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Improving the resilience of agriculture to climate change

Agricultural production systems are sensitive to extreme weather, climate events, and to other environmental and socio-economic shocks. Evaluating the resilience of such systems helps to understand the sector’s ability to maintain functions (e.g. fair incomes) and services (e.g. food production; ensuring biodiversity) vis-à-vis increasing climate variability and extreme weather events. Influencing factors include socio-economic conditions, innovation, governance, and biophysical aspects. Strengthening resilience for present and future generations involves short-term adjustments in practices and management, as well as long-term transformational changes in response to climate and other disturbances.

I.9 - Agricultural Sector Resilience Dashboard rationale

Summary

The Agricultural Sector Resilience Indicator (I.9/C.45) synthesizes the status and progress of various factors affecting the agricultural sector resilience. It builds on components from the Performance Monitoring and Evaluation Framework (PMEF), and other sources such as EUROSTAT, JRC, and EEA. This composite indicator provides an overall indication of agricultural resilience capacity for each Member State (MS) and the EU27.

The current I.9 indicator is composed of one financial component:

• The Agricultural Factor Income Resilience index (AFIRi) which derives from the PMEF C.25 indicator - Agricultural factor income stability.

And three bio-physical components:

• The Agriculture Production Resilience index (APRi) of annual cereals production.
• The Water Resilience index (WRi), which is closely linked with the PMEF I.17/C.38 indicator - Water exploitation index plus (WEI+) that addresses regional and monthly aspects of water scarcity for the agricultural sector.
• The Soil Resilience index (SRi) that uses annual modelled data of Soil organic carbon stock in agricultural land (from C.40 data), including regional change of modelled carbon stocks.

These four initial indicators were chosen for their relevance and data availability, forming a framework that can expand to include new components.

Importance

The I.9 indicator is crucial for monitoring and identifying factors enhancing the resilience of the agricultural sector, particularly to climate change. It helps policymakers understand how well the sector can adapt to and recover from climate-related stresses, ensuring long-term productivity and stability. By highlighting areas needing improvement, it supports the development of targeted interventions and sustainable practices.

Methodology

The I.9 indicator is derived from data collected by PMEF, EUROSTAT, JRC, and EEA. The methodology involves comparing the evaluation period (-) to a reference period (-), each spanning 15 years. These periods, chosen sufficiently long to appreciate statistically relevant changes in impacts of climate variability caused by one or more events, will be updated in future releases. The components and the composite indicator are measured using two metrics: Status and Progress. Resilience status is characterized relative to the median resilience of the 27 MS of the European Union in the reference period. Progress compares the evolution of status from the reference to the evaluation period. To avoid over-interpretation of results, a component specific JRC-defined threshold defines a bandwidth of stable conditions, i.e. without relevant change compared to previous period.

The status of each component for a particular period is calculated as:

• 0: Below the EU27 median, indicating low resilience.
• 0.5: Within the EU27 median, indicating average resilience.
• 1: Above the EU27 median, indicating high resilience.

Progress in relation to the reference period for each component is calculated as:

• 0: Decline, showing worsening conditions.
• 0.5: No change, indicating stable conditions within a bandwidth
• 1: Positive, showing progress.

The numerical values of the status and progress of the composite indicator are obtained through the sum of these scores that can vary between 0 and 4 for both status and progress, where 0 represents the lowest resilience and 4 the highest. Below are four examples of countries with markedly different results, illustrating how various countries can have differing levels of resilience and progress, reflecting their unique agricultural practices, climate conditions, and policy implementation:

Country A:

• Status Score: 3.5 (High resilience)
• Progress Score: 2.0 (Moderate progress)
• Interpretation: Country A has a high current resilience and moderate progress, which is a positive outcome but suggests room for further progress. The progress score of 2.0 indicates either two components with a score of 0 (declining resilience) and two indicators with a score of 1 (improving resilience), or four components each with a score of 0.5 (no change in resilience).

Country B:

• Status Score: 1.5 (Low to medium resilience)
• Progress Score: 3.0 (Significant progress)
• Interpretation: Country B has a relatively low overall resilience score but has made significant progress, likely due to effective management changes in the sector or other factors. The status score of 1.5 can result from various indicator combinations. While the progress score of 3 indicates substantial improvement in at least two components.

Country C:

• Status Score: 4.0 (Highest resilience)
• Progress Score: 0.5 (Minimal progress)
• Interpretation: Country C has the highest current resilience, with a status score of 4.0 showing that all components are above the EU median. The minimal progress suggests that resilience of country C reached a plateau for one of the indicators, while the three others decreased compared to the reference period.

Country D:

• Status Score: 0.5 (Lowest resilience)
• Progress Score: 1.0 (Some progress)
• Interpretation: Country D has the lowest current resilience among EU MS, with most components are below the median. While concerning, the progress score of 1.0 shows some progress has been made, indicating initial steps in the right direction but requiring more effort. The progress score of 1.0 can be explained by positive value of one component, or two components remaining the same, while all others have shown a decline.

Implications for Agricultural Resilience

The I.9 indicator is essential for understanding agricultural resilience, in particular to climate change. It highlights strengths and weaknesses in the sector, guiding policy and management decisions to enhance resilience. High values suggest a robust capacity to withstand and adapt to climate impacts, while low progress values indicate areas needing significant improvement. This information is vital for ensuring food security, stable rural livelihoods, and sustainable agricultural practices.

Agriculture Production Resilience Index (APRi) rationale

Description

The Agriculture Production Resilience Index (APRi) measures the stability of agricultural cereal crop production in response to climate variability and other stressors. APRi provides a comprehensive assessment of agricultural production resilience across Member States (MS) and the EU27, reflecting the sector's capacity to maintain productivity under changing environmental conditions.

Importance

The APRi is crucial for monitoring the resilience of agricultural production to climate change and other disruptions. Stable agricultural production ensures food security, supports rural livelihoods, and maintains economic stability of farmers. By identifying significant changes in fluctuations of production levels, APRi helps policymakers and farmers to identify strategies to enhance resilience, ensuring consistent agricultural output and contributing to sustainable development.

Resilience in crop production systems can be enhanced through adaptation and management practices. However, extreme weather conditions such as heat stress and drought can cause significant production losses. APRi reflects these dynamics, providing a comprehensive measure of crop production system resilience across MS and the EU27, offering large-scale insights into the capacity of agricultural systems to maintain productivity under varying environmental conditions.

APRI is a policy relevant resilience metric quantifying the stability of the cereal crop system using reported national cereal crop production. Following ecological resilience principles, it incorporates the interplay of weather and climate driven regional yield fluctuations, market driven crop choices, and regional shifts in crop area among individual cereals and their varieties. The index supports policy and management strategies that strengthen the entire cereal production system.

Methodology

The APRi is derived from statistical data collected by EUROSTAT and processed at the JRC. Cereals (grains, other cereals, and some fodder crops) are, after vegetables, the second-largest crop category in terms of economic value in the EU. Other crops may respond differently to changes in weather and climate.

This methodology involves analysing trends, and anomalies in agricultural production to assess resilience. APRi uses a theoretical framework consistent with the ecological definition of resilience. Its value is calculated from the reciprocal of the squared coefficient of the normalized variance of detrended time series, providing a comprehensive assessment of agricultural production resilience across Member States (MS) and the EU27.

Implications for Agricultural Resilience

APRi highlights the importance of maintaining stable agricultural production levels to support resilience to climate change. High APRi values indicate robust production stability, which helps agriculture withstand environmental and economic stresses, supports sustainable food supply, and contributes to overall agricultural sustainability. Low APRi values signal the need for improved agricultural practices and policies to enhance resilience and ensure long-term productivity. Geographic and climatic factors significantly influence production resilience, necessitating tailored strategies to address regional challenges and optimize agricultural performance across diverse environments.

Agricultural factor income Resilience Index (AFIRi) rationale

Description

The Agricultural Factor Income Resilience Index (AFIRi) measures the stability of agricultural income per Agricultural Work Unit. Agricultural factor income reflects the remuneration of all production factors (i.e. total of the costs for land, capital and labour) in the agricultural sector. This indicator, part of the Performance Monitoring and Evaluation Framework (PMEF) as indicator C.25, represents the net value added at factor cost. It integrates data from EUROSTAT’s Economic Accounts for Agriculture (EAA) and Agricultural Labour Input Statistics, providing a comprehensive assessment of agricultural income resilience across Member States (MS) and the EU27.

Importance

AFIRi is crucial for monitoring the economic stability of the agricultural sector, ensuring farmers can maintain their livelihoods and invest in sustainable practices. Stable agricultural income supports rural communities, reduces poverty, and promotes economic stability. By identifying areas with large variability in income, AFIRi helps policymakers implement strategies to enhance income stability, ensuring the resilience of the agricultural sector to economic, climate and environmental shocks.

Methodology

AFIRi is derived from data collected by EUROSTAT and elaborated by the JRC. The methodology involves analysing trends and anomalies in agricultural production to assess resilience. Analogous to APRi, AFIRi calculates a resilience score based on the squared coefficient of variance of detrended and normalized time series.

Implications for Agricultural Resilience

AFIRi highlights the importance of maintaining stable agricultural income levels to support resilience to economic and environmental challenges. High AFIRi values indicate robust economic stability, enabling farmers to invest in sustainable practices and innovations, thus ensuring long-term productivity. Low AFIRi values signal the need for policy interventions to stabilize incomes, enhance resilience, and ensure the agricultural sector's sustainability. This indicator is also relevant for monitoring progress towards the UN Sustainable Development Goals, particularly in reducing income inequality and promoting sustainable economic growth.

Water Resilience Index (WRi) rationale

Summary

WRi is based on the methodology of the widely used Water Exploitation Index (WEI+). While WEI+ evaluates the total water consumption of all sectors as a proportion of the renewable freshwater resources available in a specific region and period, the WRi focuses on regions and months where water abstraction (in particular irrigation) is dominated by agriculture.

By identifying river basins with high water stress, the indicator informs water management policies and practices to ensure long-term water availability and agricultural resilience.

Importance

The WRi is essential for assessing the sustainability of water use in agriculture, while also accounting for other water uses. It also helps to identify agricultural regions and periods with high water stress, informing water management strategies to ensure long-term water availability and agricultural resilience. For reference, WEI+, based on MS reporting, is part of the Performance Monitoring and Evaluation Framework (PMEF) for the Common Agricultural Policy (CAP) from 2023-2027, specifically under Indicator C.38/I.17, which aim to reduce pressure on water resources.

Methodology

For WRi we use calculations using the coupled LISFLOOD-EPIC hydrological-agronomic model at 5km resolution, operated by the Joint Research Centre (JRC). Future versions will use finer resolutions. LISFLOOD-EPIC, forced with meteorological data, simulates the full water cycle from precipitation, water use in economic sectors, to flows and stocks of water in river basins, lakes and artificial reservoirs, and groundwater. Human water abstraction and return flows from multiple sectors (irrigation, livestock, domestic use, industry and energy) are considered. Crop irrigation requirements are dynamically simulated using the embedded crop growth components from the EPIC model, and observed weather data. Similar to WEI+, monthly WRi values range from 0 to 1. Values between 0-0.1 indicate “low water stress”, 0.1-0.2 indicate “moderate water stress”, 0.2-0.4 indicate “water stress”, and values above 0.4 indicate unsustainable “severe water stress”. Using gridded model simulations, the model highlights the temporal (monthly) and regional (watersheds within countries) changes and trends in WRi, focussing on regions where water abstraction is dominated by agriculture. The overall MS WRi resilience score in the reference and evaluation period is calculated using a river basin area and temporally weighted average exceeding WEI+>0.2, excluding regions where agriculture is not dominating. The EEA defines regions with a water stress level above 20% (0.2) as water-stressed, between 10% and 20% as moderately stressed, and below 10% as low stress.

Implications for Agricultural Resilience

The WRi highlights areas and months when agriculture is facing significant water stress. High values necessitate the implementation of better water management and conservation practices to maintain agricultural productivity. Ensuring low WEI+ values supports stable agricultural outputs, reduces vulnerability to climate change, and promotes long-term sustainability in water use.

Soil Resilience Index (SRi) rationale

Description

The Soil Resilience Index (SRi) estimates the total organic carbon (SOC) content in the agricultural topsoils (40cm depth), a crucial indicator of soil health and fertility. SOC, derived from plant residues decomposed by microbes, fungi, and animals, plays a key role in soil processes. Conservation of SOC is also an important for the mitigation of GHG emissions. A related indicator C.40/I.11 SOC in agricultural land is part of the Performance Monitoring and Evaluation Framework (PMEF), with, with data is sourced from the LUCAS (Land Use and Coverage Area Survey) from 27,000 topsoil samples, during a limited amount of specific survey years. For SRi, we use the DayCent biogeochemical model, trained with LUCAS data, to estimate the evolution of annual SOC stock values, including climate impacts and agro-management effects. This component provides a comprehensive measure of soil resilience across Member States (MS) and the EU27, reflecting soils' capacity to maintain productivity and support sustainable agriculture under varying environmental conditions.

Importance

High levels of SOC improve soil structure, water retention, and nutrient cycling, leading to improved crop yields as well as reduced erosion and GHG emissions. Healthy soils with high SOC content are essential for maintaining agricultural sustainability and mitigating climate impacts. Monitoring SOC helps identify areas needing better soil management practices, ensuring long-term sustainability. The SRi is a crucial proxy for soil health and fertility, which are fundamental to sustainable agricultural productivity and resilience to climate change.

Methodology

The SRi is derived from SOC calculated using the Daycent process based ecosystem model, trained with observations from the LUCAS soil survey. DayCent estimates SOC stock values and changes over time, with model outputs downscaled to a 100-meter resolution using machine learning to produce high-resolution maps. DayCent model (version Gaec_v18) simulates the daily dynamics of carbon, nitrogen, phosphorus, and sulfur between soil, plants, and the atmosphere. It considers site characteristics, daily meteorological data, and management practices such as crop rotation, tillage, grazing, irrigation, and fertilization. DayCent is chosen for its detailed representation of soil biogeochemistry, validated performance at the European level, and ability to simulate various agricultural management practices.

Implications for Agricultural Resilience

The SRi highlights the importance of maintaining high SOC levels to support agricultural resilience to climate change. High SRi values indicate robust soil health, which helps agriculture withstand climate stresses, supports sustainable crop production, and contributes to overall environmental health. Low SRi values signal the need for improved soil management practices to enhance resilience and ensure long-term agricultural productivity.

Beside agricultural practice, the latitude and longitude of a country or region significantly influence SOC levels, with climatic conditions and soil types varying markedly with location. For instance, Mediterranean regions typically exhibit lower SOC levels compared to Northern European regions due to warmer temperatures, drier conditions, and different vegetation types. These factors contribute to the decomposition rate of organic matter, impacting SOC accumulation. Consequently, countries in the Mediterranean may have inherently lower SOC levels than those located further north, where cooler climates and higher moisture levels favour greater organic matter accumulation. Understanding these geographic and climatic variations is crucial for tailoring soil management practices to enhance soil health and resilience across different regions.

Connections with other PMEF indicators and existing monitoring tools

Performance monitoring and evaluation framework (PMEF)

Resilience dashboards

Agri-food Data Portal

Main References

Catarino, R. and Dentener, F., I.9 Agricultural Sector Resilience Progress Indicator - Methodology, Implementation, and Future Development, Publications Office of the European Union, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/7559119, JRC143715

Bisselink, B., Bernhard, J., Gelati, E., Adamovic, M., Guenther, S., Mentaschi, L., Feyen, L., De Roo, A., 2020. Climate change and Europe’s water resources. Publications Office, Luxembourg. https://doi.org/10.2760/15553

European Commission, Joint Research Centre, De Roo, A., Bisselink, B., Trichakis, I., Water-Energy-Food-Ecosystems pathways towards reducing water scarcity in Europe – Analysis using the Water Exploitation Index Plus, Publications Office of the European Union, 2023, https://data.europa.eu/doi/10.2760/478498

Lugato, E., Leip, A., Jones, A., 2018. Mitigation potential of soil carbon management overestimated by neglecting N2O emissions. Nature Climate Change 8, 219–223. https://doi.org/10.1038/s41558-018-0087-z

Zampieri, M., Weissteiner, C.J., Grizzetti, B., Toreti, A., van den Berg, M., Dentener, F., 2020. Estimating resilience of crop production systems: From theory to practice. Science of The Total Environment 735, 139378. https://doi.org/10.1016/j.scitotenv.2020.139378

Past releases of the dashboard

This is release:

There are no earlier releases.

Contacts for dashboard content

For questions, comments or suggestions on the I.9 Agricultural Sector Resilience Dashboard and datasets, please contact the functional mailbox: EU-I9-resilience@ec.europa.eu.

Credits

The I.9 resilience indicator was conceptually designed by JRC, DG AGRI, and DG CLIMA as a contribution to the Performance Monitoring and Evaluation Framework (PMEF). Summary results will be made available on the AGRI FOOD portal. The Resilience Agricultural Sector Resilience Progress Indicator (I.9) Dashboard was designed by R. Catarino and F. Dentener, and implemented by A. Caivano and J. Castro Malet.

M. Zampieri designed the methodology for the Agriculture Production Resilience Index (APRi) and the Agricultural Factor Income Resilience Index (AFIRi). E. Lugato provided regional DayCent simulation results of SOC for the Soil Resilience Index (SRi). B. Bisselink provided LISFLOOD-EPIC regional and monthly model results of water balances for the Water Resilience Index (WRi).

Agriculture Production Resilience Index (APRi)

By following these steps, the Agriculture Production Resilience Index (APRi) can be calculated, providing insights into the resilience and progress of agricultural production systems across different member states.

Step 1: Download Data

Download the dataset from Open Data.

Step 2: Detrend and Normalize Data

Fit a third-order polynomial trend for each country over the full study period. Then normalize and detrend the data by dividing the polynomial fitted values by the reported values for each year and member state. Detrending data isolates the variability caused by short-term climate stressors, such as extreme weather events, while normalization ensures comparability across member states.

Step 3: Calculate Inverse of Squared Standard Deviation for Detrended Data

Calculate the standard deviation of the detrended data for each period. Then calculate the inverse of the squared standard deviation for each period.

Note: The inverse of the squared standard deviation serves as a measure inversely proportional to the data's variability. Thus, a higher value for the inverse of the squared standard deviation suggests greater stability or predictability, while a lower value indicates more variability and less predictability. Dimensionless values of APRi can range from less than 5 (high production variability, low resilience) to more than 400 (high stability, high resilience).

Step 4: Calculate Percentiles

Calculate the following percentiles for the detrended data across all member states:

• 12.5th percentile
• 25th percentile
• 37.5th percentile
• 62.5th percentile
• 87.5th percentile

Step 5: Final Resilience Status Index Data

For each period, assign a resilience status based on the inverse squared standard deviation:

• Resilience Status = 1 if above the 62.5th percentile
• Resilience Status = 0 if below the 37.5th percentile
• Resilience Status = 0.5 if between the 37.5th and 62.5th percentiles

Step 6: Final Resilience Progress Index Data

Calculate the ratio for each member state:

Assign a progress score based on the ratio:

• Progress = 1 if Ratio>1+threshold
• Progress = 0 if Ratio<1−threshold
• Progress = 0.5 if 1−threshold ≤ Ratio ≤1+threshold

With threshold = 0.1 (i.e. 10 %). This value reflects a substantial change of the number and/or magnitude of annual fluctuations with the two periods.

Agricultural Factor Income Resilience Index (AFIRi)

By following these steps, the Agricultural Factor Income Resilience Index (AFIRi) can be calculated, providing insights into the resilience and progress of agricultural production systems across different member states.

Step 1: Download Data

Download the dataset from Open Data.

Step 2: Detrend and Normalize Data

Fit a third-order polynomial trend for each country over the full study period. Then normalize and detrend the data by dividing the polynomial fitted values by the reported values for each year and member state. Detrending data isolates the variability caused by short-term climate stressors, such as extreme weather events, while normalization ensures comparability across member states.

Step 3: Calculate Inverse of Squared Standard Deviation for Detrended Data

Calculate the standard deviation of the detrended data for each period. Then calculate the inverse of the squared standard deviation for each period. Dimensionless values of AFIRi can range from less than 20 (high income variability) to more than 400 (low variability).

Note: The inverse of the squared standard deviation serves as a measure inversely proportional to the data's variability. Thus, a higher value for the inverse of the squared standard deviation suggests greater stability or predictability, while a lower value indicates more variability and less predictability. Consequently, the higher the inverse of the squared standard deviation, the greater the resilience of the system.

Step 4: Calculate Percentiles

Calculate the following percentiles for the detrended data across all member states:

• 12.5th percentile
• 25th percentile
• 37.5th percentile
• 62.5th percentile
• 87.5th percentile

Step 5: Final Resilience Status Index Data

For each period, assign a resilience status based on the inverse squared standard deviation:

• Resilience Status = 1 if above the 62.5th percentile
• Resilience Status = 0 if below the 37.5th percentile
• Resilience Status = 0.5 if between the 37.5th and 62.5th percentiles

Step 6: Final Resilience Progress Index Data

Calculate the ratio for each member state:

Assign a progress score based on the ratio, and a threshold set as 0.05:

• Progress = 1 if Ratio>1+threshold
• Progress = 0 if Ratio<1−threshold
• Progress = 0.5 if 1−threshold ≤ Ratio ≤1+ threshold

With threshold = 0.1 (i.e. 10 %). This value reflects a substantial change of the number and/or magnitude of annual fluctuations with the two periods.

Water Resilience Index (WRi)

By following these steps, the Water Resilience Index (WRi) can be calculated, providing insights into the resilience and progress of agricultural production systems across different member states.

Step 1: Download Data

Download the dataset from Open Data.

Step 2: Calculate the regional agricultural water consumption ratio per river basin region

To calculate the Regional agricultural water consumption ratio per river basin region, please follow the formula showed below:

Where:

• C: Consumption requirement for irrigation (m³/month/region).
• L: Local water available (m³/month/region), calculated by precipitation- evapotranspiration
• U: Required consumption (m³/month/region).
• I: Upstream inflow available (m³/month/region).
• D: a threshold value (0.5) indicating when agricultural water abstraction dominates total consumption in a region.

Step 3: Calculate the number of months exceeding threshold M

For each reference and evaluation period, and for each region, sum the number of months where WRi > M, with M = 0.2 serving as the threshold for water stress. In regions where agriculture accounts for less than 50% (D = 0.5) of the water use, a value of zero is assigned, thus isolating unsustainable water use specifically caused by agriculture.

Step 4: Calculate the ratio of exceedance occurrences per period for each zone

For each period and each region, divide the total number of months where R > M (Step 3) by the total number of months in the respective period. Then multiply this value by the area of each river-basin area in a country

Step 5: Calculate the National weighted WRi

Calculate the national weighted WRi by dividing the sum of Step 4 by the total national area for each member state. This number takes values between 0 and 1 (dimensionless), where 1 means that in in all months and all regions, water consumption by agriculture in agriculturally dominated regions was within the sustainability limits.

Step 6: Calculate Percentiles

Calculate the following percentiles for the STEP 5 across all member states:

• 12.5th percentile
• 25th percentile
• 37.5th percentile
• 62.5th percentile
• 87.5th percentile

Step 7: Final Resilience Status Index Data

For each period, assign a resilience status based on the inverse squared standard deviation:

• Resilience Status = 1 if above the 62.5th percentile
• Resilience Status = 0 if below the 37.5th percentile
• Resilience Status = 0.5 if between the 37.5th and 62.5th percentiles

Step 8: Final Resilience Progress Index Data

Calculate the ratio for each member state:

Assign a progress score based on the ratio, and a threshold set as 0.05:

• Progress = 1 if Ratio>1+threshold
• Progress = 0 if Ratio<1−threshold
• Progress = 0.5 if 1−threshold ≤ Ratio ≤1+ threshold

With threshold = 0.01 (i.e. 1 %)

Soil Resilience Index (SRi)

By following these steps, the Soil Resilience Index (SRi) can be calculated, providing insights into the resilience and progress of agricultural production systems across different member states. The simulated data for the SRi is based on key Good Agricultural and Environmental Conditions (GAECs) and includes the evaluation of Soil Organic Carbon (SOC) stocks and their rate of change over specified periods.

The SRi is designed to reflect two important characteristics of soil status: the SOC stock (indicative of "soil health") and the rate of change of SOC over time. These two components are weighted to provide a comprehensive measure of soil resilience. The analysis spans two 15-year periods: a reference period and an evaluation period. This timeframe attempts to capture sufficient temporal change and providing robust data for analysis.

Step 1: Download Data

Download the dataset from Open Data.

Step 2: Calculate the average SOC stocks for the different periods for each NUTS 2 region

Calculate the SOC stock (Mg/ha) for each NUTS 2 region for both the reference and evaluation periods. Regional values of SOC range from less than 40 Mg/ha to more than 200 Mg/ha in all NUTS2 regions in the EU27.

Step 3: Calculate regional SRi_reg

We use a threshold corresponding to the 75 percentile SOC value of EU27 NUTS2 regions to define ‘healthy’ soil. This value corresponds to ca. 90 Mg/ha. This regional threshold is motivated by science based assessments that more than 60 % of EU’s soils is currently considered ‘unhealthy’. The regional Sri (SRi_reg) score is:

Step 4: Calculate the national level SRi

Determine the number of NUTS 2 regions per member state, and then calculate the total national area by summing the areas of all NUTS 2 regions within a member state. Then compute the area-weighted average SRi for each NUTS 2 region multiplying the SOC value for each NUTS 2 region by its respective area, and then the weighted SOC values for all NUTS 2 regions are summed.

Finally, calculate the national weighted average SOC by dividing the total weighted SOC by the total national area, and determine the mean SOC per year for both the reference and evaluation periods. And compute national SRi from area weighted regional SRi.

Step 5: Calculate Percentiles

Calculate the following percentiles for the STEP 5 across all member states:

• 12.5th percentile
• 25th percentile
• 37.5th percentile
• 62.5th percentile
• 87.5th percentile

Step 6 Final Resilience Status Index Data

For each period, assign a resilience status based on the inverse squared standard deviation:

• Resilience Status = 1 if above the 62.5th percentile
• Resilience Status = 0 if below the 37.5th percentile
• Resilience Status = 0.5 if between the 37.5th and 62.5th percentiles

Step 7: Final Resilience Progress Index Data

Calculate the ratio for each member state:

Assign a progress score based on the ratio, and a threshold set as 0.05:

• Progress = 1 if Ratio>1+threshold
• Progress = 0 if Ratio<1−threshold
• Progress = 0.5 if 1−threshold ≤ Ratio ≤1+ threshold

With threshold = 0.01 (i.e. 1 %).

The threshold for substantial change between reference and evaluation period is motivated by the 4 per mille initiative, which sets an overall ambitious annual rate of change. As the evaluation period shifts with 6 years, this translates in a 1.6 % change over 6 years.