Multicriteria evaluation system for EOMs use

Towards climate-smart sustainable management of agricultural soils EOM4SOIL Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use Due date of deliverable: MX Actual submission date: 15.11.2024 Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use GENERAL DATA Grant Agreement: 862695 Project acronym: EJP SOIL Programme Ktle: Towards climate-smart sustainable management of agricultural soils Programme website: www.ejpsoil.eu Project Ktle: EOM4SOIL Project website: Start date of the project: February 1st, 2020 Project duraKon: 60 months Name of lead contractor: INRAE Funding source: H2020-SFS-2018-2020 / H2020-SFS-2019-1 Type of acKon: European Joint Project COFUND DELIVERABLE NUMBER: WP6 – T1. DELIVERABLE TITLE: MulKcriteria evaluaKon system for EOMs use DELIVERABLE TYPE: Report WORK PACKAGE N: WP6 WORK PACKAGE TITLE: MulKcriteria evaluaKon system for EOMs use DELIVERABLE LEADER: INRAE AUTHOR: DOI: Seheno Rinasoa, Sabine Houot, Mariana Moreira, Anne Schaub, Hélène Lagrange, Barcauskaite Karolina, Ulfet Erdal, Janjo de Haan, Bruno Huyghebaert, Florent Levavasseur LICENSE DISSEMINATION LEVEL: CC BY 4.0 (or choose another type) CO/PU Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use Table of contents List of Tables .............................................................................................................................. 1 List of Figures ............................................................................................................................ 1 List of acronyms and abbreviations ........................................................................................... 2 1. INTRODUCTION ............................................................................................................... 3 2. MATERIALS AND METHODS ......................................................................................... 4 2.1. PROLEG, an integrated multicriteria evaluation tools ....................................................... 4 2.2. Data collection and analysis ................................................................................................ 5 2.2.1. Data collection .................................................................................................................. 5 2.2.2. Simulation and data analysis ............................................................................................ 6 3. RESULTS AND DISCUSSIONS ......................................................................................... 7 3.1. Varied scenarios of cropping systems using external organic matter .................................. 7 3.2. Nutrient contributions and soil balances: N supply, P and K balances ............................... 8 3.3. Shortand long-term changes in SOC storage and their effects on available water capacity, soil density, and microbial biomass following EOM types ...................................................... 13 3.4. Variations N losses: nitrate leaching, nitrous oxide emissions, and ammonia volatilization following application of different EOM types ......................................................................... 14 3.5. EOM effect on soil trace element content ......................................................................... 18 3.6. Greenhouse gas emissions from different types of EOM: comprehensive comparison with and without storage and treatment ........................................................................................... 20 3.7. Limitations and practical guidelines .................................................................................. 23 3.7.1. Limitations of tools ........................................................................................................ 23 3.7.2. Representativeness of the simulated case studies ........................................................... 24 3.7.3. Practical guidelines ......................................................................................................... 24 4. CONCLUSION ................................................................................................................... 25 List of references ...................................................................................................................... 26 SUPPLEMENTARY INFORMATION ................................................................................ 30 Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 1 List of Tables Table 1 : Calculation of target variable values: the differences between outcomes under EOM application and mineral fertilizer across scenario groups .......................................................... 6 Table 2 : Summary of the different case studies considered in our study across different regions in France, Belgium, Turkey, Lithuania, and the Netherlands ..................................................... 8 List of Figures Figure 1 : Map of selected countries for data collection (source: https://www.mapchart.net/europe.html ) .................................................................................... 5 Figure 2 : Mean yearly Nitrogen (N), carbon (C), phosphorus (P), and potassium (K) input rates according to the nutrient content, rate and frequency of application of EOM, for the different scenarios.. ................................................................................................................................. 11 Figure 3 : Difference in soil N supply (ΔNsupply ), in the first year (year 1) and after 30 years (year 30) between the scenarios with external organic matter application (EOM) and the reference scenario under mineral treatment (without EOM), annual phosphorus (Pbalance) and potassium (Kbalance) balance for the scenarios with EOM application. ..................................... 12 Figure 4 : Variation in soil carbon storage (ΔCstorage),in the topsoil horizon, in the first year (year 1) and after 30 years (year 30) of external organic matter application (EOM) compared to soil carbon storage in mineral treatment (without EOM); and variation in available water capacity (Δawc), soil density (Δdsoil), and microbial biomass (Δmmicrobial biomass) after 30 years of EOM application compared to the mineral treatment (without EOM) ................................ 15 Figure 5 : Difference in nitrate lixiviation (ΔNO3leached), nitrous oxide emissions (ΔN2Oemission) , and ammonia volatilization (ΔNH3volatilized), in the first year (year 1) and after 30 years (year 30) between the scenarios with external organic matter application (EOM) and the reference scenarios without EOM ............................................................................................................ 16 Figure 6 : Differences in cadmium (ΔCdsoil), copper (ΔCusoil), nickel (ΔNisoil), lead (ΔPbsoil), zinc (ΔZnsoil), mercury (ΔHgsoil), and chromium (ΔCrsoil) soil contents after 30 years between the scenarios with external organic matter application (EOM) and the reference scenarios without EOM. ........................................................................................................................... 19 Figure 7 : Differences in greenhouse gas (GHG) emissions under EOM application and mineral fertilizers (without EOM application), in the first year of EOM application (Year 1)and after 30 years of EOM application, with and without the GHG emission from the treatment and storage. .................................................................................................................................................. 22 Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 2 List of acronyms and abbrevia7ons AMG Automated model generation ANOVA Analysis of variance awc Available water capacity C Carbon Cd Cadmium Cr Chromium Cu Copper d Soil density EOM External organic matter EU European union GHG Greenhouse gas Hg Mercury K Potassium LTE Long term field experiment N Nitrogen N₂O Nitrous oxide NH₃ Ammonia NH₄-N Ammonium nitrogen Ni Nickel NO3 Nitrate P Phosphorus Pb Lead SOC Soil organic carbon SOM Soil organic matter STICS Simulateur multidisciplinary pour les cultures standard TE Trace elements WP Work package Year 1 Firt year of application Year 30 30 years of application Zn Zinc Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 3 1. INTRODUCTION Minimizing the ecological footprint of food production is a crucial strategy for promoting sustainability in agriculture, and one effective approach is recycling external organic materials (EOM) into a valuable resource. The application of EOM in agricultural soils is essential for enhancing crop yield (Caron et al., 2015; Chen et al., 2024), maintaining soil fertility (Maffia et al., 2024; Paradelo et al., 2024), and mitigating climate change impacts (Hall et al., 2022). This approach, which aligns the interactions among agriculture, the environment, and waste management, has led to continuous advancements in research on the EOM recycling in agricultural soil systems. Several studies have thoroughly investigated the significance of EOM in promoting sustainable agricultural practices across various aspects: soil health, crop productivity, environmental considerations, and integration into the circular economy (Brichi et al., 2023). Prominent research themes include enhancing carbon (C) sequestration in soils (Bai et al., 2023), optimizing nitrogen (N) management to improve efficiency (Chen et al., 2022), and increasing the availability of key soil nutrients, such as phosphorus (P) (Kataria et al., 2024). Assessing the risks related to soil contamination from repeated EOM applications has also become a major focus (Singh Gautam et al., 2024; Zhang et al., 2024). However, most of these evaluations were conducted in controlled research settings with specific experiments. The effects of EOM application to agricultural soils are influenced by factors such as EOM type (origin and treatment), cropping systems, soil characteristics and climate. These factors vary widely across different regions and countries. For instance, Ogle et al., (2005) demonstrated that organic amendments enhance C storage more effectively in moist tropical climates than in temperate regions. Wang et al. (2024) added that temperature and precipitation are correlated with soil organic carbon (SOC) content. Additionally, Bai et al. (2023) highlighted that organic amendments have a stronger long-term impact on SOC storage in arid, alkaline soils compared to humid, acidic soils. The effects of EOM are also strongly influenced by the application rate and frequency (Zhou et al., 2022). Treatments such as anaerobic digestion to produce digestate or composting of manure can significantly influence the effects of applying these EOM on N availability, N losses, and greenhouse gas (GHG) emission compared to raw manure (Crolla et al., 2013; Khadim et al., 2024). Moreover, the use of EOM by farmers also depends on various drivers (e.g., perceived effects on soil quality, influence of social referent) and barriers (financial, EOM availability, perceived EOM pollution) (Hijbeek et al., 2019). Overall, these findings underscore the importance of considering these factors when evaluating the effects of EOM in agricultural systems. Long term field experiment (LTE) aimed at studying the EOM effects are often constrained by their substantial economic burdens, which limit their capacity to capture the full variability of influencing factors, such as cropping systems, EOM types, soil properties, and climate. As a result, LTE designs frequently simplify cropping systems (e.g., through short rotations or removal of crop residues) and apply EOM at higher rates and frequencies than typically observed in agricultural practice, facilitating a more rapid detection of EOM effects. However, these methodological adjustments may limit the applicability of LTE findings to standard agricultural systems and diverse environmental contexts. This gap emphasizes the urgent need for practical research that closely aligns with real-world farming Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 4 scenarios, addressing the economic and environmental challenges agricultural producers face following EOM application. To address the trade-offs and maximize the benefit of the EOM application, it is essential to align research on EOM effects with supportive policy and promote adaptive changes in farmers' practices. Levavasseur and Houot (2022) introduced the PROLEG tool, an integrated approach for multi-criteria evaluation of EOM application effects. This tool can explicitly consider different EOM, soil types, cropping system and climates. By incorporating these parameters, the PROLEG tool can significantly aid in evaluating various EOM application scenarios, offering insights into maximizing agronomic and environmental benefits while minimizing losses and negative impacts, such as GHG emissions and soil contamination. Currently, the tool has been used only in a specific region. However, expanding the use of the tool across a broader range of case studies with varied pedoclimatic and cropping system conditions could provide valuable data on EOM effects, to optimize its applications in diverse farming systems for both shortand long-term effects. In the context of increasing environmental challenges and the need for sustainable agricultural practices, this study aims to comprehensively assess the multiple effects of EOM recycling across various regional conditions in Europe, emphasizing the current utilization of EOM. This assessment will develop practical guidelines for optimizing EOM application in diverse European agricultural systems. To achieve these objectives, the research will define existing cropping systems, both with and without EOM, through representative case studies that consider different soils, climates, and cropping practices across contrasted regional conditions in Europe. Subsequently, the agronomic and environmental performance of these cropping systems will be simulated. Insights derived from these simulations will facilitate the formulation of actionable strategies to enhance EOM management, while also contributing to broader efforts aimed at promoting sustainable farming practices and mitigating climate change impacts across various European regions. 2. MATERIALS AND METHODS 2.1. PROLEG, an integrated multicriteria evaluation tools The PROLEG tool is used to evaluate the EOM application in agricultural fields. This tool integrates various models and functions to predict the agronomic and environmental performance of cropping systems, particularly those that recycle organic wastes. The tool, developed in the R programming language, uses the AMG model (Clivot et al., 2019) to predict both the soil organic matter (SOM) and SOC content and stocks. It integrates the Alfam model (Hafner et al., 2019) to simulate ammonia (NH3) volatilization under EOM applications. The soil-crop model STICS (Simulateur multidisciplinary pour les cultures standard) (Brisson et al., 2009) is used to predict N (except NH3) and water fluxes through the soil. Additionally, it incorporates some pedotransfer functions to estimate the evolution of different soil properties along SOC. The GHG balance is also computed in the tool. An important feature of the tool is its ability to calculate the required amount of mineral N fertilizer to fulfill crop N requirements, considering the nutrients provided by the soil, both in the short term and cumulatively over repeated EOM applications in long term. For P and K, a simplified approach computes the P Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 5 and K balance based on P and K inputs from EOM applications and crop exports. In case of a negative balance, the tool adjusts the amount of minerals P and K applied. The simulation generated by this tool is based on existing database parameter tables for EOM characteristics, machinery, and crops and on the description of the cropping system, soil characteristics and climate. For a full description of the PROLEG tool, see Levavasseur et al., (2023). 2.2. Data collection and analysis 2.2.1. Data collection Data on how farmers currently use EOM was collected in collaboration with experts from various European institutes to capture diverse pedo-climatic and cropping system conditions. Partners were approached for data collection. However, challenges like time constraints and varying expertise made it difficult to gather data from all regions. The study focused on representative datasets from farmers actively using EOM. The selected EOM types included those commonly used or emerging, based on the cropping systems of each region. The study covered four contrasted regions in France, along with areas in Wallonia (Belgium), the Netherlands, Lithuania, and Turkey, providing a broad representation of agricultural practices across Europe (Figure 1). The climates utilized in this study were specified for each study area. Data were collected from the Gridded Agro-Meteorological Data in Europe (https://agri4cast.jrc.ec.europa.eu/DataPortal/Index.aspx). Different EOM scenarios were grouped based on their pedoclimatic and cropping system conditions. Each group also includes a reference scenario that uses only mineral fertilizers as a control, allowing for direct comparisons between the effects of EOM and mineral treatments Figure 1 : Map of selected countries for data collection (source: https://www.mapchart.net/europe.html ) Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 6 2.2.2. Simulation and data analysis The annual nutrient inputs (N, P, K) provided by the different types of EOM in the cropping systems were calculated based on the mean characteristics of the EOM incorporated into the model (i.e., the potential specific EOM characteristics in a given area were not considered, due to limited data availability). Simulations were conducted using the PROLEG tool to evaluate performance over a five-year climatic period (2017-2021). The simulations were conducted for two different periods: the first year of EOM application (Year 1) and after 30 years of continuous EOM use (Year 30). These periods were selected to assess both the short-term and long-term impacts of EOM on cropping systems. In all scenario simulations, N, P, and K applications were adjusted by the tool. The parameters assessed included N supply, P and K balances, and N losses through NO3 leaching, NH₃ volatilization, and nitrous oxide (N₂O) emissions. Additional soil quality indicators, such as soil C storage, available water capacity (awc), soil density (dsoil), and microbial biomass (mmicrobial biomass) were evaluated. The greenhouse gas balance was computed, considering, soil C storage, soil N2O emissions (direct and indirect), GHG emissions related to mineral fertilizer and EOM productions and to fuel consumption. The increase in soil trace metal content was computed for cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), zinc (Zn), mercury (Hg), and chromium (Cr), considering both EOM and mineral P fertilizer input. The target variables were calculated as the difference between the outcomes with EOM application and those with mineral-only treatments within each group of scenarios, except for the P and K balances, which were recorded as absolute values for each scenario. Table 1 presents the equations used to compute these differential effects. Table 1 : Calculation of target variable values: the differences between outcomes under EOM application and mineral fertilizer across scenario groups Equation for the target variables Nutrient availability N supply 𝛥𝑁!"##$%(𝑂𝑊)= 𝑁!"##$%(𝑂𝑊)− 𝑁!"##$%(𝑚𝑖𝑛𝑒𝑟𝑎𝑙) P balance 𝑃&'$'()*(𝑂𝑊) K balance 𝐾&'$'()*(𝑂𝑊) Nitrogen losses Ammonium lixiviation 𝛥𝑁𝐻+$*'),*-(𝑂𝑊)= 𝑁$*'),*-(𝑂𝑊)− 𝑁$*'),*-(𝑚𝑖𝑛𝑒𝑟𝑎𝑙) N ammonia volatilization 𝛥𝑁𝐻./0$'12$*3*-(𝑂𝑊)= 𝑁/0$'12$*3*-(𝑂𝑊)− 𝑁/0$'12$*3*-(𝑚𝑖𝑛𝑒𝑟𝑎𝑙) Nitrous oxide emissions 𝛥𝑁4𝑂*52!!20((𝑂𝑊)= 𝑁4𝑂*52!!20((𝑂𝑊)− 𝑁4𝑂*52!!20((𝑚𝑖𝑛𝑒𝑟𝑎𝑙) Soil quality indicators Carbon storage 𝛥𝐶!106'7*(𝑂𝑊)= 𝐶!106'7*(𝑂𝑊)− 𝐶!106'7*(𝑚𝑖𝑛𝑒𝑟𝑎𝑙) Available water capacity 𝛥𝑎𝑤𝑐8(𝑂𝑊)=𝑎𝑤𝑐(𝑂𝑊)−𝑎𝑤𝑐8(𝑚𝑖𝑛𝑒𝑟𝑎𝑙) Soil density 𝛥6𝑑!02$(𝑂𝑊) = 𝑑!02$(𝑂𝑊) − 𝑑!02$(𝑂𝑊) Microbial biomass 𝛥𝑚52)60&2'$8&205'!!8(𝑂𝑊)= 𝑚52)60&2'$8&205'!!8(𝑂𝑊)− 𝑚52)60&2'$8&205'!!8(𝑚𝑖𝑛𝑒𝑟𝑎𝑙) Environmental impact indicator Elements of trace metals 𝛥𝐸𝑇𝑀!02$(𝑂𝑊) = 𝐸𝑇𝑀!02$(𝑂𝑊)−𝐸𝑇𝑀!02$(𝑚𝑖𝑛𝑒𝑟𝑎𝑙) Greenhouse gase emissions 𝛥𝐺𝐻𝐺*52!!20(8(𝑂𝑊)=𝐺𝐻𝐺*52!!20(8(𝑂𝑊)−𝐺𝐻𝐺*52!!20(8(𝑚𝑖𝑛𝑒𝑟𝑎𝑙) A one-way analysis of variance (ANOVA) was applied to evaluate the effects of the simulation period (Year 1 and Year 30) on N supply, N losses, C storage, and GHG emissions. The ANOVA was conducted using R software (version 4.4.1) with a significance threshold set at P<0.05. The results of the simulations, using all target variables in Table 1, were visualized using boxplots Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 13 According to Figure 2, the average Kbalance across the evaluated EOM scenarios was predominantly negative, with average value of -26.83 kg K₂O ha⁻¹ year⁻¹. Positive Kbalance values were only observed with scenarios with application of horse manure, and cattle manure and slurry digestate in Brittany (Figure A5). All negative Kbalance value suggests that the application of K inputs from EOMs alone may be insufficient to meet the K requirements of crops, potentially leading to K deficiencies and could decrease the crop yield over time if additional K inputs, such as mineral fertilizers, are not applied (Singh V. K. and Dwivedi, 2021). 3.3. Shortand long-term changes in SOC storage and their effects on available water capacity, soil density, and microbial biomass following EOM types Applying different EOMs in the field leads to variations in the amount of carbon (C) added to the soil, which is critical in enriching agricultural soils by significantly increasing SOC levels. In this study, C inputs from EOM to the soil ranged from 110 to 1237 kg C ha⁻¹ year⁻¹ (Figure 2). The highest average annual C input rates, exceeding 750 kg C ha⁻¹ year⁻¹, were observed in the Versailles Plain scenarios with the application of green waste compost and horse manure, in the Alsace and Brittany scenarios with cattle manure, and Wallonia-Belgium scenario with a combination of cattle manure and other EOMs (cattle manure compost, sugar scum, or cattle slurry) (Figure A1). In contrast, biowaste digestate, limed urban sludge in the Versailles Plain, and energy crop digestate in Lauragais scenarios contributed less than 300 kg C ha⁻¹ year⁻¹ in C inputs. These results suggest a marked differentiation in C input levels depending on the EOM scenario and highlight regional disparities, with some regions receiving significantly more C inputs compared to others. As illustrated in Figure 4, the SOC storage simulation results demonstrate a substantial initial contribution to C storage under EOM application, showing an average change in ΔCstorage of 392 kg C ha⁻¹ year⁻¹, compared to mineral treatments during the first year of application. However, after 30 years of EOM application, a marked decline in ΔCstorage was observed, with mean values decreasing to 68 kg C ha⁻¹ year⁻¹(Figure 4). While an amount of C was stored initially, the long-term effectiveness of C storage decreased, suggesting that the soil reached a new equilibrium with regard to the considered C input, leading to minimal long-term carbon gains (Moinet et al., 2023). Furthermore, the results also showed considerable variation in SOC storage depending on the type of EOM applied. Specifically, the application of cattle manure, either alone or in combination with other EOMs, as well as green waste compost and cattle manure slurry digestate, resulted in significantly higher ΔCstorage values, exceeding 400 kg C ha⁻¹ year⁻¹ (Figure 4), with a peak of 792 kg C ha⁻¹ year⁻¹ observed in the first year of application (Figure 2). This initial high storage contribution regressed over the long term, with values between 70 and 176 kg C ha⁻¹ year⁻¹ after 30 years of continuous application of these EOM in cropping systems (Figure 4). For other types of EOMs, contributions to SOC storage followed a similar trend, though with slightly lower initial ΔCstorage values. In scenario involving limed urban sludge, biowaste digestate, pig slurry, pig slurry digestate, or energy crop digestate, the EOM application also contributed to SOC, with slightly lower initial ΔCstorage values varied from 34 to 245 kg C ha⁻¹ year⁻¹ in the first year, subsequently declining to values between 2 and 25 kg C ha⁻¹ year⁻¹ by the 30th year (Figure 4). These findings suggest that the stabilization of C storage is primarily related to EOM characteristics and application rates Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 14 (Levavasseur et al., 2020; Yadav et al., 2024). Consequently, changes in water storage capacity (Δawc), soil density (Δdsoil), and microbial biomass (Δmmicrobial biomass) relative to mineral treatment appeared to vary systematically according to SOC storage levels (Figure 4). The most pronounced changes after 30 years of application were observed in scenarios with cattle manure, with or without combinations of other EOM types, cattle manure and slurry digestate, green waste compost, and green waste and sludge compost compared to limed urban sludge, biowaste digestate, cattle slurry, energy crop digestate, pig slurry, and pig slurry digestate; with Δawc ranging from 1 to 4.48 mm, Δdsoil from -0.05 to -0.02, and Δmmicrobial biomass from 2 500 to 9 515 µg g⁻¹ soil. The simulated increase in awc and the simulated decrease in dsoil remained however limited (Figure 4). 3.4. Variations N losses: nitrate leaching, nitrous oxide emissions, and ammonia volatilization following application of different EOM types The tools simulated the N losses that occur following EOM application, including nitrate (NO3) leaching, nitrous oxide (N2O) emissions, and ammonia (NH3) volatilization, all of which may evolve after the automatic adjustment of mineral N fertilization based on crop needs and simulated N supply. According to Figure 5, the simulation results demonstrated a significant impact of the simulation period on the variation of nitrate leaching (ΔNO3leached), nitrous oxide emissions (ΔN2Oemission), and ammonia volatilization (ΔNH3volatilezed). This indicates that N losses are strongly affected by the repeated application of EOM over time. In the first year of EOM application, the average ΔNO3leached was 1.23 kg N ha⁻¹ year⁻¹, increasing substantially to 4.48 kg N ha⁻¹ year⁻¹ after 30 years of continuous EOM application (Figure 5). These simulations highlight the long-term effect of applying EOMs, which increases SOM storage. The resulting N mineralization from the higher SOM likely drives the observed increase in NO₃ leaching over time, even if mineral N fertilization was adjusted. Therefore, continuous EOM application appears to boost N availability in the soil, potentially leading to higher N losses, especially through NO₃ leaching. Although some reductions were observed, with negative ΔNO3leached values, in scenarios with cattle manure and slurry digestate in Brittany; cattle manure in Alsace; and green waste compost and cattle manure in Gers (Figure A 6). However, scenario with biowaste digestate in the plains of Versailles, pig slurry digestate, cattle slurry digestate, and pig slurry in Brittany, as well as pig slurry in Alsace, showed a high value of ΔNO3leached, ranging from 3.2 to 12.17 kg N ha⁻¹ year⁻¹ (Figure A 6), compared to compost and manure, in the first year of application. These results highlight a potential drawback of EOMs, which supply high N in soil (Figure 3), as they release large amounts of mineral-N, which are readily available to plants but may be prone to leaching if not fully straight away absorbed by the crop, compared to organic amendments that provide a more gradual release of organic-N (Eriksen et al., 2004). By year 30, the increase in ΔNO3leached was considerable, reaching from 5.76 to 30.75 kg N ha⁻¹ year⁻¹, in scenarios with the application of horse manure, green waste compost, and biowaste digestate in the plains of Versailles; cattle manure, pig slurry, pig slurry digestate, and cattle slurry digestate in Brittany; and pig slurry, cattle manure, and green waste and sludge compost in Alsace, where was observed the highest ΔNO3leached exceeded 20 kg N ha⁻¹ year⁻¹ (Figure A 6). Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 15 Figure 4 : Variation in soil carbon storage (ΔCstorage),in the topsoil horizon, in the first year (year 1) and after 30 years (year 30) of external organic matter application (EOM) compared to soil carbon storage in mineral treatment (without EOM); and variation in available water capacity (Δawc), soil density (Δdsoil), and microbial biomass (Δmmicrobial biomass) after 30 years of EOM application compared to the mineral treatment (without EOM) Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 16 Figure 5 : Difference in nitrate lixiviation (ΔNO3leached), nitrous oxide emissions (ΔN2Oemission) , and ammonia volatilization (ΔNH3volatilized), in the first year (year 1) and after 30 years (year 30) between the scenarios with external organic matter application (EOM) and the reference scenarios without EOM Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 17 In parallel, an increase in N2O emissions (ΔN2Oemission) was also observed, with the average for all EOMs rising from 0.158 kg N ha⁻¹ year⁻¹ in the first year to 0.286 kg N ha⁻¹ year⁻¹ after 30 years of EOM applications. These N2O emissions were notably higher in scenarios with the application of biowaste digestate in the plains of Versailles, pig slurry in Alsace, and energy crop digestate in Lauragais, with ΔN2Oemission exceeding 0.4 kg N ha⁻¹ year⁻¹ in both timeframes (Figure A7). This can be associated with the timing of application during the late winter (Table A1) (Li et al., 2024; Winkhart et al., 2024). The scenario with the application of cattle manure, cattle slurry, and cattle manure compost also showed a significant level of ΔN2Oemission (ΔN2Oemission > 0.29 kg N ha⁻¹ year⁻¹), in the first year of application. After 30 years of application, ΔN2Oemission potentially increased in scenarios using horse manure, green waste compost, cattle manure, cattle slurry, cattle manure compost, and certain cattle manure and slurry digestates, as well as pig slurry digestate (Figure A5). These emissions were higher both in comparison to mineral fertilizer alone and relative to ΔN2Oemission observed in the first year (Figure A7). These increases in N2O emissions over time could be attributed to: (1) the characteristics of these organic amendments, which contribute significantly to soil SOM levels and likely promote increased soil N mineralization (Kelley et al., 2024); (2) the crop management systems, particularly if the soil is not adequately covered during periods of higher N availability induced by mineralization. The excess mineralized N, if not absorbed by the crop, can increase NO3 leaching and denitrification losses, ultimately leading to higher N2O emissions (Saha et al., 2021). These findings suggest that effective management of EOM application, particularly in terms of timing and type, is crucial to minimize N losses and mitigate their environmental impact. The studies reviewed provide insights into the factors influencing these emissions and potential mitigation strategies. In contrast, ΔN2Oemission exhibited a potential reduction after 30 years in scenarios with cattle manure, cattle slurry, pig slurry, pig slurry digestate, and cattle manure and slurry digestates. This decrease was observed relative to the 30th-year mineral treatment and compared to the initial year’s ΔN2Oemission (Figure 5). Following the adjustment of mineral N fertilization based on crop needs, over time, EOM applications reduced the reliance on mineral N fertilizers, which are typically associated with increased N2O emissions in the field. In contrast, nitrogen losses through ammonia volatilization (NH3volatilized) exhibited an overall decline, with ΔNH3volatilized decreasing from an initial average of 3.08 kg N ha⁻¹ year⁻¹ in the first year to 1.48 kg N ha⁻¹ year⁻¹ by the 30th year of continuous EOM application (Figure 5). This result suggests that long-term EOM applications can enhance soil N supply while decreasing reliance on mineral N fertilizers, which may have contributed to the observed reduction in NH3 volatilization compared to mineral-only treatments over time. The type of EOM applied also strongly influenced NH3 volatilization rates. For instance, the application of certain EOM types—including green waste compost, limed urban sludge, green waste and sludge compost, biowaste digestate, and cattle manure compost—demonstrated a potential to reduce NH₃ volatilization relative to mineral treatments, even in the first year, with negative ΔNH3volatilized values. Over the 30 years, this reduction became more pronounced, particularly with amendments such as cattle manure, horse manure, biowaste digestate, and energy crop digestate, each showing ΔNH3volatilized values of -10 kg N ha⁻¹ year⁻¹ or lower (Figure A5). This confirms a reduced need for mineral N fertilization, which in turn further limits ammonia Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 18 volatilization. However, the scenarios with cattle slurry, cattle manure and slurry digestate, pig slurry, and pig slurry digestate application were associated with the highest ΔNH3volatilized values due to their high ammonium nitrogen (NH₄-N) content, which is highly susceptible to volatilization. ΔNH3volatilized of these EOM scenarios ranged from 5 to 13 kg N ha⁻¹ year⁻¹ in the first year, and 3 to 12 kg N ha⁻¹ year⁻¹ after 30 years of application (Figure 5). Nonetheless, N volatilization losses are strongly affected by factors such as application timing and immediate incorporation into the soil, both of which can either exacerbate or mitigate ammonia volatilization (Dannenmann et al., 2024; Hwang et al., 2022; Pedersen and Hafner, 2023). 3.5. EOM effect on soil trace element content The application of EOMs introduces varying levels of trace element (TE) into the soil, influenced by the type, dosage, and frequency of EOM application. While the PROLEG tool does not simulate the availability of these metals, it can evaluate their accumulation— specifically for Zn, Cu, Pb, Ni, Hg, Cr, and Cd—as shown in Figure 6. Overall, applications of green waste compost, limed urban sludge, cattle manure with cattle manure compost, cattle manure and slurry digestate contributed the highest content of TE to the soil, with each of these EOM types increasing the concentration of at least three TE compared to other EOM applications (Figure 6). In more detail, zinc levels showed the highest increase among TE following EOM application, with notable accumulation in scenarios with green waste compost, cattle manure, and combinations involving cattle manure with either cattle slurry, cattle manure compost, or sugar scum, where ΔZnₛₒᵢₗ exceeded 0.12 mg kg⁻¹ soil year-1. Copper concentrations also rose significantly, particularly with applications of limed urban sludge, green waste compost, pig slurry, pig slurry digestate, and green waste and sludge compost, as well as cattle manure combined with cattle manure compost, with ΔCuₛₒᵢₗ increases exceeding 0.04 mg kg⁻¹ soil year1. Similarly, lead content in soil increased markedly in scenarios using horse manure, green waste compost, green waste and sludge compost, and cattle manure paired with either sugar scum or cattle manure compost in Belgium, resulting in ΔPbₛₒᵢₗ levels above 0.02 mg kg⁻¹ soil year-1 —substantially higher than other EOMs. For nickel, concentrations increased most prominently under applications of horse manure, green waste compost, limed urban sludge, cattle manure with sugar scum, and cattle manure and slurry digestate, where ΔNiₛₒᵢₗ surpassed 0.0023 mg kg⁻¹ soil year-1. Although mercury levels were generally low, measurable increases occurred with applications of horse manure, green waste compost, limed urban sludge, cattle slurry, cattle manure and slurry digestate, and green waste and sludge compost, with ΔHgₛₒᵢₗ reaching up to 4.8 x 10⁻⁵ mg kg⁻¹ soil year-1. These findings highlight the considerable variability in TE accumulation across different EOM treatments, underscoring the importance of selecting appropriate EOMs to balance agronomic benefits with potential risks of TE buildup in soil. In contrast to other trace elements, cadmium and chromium levels decreased following the application of EOM. The EOM application enhances the P balance in the field, allowing for a reduction in mineral P fertilizer use. The reduction in mineral P inputs has contributed to decreased levels of Cd and Cr in the soil. The effect was particularly pronounced in scenarios Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 19 with cattle manure, cattle manure and slurry digestate, a combination of cattle manure and cattle slurry, pig manure, and pig manure digestates. Under these EOM, the changes in soil Cd (ΔPbₛₒᵢₗ) and Cr (ΔCrₛₒᵢₗ) levels ranged from –0.0012 to –0.0005 mg kg⁻¹ soil year⁻¹ and from – 0.014 to –0.005 mg kg⁻¹ soil year⁻¹, respectively. This suggests that higher P contributions from EOMs reduce the need for mineral P fertilizers, thereby further lowering Cd and Cr contamination in the soil (Mamun et al., 2022; Suciu et al., 2022). Figure 6 : Differences in cadmium (ΔCdsoil), copper (ΔCusoil), nickel (ΔNisoil), lead (ΔPbsoil), zinc (ΔZnsoil), mercury (ΔHgsoil), and chromium (ΔCrsoil) soil contents after 30 years between the scenarios with external organic matter (EOM) application and the reference scenarios (without EOM). Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 20 However, an exception was observed for Cr with the application of green waste compost and green waste and sludge compost, which led to an increase in soil Cr concentrations, with ΔCrₛₒᵢₗ reaching up to 0.32 mg kg⁻¹ soil year-1. This increase can be attributed to the low P content of these composts, necessitating higher mineral P application rates to meet crop P needs and thereby introducing additional Cr into the soil. Furthermore, these composts contain inherently higher levels of Cr, significantly contributing to Cr accumulation in the soil. These findings highlight the dual role of EOMs in nutrient management and contamination risk. While EOMs can reduce reliance on mineral fertilizers and lower Cd and Cr contamination, specific compost types, such as green waste and sludge, may require careful management to prevent excess Cr accumulation. 3.6. Greenhouse gas emissions from different types of EOM: comprehensive comparison with and without storage and treatment The application of EOM to soil is widely recognized as an effective practice for mitigating GHG emissions through C sequestration. Overall, as illustrated in Figure 7, substantial reductions in GHG emissions were observed during the first year of EOM application compared to mineral treatments, without taking into account the GHG emissions from EOM storage and treatment. However, after 30 years of continuous EOM application, the effectiveness of EOM in reducing emissions decreased significantly compared to mineral treatments. When accounting for emissions from storage and treatment, some fields treated with EOM showed an overall increase in emissions with positive ΔGHGemission, highlighting potential trade-offs in sustained EOM application. In the first year of application, the average ΔGHGemission value was -1549.45 kg CO₂ eq ha⁻¹ year⁻¹, but over time, it increased to -394 kg CO₂ eq ha⁻¹ year⁻¹, after 30 years of continuous EOM application without considering the GHG emission from the storage and the treatment (Figure 7). As illustrated in Figure A9, this reduction in the GHG emission balance was primarily driven by the initial increase in SOC storage, and a decrease in GHG emissions associated with NPK fertilizer production for some scenarios (e.g. with application of digestate). However, as SOC stabilized and ΔCstorage also declined over time, this logically led to a lower GHG emission balance with repeated EOM applications and increased ΔGHGemission compared to the first year of application. Additionally, over time, a slight decrease in GHG emissions associated with NPK fertilizer production was observed (Figure A10), following the saving fertilizers in both N or P according to the type of EOM applied. However, a slight increase in N2O also contributed to an increase of ΔGHGemission (Figure A10). In more detail, GHG emission reductions were notably higher in scenarios with cattle manure, cattle manure and slurry digestate, and cattle manure with cattle slurry, with ΔGHGemission values below -2300 kg CO₂ eq ha⁻¹ year⁻¹ (excluding storage and treatment emissions) in Brittany (Figure A9), related to the high ΔCstorage observed in the first year of application. A reduction in ΔGHGemission values, up to -1300 kg CO₂ eq ha⁻¹ year⁻¹ compared to mineral treatments, was also observed with applications of green waste compost and horse manure in the Versailles plain scenarios, cattle manure and green waste and sludge compost in Alsace, and cattle manure with cattle manure compost or sugar scum in Wallonia, Belgium (Figure A9). These results suggest that EOM applications, which significantly contribute to SOC storage in the soil, lead to Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 21 substantial GHG emission reductions, particularly in the initial years of application. Conversely, lower reductions were noted in scenarios with limed urban sludge, biowaste digestate, energy crop digestate, cattle slurry, or pig slurry alone, in the first year of EOM application. Over a 30year application period, the impact of EOM on ΔGHGemission decreased for all scenarios, with reductions ranging from -1051 to 253 kg CO₂ eq ha⁻¹ year⁻¹ depending on the type of EOM (Figure 7). The application of cattle manure, cattle manure and slurry digestate, and cattle manure applied with cattle slurry consistently resulted in greater reductions in GHG emissions compared to other EOM treatments in the long-term effect, regardless of the GHGs emitted during EOM storage and processing Figure 7). However, when accounting for GHG emissions from storage and processing, a similar trend was observed in the initial year, with reductions noted in GHG emissions tied to storage and processing phases. The average ΔGHGemissions value due to storage and treatment were quantified at -459.6 kg CO₂ eq ha⁻¹ year⁻¹. Contrastingly, after 30 years of EOM application, ΔGHGemission increased to 695.9 kg CO₂-eq ha⁻¹ year⁻¹, indicating an accrual of emissions over time. Notably, the highest ΔGHGemissions following 30 years were observed with applications of green waste compost in the Plaine de Versailles, Gers, and Lauragais regions; cattle manure and its compost variant in Wallonia, Belgium; as well as limed sludge, biowaste digestate in the Plaine de Versailles, and energy crop digestate in Lauragais. These applications showed ΔGHGemission values between 990 and 4341 CO₂ eq ha⁻¹ year⁻¹ (Figure A10). These results highlight the short-term GHG mitigation potential of EOM applications, although long-term sustainability depends on storage and treatment practices as well as the specific type of EOM used. This data suggests that emissions from storage and treatment may progressively offset the GHG mitigation potential of EOM applications. These insights prompt a broader question on the allocation of process emissions to agricultural sectors, particularly when processing urban waste. Indeed, in the emissions inventory used in PROLEG (Yadav et al., 2024); whereas most of the emissions related to manure were not allocated to manure but rather to breeding activities, emissions during composting of urban waste were allocated to the compost. Additionally, it raises the consideration of whether alternative uses for these EOMs could yield a different GHG emissions profile. Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 22 Figure 7 : Differences in greenhouse gas (GHG) emissions under EOM application and mineral fertilizers (without EOM application), in the first year of EOM application (Year 1)and after 30 years of EOM application, with and without the GHG emission from the treatment and storage. Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 29 Winkhart, F., Schmid, H., Hülsbergen, K.-J., 2024. Effects of Biogas Digestate on Winter Wheat Yield, Nitrogen Balance, and Nitrous Oxide Emissions under Organic Farming Conditions. Agronomy 14. https://doi.org/10.3390/agronomy14081739 Wysocka-Czubaszek, A., 2019. Dynamics of Nitrogen Transformations in Soil Fertilized with Digestate From Agricultural Biogas Plant. Journal of Ecological Engineering 20, 108–117. https://doi.org/10.12911/22998993/93795 Yadav, R.K., Purakayastha, T.J., Bhaduri, D., Das, R., Dey, S., Sukumaran, S., Walia, S.S., Singh, R., Shukla, V.K., Yadava, M.S., Ravisankar, N., 2024. Development of unique soil organic carbon stability index under influence of integrated nutrient management in four major soil orders of India. J Environ Manage 360, 121208. https://doi.org/https://doi.org/10.1016/j.jenvman.2024.121208 Zhou, Z., Zhang, S., Jiang, N., Xiu, W., Zhao, J., Yang, D., 2022. Effects of organic fertilizer incorporation practices on crops yield, soil quality, and soil fauna feeding activity in the wheat-maize rotation system. Front Environ Sci 10. https://doi.org/10.3389/fenvs.2022.1058071 Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 30 SUPPLEMENTARY INFORMATION LIST OF TABLES Table A 1 : Overview of external organic matter (EOM) applications in cropping systems: position in rotation, fresh matter doses per hectare, and application periods across regions of France, Wallonia-Belgium, Turkey, Lithuania, and the Netherlands ......................................... ii LIST OF FIGURES Figure A 1 : Nitrogen (N), phosphorus (P), and carbon (C) input rates according to the doses of external organic matter (EOM) applied to cropping systems. Letters A to N follow the EOM types on the x-axis, representing scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2. Nutrient quantities applied were calculated based on the EOM doses in each cropping system, as detailed in Table A1. ................................................................................................................................. i Figure A 2 : Increase in soil N supply, at the initial situation and after 30 years of EOM application compared to the soil N supply in the mineral treatment (without EOM) in each simulated scenario group. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2 ................................................................................................................................................. viii Figure A 3 : Added nitrogen (N) supply and carbon (C) storage per hectare per year from the application of external organic matter (EOM) compared to mineral treatments. ..................... ix Figure A 4 : Annual phosphorus (P) balance all fields receiving mineral fertilizer (without EOM) and under EOM application. Letters A to N represent scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2 ........................................................................................................................................ x Figure A 5 : Annual potassium (K) balance all fields receiving mineral fertilizer (without EOM) and under EOM application. Letters A to N represent scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2 ................................................................................................................................................... xi Figure A 6 : Increase in nitrate (N3O) leaching, at the initial situation and after 30 years of EOM application compared to the leached N in the mineral treatment (without EOM) in each simulated scenario group. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2 .................................................................................................................................................. xii Figure A 7 : Increase in nitrous oxide (N2O) emissions, at the initial situation and after 30 years of OW application compared to the leached N in the mineral treatment (without OW) in each simulated scenario group. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2. ................................................................................................................................................. xiii Figure A 8 : Increase in ammonia (NH3) volatilization, at the initial situation and after 30 years of EOM application compared to the leached N in the mineral treatment (without EOM) in each Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use 31 simulated scenario group. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2. ................................................................................................................................................. xiv Figure A 9 : Differences in greenhouse gas (GHG) emissions under EOM application and mineral fertilizers (without EOM application), in the first year of EOM application (Year 1), without the GHG emission from the treatment and storage. The stacked bars indicate the different sources of GHG in the cropping system. The red point in each bar represents the total values of GHG emissions for each treatment. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2 .................................................................................................................. xv Figure A 10 : Differences in greenhouse gas (GHG) emissions under EOM application and mineral fertilizers (without EOM application), after 30 years of EOM application (Year 30), without the GHG emission from the treatment and storage . The stacked bars indicate the different sources of GHG in the cropping system. The red point in each bar represents the total values of GHG emissions for each treatment. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2. ................................................................................................................ xvi Figure A 11 : Differences in greenhouse gas (GHG) emissions under EOM application and mineral fertilizers (without EOM application), in the first year of EOM application (Year 1), with the GHG emission from the treatment and storage. The stacked bars indicate the different sources of GHG in the cropping system. The red point in each bar represents the total values of GHG emissions for each treatment. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2 ................................................................................................................ xvii Figure A 12 : Differences in greenhouse gas (GHG) emissions under EOM application and mineral fertilizers (without EOM application), after 30 years of EOM application (Year 30), with the GHG emission from the treatment and storage . The stacked bars indicate the different sources of GHG in the cropping system. The red point in each bar represents the total values of GHG emissions for each treatment. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2. .............................................................................................................. xviii Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use i Figure A 1 : Nitrogen (N), phosphorus (P), and carbon (C) input rates according to the doses of external organic matter (EOM) applied to cropping systems. Letters A to N follow the EOM types on the x-axis, representing scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2. Nutrient quantities applied were calculated based on the EOM doses in each cropping system, as detailed in Table A1. Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use ii Table A 1 : Overview of external organic matter (EOM) applications in cropping systems: position in rotation, fresh matter doses per hectare, and application periods across regions of France, Wallonia-Belgium, Turkey, Lithuania, and the Netherlands Cropping systems: doses, application period and stage France Vers ail les pl ain - A Rapeseed Winter wheat Mustard (b) Grain maize Winter wheat Winter barley Biowaste digestate 14 t ha-1 Before the sowing Late summer 20 t ha-1 At the early stem elongation stage Early spring Green waste compost 30 t ha -1 At the early vegetative stage Late summer Horse manure 20 t ha -1 At the early vegetative stage Late summer Limed urban sludge 10 t ha-1 Before the sowing Late summer Vers ail les pl ain - B Rapeseed Winter wheat Winter wheat Winter barley Biowaste digestate 14 t ha-1 Before the sowing Late summer 20 t ha -1 At the early stem elongation stage Late summer Green waste compost 30 t ha-1 Before the sowing Late summer Horse manure 20 t ha-1 Before the sowing Late summer Limed urban sludge 10 t ha -1 Before the sowing Late summer (a) Group refers to the scenario group within each region based on variations in cropping systems and soil types used in the simulation. (b) Nitrogen catch crop (c) Energy cover crop Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use iii Cropping systems : Doses, application period and stage France Brittany - C & D Winter wheat Mustard (b) Silage maize Cattle manure 30 t ha -1 Before the sowing Early spring Cattle manure & cattle slurry Cattle manure 20 t ha-1 Before the sowing Early spring Cattle slurry 18 t ha-1 Before the sowing Spring Cattle manure & slurry digestate 30 t ha-1 At the early stem elongation stage Early spring 30 t ha -1 Before the sowing Spring Cattle slurry 45 t ha -1 Before the sowing Spring Pig slurry 27 t ha-1 At the early stem elongation stage Early spring 27 t ha -1 At the sowing Spring Pig slurry digestate 30 t ha-1 At the early stem elongation stage Early spring 30 t ha-1 Before the sowing Spring Brittany - E & F Winter wheat Rye or barley (c) Silage maize Cattle manure 30 t ha-1 At the early stem elongation stage Early spring Cattle manure & slurry digestate 30 t ha-1 At the early stem elongation stage Late winter 30 t ha -1 Before the sowing Spring Cattle slurry 45 t ha-1 Before the sowing Early spring Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use iv Table A 1 (continued ) : Overview of EOM applications in cropping systems: position in rotation, fresh doses per hectare, and application periods across regions of France, Wallonia-Belgium, Turkey, Lithuania, and the Netherlands Cropping systems : Doses, application period and stage France Alsace - G & H Grain maize Grain maize Grain maize Winter wheat Mustard (b) Soyabean Cattle manure 22 t ha-1 Before the sowing Early autumn 23 t ha-1 Before the sowing Summer Green waste & sludge compost 10 t ha -1 Before the sowing Early autumn 10 t ha -1 Before the sowing Summer Pig slurry 31.5 t ha-1 Before the sowing Late winter 31.5 t ha-1 Before the sowing Late winter 31.5 t ha -1 Before the sowing Late winter GersI Grain maize Grain maize Grain maize Cattle manure 20 t ha-1 Before the sowing Late winter 20 t ha-1 Before the sowing Late winter 20 t ha -1 Before the sowing Late winter Green waste compost 9 t ha-1 Before the sowing Late winter LauragaisJ Durum wheat Sunflower Green waste compost 30 t ha-1 Before the winter Early autumn LauragaisK Durum wheat Sunflower Rye or barley (c) Energy crop digestate-1 25 t ha-1 Before the sowing Early autumn Energy crop digestate-2 25 t ha -1 At the early stem elongation stage Early spring (a) Group refers to the scenario group within each region based on variations in cropping systems and soil types used in the simulation. (b) Nitrogen catch crop (c) Energy cover crop Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use v Table A 1 (continued ) : Overview of EOM applications in cropping systems: position in rotation, fresh doses per hectare, and application periods across regions of France, Wallonia-Belgium, Turkey, Lithuania, and the Netherlands Cropping systems : Doses, application period and stage Belgium Wallonia BrugeletteL Mixture of legumes (b) Sugar beet Winter wheat Mixture of legumes (b) Consumption potatoes Winter wheat Cattle slurry & Cattle manure Cattle manure 20 t ha-1 Before the sowing Summer Cattle slurry 15 t ha-1 Before the sowing Early spring Cattle manure 15 t ha-1 Before the sowing Summer Wallonia AwansM Winter wheat Rapeseed Crucifers (b) Consumption potatoes Winter wheat Mixture of legumes (b) Peas for canning Mixture of legumes (b) Sugar beet Cattle manure & Cattle manure compost Cattle manure compost 25 t ha-1 Before the sowing Summer Cattle manure 25 t ha-1 Before the sowing Late summer Wallonia Huy (Solières) - N Mixture of legumes (b) Sugar beet Winter wheat Rapeseed Mixture of legumes (b) Silage maize Winter wheat Cattle manure & Sugar scum Cattle manure 20 t ha-1 Before the sowing Summer Cattle manure 20 t ha-1 Before the sowing Summer Sugar scum 10 t ha-1 Before the sowing Summer (a) Group refers to the scenario group within each region based on variations in cropping systems and soil types used in the simulation. (b) Nitrogen catch crop (c) Energy cover crop Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use vi Cropping systems : Doses, application period and stage Turkey Adana – O & P Winter wheat Grain maize Certified compost Turkey 5 t ha-1 Before the sowing Early autumn 5 t ha-1 Before the sowing Autumn Winter wheat Sunflower 5 t ha-1 Before the sowing Early autumn Winter wheat Grain maize Cattle manure 20 t ha-1 Before the sowing Autumn 20 t ha-1 Before the sowing Late spring Winter wheat Sunflower 20 t ha-1 Before the sowing Early autumn Lithuania Anykščiai - Eastern Winter wheat Spring barley Cosumption potatoes Spring rapeseed Green waste compost 23.7 t ha-1 Before the sowing Early spring 23.7 t ha -1 Before the sowing Early spring 23.7 t ha-1 Before the sowing Spring Green waste and sludge compost 15.5 t ha-1 Before the sowing Early spring 15.5 t ha -1 Before the sowing Early spring 15.5 t ha-1 Before the sowing Spring Cattle manure compost 44.5 t ha-1 Before the sowing Early spring 44.5 t ha -1 Before the sowing Early spring 44.5 t ha-1 Before the sowing Early spring Poultry manure 16.5 t ha -1 Before the sowing Early spring 16.5 t ha -1 Before the sowing Early spring 16.5 t ha -1 Before the sowing Early spring (a) Group refers to the scenario group within each region based on variations in cropping systems and soil types used in the simulation. (b) Nitrogen catch crop (c) Energy cover crop Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use vii Table A 1 (continued ) : Overview of EOM applications in cropping systems: position in rotation, fresh doses per hectare, and application periods across regions of France, Wallonia-Belgium, Turkey, Lithuania, and the Netherlands Cropping systems : Doses, application period and stage Netherlands Drenthe Starch potatoes Spring barley Grass cover crop (b) Starch potatoes Sugar beet Pig slurry 20 t ha-1 Before the sowing Early spring 20 t ha-1 Before the sowing Early spring 20 t ha-1 Before the sowing Early spring Green waste compost & pig slurry Green waste compost 20 t ha-1 Before the sowing Early spring Pig slurry 20 t ha-1 Before the sowing Spring Green waste compost 20 t ha-1 Before the sowing Early spring Pig slurry 20 t ha-1 Before the sowing Spring Green waste compost 20 t ha-1 Before the sowing Early spring Pig slurry 20 t ha-1 Before the sowing Spring (a) Group refers to the scenario group within each region based on variations in cropping systems and soil types used in the simulation. (b) Nitrogen catch crop (c) Energy cover crop Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use xiv Figure A 8 : Increase in ammonia (NH3) volatilization, at the initial situation and after 30 years of EOM application compared to the leached N in the mineral treatment (without EOM) in each simulated scenario group. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2. Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use xv Figure A 9 : Differences in greenhouse gas (GHG) emissions under EOM application and mineral fertilizers (without EOM application), in the first year of EOM application (Year 1), without the GHG emission from the treatment and storage. The stacked bars indicate the different sources of GHG in the cropping system. The red point in each bar represents the total values of GHG emissions for each treatment. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2 Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use xvi Figure A 10 : Differences in greenhouse gas (GHG) emissions under EOM application and mineral fertilizers (without EOM application), after 30 years of EOM application (Year 30), without the GHG emission from the treatment and storage. The stacked bars indicate the different sources of GHG in the cropping system. The red point in each bar represents the total values of GHG emissions for each treatment. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2. Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use xvii Figure A 11 : Differences in greenhouse gas (GHG) emissions under EOM application and mineral fertilizers (without EOM application), in the first year of EOM application (Year 1), with the GHG emission from the treatment and storage. The stacked bars indicate the different sources of GHG in the cropping system. The red point in each bar represents the total values of GHG emissions for each treatment. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2. Deliverable WP6 – T1. Mul3criteria evalua3on system for EOMs use xviii Figure A 12 : Differences in greenhouse gas (GHG) emissions under EOM application and mineral fertilizers (without EOM application), after 30 years of EOM application (Year 30), with the GHG emission from the treatment and storage. The stacked bars indicate the different sources of GHG in the cropping system. The red point in each bar represents the total values of GHG emissions for each treatment. Letters A to N represent the scenario groups within each region based on variations in cropping systems and soil types used in the simulation, as indicated in Table 2.