;=\u0026thinsp;0\u003c/em\u003e), select at least one of the sampling locations used for the baseline so that a minimum of four points are selected per soil type of each land use/management system. Attempt to select points that will cover the variation of the covariates used (in the example of the RAF Leeming base, elevation) within the soil type. Additional points should be included in case the range of the used covariate (e.g. elevation) is not covered with only four samples. Note that for monitoring purposes (i.e. re-sampling and GHG flux measurements), the same sampling locations originally designed for soil C baseline measurement will be used, albeit with a reduced dataset. This strategy should deliver a small dataset for monitoring purposes that still has at least four repetitions per soil type within each land use/management system. The same approach should be applied in consecutive monitoring surveys with the selection of the points rotating to avoid fraudulent practices. Alternatively, for monitoring soil C stocks only, the same sampling design used for soil C baseline measurement (described in stages 1\u0026ndash;5, i.e. considering all sampling locations) can be deployed. In this case, after analyses, samples in the same unit\u0026rsquo;s parts with identified small variations in soil C can be pooled to reduce the costs of laboratory analyses. This strategy should be particularly considered in very large target estates and/or regional and/or national scale soil C monitoring.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe implementation of the proposed five-stage protocol effectively delineated and stratified sampling points, ensuring representativeness across heterogeneous and complex estates. The protocol, deployed at RAF Leeming, UK (54.2927\u0026deg; N, 1.5317\u0026deg; W), yielded a robust strategy for establishing baseline measurements of soil C (SOC and SIC) stocks and GHG fluxes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn total, 165 soil sampling point was identified across the approximately 500-hectare RAF Leeming estate, equating to one sampling point for every three hectares within the target part of the unit. These sampling points were distributed across diverse land uses, soil types, and management practices, encompassing areas such as farmland, recreational zones, runways, and native vegetation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The stratification approach was guided by the SCORPAN framework, which integrates soil, climate, organismal, relief, parent material, age, and geographic position variables to account for landscape heterogeneity.\u003c/p\u003e \u003cp\u003eThe stratification process revealed clear differences in soil properties and potential C storage and GHG fluxes across the estate. Covariates, including soil type, historic land use, and elevation data, were critical in identifying material differences and stratifying the estate into discrete units (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Figs. A1 to A25). High-resolution topographic data, particularly elevation, was used to further subdivide units into smaller subparts, ensuring that sampling points reflected spatially explicit variability within the units (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe approach provided a quasi-random stratified soil sampling design with individual samples across units (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), providing a good covering of the target estate while ensuring no bias by the introduction of the random aspect of it. The random component is essential to mitigate user bias while systematically addressing variability across key covariates such as soil type, texture, and management history. Figures A1 to A25 illustrate the estate\u0026rsquo;s spatial heterogeneity, including variations in elevation, soil characteristics, and land use, which were captured effectively by the proposed protocol.\u003c/p\u003e \u003cp\u003eLastly, by using a stratified approach with random sampling locations, the design ensured unbiased measurements that are statistically valid under a 90% confidence level. The inclusion of multiple soil depths, (0-0.10 m, 0.10\u0026ndash;0.20 m, 0.20\u0026ndash;0.30 m, 0.30\u0026ndash;0.60 m, and 0.60\u0026ndash;0.90 m), optional but highly recommended, can provide comprehensive data on soil C distribution across profiles, further enhancing the reliability of baseline assessments. The systematic yet flexible nature of the protocol allowed for its adaptation to estates with varying levels of data availability and heterogeneity. The deployment at RAF Leeming demonstrated the protocol\u0026rsquo;s scalability and applicability to other locations with similar or different land-use characteristics.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe potential for C capture in soils and the concurrent reduction of GHG fluxes is widely acknowledged within the scientific community (Batjes, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Lal, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This consensus extends well beyond the primary goal of climate change mitigation and encompasses a range of vital ecosystem services (Moinet et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, despite its undisputable importance, no scientific consensus exists when it comes to measuring and monitoring soil C (accounting for both SOC and SIC) and soil GHG fluxes, particularly in estates with different land uses, soil types, and soil management practices (Smith et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Here we outlined a comprehensive five-stage protocol for encouraging the standardisation of the soil C baseline measurement and subsequently monitoring C changes as well as GHG fluxes. We strongly advocate that this is of ultimate importance as it will steer the process towards unbiased, reliable, as well as comparable, results.\u003c/p\u003e \u003cp\u003eAlongside skilled workers and expensive pieces of equipment, the number of samples needed (i.e. sample size) is often deemed a challenging aspect for high-accuracy soil C results (Izaurralde et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As pointed out by Lohr, (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), the determination of the sample size entails a balance between augmenting accuracy and managing the associated costs and complexities. This becomes even more important when it comes to the spatially dependent nature of soil C (Lawrence et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The results obtained here (i.e. the sampling approach) show that it is possible to have a reasonable sample size even in large estates with mixed land uses, soil types and/or soil management practices. In this study, a soil sampling point was allocated for every approximately 3 hectares of the target part of the unit (165 sampling points for approximately 500 hectares in total) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Yet, it was able to cover the range of soil types, elevation levels, and variation on other covariates, found across all land uses and management practices in the entire base (Figs. A1-A25). For the sake of comparison, the MRV protocol outlined by FAO, (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for agricultural landscapes, recommends collecting a composite sample every 10 hectares within target areas exceeding 50 hectares in size.\u003c/p\u003e \u003cp\u003eThe quasi-random stratified soil sampling design with individual samples across units, is acknowledged as a robust way of identifying even small variations across the target site (Carter \u0026amp; Gregorich, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Besides that, it should attend statistical requirements for 10% uncertainty under a 90% confidence level (Oldeman, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), providing a good covering of the target estate while ensuring no bias by the introduction of the random aspect of it. Commonly, guidelines suggest the use of systematic sampling using transects or a grid approach with composite samples sent for analysis. However, depending on the size of the target area and its heterogeneity (especially if it is an estate with different land uses, soil types and soil management practices), such an approach might lead to skewed results (i.e. under- or overestimation) (Lawrence et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with a potential increase in uncertainties and scepticism particularly with soil C programs that carry out/suggest such an approach. Based on the maps generated by our approach alone (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Figs. A1-A25), it is already possible to predict a potential large variation in soil C across this estate, which could lead to misleading results when using different methods than the one suggested here for soil sampling and measurement of the soil C baseline.\u003c/p\u003e \u003cp\u003eIt is evident that the larger the sample size the smaller the errors and uncertainties might be. However, another frequently emphasised challenge pertains to the selection of sampling points, i.e. the actual locations where soil samples should be taken. For soil C measurement, the selection of sampling points should be accurate and precise, which will ensure its faithful assessment of the actual C stocks and minimal error intervals in the estimation, respectively (World Bank, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). If the selection of sampling points is not well designed (importantly: this is dependent on the soil variable of interest) it can result in either inaccurate and imprecise, inaccurate but precise, or accurate but imprecise estimation, which could lead to bias or systematic error and the presence of random errors (Pearson et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In this present protocol, we highlight that having prior knowledge concerning variates that affect soil C variability, as suggested in the example of RAF Leeming base, is critical for meeting accuracy and precision while reducing sample size to the bare minimum. The rationale behind it is that many topographic/terrain attributes, particularly those derived from elevation (e.g., slope, curvature, water flow, TWI, etc., Figs. A15-A22), will play a key role when it comes to soil C storage (organic and inorganic), as these factors can directly affect plant and soil interactions that govern the quantity and quality of SOM inputs, decomposition rates under uncultivated soils (Minasny et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), as well as mineral dissolution and fine-scale temperatures (puro earth, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Combining it with land uses (current and historic), soil map units, and management zones (as suggested in \u003cem\u003eStage 3\u003c/em\u003e of this protocol) for stratification and sampling design, is expected to address potential limitations such as enhanced quantification compared to a simple random sampling approach (Mueller et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and potentially inconsistent temporal results (Franzen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). It is also pertinent to note that many chemical and biological factors can affect soil C storage (Vicca et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this context, the current protocol is anticipated to encompass the capacity to account for variations in spatial distribution linked to these factors. Selection of sampling points without prior knowledge will always lead to bias (even if it is carried out randomly) and, therefore, should be avoided at all costs, especially in estates with different land uses, soil types and soil management practices.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eCurrent knowledge and techniques being performed for the purpose of measuring and monitoring soil C are still too vague. For instance, in the Puro protocol (puro earth, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), there is a reference to the requirement for in-field soil C measurements by participants engaged in C crediting programs (related to C inorganic forms). However, there was no explicit stipulation regarding sampling design for both baseline and/or monitoring assessments. Similarly, in the VERRA methodology designed for SOC credits (VERRA, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the need for direct measuring at t\u0026thinsp;=\u0026thinsp;0 (i.e. baseline) and subsequent monitoring at intervals (e.g. approximately every 5 years) is acknowledged, but no explicit method is provided in this regard. The methodology, however, stresses the need for procedures to be unbiased and representative citing, for instance, the use (or adaptation) of published handbooks/protocols such as the ones provided in the FAO Soils Portal (FAO, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); and/or the ISO standards on soil sampling (ISO, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); and/or the IPCC Good Practice Guidance LULUCF (IPCC, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). While those methodologies offer valuable guidance on how, what, and why soil sampling should be conducted, they are often either more generic or focussed only on agricultural landscapes, rather than being tailored specifically for soil C assessments in estates with different land uses, soil types, and soil management practices. Furthermore, the potential for methodological adaptation may introduce biases into the results, as previously discussed. Similarly, recent research has introduced intriguing methodologies for measuring and monitoring soil C storage, but, again, predominantly concentrating on agricultural lands and most importantly not considering SIC (de Gruijter et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Manning et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rather simple rationale outlined in this five-stage protocol could improve our current knowledge and techniques, particularly those proposed in methodologies such as FAO, (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and World Bank, (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), without bringing an extra layer of complexity. In addition, whilst this protocol aims to standardise the measurement and monitoring of soil C (SOC and SIC) and GHG fluxes, we underscore that the same samples could potentially be used for the measurement of other soil variables. Ultimately, we highlight the significance of understanding spatial heterogeneity, particularly with regard to plant and soil interactions and soil physicochemical characteristics, which has already been heavily emphasised in the advancement of sustainable strategies for agricultural crop cultivation (AbdelRahman \u0026amp; Arafat, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, it is imperative to consider this aspect when measuring/assessing soil C and/or GHG fluxes. We also advocate for an ongoing enhancement of this protocol through collaboration with fellow researchers, institutions, organisations, and practitioners (e.g. farmers, technicians, and soil analysis laboratories) who are actively engaged in soil C programs.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe present a five-stage protocol for the systematic selection of sample locations in complex estates for surveys of factors such as soil C where a robust baseline is needed to underpin monitoring strategies designed to determine the effects of soil management interventions. After defining the boundary of the estate (\u003cem\u003eStage 1\u003c/em\u003e), the land is stratified into different land use types (\u003cem\u003eStage 2\u003c/em\u003e). \u003cem\u003eStage 3\u003c/em\u003e considers a wide range of covariates, including previously measured soil properties (particularly, texture, pH and SOM), soil type (clay, sandy, silty, peaty), past/historic land uses and management systems that affect plant and soil interactions, as well as landform, and climate. Elevation data define topography/terrain covariates, including slope (degrees), flow direction, flow accumulation, basin, aspect, curvature, hillshade and indexes such as the Topographic Wetness Index and the Topographic Position Index. At this point, several geographic information system layers have been produced to inform \u003cem\u003eStage 4\u003c/em\u003e, in which the domains identified in \u003cem\u003eStage 3\u003c/em\u003e are subdivided to give areas within which sample locations can be regarded as having shared characteristics. \u003cem\u003eStage 5\u003c/em\u003e allocates sampling locations according to a required statistical design, whilst avoiding domain boundaries, known buried services and other artificial constraints. In the case study reported here, within an area of 500 ha overall, the systematic five-stage approach led to designation of 165 sampling locations that represent variability in land use type and natural conditions. Following this protocol, a robust strategy for soil C stock baseline measuring and monitoring (SOC and SIC) and GHG fluxes can be set into motion and spatial-temporal variations accurately assessed, especially when interventions are deployed. Whilst we encourage the use of such a protocol, it is crucial to underscore that it should remain a dynamic and evolving framework. Inputs from fellow researchers, institutions, entities, and practitioners (including, farmers, technicians, and soil analysis laboratories), must be actively incorporated into its development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank Dr. Charlie Durham for his independent review of the manuscript. We also express our gratitude to Prof. Oliver Heidrich for his invaluable support throughout the study, including insightful discussions, guidance, and managing the funding provided by the UK Ministry of Defence. Our heartfelt thanks go to RAF Leeming, particularly the RAFX (eXperimental) team, for their openness and willingness to assist in every aspect of this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdelRahman MAE, Arafat SM (2020) An Approach of Agricultural Courses for Soil Conservation Based on Crop Soil Suitability Using Geomatics. \u003cem\u003eEarth Systems and Environment\u003c/em\u003e, 4, 273\u0026ndash;285, (At: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s41748-020-00145-x\u003c/span\u003e\u003cspan address=\"10.1007/s41748-020-00145-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberta Government (2012) \u003cem\u003eQuantification protocol for conservation cropping (version 1.0).\u003c/em\u003e (At: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://open.alberta.ca/publications/9780778596288#summary\u003c/span\u003e\u003cspan address=\"https://open.alberta.ca/publications/9780778596288#summary\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson K, Peters G (2016) The trouble with negative emissions. \u003cem\u003eScience\u003c/em\u003e, 354, 182\u0026ndash;183, (At: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aah4567.)\u003c/span\u003e\u003cspan address=\"10.1126/science.aah4567.)\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustralian Government (2018) \u003cem\u003eThe Supplement to the Carbon Credits (Carbon Farming Initiative\u0026mdash;Measurement of Soil Carbon Sequestration in Agricultural Systems) Methodology Determination 2018\u003c/em\u003e. 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World Bank, Washington DC (2021) (At: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdl.handle.net/10986/35923\u003c/span\u003e\u003cspan address=\"http://hdl.handle.net/10986/35923\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e License: CC BY 3.0 IGO.)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatements \u0026amp; Declarations\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThis work was financially supported by the United Kingdom Ministry of Defence's Defence Innovation Fund Top-Level Budget Ideas Scheme (61182036). The work was part of the ViTAL Living Lab project work package 4. The data collection for this study comprises original work conducted by the first author, Caio F. Zani. Caio F. Zani took the lead in writing, with input from Arlete S. Barneze and David A. C. Manning. The authors of this study declare no conflict of interest. The authors also have no relevant financial or non-financial interests to disclose. No specific data was generated in this study but any specific aspect such the maps or any other that support the findings of this study are mostly available in both in the main body of the paper and in the supplementary material of this article. These can also be provided on request from the corresponding author\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"baseline, carbon cycle, ecosystems, land management, reporting, verification","lastPublishedDoi":"10.21203/rs.3.rs-5677695/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5677695/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims\u003c/h2\u003e \u003cp\u003ePlant-soil interactions are critical in governing soil carbon (C) stocks and greenhouse gas (GHG) fluxes, but they vary significantly across land uses, soil types, and soil management practices. Finding potential intervention that could enhance soil C and GHG fluxes relies on reliable baseline data that capture these variations. Complex estates, characterised by such heterogeneous conditions, require standardised protocols to ensure reproducibility and comparability across sites.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study introduces a five-stage protocol for systematically measuring and monitoring soil C stocks (including organic and inorganic forms) and GHG fluxes. The protocol is designed for \"Time-Zero\" (T\u0026thinsp;=\u0026thinsp;0) baseline assessments and the strategic selection of monitoring sites for long-term soil sampling and GHG flux measurements. The approach was tested at RAF Leeming (Yorkshire, UK), a estate with varied land uses, soil types, and management practices.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe protocol provides a rigorous, reproducible and adaptable framework for obtaining robust baseline data. It facilitates the quantification of soil C and GHG fluxes, while it can guide site-specific interventions, ensuring that aspects such as plant and soil interactions are considered for comparability purposes. Its design is scalable, with applications extending to urban areas, military installations, airports, and other managed estates.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe proposed protocol enables standardised, transparent soil C and GHG monitoring to meet internationally accepted standards. We advocate for its broad implementation across estates with varying land uses and soil characteristics to support sustainable soil management and climate mitigation efforts.\u003c/p\u003e","manuscriptTitle":"A five-stage protocol for systematic measuring and monitoring soil carbon and greenhouse gas fluxes in complex estates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 16:56:57","doi":"10.21203/rs.3.rs-5677695/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-04-04T04:49:51+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-02-13T07:44:34+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-07T10:12:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2025-01-06T22:12:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-06T13:01:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2025-01-03T09:38:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"72c3a69e-826e-4536-8780-dad08bf8cdff","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T16:05:11+00:00","versionOfRecord":{"articleIdentity":"rs-5677695","link":"https://doi.org/10.1007/s11104-025-07703-0","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2025-07-25 15:57:21","publishedOnDateReadable":"July 25th, 2025"},"versionCreatedAt":"2025-01-09 16:56:57","video":"","vorDoi":"10.1007/s11104-025-07703-0","vorDoiUrl":"https://doi.org/10.1007/s11104-025-07703-0","workflowStages":[]},"version":"v1","identity":"rs-5677695","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5677695","identity":"rs-5677695","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}