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The present European Union (EU) Sewage Sludge Directive (86/278/EEC) is undergoing modifications aimed at enhancing its applicability in the agricultural sector. The Directive's existing limit values for heavy metal concentrations in soils are in the process of being revised. However, to comprehensively understand their effects on EU agricultural lands, additional evaluations are necessary. This is particularly important given that ecological risk assessments are often performed on a site-specific basis, potentially overlooking broader regional implications. The main objective of the current work is to introduce a methodological approach to quantify the impact of sewage sludge (SS) application on agricultural soils in the EU and the United Kingdom. Concentrations of heavy metals (HMs) (Cd, Cu, Hg, Ni, Pb and Zn) in agricultural land from Land Use/Land Cover Area Frame Survey (LUCAS) 2009 topsoil database were used as a baseline. Maximum quantities of SS that can be safely applied to agricultural lands were obtained by a modeling procedure was used to determine the maximum safe quantities of SS that can be applied to agricultural lands for each country within the European Member States and the United Kingdom. Accumulation of HMs in soils was modelled by using a representative SS composition, distributed over 10 successive years at 5 Mg ha−1 year−1 rate. Ecological risk impact was assessed by using both the single ecological risk index (Er) and the integrated potential ecological risk index (RI). Maximum quantities of SS applied on agricultural soils in EU + UK were estimated to be 45 Mg ha−1 at the country level. We found that 19% of agricultural land (around 28,471,900 ha) in the EU + UK shows a higher RI than moderate risk after long time application of the representative SS. We show that the combination of the HM concentrations from the LUCAS topsoil survey and assumptions on the SS composition and soil HM partitioning can be used to define the actual and potential soil pollution rate in EU + UK. We demonstrate that the proposed methodology can be used by policymakers, farmers, regional authorities and other stakeholders, with possible adaptions based on local in-depth soil and SS knowledge.

Ecological risk assessment of heavy metals from application of sewage sludge on agricultural soils in Europe - Yunta - 2024 - European Journal of Soil Science - Wiley Online Library

Soil protists have vital roles as major microbiome predators in soil functioning and plant performance. Protists are also suggested to be the most responsive microbial group to external changes, such as anthropogenic land use types. While protists were long used as models for biogeography such as to investigate if ‘everything is everywhere’ among microbes, their biogeography at the taxonomic level has never been explored in depth at the continental scale and linked to anthropogenic drivers. Here we evaluated how land-use types affect the diversity and structure of soil protist communities across 885 locations in Europe based on the European Commission's Land Use and Coverage Area frame Survey (LUCAS). We observed higher α-diversity of soil protists but lower community structure dissimilarity (β-diversity) in croplands compared with woodlands, with grasslands in an intermediate position. The diversity of protist groups with a broader spatial niche was higher in croplands, whereas taxa with a narrower niche increased in woodlands. The importance of climate factors on α-diversity variations reduced as land use intensity increased, but the opposite trend was observed for the effect of soil properties. Our study suggests that there is an interaction between land use type, environmental effects, and spatial niche attributes of soil protist groups, highlighting the importance of land-use type on the dynamics of protist communities.

https://www.sciencedirect.com/science/article/pii/S0038071724001482

Soil microbial biomass and activity strongly depend on land use, vegetation cover, climate, and soil physicochemical properties. In most cases, this dependence was assessed by one-to-one correlations while by employing network analysis, information about network robustness and the balance between stochasticity and determinism controlling connectivity, was revealed. In this study, we further elaborated on the hypothesis of Smith et al. (2021) that cropland soils depended more on climate variables and therefore are more vulnerable to climate change. We used the same dataset with that of Smith et al. (2021) that contains seasonal microbial, climate and soil variables collected from 881 soil points representing the main land uses in Europe: forests, grassland, cropland. We examined complete (both direct and indirect relationships) and incomplete networks (only direct relationships) and recorded higher robustness in the former. Partial Least Square results showed that on average more than 45% of microbial attributes' variability was predicted by climate and habitat drivers denoting medium to strong effect of habitat filtering. Network architecture slightly affected by season or land use type; it followed the core/periphery structure with positive and negative interactions and no hub nodes. Microbial attributes (biomass, activity and their ratio) mostly belong to core block together with Soil Organic Carbon (SOC), while climate and soil variables to periphery block with the exception of cropland networks, denoting the higher dependence between microbial and climate variables in these latter. All complete networks appeared robust except for cropland and forest in summer, a finding that disagrees with our initial hypothesis about cropland. Networks' connectivity was controlled stronger by stochasticity in forest than in croplands. The lack of human interventions in forest soils increase habitat homogeneity enhancing the influence of stochastic agents such as microbial unlimited dispersal and/or stochastic extinction. The increased stochasticity implies the necessity for proactive management actions.

Connections between soil microbes, land use and European climate: Insights for management practices - ScienceDirect

Soil loss by water erosion represents a key threat to land degradation worldwide. This study employs an integrated quantitative modelling approach to estimate the long-term global sustainability impacts of soil erosion. The global biophysical model estimates a mean increase of soil erosion rates of between 30-66% over the period 2015-2070 under alternative climate-economic scenarios, assuming different greenhouse gas concentration trajectories. In a subsequent step, projected soil erosion rates are converted into land productivity losses and inputted into an economic global simulation model to identify those regional hotspots where the greatest market tensions are expected to occur.The headline result is that soil erosion presents a major challenge to food security in vulnerable regions (Africa and some tropical regions). Indeed, for certain crops (particularly oilseeds) the threat of shortages is potentially significant. Secondly, exploring different long-term socioeconomic-environmental pathways quantitatively confirms the merits of sustainable management practises in coping with anticipated market and environmental stresses arising from soil erosion. Finally, free (and fair) trade is essential to allow less affected regions to expand (marginally) their production, thereby cushioning the market tensions that are expected to occur in more acutely affected areas of the world.

Remaining Loyal to Our Soil: A Prospective Integrated Assessment of Soil Erosion on Global Food Security by Martina Sartori, Emanuele Ferrari, Robert M'Barek, G. Philippidis, Kirsten Boysen-Urban, Pasquale Borrelli, Luca Montanarella, Panos Panagos :: SSRN

The use of cover crops (CCs) is a promising cropland management practice with multiple benefits, notably in reducing soil erosion and increasing soil organic carbon (SOC) storage. However, the current ability to represent these factors in land surface models remains limited to small scales or simplified and lumped approaches due to the lack of a sediment-carbon erosion displacement scheme. This precludes a thorough understanding of the consequences of introducing a CC into agricultural systems. In this work, this problem was addressed in two steps with the spatially distributed CE-DYNAM model.First, the historical effect of soil erosion, transport, and deposition on the soil carbon budget at a continental scale in Europe was characterized since the early industrial era, using reconstructed climate and land use forcings. Then, the impact of two distinct policy-oriented scenarios for the introduction of CCs were evaluated, covering the European cropping systems where surface erosion rates or nitrate susceptibility are critical. The evaluation focused on the increase in SOC storage and the export of particulate organic carbon (POC) to the oceans, compiling a continental-scale carbon budget. The results indicated that Europe exported 1.95 TgC/year of POC to the oceans in the last decade, and that CCs can contribute to reducing this amount while increasing SOC storage. Compared to the simulation without CCs, the additional rate of SOC storage induced by CCs peaked after 10 years of their adoption, followed by a decrease, and the cumulative POC export reduction stabilized after around 13 years. The findings indicate that the impacts of CCs on SOC and reduced POC export are persistent regardless of their spatial allocation adopted in the scenarios. Together, the results highlight the importance of taking the temporal aspect of CC adoption into account and indicate that CCs alone are not sufficient to meet the targets of the 4‰ initiative. Despite some known model limitations, which include the lack of feedback of erosion on the net primary productivity and the representation of carbon fluxes with an emulator, the current work constitutes the first approach to successfully couple a distributed routing scheme of eroded carbon to a land carbon model emulator at a reasonably high resolution and continental scale. SHORT ABSTRACT: A spatially distributed model coupling erosion, transport, and deposition to the carbon cycle was developed. Then, it was used to simulate the impact of cover crops on both erosion and carbon, to show that cover crops can simultaneously increase organic carbon storage and reduce particulate organic carbon export to the oceans. The results seemed persistent regardless of the spatial distribution of cover crops.

Including land management in a European carbon model with lateral transfer to the oceans - PubMed

Nowadays, there is contrasting evidence between the ongoing continuing and widespread environmental degradation and the many means to implement environmental sustainability actions starting from good policies (e.g. EU New Green Deal, CAP), powerful technologies (e.g. new satellites, drones, IoT sensors), large databases and large stakeholder engagement (e.g. EIP-AGRI, living labs). Here, we argue that to tackle the above contrasting issues dealing with land degradation, it is very much required to develop and use friendly and freely available web-based operational tools to support both the implementation of environmental and agriculture policies and enable to take positive environmental sustainability actions by all stakeholders. Our solution is the S-DSS LANDSUPPORT platform, consisting of a free web-based smart Geospatial CyberInfrastructure containing 15 macro-tools (and more than 100 elementary tools), co-designed with different types of stakeholders and their different needs, dealing with sustainability in agriculture, forestry and spatial planning. LANDSUPPORT condenses many features into one system, the main ones of which were (i) Web-GIS facilities, connection with (ii) satellite data, (iii) Earth Critical Zone data and (iv) climate datasets including climate change and weather forecast data, (v) data cube technology enabling us to read/write when dealing with very large datasets (e.g. daily climatic data obtained in real time for any region in Europe), (vi) a large set of static and dynamic modelling engines (e.g. crop growth, water balance, rural integrity, etc.) allowing uncertainty analysis and what if modelling and (vii) HPC (both CPU and GPU) to run simulation modelling ‘on-the-fly’ in real time. Two case studies (a third case is reported in the Supplementary materials), with their results and stats, covering different regions and spatial extents and using three distinct operational tools all connected to lower land degradation processes (Crop growth, Machine Learning Forest Simulator and GeOC), are featured in this paper to highlight the platform's functioning. Landsupport is used by a large community of stakeholders and will remain operational, open and free long after the project ends. This position is rooted in the evidence showing that we need to leave these tools as open as possible and engage as much as possible with a large community of users to protect soils and land.

Land Degradation & Development | Environmental & Soil Science Journal | Wiley Online Journal

In the European Union (EU), a common understanding of the potential harmful effect of sewage sludge (SS) on the environment is regulated by the Sewage Sludge Directive 86/278/EEC (SSD). Limit values (LVs) for concentrations of heavy metals in soil are listed in Impact Assessment of this directive, and they were transposed by EU member states using different criteria. Member states adopted either single limit values or based on soil factors such as pH and texture to define the maximum limit values for concentrations of heavy metals in soils. Our work presents the first quantitative analysis of the SSD at the European level by using the Land Use and Coverage Area Frame Survey (LUCAS) 2009 topsoil database. The reference values at the European level were arranged taking into account the upper value (EU_UL) and the lower value (EU_LL) for each heavy metal (arsenic, cadmium, copper, chromium, mercury, nickel, lead, and zinc) as well as taking into account the pH of the soil (cadmium, copper, mercury, nickel, lead, and zinc) as introduced in the SSD Annex IA. Single and integrated contamination rate indices were developed to identify those agricultural soils that exceeded the reference values for each heavy metal. In total, 10%, 36%, and 19% of the LUCAS 2009 topsoil samples exceeded the limit values. Additionally, 12% and 16% of agricultural soils exceeded the concentration of at least one single heavy metal when European LVs were fixed following the soil pH in Strategy II compared to those national ones in Strategy I. Generally, all member states apply similar or stricter limit values than those laid down in the SSD. Our work indicates that choosing LVs quantitatively affects further actions such as monitoring and remediation of contaminated soils. The actual soil parameters, such as heavy metal concentrations and soil pH values from the LUCAS 2009 topsoil database, could be used by SSD-involved policy stakeholders not only to lay down the LVs for concentrations of heavy metal in soils but also for monitoring the SSD compliance grade by using the LUCAS surveys over time (past and upcoming LUCAS datasets).

https://link.springer.com/article/10.1007/s11356-024-31835-y

Future phosphorus (P) shortages could seriously affect terrestrial productivity and food security. We investigated the changes in topsoil available P (AP) and total P (TP) in China's forests, grasslands, paddy fields, and upland croplands during the 1980s–2010s based on substantial repeated soil P measurements (63,220 samples in the 1980s, 2000s, and 2010s) and machine learning techniques. Between the 1980s and 2010s, total soil AP stock increased with a small but significant rate of 0.13 kg P ha−1 year−1, but total soil TP stock declined substantially (4.5 kg P ha−1 year−1) in the four ecosystems. We quantified the P budgets of soil–plant systems by harmonizing P fluxes from various sources for this period. Matching trends of soil contents over the decades with P budgets and fluxes, we found that the P-surplus in cultivated soils (especially in upland croplands) might be overestimated due to the great soil TP pool compared to fertilization and the substantial soil P losses through plant uptake and water erosion that offset the P additions. Our findings of P-deficit in China raise the alarm on the sustainability of future biomass production (especially in forests), highlight the urgency of P recycling in croplands, and emphasize the critical role of country-level basic data in guiding sound policies to tackle the global P crises.

Global Change Biology | Environmental Change Journal | Wiley Online Library

The EU Soil Strategy 2030 aims to increase soil organic carbon (SOC) in agricultural land to enhance soil health and support biodiversity as well as to offset greenhouse gas emissions through soil carbon sequestration. Therefore, the quantification of current SOC stocks and the spatial identification of the main drivers of SOC changes is paramount in the preparation of agricultural policies aimed at enhancing the resilience of agricultural systems in the EU. In this context, changes of SOC stocks (Δ SOCs) for the EU + UK between 2009 and 2018 were estimated by fitting a quantile generalized additive model (qGAM) on data obtained from the revisited points of the Land Use/Land Cover Area Frame Survey (LUCAS) performed in 2009, 2015 and 2018. The analysis of the partial effects derived from the fitted qGAM model shows that land use and land use change observed in the 2009, 2015 and 2018 LUCAS campaigns (i.e. continuous grassland [GGG] or cropland [CCC], conversion grassland to cropland (GGC or GCC) and vice versa [CGG or CCG]) was one of the main drivers of SOC changes. The CCC was the factor that contributed to the lowest negative change on Δ SOC with an estimated partial effect of −0.04 ± 0.01 g C kg−1 year−1, while the GGG the highest positive change with an estimated partial effect of 0.49 ± 0.02 g C kg−1 year−1. This confirms the C sequestration potential of converting cropland to grassland. However, it is important to consider that local soil and environmental conditions may either diminish or enhance the grassland's positive effect on soil C storage. In the EU + UK, the estimated current (2018) topsoil (0–20 cm) SOC stock in agricultural land below 1000 m a.s.l was 9.3 Gt, with a Δ SOC of −0.75% in the period 2009–2018. The highest estimated SOC losses were concentrated in central-northern countries, while marginal losses were observed in the southeast.

Global Change Biology | Environmental Change Journal | Wiley Online Library

Phosphorus (P) is an essential nutrient for all crops, yet its excess negatively affects public health, the environment, and the economy. At the same time, rock P is a critical raw material due to its importance for food production, the finite geological deposits, and its unequal regional distribution. As a consequence, nutrient management is addressed by numerous environmental policies. Process-based biogeochemical models are valuable instruments to monitor the P cycle and predict the effect of agricultural management policies. In this study, we upscale the calibrated DayCent model at European level using data-derived soil properties, advanced input data sets, and representative management practices. Our results depicted a P budget with an average P surplus (0.11 kg P ha−1 year−1), a total soil P (2240.0 kg P ha−1), and available P content (77.4 kg P ha−1) consistent with literature and national statistics. Through agricultural management scenarios, we revealed a range of potential changes in the P budget by 2030 and 2050, influenced by the interlink of P with biogeochemical carbon and nitrogen cycles. Thus, we developed a powerful assessment tool capable of i) identifying areas with P surplus or deficit at high spatial resolution of 1 km2, (ii) pinpointing areas where a change in agricultural management would be most urgent to reach policy goals in terms of environmental pollution, food security and resource efficiency of a critical raw material, and iii) assessing the response of the P cycle to modifications in agricultural management.

https://www.sciencedirect.com/science/article/pii/S0048969723057704

Excavated soil and rock (ESR) and dredging spoils (DDS) account for 23 % of the total EU waste generation in 2020. This study performs a life cycle assessment and life cycle costing to quantify the potential environmental and cost savings resulting from increasing the level of ESR and DDS prepared for reuse and recycled in comparison to the business-as-usual practice. Scenarios for the waste management pathways based on the status quo, technical feasibility or normative impositions are assessed, including the potential contribution to achieving the European Green Deal goals. Results show that promoting preparing for reuse and recycling could lead to non-negligible GHG reductions (up to 3.6 Mt. CO2 eq.) and economic savings (EUR 12.3 billion) annually. Depending upon the scenario, 0.2 % to 1 % of the net annual GHG emissions reductions sought by the European Green Deal could be facilitated by scaling up improved circular management of ESR and DDS at the EU level. Finally, the study highlights the main barriers to scaling up to more circular (i.e., preparing for reuse and recycling) and better performing management options in Europe. The results provide new insights for the European Green Deal and circular economy policymaking for CDW.

Management of excavated soil and dredging spoil waste from construction and demolition within the EU: Practices, impacts and perspectives - ScienceDirect

The so-called soil-landscape model is the central paradigm which relates soil types to their forming factors through the visionary Jenny’s equation. This is a formal mathematical expression that would permit to infer which soil should be found in a specific geographical location if the involved relationship was sufficiently known. Unfortunately, Jenny’s is only a conceptual expression, where the intervening variables are of qualitative nature, not being then possible to work it out with standard mathematical tools. In this work, we take a first step to unlock this expression, showing how Machine Learning can be used to predictably relate soil types and environmental factors. Our method outperforms other conventional statistical analyses that can be carried out on the same forming factors defined by measurable environmental variables.

Disentangling Jenny’s equation by machine learning | Scientific Reports