Methodology

A small-sized company, a university canteen, or an individual citizen currently have no easy way of knowing the environmental impact of the food they buy, nor the environmental and socio-economic context of where that food comes from. Existing tools are either too technical, or behind a paywall, which makes access to knowledge a barrier. Yet recently, there have been critical developments on the environmental impact of food. Many datasets were published in recent years. They can be brought together to fill this gap.

The origin of production information in the tool are combined with Food and Agricultural Organization (FAO) trade data and subnational production area from Spatial Production Allocation Model (SPAM).

Trade

FAO Detailed Trade Matrix (FAO, 2025) is filtered to provide import quantities. FAO Supply Chain Utilization Accounts (SUA) and FAOSTAT Production data, then, are used to provide domestic production volumes. Simply:

• All bilateral import flows and domestic production from year 2018 onwards are separately added up to a single cumulative volume for each exporter: total supply = domestic production + import.
• Each source receives a percentage share: percentage share = source volume / total supply x 100.
• Sources are sorted by share and top 20 are retained.
• Countries with production but no imports are added as 100% domestic producer.

Re-export:FAO bilateral import data records the direct trading partner, not the original producer. Cocoa arriving in Germany from the Netherlands may have originated in Ivory Coast. To address this, all trade flows are first converted to raw commodity equivalents (e.g., cocoa butter back to cocoa beans, using published extraction rates). Each country is then allowed to export up to 110% of its own production before the surplus is treated as re-exported material. The 10% allowance accommodates normal stock fluctuations and the small discrepancies that arise between how production and trade are reported, and avoids the abrupt cliff effect of a hard threshold. The flagged surplus is reassigned to the country's own import sources, while genuine domestic production is preserved.

Sensitivity. Adjusting this allowance between 0% and 20% leaves the results for major re-exporters (Netherlands, Belgium, Singapore, Malaysia, which export 3-10 times their domestic production) essentially unchanged, since these countries are already corrected regardless of the exact threshold chosen. The correction matters most for countries whose exports narrowly exceed production, where the flagged share can shift by up to 16 percentage points. For most importing countries, this translates to sourcing shifts below 1 percentage point; in the handful of worst cases involving a single borderline source, the shift can reach 6 percentage points.

Coffee disaggregation.FAO reports all coffee trade under a single item (“Coffee, green”), but arabica and robusta have distinct production geographies and environmental footprints. Bilateral coffee flows are disaggregated into arabica (SPAM code COFF) and robusta (RCOF) using each source country's production ratio derived from SPAM 2020 gridded crop allocation data. For example, a shipment from Brazil is split 79% arabica / 21% robusta, reflecting Brazil's national production mix; from Viet Nam, 4% arabica / 96% robusta. For domestic production, the consuming country's own ratio is applied. This yields 87 countries with computed shares, of which 38 are robusta-dominant.

Extraction rates are sourced from Zhao et al. (2025) for single-output products and from industry physical mass yields for joint-product processing such as oilseed crushing.

After the correction, countries without actual production of the commodity are filtered out entirely.

Subnational sourcing

This stage breaks down country sources into sub-national source regions and provinces. For this, SPAM production rasters (IFPRI, 2019; 2024), GPW livestock headcount layers (Parente et al., 2025), FAOSTAT and Database of Global Administrative Areas (GADM, 2024) are used.

Sourcing for crops

• SPAM crop production rasters are converted to intensity, tonnes per hectare.
• SPAM rasters are then reprojected to the 1 km pipeline template via bilinear average resampling, preserving the production density.
• Production densities are summed over administrative areas at level 1 (regional level) and 2 (province-level).

Sourcing for livestock

Global Pasture Watch livestock headcount layers (Parente et al., 2025) provide per pixel animal counts for cattle, buffalo, sheep, and goats at 1 km resolution, calibrated to FAOSTAT national headcount totals.

• FAO national production totals are divided by total animal heads per country to get a country-specific yield per animal, which is then applied to the per-pixel headcount map to estimate how much production occurs at each pixel.
• Production amounts per pixel are summed over administrative areas at level 1 and 2.

In both livestock and crops sourcing, each region receives a percentage share of national production. Regions contributing less than 0.01% are dropped.

When a user selects a commodity and importing country, both stages are combined to produce a full breakdown of which regions likely produced the commodity.

Statistical land occupation (sLO) is the agricultural area of land required to produce one kilogram of a commodity (Fitts et al. 2025). SPAM 2020, Global Pasture Watch (Parente et al., 2025) are used to estimate sLO.

Crop land occupation

At each pixel, SPAM physical area rasters and production rasters are both converted to density values (per hectare) and reprojected to the 1 km pipeline template. Land occupation is thus calculated by dividing the physical crop area by the production volume. Locations with negligible area or production are excluded to avoid unreliable values.

These pixel-level values are then averaged across each administrative region using a production-weighted mean: locations that produce more have proportionally more influence on the regional average. This ensures the regional factor reflects the actual production mix, excluding marginal production areas dominating the regional average. Regions where production falls below the 1st percentile of the crop's distribution and land occupation simultaneously exceeds the 98th percentile are excluded. These arise from SPAM allocating cropland area to regions with near-zero production, producing implausibly high per-kilogram values. These regions are also removed from the production sourcing data, as the underlying allocation is suspect.

Land occupation for livestock

Livestock land occupation measures the pasture area required per kilogram of product. Pasture area is derived from GPW (Parente et al., 2025), using only cultivated grassland class. Natural or semi-natural grasslands are excluded to keep a conservative estimate of managed land. GPW data at 30 m resolution is aggregated to the 1 km analysis grid by calculating the fraction of each pixel classified as cultivated grassland because processing at the native 30 m resolution would be computationally unmanageable with the author's hardware.

At each location, pasture area from GPW is divided by the number of animal heads from GPW to get hectares per head. Two filters are applied: pixels where pasture covers less than 5% of the pixel are excluded because small classification errors would produce unstable values and pixels with more than 100 heads per hectare are excluded because these represent intensive operations (such as feedlots) where the pasture-based land occupation concept does not apply.

The hectares per head values are then converted to per-kilogram values using the country-specific yield factor from the subnational sourcing stage, and averaged across administrative regions using the same production-weighted approach as for crops.

In some countries with very small production totals (for example, Belize reporting less than 1 tonne of goat meat), the yield factor becomes unrealistically high. This produces extreme land occupation values that are not meaningful. Around 3,100 such values are flagged as missing data in the tool.

Country-level sLO values, aggregated to production-weighted global averages and converted to final-product functional units using published extraction rates, fall within the 5th-95th percentile range reported by Poore & Nemecek (2018) for 15 of 19 tested commodities, including wheat, maize, rice, cassava, sugarcane, groundnuts, palm oil, sunflower oil, rapeseed, coffee, cocoa, banana, other pulses, beef, and milk. Four commodities fall outside this range: barley (reflecting a functional-unit mismatch between kg beer and kg barley in the reference), potatoes and soybean oil (marginally above the 95th percentile), and sugar beet (~2× above, suggesting more aggressive European expansion detection in this pipeline).

Statistical land use change (sLUC) is the area of natural ecosystem converted to agriculture per kilogram of commodity produced, estimated with statistical attribution rather than direct causal tracing to specific regions. SPAM 2010 and 2020, Hansen Global Forest Change, GPW, Global drivers of forest loss at 1 km resolution (Sims et al., 2025), Global annual wetland dataset (Zhang et al., 2024) and FAOSTAT are used to estimate ecosystem loss, following the approach from Fitts et. al. (2025).

Detection of ecosystem loss

Forest, grassland/shrubland, wetland, and an additional GLCLU-derived gap-fill for savanna/open-woodland biomes are estimated. Forest loss is detected from Hansen Global Forest Change data (Hansen et al., 2013), covering the period 2010–2024, a 15-year window chosen to capture recent ecosystem conversion patterns relevant to current production systems. Grassland and shrubland loss is derived from GPW (Parente et. al., 2025) annual classifications over the same period. Wetland loss is derived from the Global Annual Wetland Dataset (Zhang et al., 2024) for 2010–2022. All 30 m loss layers are aggregated to the 1 km analysis grid. Where ecosystem types overlap (e.g. forested wetland) the loss is counted only once to avoid double-counting.

GLCLU gap-fill for savanna and open-woodland conversion. GPW's grassland and shrubland definitions exclude conversion in savanna, open-woodland, and dryland biomes (cerrado, miombo, chaco, Sahel, Deccan plateau, Kazakh steppe), where the pre-conversion vegetation is short-vegetation mixed with scattered trees rather than closed-canopy grassland. To capture these, the Global Land Cover and Land Use Change dataset (Potapov et al., 2022) is used with the 9-class reclassification scheme of Kan et al. (2026). Natural vegetation (classes 2, 3, 4: short vegetation terra firma, tree cover, short vegetation wetland) converted to cropland (class 7) between 2010 and 2020. This gap-fill adds loss only at pixels where (1) the main pipeline attributes zero loss from any existing source, (2) the SPAM+GPW agricultural expansion is positive, (3) GLCLU confirms natural→cropland conversion, and (4) GACED30 or GLAD independently confirms cropland presence in 2024. The gap-fill is capped at the pixel's total agricultural expansion. This adds approximately 29 Mha globally, concentrated in documented agricultural frontiers (Brazilian cerrado, Indian Deccan, Sahel, Tanzania miombo, Kazakh steppe).

Filtering ecosystem loss

Not all ecosystem loss is caused by agriculture. The Sims et al. driver dataset classifies each forest loss pixel by its cause. Only agricultural expansion and shifting cultivation are included. Because Sims et al. only covers forest loss, grassland/shrubland and wetland losses are instead filtered by requiring that agricultural expansion (crop area or cultivated pasture increase) via SPAM 2010-2020 and GPW 2010-2024 occurred in the same location, ensuring only losses coinciding with actual agricultural growth are counted.

Additionally, SPAM crop expansion at each pixel is capped by the maximum observed cropland fraction from two independent 30 m satellite products: GACED30 (Chen et al., 2026) and GLAD Annual Croplands (Khan et al., 2025). Pixels where both products detect zero cropland cannot receive SPAM-allocated expansion. This cap eliminates approximately 15% of SPAM's total crop expansion allocation globally.

Users of the tool need to be cautious as there are uncertainties about this assumption. SPAM crop area and GPW pasture area expansion are re-modelled estimates, not ground-truth observations.

Crop and pasture allocation

At each pixel, ecosystem loss is shared among crops and pasture based on which crops expanded their area between 2010 and 2020, using SPAM 2010 and SPAM 2020, and which GPW pasture areas expanded between 2010 and 2024.

If soybean expanded by 40 hectares and maize by 10 hectares at a pixel, soybean receives 80% of the ecosystem loss and maize 20%, regardless of their current area shares. This approach attributes loss to the crops that actually grew, rather than those that simply dominate current area. Where no crop or pasture expansion is detected, the ecosystem loss at that location is not allocated to any commodity.

Calculating sLUC

Ecosystem loss area is divided by production volume to get km² of ecosystem lost per kilogram produced. A value of 0.5 km²/kg means half a square kilometre of ecosystem was converted for every kilogram produced. Production volumes from SPAM 2020 are scaled to 2024 using FAO national production ratios to align with the loss observation period. Regions where production falls below the 1st percentile of the crop's distribution and ecosystem loss intensity simultaneously exceeds the 98th percentile are excluded. Unlike land occupation and water use, a simple percentile threshold on sLUC alone would remove real deforestation frontiers where high per-kilogram conversion is the expected signal. The combined filter instead targets misattribution artifacts, where trace SPAM production allocations coincide with ecosystem loss driven by other commodities in the same location.

Temporal considerations

The loss observation period is 2010–2024, but production data reflects 2020 scaled to 2024. This means loss events from before 2020 are charged to current production, i.e. land converted in 2012 is attributed to today's output. This is a deliberate framing: sLUC measures the average ecosystem conversion risk embedded in current production, not a causal attribution of past deforestation to past output.Ecosystem loss at this scale can take place over long periods, and once an area is converted, today's production is sourced from that converted land. The impact does not stop with the conversion year, it degrades nearby ecosystems and can drive species toward extinction over longer time periods.

The GLCLU gap-fill layer has a slightly narrower window (2010–2020, from two-epoch GLCLU snapshots) than the other loss bands (2010–2022 for wetland, 2010–2024 for forest and GPW grass/shrub).

Sensitivity.The pipeline combines multiple satellite products, each with a known detection accuracy. A scenario test - removing the savanna gap-fill at one end, allowing maximum uncapped grassland conversion at the other - places the global attributed ecosystem loss between 258 and 328 million hectares, around a central estimate of 287 million hectares. A separate layer-by-layer test, using the validation accuracy reported in each underlying dataset, gives a combined uncertainty of roughly ±8% if the layers' errors are independent, rising to around ±15% if they move together. Forest loss detection is the single largest contributor to this range. sLUC values should be read as best estimates within this band rather than precise figures.

Blue water consumption is the volume of water drawn from rivers, lakes, and groundwater per kilogram of crop produced. Rainfall is excluded because it is a natural resource that would be consumed regardless of whether the land is farmed or not. To estimate blue water consumption, dataset by Chukalla et al. (2025) is used. At each pixel, water consumption is divided by production volume to get cubic metres of water per kilogram. These values are averaged across administrative regions using the same production-weighted approach as for land occupation. Regions where the production-weighted coverage ratio, the share of regional production covered by water consumption data, falls below the 1st percentile of the crop's distribution are excluded. These represent spatial mismatches between the water consumption and crop production rasters, where the water factor is computed from a small, unrepresentative subset of the production area.

Blue water is reported for crops only. Livestock water footprints (drinking and process water) are not included. Users who want to account for water used in animal feed production can query the relevant feed crops (such as maize or soy) separately. Estimates for direct water consumption for livestock can be accessed via FAO's GLEAM V3 Water tool. To estimate soy feed used for the livestock, Soy Footprint Calculator can be used.

Coverage for the water use is also limited globally. Users may see production data for regions where no water use data is available.

Nitrogen and phosphorus from agriculture run off into rivers and lakes, contributing to eutrophication and dead zones. The tool estimates nutrient loading per kilogram of crop produced, using data from Hogeboom et al. (2026), which builds on the work of Mekonnen & Hoekstra (2015, 2018).

Nutrient loading rates are provided at the watershed level (HydroBasins Level 6) for 12 crop groups rather than individual crops. Each crop in the tool is mapped to one of 12 nutrient crop groups, such as cereals, oil crops, stimulants, or roots and tubers, to match the nutrient loading data.

To calculate nutrient pollution per kilogram, the loading rate (kg of nitrogen or phosphorus per hectare) is multiplied by the land occupation factor (hectares per kilogram) already calculated for each region.

For simplicity and consistency reasons, nutrient loading is reported at administrative region level rather than watershed level. Where a region overlaps multiple watersheds, the loading rate is calculated as an area-weighted mean, which means larger watersheds contribute proportionally more to the regional estimate than smaller ones.

Nutrient pollution is reported for crops only. As with water, users can query feed crops separately to estimate livestock-related nutrient impacts. The SBTN Nutrient Navigator itself cautions that significant uncertainty remains even at the sub-catchment level: values should be treated as indicative rather than precise. In some regions with very low crop yields, the combination of nutrient loading and high land occupation produces implausibly high values. Values above 1.0 kg nutrient per kg product are filtered.

The tool estimates cropland greenhouse gas emissions per kilogram of product, using spatially explicit data from Cao et al. (2026). Emission sources include fertiliser application, manure management on cropland, crop residues, peatland cultivation, burning, and rice methane. All emissions are converted to CO₂ equivalents using IPCC AR6 global warming potentials (IPCC, 2021).

Deforestation emissions, the CO₂ released from biomass and soil carbon when forest is cleared for agriculture, are added on top of the Cao et al. on-farm emissions using the WRI GCSC dataset (Fitts et al., 2025). GCSC provides per-commodity, per-ADM2 emission factors for 42 crop categories with annual values for 2020, 2021, 2022, 2023, and 2024. To produce a single stable factor per region, the tool uses a production-weighted average of the five annual values. Only forest-loss carbon is covered. Savanna clearing and peatland oxidation beyond forest margins are not included.

At each location, emissions from all sources (including deforestation where applicable) are summed and divided by production volume to get kg CO₂e per kilogram produced. The tool also shows the breakdown by emission source for each region.

Enteric fermentation, the methane produced by ruminant digestion, which is the largest single agricultural GHG source, is not included. This is a spatially explicit cropland emission dataset, and enteric fermentation is a process that would require a separate attribution. As with water and nutrients, emissions are reported for crops only. When relevant datasets are published or accessed in the future, this data can be added to the tool.

Users can query feed crops separately to estimate livestock-related emissions.

Each impact metric is converted to a monetary value using country-specific value factors from the International Foundation for Valuing Impacts (IFVI). Land occupation, ecosystem loss, blue water consumption, and nitrogen and phosphorus pollution each have a corresponding value factor expressed in USD or EUR per unit of impact. Each crop in the tool is mapped to the nearest IFVI commodity category.

Monetary values are presented as context to help users understand the relative scale of different impacts on society in monetary manner.

The tool provides contextual information about the ecological condition of each sourcing region at ecosystem level and species level.

Ecosystem Integrity Index (EII)

The EII measures how much an ecosystem keeps its natural condition. Except genetic level, it covers complexity of an ecosystem, measuring functional, biological, and physical aspects (Hill et. al., 2022). The final score is the minimum of three components because an ecosystem degraded in any single dimension is compromised regardless of the other two. It's worth noting that the EII has been listed as a Component Indicator for the Kunming-Montreal Global Biodiversity Framework and is recognised by both TNFD and SBTN.

All component scores are averaged across each administrative region to produce a single value per region. This is the value displayed in the tool.

Functional integrity

Functional integrity measures how close observed net primary productivity is to what would be expected under natural conditions. Actual productivity is derived from VIIRS satellite data (Zhao et al., 2025), using a 2018–2022 five-year mean to average out interannual climate variability. Potential (natural) productivity comes from the LUIcube dataset (Matej et al., 2025), which uses the LPJ-GUESS vegetation model to simulate what vegetation would produce without human land use.

Because the vegetation model systematically misestimates productivity in some regions, a Random Forest bias correction is applied. The model is trained on 500,000 pristine pixels (stratified by ecoregion) to learn the ratio between satellite-observed and model-predicted productivity at undisturbed sites, using 26 environmental predictors covering climate (CHELSA 2.1), terrain (MERIT-DEM), soil properties, cloud cover, and biogeography. This corrected potential is then applied globally. Spatial cross-validation using 5° geographic blocks yields R² = 0.71 and MAE = 0.15.

The score is built from two measures: one captures how far productivity has shifted relative to its natural level (a forest producing half its potential scores the same as one producing double), and the other captures the raw size of the gap between actual and potential productivity. Averaging both ensures the score is sensitive to both proportional and absolute changes. A score of 1.0 means productivity matches natural conditions; lower scores indicate greater departure. Importantly, the formula is symmetric: both underproduction (e.g. deforested land at 70% of natural) and overproduction (e.g. fertilised cropland at 157% of natural) reduce the score, because either departure indicates the ecosystem is no longer functioning as it naturally would.

The hardest part of functional integrity is estimating what productivity should be at a location under natural conditions. To test this, the model's predictions were compared against actual productivity measurements from 135 unmanaged field sites worldwide (Rodal et al., 2025). The correlation was Spearman r = 0.617 (p < 10⁻¹⁵), meaning the model captures real variation in natural productivity across different ecosystems, a meaningful result given that predicting a counterfactual (“what would nature do here without humans?”) is inherently difficult.

IUCN I–II protected areas show significantly higher functional integrity than non-protected land, with a mean delta of +0.087 across 14 biomes (13 of 14 significant after FDR correction). The effect is smaller than structural integrity (+0.140) because productivity is less sensitive to protection status than vegetation structure: managed forests and cropland can maintain near-natural productivity levels even when structurally degraded.

Two independent estimates of potential natural NPP, one based on RF bias-correction of a process model (present work, 26 predictors) and one based on machine learning trained directly on pristine areas (Leutner 2025, ~9 predictors), show strong pixel-level agreement (Spearman r = 0.912, n = 8.8M) and very strong ecoregion-level agreement (r = 0.938, n = 812), confirming that the counterfactual productivity baseline is reproducible across methods. Despite this agreement in potential NPP, the two functional integrity scores diverge at pixel level (r = 0.155). The divergence has two identified sources: the two satellite NPP products (VIIRS vs CLMS/Sentinel-3) agree closely in temperate and arid regions but diverge by 40–100% in tropical forests where persistent cloud cover amplifies retrieval differences; and the Leutner formulation includes a seasonality component absent in the present work, which dominates divergence in boreal and polar regions.

To benchmark the functional integrity metric, the same within-biome PA test was applied using identical data and methodology. The present metric identifies significantly higher functional integrity in strict protected areas across 12 of 14 biomes (mean Cohen's d = 0.42), and correctly orders four levels of protection strictness (strict > moderate > sustainable > non-PA) in 12 of 14 biomes. The Landbanking Group functional integrity (resampled to 1 km) passes in 5 of 14 biomes with a near-zero mean effect size. The difference likely reflects the use of 26 environmental predictors (including cloud frequency and six soil properties) and a process-model prior in the present work, compared to ~9 predictors without a process-model baseline.

Limitation: Pixels with potential NPP below 50 gC/m²/yr (deserts, ice) are excluded because the observed/potential ratio becomes unstable at near-zero values.

Structural integrity

Structural integrity covers physical intactness of an ecosystem area, including the size, shape and connectivity of habitats, as well as complexity of the vegetation. An area with high structural integrity is characterised by large, connected areas of natural vegetation with intact canopy structure, free from significant human modification (Hill et al., 2022; Leutner, 2025).

The Hill et al. (2022) and Leutner (2025) frameworks measure structure through human pressure by mapping infrastructure, agriculture, and urbanisation and inferring that these pressures degrade ecosystem structure. This tool extends that approach by combining pressure-based measurement with direct observation of vegetation structure from three independent satellite sources, capturing degradation that pressure proxies alone cannot detect.

Three satellite-derived layers each measure a different aspect of vegetation structure. Each has a Random Forest model that predicts the expected value under pristine conditions from 29 environmental predictors. The final score at every pixel is the minimum across all layers that have valid data at that location, ensuring that degradation detected by any single source is reflected in the score.

GEDI measures forest canopy structure from spaceborne lidar (Burns et al., 2024) using four metrics: foliage height diversity, canopy cover, canopy height, and aboveground biomass density. The structural score for each metric is the ratio of observed to expected value, capped at 1.0, and the four are averaged into a composite.

GPW Vegetation Heightmeasures short vegetation height using ICESat-2 and Landsat fusion (Parente et al., 2025), capturing grassland, shrubland, and tundra structure that GEDI's pulse width cannot resolve below ~2 m. Pixels below 0.5 m are excluded because ICESat-2 cannot reliably separate vegetation from terrain at that height.

CTrees Aboveground Biomassmeasures aboveground biomass using a fusion of GEDI, ICESat-2, and Landsat data (Saatchi, 2025), providing an independent biomass estimate. Pixels below 5 Mg/ha are excluded because the product's global bias (−4.9 Mg/ha) exceeds the observed value at that threshold.

The Beyer landscape fragmentation score is adapted from Beyer et al. (2020) using the Global Human Modification index (Theobald et al., 2025) as input. It integrates habitat quality, area, and spatial configuration, meaning a high-quality cell surrounded by other high-quality cells scores higher than an isolated one. This captures fragmentation effects that pixel-level vegetation metrics miss.

The minimum operator ensures complementary detection: a deforested pixel shows low GEDI integrity even if vigorous pasture grass produces high GPW integrity. Conversely, an overgrazed grassland with no GEDI data is captured by low GPW integrity. And a city like London, where GEDI reads high canopy in scattered parks, is correctly scored near zero by the Beyer fragmentation layer.

Between 70°N and 76°N, satellite observations (GPW height, CTrees biomass) exist but several predictor rasters are incomplete, preventing the Random Forest from producing integrity scores directly. In this zone, GPW and CTrees integrity are gap-filled using observation-modulated ecoregion medians from lower latitudes (56–70°N), preserving spatial variation from the actual satellite data rather than assigning flat values per stratum.

Compositional Integrity

The Biodiversity Intactness Index (BII) measures the average abundance of native species relative to undisturbed conditions, on a scale of 0 to 1. It is derived from the 100 m global BII product (Gassert et al., 2022; Impact Observatory & Vizzuality, 2022), aggregated to 1 km resolution. BII is based on the PREDICTS database, a global collection of local biodiversity surveys, and statistical models that relate species abundance to land-use pressures (Newbold et al., 2016). It is used as-is, with no further adjustment.

SBTN Water Layers

The tool also displays two water stress indicators from SBTN's State of Nature assessment (Camargo et al., 2024), providing context about pressure on the local water system.

Water availability risk reflects how much demand is being placed on available water resources, combining baseline water stress, water depletion, and blue water reduction into a single score.

Water pollution risk reflects the potential for nutrient pollution in waterways, combining coastal eutrophication potential, nitrate levels, and periphyton growth potential.

Both are scored on a 1–5 scale, where 1 means low concern and 5 means very high concern. The score is the worst of the three underlying indicators. If any one shows high concern, the region is flagged accordingly.

At the regional level, scores are aggregated from watershed data using area-weighted means. At the country level, scores are averaged from the official SBTN regional values to maintain consistency with SBTN's published product.

Freshwater Nutrient Pollution

Freshwater nutrient pollution characterizes the degree to which human activity has altered nutrient concentrations in waterways. The layer uses modeled nitrogen and phosphorus concentrations from McDowell (2025), which provides current and natural-reference nutrient levels for river catchments globally at approximately 1 km resolution.

For each region, the tool reports how much nitrogen and phosphorus pollution has been added by human activity, compared to what would naturally occur. This is expressed as the percentage above the natural baseline for both nutrients. For example, if nitrogen excess is 64% and phosphorus excess is 56%, it means nitrogen concentrations are 64% higher and phosphorus concentrations are 56% higher than natural conditions, due to sources such as agricultural runoff, wastewater, and urban drainage.

The nitrogen-to-phosphorus ratio determines which nutrient is limiting: ratios below 7 indicate nitrogen limitation, ratios of 7 or above indicate phosphorus limitation. The limiting nutrient is indicated because it is the one whose excess most directly drives algal growth, though both nutrients contribute to ecological degradation.

Each region is also classified as “within safe limits” or “critical” based on periphyton growth potential (PGP) thresholds from the same dataset. If the limiting nutrient concentration exceeds the threshold for undesirable algal proliferation, above 0.8 mg/L for nitrogen-limited systems or above 0.046 mg/L for phosphorus-limited systems, the region is flagged as critical.

Catchment-level values are aggregated to GADM ADM1/ADM2 regions using area-weighted means. The limiting nutrient is determined by majority vote across pixels within each region.

Land-cover Change Impacts on Future Extinctions

The LIFE index (Land-cover change Impacts on Future Extinctions; Eyres et al., 2025) estimates how many species extinctions would result from changing land cover at each location. It accounts for species richness, range size (how restricted a species' habitat is), and how much habitat each species has already lost. Locations where many narrow-range species are already close to their survival risk score highest.

Two dimensions are reported. LIFE threat measures how much biodiversity would be lost if natural land or pasture at a location were converted to cropland. LIFE restoration measures how much biodiversity could be recovered by restoring current agricultural land to natural vegetation. Both are normalised to a 0–1 scale relative to the 90th percentile of global values. A score of 1.0 means the location is at or above the top 10% of global hotspots for extinction impact. A score of 0.5 means its impact is half that threshold. Scores above 1.0 are capped at 1.0, so the most extreme hotspots all appear as 1.0.

LIFE is reported as an independent layer alongside the EII. Both normalized LIFE rasters are aggregated to GADM ADM1/ADM2 regions using the same pixel-count-weighted mean

Freshwater biodiversity

The freshwater biodiversity score follows the STAR framework (Species Threat Abatement and Restoration; Mair et al., 2021), adapted for freshwater species using the comprehensive freshwater species assessment by Sayer et al. (2025) and the spatial methodology of Ridley et al. (2025).

The score covers freshwater fish, dragonflies, crabs, and wetland-dependent amphibians, birds, mammals, and reptiles. For each location, the score considers what proportion of each species' global habitat falls within that location and how threatened the species is. More threatened species contribute more, weighted by their IUCN Red List category (Near Threatened = 1, Vulnerable = 2, Endangered = 3, Critically Endangered = 4; IUCN, 2025). Species habitat is estimated by overlaying IUCN range maps (IUCN, 2025) with wetland extent data from GLWD v2 (Lehner et al., 2025). Species with unmapped habitat types use the full range polygon as a precautionary fallback.

Higher scores indicate locations where conservation action would have the greatest potential to reduce the extinction risk of freshwater species. Regional scores are computed as the mean per-pixel value within the region, making scores comparable across regions of different sizes regardless of total area.

Mycorrhizal fungi

The tool reports the diversity and rarity of two types of mycorrhizal fungi, underground organisms that form symbiotic relationships with plant roots, sustaining plant life and helping regulate biogeochemical cycles (Van Nuland et al., 2025). Arbuscular mycorrhizal (AM) fungi partner with roughly 80% of plant species, including most crops, and show highest diversity near the equator. Ectomycorrhizal (EcM) fungi are primarily associated with trees and show highest diversity at northern and southern latitudes.

For each type, a single score is shown based on rarity-weighted richness — a metric that combines how many species are present with how restricted their ranges are, so that locations hosting many narrow-range species score highest. The scores are derived from machine-learning predictions trained on over 2.8 billion fungal DNA sequences from 25,000 soil samples worldwide.

AM fungi scores are most relevant for cropland, while EcM scores are most relevant for forests and agroforestry. The scores reflect the predicted natural fungal community at each location.

Soil Health Indicators

Three soil indicators estimate soil health by scoring against what would be expected under natural (pristine) conditions. Each indicator uses a scoring approach matched to the ecological behaviour of the variable it measures. All soil scores are pre-computed at 1 km resolution and averaged across each administrative region.

Soil Organic Carbon

Soil organic carbon (SOC) is the single most integrative indicator of soil health. It controls water retention, nutrient cycling, and structural stability, and its depletion through tillage, overgrazing, or deforestation is the primary chemical signal of soil degradation worldwide.

The score measures how much organic carbon remains in the soil compared to what would be there under natural conditions. A Random Forest model trained exclusively on pristine reference sites, pixels inside strictly protected areas (IUCN categories I–III) with low human modification, no recent forest loss, and no detectable agriculture or built-up land, predicts the expected natural SOC at every location from 30 environmental covariates covering climate (CHELSA 2.1), terrain (MERIT-DEM), soil texture, lithology, fire frequency, peatland extent, and ecoregion classification. The model learns from 500,000 pristine sample locations, drawn proportionally from every ecoregion on Earth. SOC values are log-transformed before training to handle the wide range of natural carbon levels across climates.

The model's predictions are calibrated against 2,999 pristine soil profiles with lab-verified carbon values (from a global compilation of 331,071 profiles; Hengl & Gupta, 2025). The final score places each pixel's observed SOC within this calibrated natural range. Scores above 0.45 align with the upper half of pristine land (strict protected areas with near-zero human modification, median 0.425, P75 0.475); lower scores indicate depletion.

Score labels:Natural (≥ 0.45): above the pristine median. Slightly below natural (0.35–0.45): minor departure, still within the pristine IQR. Below natural (0.25–0.35): moderate depletion. Depleted (0.1–0.25): significant depletion. Severely depleted (< 0.1): well below pristine range.

Soil Phosphorus

Phosphorus is an essential plant nutrient, but soil can hold too little or too much of it. In regions where crops are harvested for decades without fertiliser input, phosphorus gets depleted. In regions with heavy fertiliser use, phosphorus accumulates beyond what plants can absorb. This excess suppresses mycorrhizal fungi, disrupts zinc and iron availability, reduces microbial diversity, and washes into waterways where it triggers algal blooms. Because both deficit and excess degrade soil health, this indicator scores in both directions.

The scoring follows the same approach as organic carbon: a model trained on undisturbed sites predicts what phosphorus levels should look like naturally at every location, using the same 30 environmental covariates. This prediction is calibrated against 2,883 laboratory measurements from pristine sites (McDowell et al., 2023). Each pixel is then scored by where its observed phosphorus falls within that calibrated natural range.

Score labels (bidirectional):Severe deficit (< 0.3): well below natural range. Deficit (0.3–0.35): significant depletion. Moderate deficit (0.35–0.45): minor depletion. Natural (0.45–0.55): within expected range. Moderate excess (0.55–0.65): mild accumulation. Excess (0.65–0.7): significant accumulation. Severe excess (> 0.7): heavy fertiliser enrichment and associated risks.

Nitrogen Deposition

Excess nitrogen from the atmosphere acts on ecosystems in two ways. First, it over-fertilises the soil, giving an advantage to fast-growing, nitrogen-hungry species that crowd out specialists adapted to low-nutrient conditions (terrestrial eutrophication). Second, as soil microbes process the added nitrogen, they release hydrogen ions that gradually acidify the soil and deplete the mineral base that plants and soil organisms depend on. This indicator implements the Science Based Targets Network's Draft Land Target V2 methodology (SBTN, 2025) at 1 km resolution with one specific refinement.

Unlike organic carbon and phosphorus, this indicator works with a tolerance threshold. Every location has a critical nitrogen load: the maximum amount of atmospheric nitrogen the natural ecosystem can absorb before species begin to disappear and soil chemistry starts to shift. These thresholds come from decades of field experiments (Bobbink et al., 2010), compiled into critical loads for 14 global biome types by Schulte-Uebbing et al. (2022).

The refinement recognizes that soil pH determines how much buffering capacity the soil has left: pH < 4.2 means the soil has exhausted its base mineral reserves (most protective threshold); pH > 6.2 means carbonate reserves continuously neutralise incoming acidity (least protective threshold); 4.2 ≤ pH ≤ 6.2 means the soil is actively drawing down finite reserves (mid-range threshold). Following SBTN Draft Land V2, a 10% safety buffer is applied: the target is set at 90% of the critical load.

Current deposition is from Zhu et al. (2025), a 0.125° (~15 km) global dataset of total atmospheric nitrogen deposition resampled to the 1 km grid. Soil pH is from OpenLandMap (2020–2022, 0–30 cm). Biome classification follows RESOLVE Ecoregions 2017 (Dinerstein et al., 2017).

Globally, 12.7% of land pixels are in exceedance, with a mean overshoot of 5.6 kg N/ha/yr over exceeded pixels. Maximum exceedance reaches 55.7 kg N/ha/yr in industrial East Asian hotspots.

Interpretation note: SOC and Olsen P scores are standalone soil health indicators, not components of the EII composite (which uses the minimum of BII, NPP integrity, and structural integrity). The CDF-based scores (0–1, baseline ~0.5) are more informative for soil diagnostics than a normalised integrity score because they preserve the direction of departure from pristine.

Aboveground Biomass

Aboveground biomass (AGB) estimates the total dry mass of live woody vegetation per unit area. It is reported as a direct observation, not scored against a pristine baseline, because biomass is most useful as an absolute stock measurement: how much carbon is stored at a location, and how that stock has changed over time.

The data comes from the CTrees global AGB product (Saatchi, 2025), which maps biomass annually from 2000 to present at 100 m resolution using a machine learning framework that integrates satellite imagery (Landsat, ALOS PALSAR, GEDI, ICESat-2) with over 500,000 ground inventory plots and extensive airborne LiDAR measurements. Ecoregion-specific models ensure regional variability is captured across boreal, temperate, and tropical forests as well as woodlands, savannas, and wetlands. The 100 m product is aggregated to 1 km for use in this tool.

The tool displays current biomass stock and net change since 2010, giving a direct reading of how much carbon the landscape holds and whether it is gaining or losing biomass. AGB values are reported in Mg/ha (tonnes of dry matter per hectare). To convert to carbon stock, multiply by 0.5. To convert carbon to CO₂ equivalent, multiply by 3.667.

Soil Carbon Stock

Soil stores enormous amounts of carbon, globally more than all forests and the atmosphere combined. This layer shows how much organic carbon is stored in the soil at each location, measured in tonnes of carbon per hectare for the full 0–100 cm soil profile.

The data comes from the OpenLandMap-soildb product (Hengl et al., 2025), which uses machine learning trained on over 216,000 soil samples worldwide to map soil carbon density at 120 m resolution, aggregated to 1 km for this tool. The product predicts carbon density directly rather than combining separate soil carbon and bulk density maps, which improves accuracy.

The tool shows the total carbon stock for the top meter of soil, along with how much comes from each depth layer (0–30 cm, 30–60 cm, 60–100 cm). This breakdown matters because topsoil carbon (0–30 cm) is most vulnerable to disturbance: tillage, erosion, and land clearing deplete the upper layer first. In peatlands and boreal forests, a larger share of carbon sits deeper underground, where it is more stable but can be released by drainage or permafrost thaw.

The tool also shows how soil carbon stock has changed over approximately 20 years (2000–2005 vs 2020–2022). Large losses from deforestation or peat drainage are clearly detectable at this timescale. Slower changes, such as gradual carbon recovery from improved land management, are close to the detection limit and should be interpreted as indicative trends rather than precise measurements. Accuracy is highest in Europe and North America where the model has the most training data.

The tool provides socioeconomic information alongside environmental data, helping users understand the human context of production regions.

All socioeconomic indicators are computed at regional and provincial level, with country-level values used as fallback where subnational data is missing. These indicators allow users to see where high environmental impact coincides with high deprivation, smallholder dominance, or indigenous land presence.

Agricultural dependency

Agricultural dependency measures what share of a region's economy comes from agriculture, using gridded GDP data (Shoji et al., 2025). Regions with high agricultural dependency are more economically vulnerable to changes in commodity markets or sourcing decisions.

Relative Deprivation Index

The Gridded Relative Deprivation Index (GRDI v1.10; CIESIN, 2025) is a combined measure of multidimensional poverty at 1 km resolution. It combines five components into a weighted average: subnational Human Development Index, infant mortality rate, child dependency ratio, built-up area, and nighttime lights intensity. Scores range from 0 to 100, with higher values indicating greater deprivation. The index measures relative deprivation, how deprived an area is compared to others, rather than absolute poverty levels.

Human Development Index

The tool uses a subnational HDI surface derived from satellite imagery and machine learning (Sherman et al., 2023). This means regions within the same country can differ substantially, a capital city and a rural province will show different scores. Values below 0.55 indicate low human development, 0.55–0.70 medium, 0.70–0.80 high, and above 0.80 very high.

Farm size

Mean farm size per region is derived from Fortin et al. (2026) for year 2020 and classified into five tiers: smallholder (below 2 hectares), small-scale (2–20 hectares), medium-scale (20–200 hectares), large-scale (200–1,000 hectares), and very large-scale (above 1,000 hectares). Smaller farms typically indicate smallholder-dominated landscapes where livelihoods depend directly on the land, and where communities have lower capacity to absorb abrupt changes in sourcing practices or market access.

People on agricultural land

This indicator estimates the population whose livelihood is tied to local agricultural production. It uses LandScan population data (Lebakula et al., 2025) weighted by the fraction of each location that is cropland or grazing land, drawn from the LUIcube land-use dataset (Matej et al., 2025). A mixed suburban pixel with 30% farmland contributes 30% of its population, not all or nothing. This gives a realistic estimate of how many people might rely on agriculture in each region.

Indigenous and community lands

The tool reports whether a region overlaps with indigenous or community territories from the LandMark Global Platform (LandMark, 2025). Both formally recognised territories with legal boundaries and indicative customary-tenure areas where communities exercise de facto rights without formal recognition are included. The combined coverage is displayed as a percentage of the region's area. Regions with indigenous land presence warrant attention to Free, Prior and Informed Consent (FPIC) for any demand-side intervention. Changes in sourcing practices can directly affect communities with deep ties to the land.

Coverage limitations.The LandMark platform only reflects territories that have been published to the dataset, and global coverage is uneven. Many regions with well-known indigenous or customary tenure, particularly across Southeast Asia (e.g. Indonesia, the Philippines, parts of Vietnam and Laos), appear with zero or near-zero overlap in the source. Territories may be missing because they are not yet formally mapped, or because communities and governments have chosen not to release spatial data publicly for security, sovereignty, or data-protection reasons. Absence of overlap in the tool therefore does not mean absence of indigenous communities on the ground, and users working in these regions should treat a "no overlap" result as inconclusive rather than exonerating.

When a user views results for a specific commodity and region, the tool shows where that region's impact sits relative to all other producing regions globally, as a percentile rank. The comparison is made against regions at the same administrative level. Subnational regions are compared against other subnational regions.

Regions that together account for less than 0.5% of global production are excluded from the comparison, as they would risk obscuring meaningful signals in the benchmark.

The global median displayed alongside each result represents the intensity level that half of all producing regions outperform. The map colour scale uses the same anchor: yellow marks the median, red marks twice the median or above.

Trade data and sourcing

The tool uses FAO bilateral trade data, which records the direct trading partner rather than the original producer. The continuous re-export correction addresses most of this (see Section 02), though residual inaccuracies remain for complex multi-step processing chains. The subnational production pattern is fixed at 2020 (from SPAM); regions where production has shifted significantly since then will carry outdated spatial weights until newer data becomes available.

Livestock

Livestock land occupation relies on GPW livestock headcount (Parente et al., 2025), which includes intensive confined operations (feedlots). These systems occupy very little pasture per animal, producing near-zero land occupation values that may not reflect the full land footprint when feed crops are considered.

Greenhouse gas emissions

Enteric fermentation is not included. The Cao et al. (2026) dataset covers on-cropland emissions only.

Nutrient pollution

Values above 1.0 kg nutrient per kg product are implausibly high and are set to missing. These arise from regions with very small production volumes where the calculation amplifies noise.

Ecosystem integrity

GEDI LiDAR coverage ends at ±52° latitude. Above this, structural integrity relies on GPW height, CTrees biomass, and the Beyer fragmentation score; below 0.5 m (GPW) and 5 Mg/ha (CTrees) the observed/expected ratio is uninformative and those pixels are excluded. In deserts and sparse tundra, only the Beyer score contributes. VIIRS satellite productivity data saturates in the densest tropical forests, making functional integrity scores in parts of the Amazon and Congo Basin slightly conservative. Pixels with potential NPP below 50 gC/m²/yr are excluded entirely.

Underlying satellite products

Hansen forest loss detection has lower accuracy in dry and sparse forests and cannot detect repeat loss on the same pixel (Fitts et al., 2025). The Sims et al. driver map cannot distinguish loss in natural forests from orchard or plantation replanting, which may inflate agricultural conversion estimates in regions with long-standing tree crop cultivation such as parts of Malaysia and Indonesia.

Species and fungi data

The LIFE index normalises scores to the 90th percentile, meaning the very highest extinction-sensitivity hotspots are all clipped to 1.0 and cannot be distinguished from one another within the tool.

Mycorrhizal fungi predictions from Van Nuland et al. (2025) are modelled from species distribution models with limited ground-truth validation outside Europe and North America.

Freshwater biodiversity regional scores are computed as the mean per-pixel value within the region, reflecting average score intensity rather than total accumulated value, making scores comparable across regions of different sizes.

Soil health indicators

In some broadly cleared regions (e.g. Western Australian wheatbelt), the SOC model's predicted natural baseline converges toward the current degraded state, masking real depletion. The phosphorus calibration filter is deliberately looser than the SOC filter (n = 2,883 vs strict filter of n = 63), which may introduce minor bias. Nitrogen deposition critical loads are predominantly derived from European and North American field studies with limited direct tropical evidence. Hard pH-class boundaries at 4.2 and 6.2 introduce artificial jumps affecting ~2.9% of land.

Water stress context

SBTN water stress scores are published at watershed and regional level. Provincial (ADM2) values are re-aggregated from watershed polygons, and country values are simple averages of regional values rather than area-weighted, which can slightly bias country summaries.

A quick reference table listing every variable the tool reports, its unit, and the primary data source.

The indicators in this tool are not independent. They form a network of synergies (green: indicators that improve together) and tradeoffs (orange: one improves while the other degrades). The graph below shows Spearman rank correlations across ~47,000 regions worldwide.

Direct relationships are ecologically straightforward: one variable mechanistically influences the other (e.g. more biomass means more soil carbon). Indirect (dashed lines) means both variables respond to a shared underlying driver rather than directly causing each other (e.g. biodiversity and deprivation both track remoteness from cities). The Min |ρ| slider filters by correlation strength: higher values show only the strongest relationships, lower values reveal the full web of weaker connections.

Method-level changes are listed here.

• 2026-04-15 — [GHG emissions]Deforestation emissions added on top of Cao et al. (2026) on-farm emissions using the WRI GCSC dataset (Fitts et al., 2025 Technical Note), as a production-weighted average of 2020–2024 annual per-commodity, per-ADM2 factors. Surfaced as a new "Deforestation" slice in the emission source breakdown.
• 2026-04-15 — [ecosystem loss] GLCLU gap-fill added for savanna/open-woodland conversion in cerrado, miombo, chaco, Sahel, Deccan, and Kazakh steppe biomes (Potapov et al. 2022 with Kan et al. 2026 9-class reclassification; approximately 29 Mha global contribution, gated on SPAM+GPW agricultural expansion and GACED30/GLAD cropland presence).
• 2026-04-15 — [functional integrity] RF bias correction training target switched from single-year VIIRS/LUIcube ratio to 2018–2022 five-year mean ratio, to reduce interannual noise and provide a more stable climatological baseline.
• 2026-05-01 — [structural integrity] Expanded from 2-layer (GEDI + Beyer) to 4-layer composite: GEDI canopy structure, GPW Vegetation Height (short vegetation), CTrees Aboveground Biomass, and Beyer landscape fragmentation. Minimum operator across all valid layers. Predictor stack expanded from 22 to 29 features.
• 2026-05-01 — [functional integrity] Predictor stack expanded from 22 to 26 features (CHELSA 2.1 replaces WorldClim; MERIT-DEM replaces SRTM). Validated against Rodal et al. (2025) field NPP database (Spearman r = 0.617).
• 2026-05-01 — [soil health] Added soil organic carbon score, soil phosphorus score, and nitrogen deposition exceedance as new nature context indicators. 30-predictor Random Forest models trained on pristine reference sites.
• 2026-05-01 — [freshwater pollution] Added freshwater nutrient pollution layer (N and P excess above natural baseline) from McDowell (2025).
• 2026-05-01 — [aboveground biomass] Added aboveground biomass stock and net change since 2010 from CTrees (Saatchi, 2025) at 1 km resolution.
• 2026-05-02 — [soil carbon stock] Added soil organic carbon stock (0–100 cm) and change since 2000–2005 from OpenLandMap-soildb (Hengl et al., 2025), with depth layer breakdown (0–30, 30–60, 60–100 cm).
• 2026-05-02 — [total carbon density] Added derived total carbon density metric combining aboveground biomass (×0.5) and soil carbon stock, with above/below ground percentage split.