Publications in Journals

Policy making and soil security require to address the sustainable management of soil resources at the landscape level. Therefore, geopedology can become highly relevant for effective policy making for achieving long term soil protection. The necessary pre-condition is the availability of a solid scientific basis and detailed data on the actual status and trends of soils within relevant landscapes. The recent emergence of high-resolution digital soil mapping techniques offers new possibilities for achieving such a knowledge base. Several examples from the European Union demonstrate that geopedology can be a valuable tool for understanding soil processes at the landscape level and design effective soil protection policies.

https://doi.org/10.1007/978-3-031-20667-2_25

Micro- and nanoplastics (MNPs) emissions and pollution are a growing concern due to their potential impact on ecosystems and human health, particularly in soil. This study conducts a comprehensive bibliometric analysis of 2,451 publications spanning from 2006 to 2023. The aim is to assess the research landscape, trends, contributors, and collaborative efforts related to MNPs in soil. Moreover, it examines the extensive research on the effects of MNPs on soil organisms, including earthworms, nematodes, and other fauna as well as the physical–chemical impacts, nanoscale interactions, and ecotoxicological effects on soil microorganisms. Utilizing network analysis, this study explores the global distribution of research across countries, institutions, authors, and keywords, shedding light on the interconnected scientific exploration. The findings reveal a consistent rise in research output over the past decade, reflecting worldwide interest in soil MNPs pollution. It also identifies influential authors and interdisciplinary clusters, highlighting their significant collaborations. Moreover, it pinpoints key institutions and leading journals in this area. Keyword co-occurrence and time-series analysis uncover seven significant research clusters. All provide insights into crucial MNPs aspects and their environmental and health implications. Our findings guide future research and inform strategies to combat MNPs pollution in soils, underscore the need for interdisciplinary approaches to address this complex challenge. In essence, our comprehensive bibliometric analysis serves as a valuable resource, it benefits researchers, policy stakeholders by promoting further research and guiding strategies to mitigate MNPs pollution in soils, in support of ecosystem preservation and human health protection.

https://doi.org/10.1016/j.ecolind.2023.111109

Camelina (C. sativa L. Crantz) is a promising biofuel crop with high potential for cultivation on marginal soils. In this work, seed yields have been modelled to assess suitable areas based on experimental field trials, meteorological data from the Monitoring Agricultural ResourceS (MARS) gridded agro-meteorological in Europe, soil properties from LUCAS, topography and land cover. Potential yields for Camelina-Barley rotation (CAMBAR) were modelled for the past 20 years using the mechanistic crop growth model ARMOSA that can estimate quantitatively several soil and water parameters and future forcing scenarios (RCP4.5 and RCP8.5) using the generation model HadGEM2-ES. The term marginal land is found in the literature to indicate unused agriculture land such as abandoned, underused, degraded and fallow. In this work, marginality is considered the economic feasibility of cultivation, which aims to identifying land where cost effective agricultural production is not possible under a given set of conditions. Marginal lands were identified when the average Camelina seed yield from the crop growth modelling was lower than average in European countries based on a comprehensive literature assessment. The analysis was targeted at regions with a predominantly Mediterranean climate. Simulation by the mechanistic crop model was carried out on a 25 km grid for soil texture, soil carbon average stock, slope and aspect found in the MARS agricultural area mask. Spatial data and subsequent editing and processing of each simulation were allocated to a 500 m spatial resolution via a Geographic Information Systems (GIS). The study area covers around 500,000 km2. The CAMBAR scenario obtained an average yield of 2468 kg ha^-1 yr^-1, with standard deviation (+- 641) due to fluctuations for extreme weather patterns. Regarding soil organic carbon (SOC), CAMBAR showed an increase of + 43 kg ha^-1 yr^-1, which aligns with other studies carried out in Mediterranean or continental climates under crop rotation with minimum tillage and straw retention. The results of the present scenarios showed a slight increase in SOC stock (0.1–0.15 % yr^-1). In regions with sufficient precipitation throughout the crop cycle, the increase of SOC is lower than the entire study area average, and in few cases, the SOC stock was drastically decreased. However, the model shows that SOC stock can increase when Camelina is introduced in rotation with cereals in areas with high desertification risk. In Spain, in the regions of Castilla La Mancha, Castilla y León and Comunidad de Madrid accounting for 40% of the total area of investigation in Spain, equivalent to an area of 88,233 km², the SOC increase was + 188, + 255 and + 236 kg ha^-1 yr^-1, respectively. The results of this work constitute a key contribution to policy development at the sub-national, national and EU level, through the investigation of low LUC/ILUC biofuel from marginal areas before these are lost due to land degradation processes and other anthropogenic impacts. Mapping the marginal areas is fundamental to showing potential for producing advanced biofuel crops.

https://doi.org/10.1016/j.agee.2023.108677

https://doi.org/10.1002/sae2.12041

Early and late blight are two diseases which pose a huge risk to both potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) crops and make farmers run at a loss. The early and automatic detection of these diseases would save time as well as enable farmers to act quickly on crops which have been affected. Machine learning and deep learning technology provide many solutions for the detection of the blight diseases in affected crops, and are common in the literature. However, explanation methods for such solutions are not common, but are necessary, considering some machine learning models are seen as black boxes. This study proposes a ResNet-9 model which detects the blight disease state of potato and tomato leaf images, which farmers can leverage. With the data obtained from the popular “Plant Village Dataset”, there were 3,990 initial training data samples. After augmenting the training set and a rigorous hyperparameter optimization procedure, the model was trained with these hyperparameter values, and examined on the test set, which contained 1,331 images. A test accuracy of 99.25%, 99.67% overall precision, 99.33% overall recall and 99.33% overall F1-score values were achieved. To fully understand the model, explanations for the proposed model were provided through saliency maps, which showed the reasoning behind the predictions of the model. It was observed that the ResNet-9 model considered the shape of the leaf, diseased areas present and general green areas of the leaf for its predictions and this makes us understand the model predictions better and see that the model behaves as expected. Our results could contribute to the testing and deployment of Convolutional Neural Network (CNN) models for classification of proximal sensing images of potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plant leaves. Further studies would benefit from this modeling framework and would have the chance to test several other variables to determine the leaf infections in an earlier stage for crop protection.

https://doi.org/10.1016/j.atech.2023.100178

Soil Bulk Density (BD) is an extremely important variable because it is an important site characterization parameter, and it is highly relevant for policy development because it is mandatory for calculating soil nutrient stocks. BD can influence soil chemical properties, land-use planning and agronomic management. The 2018 Land Use and Coverage Area Frame Survey (LUCAS) saw the unprecedented collection of BD core analysis in a subset of the locations in Europe and the United Kingdom where soil physical and chemical properties were analysed in the 2009 and the 2015 sampling campaigns. Here, we integrated the LUCAS 2018 BD sampling campaign with the mass fraction of coarse fragments previously determined in LUCAS 2009–2015 in order to provide a dataset of the volume fraction of coarse fragments and the BD of the fine earth and improve soil organic carbon (SOC) stock estimation accuracy for topsoil. BD data sampled at 0–10 and 10–20 cm were averaged to harmonize the BD with the mass fraction of coarse fragments measured in 2009, 2012 and 2015. Samples were from cropland, grassland and woodland soils, which accounted for 41%, 21% and 30%, respectively, of the total number of selected sites (n = 6059); ‘bareland’, and ‘shrubland’ accounted for 3% of the sites each, whereas ‘artificial land’ accounted for <1%. Only six samples were classified as ‘wetland’. The dataset was produced assuming the mass density of the coarse fraction to be constant across all LUCAS soil samples. We also estimated the SOC stocks associated with LUCAS 2018 BD and SOC content measurements and showed that correcting the BD by the coarse mass fraction instead of the coarse volume fraction generates SOC stock underestimation. We found the highest deviations in woodlands and shrublands. We showed that, when SOC stock is computed with coarse mass fraction, the error compared with the computation by volume may vary depending on the SOC and coarse mass fraction. This may imply a SOC stock underestimation for European soils. This dataset fits into the big framework of LUCAS soil properties monitoring and contributes both to soil awareness and soil research and assessments, which are two important objectives of the Soil Strategy and the European Soil Observatory (EUSO).

https://doi.org/10.1111/ejss.13391

The European fan palm (Chamaerops humilis, Arecaceae) is the only native palm in continental Europe, providing ecosystem services that are hard to obtain from other species. However, its populations are declining in some areas due to anthropogenic effects including climate change. Knowledge of genetic variability among natural populations is needed to establish conservation plans, to prevent genetic contamination of native stands by cultivated germplasm and to exploit it as an ornamental species. However, information on the genetic similarities among C. humilis populations is scarce. The aims of this work were to study genetic structure in C. humilis using a set of specifically designed genetic markers and to highlight genetic similarities and their relationships with geographical proximity. We sampled 301 specimens from 42 natural populations throughout the distribution area and analysed these with ten di-, tri- and tetra-nucleotide simple sequence repeats. Relationships between genetic similarities and geographical distances were analysed and populations grouped according to a genetic, geographical or national clustering. We found lower variability in populations from the eastern half of the distribution, and this lower variability was accompanied by a stronger relationship between genetic differences and spatial proximity. In addition, we found that C. humilis probably showed two patterns of spread and further differentiation: one from Morocco to southern continental Spain and then to Portugal and the Balearic Islands, and one from Morocco to Algeria, Tunisia, Sicily and continental Italy. Populations from Sardinia and France showed similarities to those from Spain and Tunisia, respectively, and may have arisen from multiple colonization events. Our results support the hypothesis that isolation on large islands may have increased diversification of the species even if all populations shared the same founder. These results have important implications for both the ecological management and the conservation of the species.

https://doi.org/10.1093/botlinnean/boac053

The use of crop residues for biofuel production has the potential to provide environmental and economic benefits to modern societies. Because of the profound impacts that crop residues have on agricultural productivity and soil health, a sustainable utilization of these residues is required. Thus, we determined crop yield and quality response for a range of biomass retention rates in grain cropping systems. Combinations of corn (Zea mays L.) stover (0, 3.33, 6.66 and 10 Mgha⁻¹) and wheat (Triticum aestivum L.) straw (0, 1.0, 2.0, and 3.0 Mgha⁻¹) were soil applied in a corn-wheat/soybean (Glycine max L. Merr.) rotation in Virginia’s Coastal Plain. Corn stover (0, 3.33, 6.66, 10 and 20 Mg ha⁻¹) was applied in a continuous corn cropping system in the Ridge/Valley province. For each system, residues were applied following grain harvest over two production cycles. Each experiment was conducted as a randomized complete block design with four replications. Two cycles of crop residue management, with retention rates of up to 20 Mg ha⁻¹ of corn stover retention in Blacksburg, and up to 13 Mg ha⁻¹ of corn stover and wheat straw in New Kent, had no effect on total nitrogen (TN) and carbon (TC) concentrations, CN ratios, bulk density (BD), soil pH, field capacity, permanent wilting point, plant available water and water aggregate stability across soil depths and aggregate sizes in Virginia. In one situation when residue management slightly affected BD (0–2.5 cm depth, NK1), differences across the sixteen total retained residues treatments were less than 5%, thus rendering them not biologically or environmentally meaningful. Overall, results of this study did not show any clear short-term impact, resulting from various rates of crop residue retention in Virginia cropping systems. These incipient negative impacts resulting from very low rates of residue return warrant further studies to corroborate whether these results are to be found following long-term scenarios of crop residue management.

https://doi.org/10.3390/agronomy13030838

In recent years there has been an increased demand for digital soil mapping (DSM) products. DSM has become the ultimate soil spatial representation framework due to its quantitative results, replicability, and uncertainty analysis. The present study aimed to map the probability distribution of the derived soil profiles (DSPs) of soil typological units (STUs). DSPs are statistical representation of the properties of the soil profiles belonging to STUs. STUs aggregate individual profiles into a group. The criteria used for grouping were homogeneity for World Reference Base (WRB) reference soil group (WRB-RSG), qualifiers (WRB-qu), and Soil Taxonomy particle size for the family classification (USDA-PS), and the belonging to a specific Soil Region. The European Soil Regions have been suggested as the primary grouping criteria for soil mapping at the European continental scale since they define continental-scale soilscapes, distinguished mainly by their climate and geology. To map DSPs, we firstly mapped STUs. The grouping criteria of STUs were mapped at 500 m spatial resolution, using a Neural Network trained on 18,707 georeferenced and analyzed soil profiles selected from the Italian national soil database. A 10% of the soil profiles were randomly sampled using a stratified sampling approach for validation. In particular, the procedure consisted of: i) mapping the grouping criteria WRB-RSG, WRB-qu, and USDA-PS, on a 500 m national grid, through Neural Network; ii) grouping soil profiles on the base of the combinations of grouping criteria (WRB-RSG, WRB-qu, USDA-PS, and Soil Regions) as mapped with the first step at each grid node, to produce a map of Soil Typological Units (STUs); iii) calculating statistics for the soil parameters of the groups of soil profiles created, to produce a map of Derived Soil Profiles (DSPs). DSPs statistics (average, standard deviation, and sample numerosity) were elaborated for the following parameters: soil rooting depth, pH (in water), soil organic carbon, clay, silt, sand, coarse fragments, and cation exchange capacity. The maps obtained were validated against the test set. The same test set was used for the comparison with the National benchmark map (Soils Map of Italy 1:1,000,000) and with the global scale SoilGrids at 250 m spatial resolution. The overall accuracy was 45.98% for the WRB-RSG map compared with the 30.74% of WRB-RSG as mapped with the Soil Map of Italy, and 28.79% as mapped with SoilGrids; 33.07% for WRB-qu compared with the 15.69% of WRB-qu as mapped with the Soil Map of Italy, and 12.45% as mapped with SoilGrids, and 45.48% for USDA-PS, not comparable with the National and Global benchmarks. Tau statistics showed a higher accuracy Kappa of our approach than in others, due to the unbalanced classes numerosity. The predictive ability in the validation of DSPs parameters resulted in a R2 of 0.35 for clay (0.16 with SoilGrids), 0.28 for sand (0.08 with SoilGrids), 0.18 for pH in water (0.21 with SoilGrids). The proposed approach produced harmonized soil type maps with higher accuracy than the previous generation of conventional field-based soil maps for the national benchmark and the calculation of the uncertainty. The STUs express variability of soil properties between groups so their knowledge might improve our understanding of the soil distribution, the planning of their management, monitoring, and the decisions for further surveys. A future challenge will be including more dynamic parameters in the criteria used to create STU, to help monitoring soil management effects.

https://doi.org/10.1016/j.geodrs.2023.e00619

Conservation agriculture (CA) is an agronomic system based on minimum soil disturbance (no-tillage, NT), permanent soil cover, and species diversification. The effects of NT on soil organic carbon (SOC) changes have been widely studied, showing somewhat inconsistent conclusions, especially in relation to the Mediterranean and humid subtropical climates. These areas are highly vulnerable and predicted climate change is expected to accentuate desertification and, for these reasons, there is a need for clear agricultural guidelines to preserve or increment SOC. We quantitively summarized the results of 47 studies all around the world in these climates investigating the sources of variation in SOC responses to CA, such as soil characteristics, agricultural management, climate, and geography. Within the climatic area considered, the overall effect of CA on SOC accumulation in the plough layer (0–0.3 m) was 12% greater in comparison to conventional agriculture. On average, this result corresponds to a carbon increase of 0.48 Mg C ha⁻¹ year⁻¹. However, the effect was variable depending on the SOC content under conventional agriculture: it was 20% in soils which had ≤ 40 Mg C ha⁻¹, while it was only 7% in soils that had > 40 Mg C ha⁻¹. We proved that 10 years of CA impact the most on soil with SOC ≤ 40 Mg C ha⁻¹. For soils with less than 40 Mg C ha⁻¹, increasing the proportion of crops with bigger residue biomasses in a CA rotation was a solution to increase SOC. The effect of CA on SOC depended on clay content only in soils with more than 40 Mg C ha⁻¹ and become null with a SOC/clay index of 3.2. Annual rainfall (that ranged between 331–1850 mm y⁻¹) and geography had specific effects on SOC depending on its content under conventional agriculture. In conclusion, SOC increments due to CA application can be achieved especially in agricultural soils with less than 40 Mg C ha⁻¹ and located in the middle latitudes or in the dry conditions of Mediterranean and humid subtropical climates.

https://doi.org/10.1111/ejss.13338

Over the last decades, the establishment of new fast-growing forest plantations has been prospering. Although the European continent has the lowest share worldwide of forest plantations out of its total forest area, the Portuguese reality contrasts this. Since the last century, eucalypt plantations have increased widely in Portugal, and nowadays, it represents 36% of the total country's forests. Consequently, the soils of these plantations are commonly targeted with intensive soil mobilization by heavy machinery before planting, which in the case of sloped areas, frequently results in bench terracing. This study aims to quantify the widespread bench terracing implementation over the last 20 years in the Caramulo Mountains in north central Portugal. To do so, an analysis of satellite imagery was performed with Google Earth Pro, which allowed determining the coverage of forest areas where new terraces have been implemented, and their respective temporal dynamics. These results were then compared with additional spatiotemporal databases on land cover, topography, and bedrock, in order to understand the drivers of terrace implementation. Till date, 15% of the forest area in the mountains of Caramulo is under terraces, and over the last 20 years, the construction rate of new terraced land decreased in time, from 4% between 2000 and 2004 to 2% between 2015 and 2019. Among the two bedrock types existent in the area, terracing was found nearly exclusive over schist bedrock type (97%), while few areas were implemented over granites (3%). Their distribution was found limited above 30° of slope angle while 39% were found implemented below 15° of slope angle, conflicting with literature recommendations. Terracing was also found to be a driver of land cover change in 12% of the newly constructed terraces, whereas 8% were constructed over previous pine plantations and 4% on shrublands. This study allowed identifying several knowledge gaps associated with terracing implementation. Therefore, the authors of this work suggest a multidisciplinary approach when planning new terraces for a better assessment of the benefits and impacts of such land management practices. 

https://doi.org/10.1002/ldr.4837

Rural fires are now a major societal concern across the world, especially where fire regimes have (apparently) intensified in terms of burnt area, intensity and recurrency. Among the indirect fire effects, fire-enhanced runoff and erosion have been an important focus of post-fire land and water management, in particular through emergency stabilization of hillslopes using a range of erosion mitigation measures. The most widely applied and – scientifically tested – measure is that of mulching with agricultural straw, in spite of concerns of introducing exogenous organic material and especially seeds of non-native higher plant species, including the straw species it- or themselves. So far, field studies in the present study region of north-central Portugal have preferred using endogenous forest residues but these studies concerned forest types for which such residues are easily available. The latter, however, is not the case for strawberry tree stands, so that straw mulch was selected in this study as a cheaper alternative to eucalypt or pine residues. This - apparently, first – post-fire erosion mitigation study in a strawberry tree (Arbutus unedo L.) stand aimed to compare post-fire sediment and organic matter losses as well as ground vegetation recovery without and with applying barley (Hordeum vulgare L.) straw mulch at a low rate of 2 Mg ha−1. The experimental set-up involved a randomized block design with a total of six geotextile-bounded erosion plots of 2 m by 5 m that were organized in three blocks, were installed and mulched roughly one month after the 17-October-2017 M-fire in inland Central Portugal, and monitored at 12 irregular intervals during the first two post-fire years. The principal findings were that: (i) especially the specific sediment losses without mulching over the first post-fire year were notably higher than those reported by the prior field studies in the region, in eucalypt and maritime pine plantations; and that the - low - mulching rate: (ii) was extremely effective in reducing these first-post-fire-year losses; but (iii) did not result in changes in the cover or floristic composition of the ground vegetation cover that were noteworthy and longer-lived than the first post-fire year.

https://doi.org/10.1016/j.ecoleng.2023.107074

https://doi.org/10.1016/j.envsci.2023.06.016

https://doi.org/10.1016/j.jenvman.2023.117478

https://doi.org/10.1029/2023JG007391

Intensive vegetable production is characterised by high nitrogen (N) application rates and frequent irrigations, promoting elevated nitrous oxide (N₂O) emissions, a powerful greenhouse gas indicative for the low N use efficiency (NUE) in these systems. The use of nitrification inhibitors (NI) has been promoted as an effective strategy to increase NUE and decrease N₂O emissions in N-intensive agricultural systems. This study investigated the effect of two NIs, 3,4-dimethylpyrazole phosphate (DMPP) and 3-methylpyrazole 1,2,4-triazole (Piadin), on N₂O emissions and 15N fertiliser recovery in a field experiment in sweet corn. The trial compared the conventional fertiliser N rate to a 20% reduced rate combined with either DMPP or Piadin. The use of NI-coated urea at a 20% reduced application rate decreased cumulative N2O emissions by 51% without yield penalty. More than 25% of applied N was lost from the conventional treatment, while a reduced N rate in combination with the use of a NI significantly decreased N fertiliser losses (by up to 98%). Across treatments, between 30 and 50% of applied N fertiliser remained in the soil, highlighting the need to account for residual N to optimise fertilisation in the following crop. The reduction of overall N losses without yield penalties suggests that the extra cost of using NIs can be compensated by reduced fertiliser application rates, making the use of NIs an economically viable management strategy for growers while minimising environmentally harmful N losses from vegetable growing systems.

https://doi.org/10.1007/s10705-021-10185-y

The reduction of the greenhouse gas nitrous oxide (N₂O) to dinitrogen (N₂) via denitrification and N₂O source partitioning between nitrification and denitrification remain major uncertainties in sugarcane systems. We therefore investigated magnitude and product stoichiometry of denitrification and production pathways of N₂O from a tropical sugarcane soil in response to increasing soil nitrate (NO₃⁻) availability.

https://doi.org/10.1007/s11368-023-03482-2

Fertilisating crops with zinc (Zn) is considered important to enhance agricultural productivity and combat human Zn deficiencies in sub-Saharan Africa. However, it is unclear on which soils Zn fertilisation can lead to higher yields and increased grain Zn concentrations. This study aimed to find soil properties that predict where soil Zn is limiting maize yields and grain Zn concentrations, and where these respond positively to Zn fertilisation.

https://doi.org/10.1007/s11104-023-06050-2

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.

https://doi.org/10.1038/s41598-023-44171-x

Environmental risk factors, such as exposure to air pollution, are linked with lung cancer. However, potential health impacts of exposure to carcinogenic pollutants in soil are less defined. In this ecological study, we evaluated at a regional scale potential associations between lung cancer mortality and the soil content of two carcinogens: arsenic and cadmium

https://doi.org/10.1093/eurpub/ckad160.1252

Land degradation is the persistent reduction in the capacity of the land to support human and other life on Earth (IPBES, 2018). This process jeopardizes the provision of ecosystem services. The Sustainable Development Goal (SDG) 15, ‘Life on Land’, includes efforts to sustainably manage and recover natural ecosystems and restore degraded land and soil. Under the umbrella of SDG 15, the United Nations Convention to Combat Desertification (UNCCD) has defined an indicator framework to monitor progress toward ‘land degradation neutrality’. We evaluated the performance of SDG 15.3.1, focusing on “…proportion of land that is degraded over the total land area” for the European Union (EU) using the TRENDS.EARTH software. We assessed the impact of alternative datasets at different spatial resolutions and policy-relevant data sources for land cover (CORINE) and soil organic carbon (SOC) stock (LUCAS). Our hypothesis was that higher spatial resolution sub-indicators would better identify the total share of degraded land and provide a clearer picture of the extent of degraded land for the target period. Land productivity trajectories were adjusted using the Water Use Efficiency index that revealed the high share of improving land reported by the NDVI trends. Therefore, it is advisable to use always a climate correction to assess land productivity trends. Replacing default datasets with alternative sub-indicators allowed the detection of 25–40% more degraded areas. Additionally, the integration with a combined proxy of land degradation (soil erosion >10 Mg ha⁻¹ yr⁻¹, and SOC concentration <1%) identified an additional 50% land degradation and revealed that a large extent of the EU needs restoration measures.

The agricultural uses of the Coordination of Information on the Environment Land Cover (CLC) dataset suffer from limitations such as temporal stationarity, low spatial resolution, broad and rather simplified grouping of classes. The study attempts to address these shortcomings, using as test site the Sperchios River catchment, Central Greece. The Greek ‘branch’ of the Land Parcel Identification System, Beneficiaries’ Declarations (BD) and CLC inventories were utilized to develop hybrid layers, deriving from their harmonization, sequential incorporation and progressive update (BD → BD-ilot → BD-ilot-CLC). The final layer constitutes the new object-oriented Land Use/Land Cover map. Remote sensing data (Sentinel-2) was used to validate the accuracy of the BD, subject to the most frequent errors. The new map retains the key advantages of CLC yet is now characterized by highly detailed spatial resolution and the explicit description of the different cultivated farmlands included.

10.1080/10106049.2022.2041107

Information on spatial and temporal changes in the soil organic carbon (SOC) and soil total nitrogen (STN) stocks are crucial to support sustainable land management strategies. However, such information is scarce mainly for arid and semi-arid regions. The present research aimed to determine SOC and STN stock dynamics with depths during the 1971–2019 period. Thus, two soil types were selected in north Tunisia: a Luvisol under forest vegetation and a Cambisol under cereal crop and analyzed for SOC, STN, rock fraction, and bulk density from the surface (0–30 cm), middle (30–50 cm), and deep layers (50–100 cm) in 1971, 2005, 2012, and 2019. SOC and STN contents decreased with depth in both soils. The Luvisol exhibited the highest SOC and STN contents. From 1971 to 2019, SOC and STN contents decreased more in the surface layer in both soils with sharpest decrease for STN than SOC. The stocks were calculated taking into account the rock fractions avoiding overestimation. Stocks recorded in the surface layer corresponded to 68% of total SOC stock and 58% of total STN stock in both soils. From 1971 to 2019, the reduction of SOC and STN stocks was more important in the Cambisol than in the Luvisol in all layers, mainly in the surface layer. Such reductions could have important implications for soil fertility and global warming

10.1007/s12517-022-09860-3

To assess carbon sequestration in the agricultural and natural systems, it is usually required to report soil organic carbon (SOC) as mass per unit area (Mg ha-1) for a single soil layer (e.g., the 0-0.3 m ploughing layer). However, if the SOC data are reported as relative concentration (g kg-1 or %), it is required to compute the SOC stock and its standard deviation (SD) for a given layer as the product of SOC concentration and bulk density (BD). For a proper computation, it is required to consider that these two variables are correlated. Moreover, if the data are already reported as SOC stock for multiple sub-layers (e.g., 0-0.15 m, 0.15-0.3 m) it is necessary to compute the SOC stock and its SD for a single soil layer (e.g., 0-0.3 m). The correlation between stocks values from adjacent and non-adjacent soil sub-layers must be accounted to compute the SD of the single soil layer.

The present work illustrates the methodology to compute SOC stock and its SD for a single soil layer based on SOC concentration and BD also from multiple sub-layers. An Excel workbook automatically computes the means of stocks and SD saving the results in a ready-to-use database.
•Computation of a carbon (SOC) stock and its standard deviation (SD) from the product between SOC concentration and bulk density (BD), being correlated variables.
•Computation of a SOC stock and its SD from the sum of SOC stocks of multiple correlated sub-layers.
•An Excel workbook automatically computes the means of SOC stocks and SD and saves the results in a ready-to-use database.

10.1016/j.mex.2022.101662

Despite phosphorus (P) being crucial for plant nutrition and thus food security, excessive P fertilization harms soil and aquatic ecosystems. Accordingly, the European Green Deal and derived strategies aim to reduce P losses and fertilizer consumption in agricultural soils. The objective of this study is to calculate a soil P budget, allowing the quantification of the P surpluses/deficits in the European Union (EU) and the UK, considering the major inputs (inorganic fertilizers, manure, atmospheric deposition, and chemical weathering) and outputs (crop production, plant residues removal, losses by erosion) for the period 2011–2019.

The Land Use/Cover Area frame Survey (LUCAS) topsoil data include measured values for almost 22,000 samples for both available and total P. With advanced machine learning models, we developed maps for both attributes at 500 m resolution. We estimated the available P for crops at a mean value of 83 kg ha−1 with a clear distinction between North and South. The ratio of available P to the total P is about 1:17.

The inorganic fertilizers and manure contribute almost equally as P inputs (mean 16 ± 2 kg P ha−1 yr−1 at 90 % confidence level) to agricultural soils, with high regional variations depending on farming practices, livestock density, and cropping systems. The P outputs came mainly from the exportation by the harvest of crop products and residues (97.5 %) and, secondly, by erosion. Using a sediment distribution model, we quantified the P fluxes to river basins and sea outlets.

In the EU and UK, we estimated an average surplus of 0.8 kg P ha−1 yr−1 with high variability between countries with some regional variations. The P annual budget at regional scale showed ample possibility to improve P management by both reducing inputs in regions with high surplus (and P soil available) and rebalancing fertilization in those at risk of soil fertility depletion.

10.1016/j.scitotenv.2022.158706