{"type": "FeatureCollection", "features": [{"id": "10.1002/joc.1276", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:14:24Z", "type": "Journal Article", "created": "2005-11-30", "title": "Very High Resolution Interpolated Climate Surfaces For Global Land Areas", "description": "(Uploaded by Plazi for the Bat Literature Project) We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950\u20132000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright \uf6d9 2005 Royal Meteorological Society.", "keywords": ["0106 biological sciences", "0301 basic medicine", "550", "Climate", "bats", "bat", "Precipitation", "precipitation", "01 natural sciences", "Error", "geographical information systems", "03 medical and health sciences", "precipitaci\u00f3n atmosf\u00e9rica", "Chiroptera", "1902 Atmospheric Science", "Animalia", "Chordata", "temperatura", "factores clim\u00e1ticos", "procesamiento de datos", "Temperature", "Uncertainty", "temperature", "Biodiversity", "15. Life on land", "GIS", "climatic factors", "Interpolation", "ANUSPLIN", "13. Climate action", "Mammalia", "sistemas de informaci\u00f3n geogr\u00e1fica", "data processing"]}, "links": [{"href": "https://doi.org/10.1002/joc.1276"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Climatology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/joc.1276", "name": "item", "description": "10.1002/joc.1276", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/joc.1276"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2005-01-01T00:00:00Z"}}, {"id": "10.3389/fsufs.2024.1272332", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:52Z", "type": "Journal Article", "created": "2024-08-15", "title": "Linking drivers of food insecurity and ecosystem services in Africa", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Food insecurity is a multidimensional and intricate problem, known to have significant implications for individuals, communities, and countries worldwide. Africa has become the continent that is experiencing this uncertainty the most. Food Security (FS) encompasses several aspects such as availability, accessibility, nutrient use, and supply system stability with time and, more recently, other obliges to governance/agency and sustainability. Knowing the interconnection between these aspects and the Ecosystems Services (ES) and understanding the relationship and interactions between FS and ES is important. Moreover, this knowledge may contribute to supporting policies that promote long-term sustainable and secure food systems. Hereby, a conceptual framework is presented, that examines interactions between food insecurity drivers and ecosystem change drivers and the combined influence on ES. Our review further introduces existing trade-offs between ES on account of agricultural intensification vs. key existing strategies to promote sustainable agricultural production. These strategies include climate-smart agriculture, sustainably managed land, and effective handling of water resources. In the end, the potential of Payment for Ecosystem Services (PES), as a suitable approach to ensuring these strategies are adopted, especially in African countries where sustainable financial incentives are currently under-explored is discussed. In resume, this review aims to make a conceptual contribution to understanding how drivers of food insecurity influence drivers of ecosystem changes, the impact of these influences on the services of ecosystems, and how sustainable agro approaches and PES introduction can help to reduce such negative impacts.</p></article>", "keywords": ["2. Zero hunger", "0301 basic medicine", "0303 health sciences", "Nutrition. Foods and food supply", "1. No poverty", "food security", "TP368-456", "15. Life on land", "Food processing and manufacture", "12. Responsible consumption", "03 medical and health sciences", "13. Climate action", "sustainable agriculture intensification", "smallholder farmers", "TX341-641", "payment for ecosystem services", "climate change adaptation"]}, "links": [{"href": "https://doi.org/10.3389/fsufs.2024.1272332"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Sustainable%20Food%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fsufs.2024.1272332", "name": "item", "description": "10.3389/fsufs.2024.1272332", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fsufs.2024.1272332"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-15T00:00:00Z"}}, {"id": "10.1021/es303829w", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:17:32Z", "type": "Journal Article", "created": "2012-12-21", "title": "Can Dispersed Biomass Processing Protect The Environment And Cover The Bottom Line For Biofuel?", "description": "This paper compares environmental and profitability outcomes for a centralized biorefinery for cellulosic ethanol that does all processing versus a biorefinery linked to a decentralized array of local depots that pretreat biomass into concentrated briquettes. The analysis uses a spatial bioeconomic model that maximizes profit from crop and energy products, subject to the requirement that the biorefinery must be operated at full capacity. The model draws upon biophysical crop input-output coefficients simulated with the Environmental Policy Integrated Climate (EPIC) model as well as market input and output prices, spatial transportation costs, ethanol yields from biomass, and biorefinery capital and operational costs. The model was applied to 82 cropping systems simulated across 37 subwatersheds in a 9-county region of southern Michigan in response to ethanol prices simulated to rise from $1.78 to $3.36 per gallon. Results show that the decentralized local biomass processing depots lead to lower profitability but better environmental performance, due to more reliance on perennial grasses than the centralized biorefinery. Simulated technological improvement that reduces the processing cost and increases the ethanol yield of switchgrass by 17% could cause a shift to more processing of switchgrass, with increased profitability and environmental benefits.", "keywords": ["2. Zero hunger", "Michigan", "Ethanol", "Biomass production", " bioenergy supply", " cellulosic ethanol", " environmental trade-off analysis", " bioeconomic modeling", " EPIC", " spatial configuration", " local biomass processing", " Crop Production/Industries", " Environmental Economics and Policy", " Production Economics", " Resource /Energy Economics and Policy", " Q16", " Q15", " Q57", " Q18", "", "02 engineering and technology", "Environment", "Models", " Theoretical", "15. Life on land", "7. Clean energy", "13. Climate action", "Biofuels", "Costs and Cost Analysis", "0202 electrical engineering", " electronic engineering", " information engineering", "Computer Simulation", "Biomass"], "contacts": [{"organization": "Egbendewe-Mondzozo, Aklesso, Swinton, Scott M., Bals, Bryan D., Dale, Bruce E.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1021/es303829w"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1021/es303829w", "name": "item", "description": "10.1021/es303829w", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1021/es303829w"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-01-11T00:00:00Z"}}, {"id": "10.1007/s11356-017-8823-x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:15:22Z", "type": "Journal Article", "created": "2017-03-24", "title": "Quantitative characterization of pore structure of several biochars with 3D imaging", "description": "Open Access16 pages, 4 figures. The final publication is available at Springer via http://dx.doi.org/10.1007/s11356-017-8823-x", "keywords": ["x-ray tomography", "Condensed Matter - Materials Science", "soil amendment", "pore structure", "ta1171", "ta1182", "Water", "Materials Science (cond-mat.mtrl-sci)", "FOS: Physical sciences", "04 agricultural and veterinary sciences", "01 natural sciences", "6. Clean water", "Diffusion", "Imaging", " Three-Dimensional", "image analysis", "Charcoal", "Image Processing", " Computer-Assisted", "0401 agriculture", " forestry", " and fisheries", "biochar", "Porosity", "soil amendments", "ta218", "water retention", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/s11356-017-8823-x.pdf"}, {"href": "https://doi.org/10.1007/s11356-017-8823-x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20and%20Pollution%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11356-017-8823-x", "name": "item", "description": "10.1007/s11356-017-8823-x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11356-017-8823-x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-24T00:00:00Z"}}, {"id": "10.5281/zenodo.7948400", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:45Z", "type": "Report", "title": "Farm management information systems as tools for revealing management zones inside the fields", "description": "INTRODUCTION and OBJECTIVES: There is a huge need to increase the productivity in agriculture to feed the world\u2019s growing population. However, this increase needs to be achieved in a sustainable way, without jeopardising the ecosystem and environment. Innovations in AgTech are accelerating this process and providing adequate solutions for optimisation of on-field decision-making, but they are often isolated and inaccessible to the farmers. The objective of our work was to design a comprehensive farm management system that takes scientific achievements and enables farmers to use them in their daily operations. MATERIAL and METHOD: In order to digitally transform the Serbian agriculture, we designed AgroSense farm management information system. It was launched in 2017 and has since gathered more than 20,000 users, whose total area equals one fourth of all farmland in Serbia. The platform has a number of modules for weather forecast, historical weather records, digital field books, satellite image processing etc., while the newest addition is the drone image processing module. This module allows 3rd party drone services to scan the fields and upload the data to the platform, after which, the images are processed and analysed. The analysis is directed towards zone management delineation, which is the first step in application of precision agriculture technologies. Zones are detected within the field as areas with homogeneous soil and elevation properties. This is done by applying k-means, an unsupervised machine learning model for clusterisation of data, i.e. pixels in this case. This algorithm minimises the intra-class variance (variance of pixels within the zone) and maximises the inter-class variance (variance between pixels from different classes. This zone delineation can be done on a pixel-level if the objective of zone delineation is e.g. choosing the right locations for soil sampling, or on the level of the tractor swath if the goal is e.g. the variable-rate application of fertiliser. The number of zones and the swath width are variable parameters, left to the user to choose, according to the size of the field, type of the equipment and other factors. RESULTS and CONCLUSIONS: The resulting platform was deployed in 2021 and tested on a number of users. It yielded excellent results and served for optimising the route and sampling location of unmanned ground vehicles (UGVs), characterisation of fields and variable application of fertiliser. Future work includes development of other algorithms for more complex image recognition tasks, such as row detection, leaf area assessment and disease/weed mapping.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "drones; precision agriculture; image processing; machine learning"], "contacts": [{"organization": "Marko, Oskar, Brdar, Sanja, Pani\u0107, Marko, Mini\u0107, Vladan, Pejak, Branislav, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7948400"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7948400", "name": "item", "description": "10.5281/zenodo.7948400", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7948400"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-16T00:00:00Z"}}, {"id": "10.1016/j.agee.2015.04.015", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:15:38Z", "type": "Journal Article", "created": "2015-04-17", "title": "Biochar Stimulates Plant Growth But Not Fruit Yield Of Processing Tomato In A Fertile Soil", "description": "Biochar addition to soil is a promising option for climate change mitigation and is recognized to exert beneficial effects on soil fertility. However, recent meta-analysis documented controversial effects on soil-plant interactions and on crop yields response. The data presented in this paper are the results of a field experiment on a processing tomato crop aiming to enhance the knowledge on the real applicability of biochar at farm scale in a high fertility alkaline soil. The effects of two biochar types on soil properties and on quantitative and qualitative parameters of processing tomato were evaluated. Biochar application significantly increased the soil carbon content, the soil cation exchange capacity and the availability of NH<inf>4</inf><sup>+</sup>, P and K. Moreover, it stimulated plant growth and N, P and base cation contents at harvest, reducing the leaf water potential in the warmer period. These results demonstrate that also intensive cultivations in fertile soil can benefit from biochar amendment.", "keywords": ["Intensive agriculture", "Soil nutrients", "2. Zero hunger", "13. Climate action", "Intensive agriculture; Processing tomato yield and quality; Soil nutrients; Ecology; Animal Science and Zoology; Agronomy and Crop Science", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Processing tomato yield and quality", "01 natural sciences", "6. Clean water", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2015.04.015"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2015.04.015", "name": "item", "description": "10.1016/j.agee.2015.04.015", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2015.04.015"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-09-01T00:00:00Z"}}, {"id": "10.1016/j.carbpol.2021.117980", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:15:57Z", "type": "Journal Article", "created": "2021-03-24", "title": "Apple pomaces derived from mono-varietal Asturian ciders production are potential source of pectins with appealing functional properties", "description": "Comprehensive chemical characterization of nine mono-varietal apple pomaces obtained from the production of ciders with PDO is described. They were rich in essential minerals, fibers (35-52.9 %), and polyphenols. High levels in GalA (11.8-21.6 %), revealed the suitability of these apple pomaces as efficient sources of pectins. Extracted pectins showed high variability in monomer composition, with degrees of methylesterification, strongly associated with pectins functional properties, ranging from 58 to 88 %. For a subset of apple pomace varieties, pectin extraction was accomplished by conventional acid heat treatment or ultrasound. Despite ultrasound-assisted extraction did not improve pectin yield, it minimized levels of 'non-pectin' components as revealed by the low content of Glc/Man, leading to the obtainment of high-purity pectin. Our work highlights the key role played by the selection of the apple variety to streamline the potential food applications (gelling/thickening agents or prebiotics) of the extracted pectins that largely depend on their structural features.", "keywords": ["2. Zero hunger", "0404 agricultural biotechnology", "Cider processing", "Carbohydrates", "Ultrasound assisted-extraction", "04 agricultural and veterinary sciences", "Phenolic content", "Apple pomace", "0405 other agricultural sciences", "Pectin", "Dietary fiber"]}, "links": [{"href": "https://doi.org/10.1016/j.carbpol.2021.117980"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Carbohydrate%20Polymers", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.carbpol.2021.117980", "name": "item", "description": "10.1016/j.carbpol.2021.117980", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.carbpol.2021.117980"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-01T00:00:00Z"}}, {"id": "10.1016/j.ijfoodmicro.2021.109043", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:16:36Z", "type": "Journal Article", "created": "2021-01-08", "title": "Unraveling the emergence and population diversity of Listeria monocytogenes in a newly built meat facility through whole genome sequencing", "description": "The food processing environments of a newly opened meat processing facility were sampled in ten visits carried out during its first 1.5\u00a0years of activity and analyzed for the presence of Listeria monocytogenes. A total of 18 L. monocytogenes isolates were obtained from 229 samples, and their genomes were sequenced to perform comparative genomic analyses. An increase in the frequency of isolation of L. monocytogenes and in the diversity of sequence types (STs) detected was observed along time. Although the strains isolated belonged to six different STs (ST8, ST9, ST14, ST37, ST121 and ST155), ST9 was the most abundant (8 out of 18 strains). Low (0 and 2) single nucleotide polymorphism (SNP) distances were found between two pairs of ST9 strains isolated in both cases 3\u00a0months apart from the same processing room (Lm-1267 and Lm-1705, with a 2 SNPs distance in the core genome; Lm-1265 and Lm-1706, with a 0 SNPs distance), which suggests that these strains may be persistent L. monocytogenes strains in the food processing environment. Most strains showed an in silico attenuated virulence potential either through the truncation of InlA (in 67% of the isolates) or the absence of other virulence factors involved in cell adhesion or invasion. Twelve of the eighteen L. monocytogenes isolates contained a plasmid, which ranged in size from 4 to 87 Kb and harbored stress survival, in addition to heavy metals and biocides resistance determinants. Identical or highly similar plasmids were identified for various sets of L. monocytogenes ST9 isolates, which suggests the clonal expansion and persistence of plasmid-containing ST9 strains in the processing environments of the meat facility. Finally, the analysis of the L. monocytogenes genomes available in the NCBI database, and their associated metadata, evidenced that strains from ST9 are more frequently reported in Europe, linked to foods, particularly to meat and pork products, and less represented among clinical isolates than other L. monocytogenes STs. It also showed that the ST9 strains here isolated were more closely related to the European isolates, which clustered together and separated from ST9 North American isolates.", "keywords": ["0301 basic medicine", "Meat", "Food Handling", "Swine", "Virulence Factors", "Tecnolog\u00eda de los alimentos", "Food processing environment", "Persistence", "03 medical and health sciences", "Manufacturing and Industrial Facilities", "Floors and Floorcoverings", "Animals", "2. Zero hunger", "0303 health sciences", "Virulence", "Whole Genome Sequencing", "Genetic Variation", "Gen\u00e9tica", "Listeria monocytogenes", "Europe", "Genes", " Bacterial", "Whole genome sequencing", "Food Microbiology", "Equipment Contamination", "Disinfectants", "Plasmids"]}, "links": [{"href": "https://doi.org/10.1016/j.ijfoodmicro.2021.109043"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Food%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.ijfoodmicro.2021.109043", "name": "item", "description": "10.1016/j.ijfoodmicro.2021.109043", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.ijfoodmicro.2021.109043"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-01T00:00:00Z"}}, {"id": "10.1016/j.optmat.2025.117551", "type": "Feature", "geometry": null, "properties": {"license": "Restricted", "updated": "2026-07-25T16:16:47Z", "type": "Journal Article", "created": "2025-09-24", "title": "Er3+/Yb3+ co-activated YNbO4 nanocrystalline phosphors: Up-conversion luminescence under the 980\u00a0nm excitation and integrated lifetime thermometry", "description": "This paper presents the structure, morphology, optical and photoluminescent properties of erbium (1 at %) and ytterbium (2 at %) doped yttrium niobium oxide (YNbO<inf>4</inf>) as a potential temperature sensor material. Obtained powder samples of fergusonite-\u03b2-like monoclinic crystalline structure of YNbO<inf>4</inf>, confirmed by X-ray diffraction analysis, showed particles of about 1\u20133 \u03bcm in size. Photoluminescence emissions were detected in the visible (Vis) and near-infrared (NIR) regions after excitation at 980 nm as a result of the energy up-conversion (UC) process. The lifetime of the most intense Er<sup>3+</sup> excited state <sup>4</sup>S<inf>3/2</inf> level measured at 300 K was 0.238 ms. Thermometric properties were recorded at different temperatures and analyzed for the first time using the luminescence emission decay method. The relative sensitivity decreases from 0.23 % to 0.085 % K<sup>\u22121</sup>, by varying the temperature from 300 to 600 K, indicating a good potential of this material for lifetime-based phosphor thermometry.", "keywords": ["Luminescence", "Powder processing", "Semiconductors", "Temperature sensing", "Nanocrystalline microstructure"], "contacts": [{"organization": "Tamara B. Iveti\u0107, Boris Banjac, Ljubica \u0110a\u010danin Far, Dragana \u0160trbac, Zoran Risti\u0107,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.optmat.2025.117551"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Optical%20Materials", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.optmat.2025.117551", "name": "item", "description": "10.1016/j.optmat.2025.117551", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.optmat.2025.117551"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2026-01-01T00:00:00Z"}}, {"id": "10.1016/j.pmatsci.2023.101186", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:16:49Z", "type": "Journal Article", "created": "2023-09-07", "title": "Recent advances in bio-based functional additives for polymers", "description": "In recent decades, the strong global concern on depletion of fossil fuel and the environmental impact of oil-based compounds has pushed towards more sustainable approaches in the development of polymer-based materials. This interest is driven by the need of a more sustainable economy and a lower dependency on fossil fuels. In this frame, the replacement of synthetic additives with natural compounds in polymer commodities is progressively taking place. Additionally, according to forecasts, the production of bio-plastics will grow exponentially in the near future. However, these materials may exhibit poor physical and mechanical properties regarding processability and end-use, which can limit their potential for applications. Therefore, academic and industrial communities are pushing their interest in fully bio-based formulations with improved performance, and tailored for specific applications, ranging from packaging to biomedicine. This review presents the most recent advances in research and development of bio-based functional additives for polymeric materials. For each type of additive, both the scientific fundamentals and the technological aspects are encompassed, with an emphasis on the current commercially available bio-based additives and their role in market uptake of environmentally friendly products. Finally, considerations about environmental, health, regulatory, and economic issues related to the use of bio-additives in plastic materials are also addressed.", "keywords": ["Processing aids", "polymer additives", "Economic and legislative issues", "Melt stabilizers", "Antimicrobials", "Plasticizers", "Functional additives", "Polymer sciences", "Bio-based polymer additives", "Compatibilizers", "Sustainability and safety", "Flame retardants", "Antioxidants"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/514127/1/1-s2.0-S0079642523001184-main%281%29.pdf"}, {"href": "https://doi.org/10.1016/j.pmatsci.2023.101186"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Progress%20in%20Materials%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.pmatsci.2023.101186", "name": "item", "description": "10.1016/j.pmatsci.2023.101186", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.pmatsci.2023.101186"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-10-01T00:00:00Z"}}, {"id": "10.1101/2021.02.13.430456", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:32Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "Abstract<p>Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.</p", "keywords": ["0301 basic medicine", "570", "Microscopy", "Silicon", "0303 health sciences", "Temperature", "root-microbe interactions", "Equipment Design", "Biological Sciences", "Environment", "15. Life on land", "Plant Roots", "630", "Fluorescence", "Soil", "03 medical and health sciences", "Seedlings", "Calibration", "Rhizosphere", "Image Processing", " Computer-Assisted", "environmental imaging", "rhizosphere", "Soil Microbiology", "Bacillus subtilis", "Lactuca"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/178939/18/e2109176118.full.pdf"}, {"href": "https://pnas.org/doi/pdf/10.1073/pnas.2109176118"}, {"href": "https://doi.org/10.1101/2021.02.13.430456"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20National%20Academy%20of%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1101/2021.02.13.430456", "name": "item", "description": "10.1101/2021.02.13.430456", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1101/2021.02.13.430456"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-13T00:00:00Z"}}, {"id": "10.1038/s41538-021-00087-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:17:49Z", "type": "Journal Article", "created": "2020-09-12", "title": "Microbiome-based environmental monitoring of a dairy processing facility highlights the challenges associated with low microbial-load samples.", "description": "Abstract<p>Food processing environments can harbor microorganisms responsible for food spoilage or foodborne disease. Efficient and accurate identification of microorganisms throughout the food chain can allow the identification of sources of contamination and the timely implementation of control measures. Currently, microbial monitoring of the food chain relies heavily on culture-based techniques. These assays are determined on the microbes expected to be present in the environment, and thus do not cater for unexpected contaminants. Many culture-based assays are also unable to distinguish between undesirable taxa and closely related harmless species. Furthermore, even when multiple culture-based approaches are used in parallel, it is still not possible to comprehensively characterize the entire microbiology of a food-chain sample.</p><p>High throughput DNA sequencing represents a potential means through which microbial monitoring of the food chain can be enhanced. While sequencing platforms, such as the Illumina MiSeq, NextSeq and NovaSeq, are most typically found in research or commercial sequencing laboratories, newer portable platforms, such as the Oxford Nanopore Technologies (ONT) MinION, offer the potential for rapid analysis of food chain microbiomes. In this study, having initially assessed the ability of rapid MinION-based sequencing to discriminate between different microbes within a simple mock metagenomic mixture of related food spoilage, spore-forming microorganisms. Subsequently, we proceeded to compare the performance of both ONT and Illumina sequencing for environmental monitoring of an active food processing facility.</p><p>Overall, ONT MinION sequencing provided accurate classification to species level, which was comparable to Illumina-derived outputs. However, while the MinION-based approach provided a means of easy library preparations and portability, the high concentrations of DNA needed to run the rapid sequencing protocols was a limiting factor, requiring the random amplification of template DNA in order to generate sufficient material for analysis.</p>", "keywords": ["0301 basic medicine", "2. Zero hunger", "03 medical and health sciences", "Nutrition. Foods and food supply", "13. Climate action", "0206 medical engineering", "TX341-641", "Metagenomics", "02 engineering and technology", "Food microbiology", "TP368-456", "Food processing and manufacture", "Article"]}, "links": [{"href": "https://www.nature.com/articles/s41538-021-00087-2.pdf"}, {"href": "https://doi.org/10.1038/s41538-021-00087-2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/npj%20Science%20of%20Food", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41538-021-00087-2", "name": "item", "description": "10.1038/s41538-021-00087-2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41538-021-00087-2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-09-11T00:00:00Z"}}, {"id": "10.1073/pnas.2109176118", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:10Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "Abstract<p>Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.</p>", "keywords": ["0301 basic medicine", "570", "Microscopy", "Silicon", "0303 health sciences", "Temperature", "root-microbe interactions", "Equipment Design", "Biological Sciences", "Environment", "15. Life on land", "Plant Roots", "630", "Fluorescence", "Soil", "03 medical and health sciences", "Seedlings", "Calibration", "Rhizosphere", "Image Processing", " Computer-Assisted", "environmental imaging", "rhizosphere", "Soil Microbiology", "Bacillus subtilis", "Lactuca"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/178939/18/e2109176118.full.pdf"}, {"href": "https://pnas.org/doi/pdf/10.1073/pnas.2109176118"}, {"href": "https://doi.org/10.1073/pnas.2109176118"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20National%20Academy%20of%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1073/pnas.2109176118", "name": "item", "description": "10.1073/pnas.2109176118", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1073/pnas.2109176118"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-13T00:00:00Z"}}, {"id": "10.1080/10942912.2020.1716796", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:19Z", "type": "Journal Article", "created": "2020-01-23", "title": "Antioxidant activity, nutritional, and phenolic composition of sweet potato leaves as affected by harvesting period", "description": "In the present study, the nutritional and phenolic composition as well as the antioxidant activity of sweet potato leaves (SPL) harvested in 3 different periods were determined and compared. Furthermore, gray relational analysis was used to compare the comprehensive nutritional value. Results showed SPL HP1 had the highest protein value (30.8 \u00b1 0.4 g/100 g dw), while SPL HP3 had the highest content of vitamin C (104.6 \u00b1 4.9 mg/100 g dw), vitamin E (5.8 \u00b1 0.4 mg/100 g dw), total polyphenol content (9.1 \u00b1 0.3 g/100 g dw), antioxidant activity (DPPH: 7.4 \u00b1 0.1 g VcE/100 g dw; ABTS: 10.6 \u00b1 0.7 g VcE/100 g dw; FRAP: 0.617 \u00b1 0.005 \u00b5mol TroloxE/100 g dw), and comprehensive nutritional value (weighted gray relational grade 0.8336). The individual phenolic composition showed the presence of six caffeoylquinic acids, caffeic acid, and two flavonoids (quercetin and isoquercetin), which were significantly different among different harvest periods. In conclusion, HP3 was an optimal period for harvesting SPL.", "keywords": ["nutritional composition", "antioxidant activity", "Harvest period", "NUTRITIONAL COMPOSITION", "Food processing and manufacture", "HARVEST PERIOD", "Phenolic composition", "0404 agricultural biotechnology", "Antioxidant activity", "TX341-641", "https://purl.org/becyt/ford/2", "Sweet potato leaves", "2. Zero hunger", "harvest period", "Nutrition. Foods and food supply", "Qu\u00edmica", "04 agricultural and veterinary sciences", "TP368-456", "PHENOLIC COMPOSITION", "sweet potato leaves", "Nutritional composition", "SWEET POTATO LEAVES", "https://purl.org/becyt/ford/2.11", "0405 other agricultural sciences", "ANTIOXIDANT ACTIVITY", "phenolic composition"]}, "links": [{"href": "https://www.tandfonline.com/doi/pdf/10.1080/10942912.2020.1716796"}, {"href": "https://doi.org/10.1080/10942912.2020.1716796"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Food%20Properties", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1080/10942912.2020.1716796", "name": "item", "description": "10.1080/10942912.2020.1716796", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1080/10942912.2020.1716796"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.1080/23311932.2024.2437574", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:21Z", "type": "Journal Article", "created": "2024-12-14", "title": "Organic carbon sequestration potential of Slovenian agricultural soil and the impact of management practices on SOC stock", "description": "Improving soil management is crucial for mitigating climate change by increasing soil organic carbon (SOC). This study addresses the question of whether Slovenian agricultural soils can increase SOC stocks and achieve the 4per1000 initiative (4p1000.org). We calculated the SOC stocks, soil carbon sequestration (SCS) potential of the fine soil fraction (<20\u2009\u00b5m) (Csd) and the SCS potential of bulk soil for different agricultural uses in Slovenia. In addition, we conducted agricultural land management scenarios to determine a possible increase in SOC stocks. The results showed an average SOC stock of 94.7 t ha\u22121 in the agricultural soils of Slovenia. High Csd values were mainly found in croplands, intensive orchards and vineyards. The Csd (0\u201330\u2009cm) amounts to 16.3\u2009Mt SOC for the entire country. In addition, our results on SCS potential were compared with similar assessments from other European countries, where the range of bulk soil SCS potential was between 0.03 and 2.8\u2030 SOC change yr\u22121. With the current management of agricultural land in Slovenia, the SCS is almost balanced (+0.1\u2030). Different management scenarios showed that efficient fertilizer use, no-till, vegetation cover with biodiverse crop rotation and keeping residues on the surface lead to a significant SOC stock increase by 19.6 t ha\u22121 in 20\u2009years, which supports the 4per1000 initiative target (10.5\u2030).", "keywords": ["soil organic carbon", "conservation agriculture", "fine soil fraction", "S", "Soil organic carbon sequestration potential", "land use", "Agriculture", "TP368-456", "soil management", "Food processing and manufacture"]}, "links": [{"href": "https://www.tandfonline.com/doi/pdf/10.1080/23311932.2024.2437574"}, {"href": "https://doi.org/10.1080/23311932.2024.2437574"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Cogent%20Food%20%26amp%3B%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1080/23311932.2024.2437574", "name": "item", "description": "10.1080/23311932.2024.2437574", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1080/23311932.2024.2437574"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-13T00:00:00Z"}}, {"id": "10.1093/bioinformatics/btac037", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:24Z", "type": "Journal Article", "created": "2022-01-26", "title": "GenomeTornadoPlot: a novel R package for CNV visualization and focality analysis", "description": "Abstract                                   Motivation                   <p>Analysis of focal copy number variations (CNVs) is highly relevant for cancer research, as they pinpoint driver genes. More specifically, due to selective pressure oncogenes and tumor suppressor genes are more often affected by these events than neighboring passengers. In cases where multiple candidates co-reside in a genomic locus, careful comparison is required to either identify multigenic minimally deleted regions of synergistic co-mutations, or the true single driver gene. The study of focal CNVs in large cancer genome cohorts requires specialized visualization and statistical analysis.</p>                                                   Results                   <p>We developed the GenomeTornadoPlot R-package which generates gene-centric visualizations of CNV types, locations and lengths from cohortwise NGS data. Furthermore, the software enables the pairwise comparison of proximate genes to identify co-mutation patterns or driver-passenger hierarchies. The visual examination provided by GenomeTornadoPlot is further supported by adaptable local and global focality scoring. Integrated into the GenomeTornadoPlot R-Package is the comprehensive PCAWG database of CNVs, comprising 2976 cancer genome entities from 46 cohorts of the Pan-cancer Analysis of Whole Genomes project. The GenomeTornadoPlot R-package can be used to perform exploratory or hypothesis-driven analyses on the basis of the PCAWG data or in combination with data provided by the user.</p>                                                   Availability and implementation                   <p>GenomeTornadoPlot is written in R script and released via github: &amp;lt;https://github.com/chenhong-dkfz/GenomeTornadoPlot/&amp;gt;. The package is under the license of GPL-3.0.</p>", "keywords": ["570", "DNA Copy Number Variations", "ddc-570", "Genomics", "Oncogenes", "004 Data processing Computer science", "Software", "ddc-004", "570 Life sciences", "004", "3. Good health"]}, "links": [{"href": "https://archiv.ub.uni-heidelberg.de/volltextserver/34483/1/btac037.pdf"}, {"href": "https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/34483/1/btac037.pdf"}, {"href": "https://academic.oup.com/bioinformatics/article-pdf/38/7/2036/49009547/btac037.pdf"}, {"href": "https://doi.org/10.1093/bioinformatics/btac037"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bioinformatics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/bioinformatics/btac037", "name": "item", "description": "10.1093/bioinformatics/btac037", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/bioinformatics/btac037"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-31T00:00:00Z"}}, {"id": "10.1093/jxb/erab174", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:26Z", "type": "Journal Article", "created": "2020-12-03", "title": "Digging roots is easier with AI", "description": "Abstract<p>The scale of root quantification in research is often limited by the time required for sampling, measurement and processing samples. Recent developments in Convolutional Neural Networks (CNN) have made faster and more accurate plant image analysis possible which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of Machine Learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model, i.e., learning from labeled examples, can effectively exclude the debris by comparing the end-results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training and the derived measurements were compared with manual measurements. After 200 minutes of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76) and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.</p>", "keywords": ["0301 basic medicine", "root phenotyping", "profile wall", "root washing", "segmentation", "deep learning", "Convolutional neural network", "04 agricultural and veterinary sciences", "15. Life on land", "Soil", "03 medical and health sciences", "core-break", "monolith", "soil coring", "Image Processing", " Computer-Assisted", "0401 agriculture", " forestry", " and fisheries", "Neural Networks", " Computer", "Software"]}, "links": [{"href": "http://academic.oup.com/jxb/article-pdf/72/13/4680/38807872/erab174.pdf"}, {"href": "https://doi.org/10.1093/jxb/erab174"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Experimental%20Botany", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/jxb/erab174", "name": "item", "description": "10.1093/jxb/erab174", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/jxb/erab174"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-02T00:00:00Z"}}, {"id": "10.1103/physrevapplied.20.044047", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:34Z", "type": "Journal Article", "created": "2023-10-18", "title": "Asymmetric spoof-fluid-spoof acoustic waveguide and its application as a  CO2  sensor", "description": "Open AccessWe study pressure acoustic propagation in asymmetric spoof-fluid-spoof acoustic waveguides and its potential application in acoustic gas sensors. First, a stable and efficient analytical method is established for fast calculation of the dispersion curves based on spectral expansion and enforcement of continuity between segments at suitable collocation points. The analysis is validated by a commercial finite element software. The geometric design of the waveguide is then optimized for the emergence of a nearly-flat dispersion curve associated with vertical geometric asymmetry. The waveguide is fabricated using 3D printing technology and the measurement results corroborate the numerical simulations. Based on the nearly-flat dispersion curve supported by this waveguide, a CO$_2$ sensor is proposed allowing to relate the phase difference measured between two points in the waveguide to the composition of the gas in the waveguide. The proposed sensor is experimentally validated in a controlled environment and the measurement results match the computational predictions well. The sensor is robust with respect to noise and signal-recording duration due to fast phase measurements and shows high sensitivity to gas concentration due to reliance on the second, nearly-flat, dispersion curve. In addition, the sensor is label-free and low-cost, while exhibiting rapid response, low-maintenance requirements and potential for measurements in a wide range of CO$_2$ concentrations without saturation issues.", "keywords": ["Signal Processing (eess.SP)", "FOS: Electrical engineering", " electronic engineering", " information engineering", "FOS: Physical sciences", "Physics - Applied Physics", "Applied Physics (physics.app-ph)", "Electrical Engineering and Systems Science - Signal Processing"]}, "links": [{"href": "https://doi.org/10.1103/physrevapplied.20.044047"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Physical%20Review%20Applied", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1103/physrevapplied.20.044047", "name": "item", "description": "10.1103/physrevapplied.20.044047", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1103/physrevapplied.20.044047"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-10-18T00:00:00Z"}}, {"id": "10.1105/tpc.20.00318", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:34Z", "type": "Journal Article", "created": "2020-10-10", "title": "ARADEEPOPSIS, an Automated Workflow for Top-View Plant Phenomics using Semantic Segmentation of Leaf States", "description": "Linking plant phenotype to genotype is a common goal to both plant breeders and geneticists. However, collecting phenotypic data for large numbers of plants remain a bottleneck. Plant phenotyping is mostly image based and therefore requires rapid and robust extraction of phenotypic measurements from image data. However, because segmentation tools usually rely on color information, they are sensitive to background or plant color deviations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. ARADEEPOPSIS (https://github.com/Gregor-Mendel-Institute/aradeepopsis) uses semantic segmentation of top-view images to classify leaf tissue into three categories: healthy, anthocyanin rich, and senescent. This makes it particularly powerful at quantitative phenotyping of different developmental stages, mutants with aberrant leaf color and/or phenotype, and plants growing in stressful conditions. On a panel of 210 natural Arabidopsis (Arabidopsis thaliana) accessions, we were able to not only accurately segment images of phenotypically diverse genotypes but also to identify known loci related to anthocyanin production and early necrosis in genome-wide association analyses. Our pipeline accurately processed images of diverse origin, quality, and background composition, and of a distantly related Brassicaceae. ARADEEPOPSIS is deployable on most operating systems and high-performance computing environments and can be used independently of bioinformatics expertise and resources.", "keywords": ["0301 basic medicine", "0303 health sciences", "Genotype", "Large-Scale Biology Articles", "Arabidopsis", "Computational Biology", "Semantics", "Workflow", "Plant Leaves", "03 medical and health sciences", "Phenotype", "Image Processing", " Computer-Assisted", "Phenomics", "Software", "Genome-Wide Association Study"]}, "links": [{"href": "https://doi.org/10.1105/tpc.20.00318"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20Plant%20Cell", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1105/tpc.20.00318", "name": "item", "description": "10.1105/tpc.20.00318", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1105/tpc.20.00318"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-09T00:00:00Z"}}, {"id": "10.1109/JSTSP.2021.3054314", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:35Z", "type": "Journal Article", "created": "2021-01-25", "title": "Tensor Decomposition Learning for Compression of Multidimensional Signals", "description": "Multidimensional signals like multispectral images and color videos are becoming ubiquitous in modern times, constantly introducing challenges in data storage and transfer, and therefore demanding efficient compression strategies. Such high dimensional observations can be naturally encoded as tensors, exhibiting significant redundancies across dimensions. This property is exploited by tensor decomposition techniques that are being increasingly used for compactly encoding large multidimensional arrays. While efficient, these methods are incapable of utilizing prior information present in training data. In this paper, a novel tensor decomposition learning method is proposed for the compression of high dimensional signals. Specifically, instead of extracting independent bases for each example, our method learns an appropriate basis for each dimension from a set of training samples by solving a constrained optimization problem. As such, each sample is quantized and encoded into a reduced-size core tensor of coefficients that corresponds to the multilinear combination of the learned basis matrices. Furthermore, the proposed method employs a symbol encoding dictionary for binarizing the decomposition outputs. Experimental results on synthetic data and real satellite multispectral image sequences demonstrate the efficacy of our method, surpassing competing compression methods while offering the flexibility to handle arbitrary high dimensional data structures.", "keywords": ["Signal Processing", "Mathematical analysis", "0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "Tensor decomposition", "02 engineering and technology", "Electrical and Electronic Engineering", "Compression (physics)", "Mathematics"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/4200690/9393360/09335033.pdf?arnumber=9335033"}, {"href": "https://doi.org/10.1109/JSTSP.2021.3054314"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Journal%20of%20Selected%20Topics%20in%20Signal%20Processing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/JSTSP.2021.3054314", "name": "item", "description": "10.1109/JSTSP.2021.3054314", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/JSTSP.2021.3054314"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-01T00:00:00Z"}}, {"id": "10.1109/TMI.2017.2743819", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:35Z", "type": "Journal Article", "created": "2017-08-24", "title": "Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior", "description": "Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.", "keywords": ["Mice", "Image Processing", " Computer-Assisted", "0202 electrical engineering", " electronic engineering", " information engineering", "Animals", "Brain", "Humans", "02 engineering and technology", "Magnetic Resonance Imaging", "Algorithms", "Markov Chains"], "contacts": [{"organization": "Marko Pani\u0107, Jan Aelterman, Vladimir Crnojevi\u0107, Aleksandra Pi\u017eurica,", "roles": ["creator"]}]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/42/8053927/08016375.pdf?arnumber=8016375"}, {"href": "https://doi.org/10.1109/TMI.2017.2743819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Transactions%20on%20Medical%20Imaging", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/TMI.2017.2743819", "name": "item", "description": "10.1109/TMI.2017.2743819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/TMI.2017.2743819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-01T00:00:00Z"}}, {"id": "10.1109/jstars.2019.2958847", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:36Z", "type": "Journal Article", "created": "2020-01-22", "title": "Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers", "description": "Open AccessThis article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain\u2014interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Do\u00f1ana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.", "keywords": ["Teledetecci\u00f3", "550", "Interferometric coherence", "Geophysics. Cosmic physics", "ta1171", "0211 other engineering and technologies", "02 engineering and technology", "01 natural sciences", "land cover mapping", "ta216", "TC1501-1800", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "ta213", "QC801-809", "[SPI.ELEC] Engineering Sciences [physics]/Electromagnetism", "interferometric coherence", "Remote sensing", "synthetic aperture radar (SAR)", "15. Life on land", "[SPI.TRON] Engineering Sciences [physics]/Electronics", "SDG 11 - Sustainable Cities and Communities", "[SPI.TRON]Engineering Sciences [physics]/Electronics", "Ocean engineering", "Synthetic aperture radar (SAR)", "[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3", ":Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3 [\u00c0rees tem\u00e0tiques de la UPC]", "13. Climate action", "Teor\u00eda de la Se\u00f1al y Comunicaciones", "Sentinel-1", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "Land cover mapping", "Copernicus"]}, "links": [{"href": "https://doi.org/10.1109/jstars.2019.2958847"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Journal%20of%20Selected%20Topics%20in%20Applied%20Earth%20Observations%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/jstars.2019.2958847", "name": "item", "description": "10.1109/jstars.2019.2958847", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/jstars.2019.2958847"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.1109/tmi.2017.2743819", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:37Z", "type": "Journal Article", "created": "2017-08-24", "title": "Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior", "description": "Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.", "keywords": ["Mice", "Image Processing", " Computer-Assisted", "0202 electrical engineering", " electronic engineering", " information engineering", "Animals", "Brain", "Humans", "02 engineering and technology", "Magnetic Resonance Imaging", "Algorithms", "Markov Chains"], "contacts": [{"organization": "Marko Pani\u0107, Jan Aelterman, Vladimir Crnojevi\u0107, Aleksandra Pi\u017eurica,", "roles": ["creator"]}]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/42/8053927/08016375.pdf?arnumber=8016375"}, {"href": "https://doi.org/10.1109/tmi.2017.2743819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Transactions%20on%20Medical%20Imaging", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/tmi.2017.2743819", "name": "item", "description": "10.1109/tmi.2017.2743819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/tmi.2017.2743819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-01T00:00:00Z"}}, {"id": "10.1111/nph.18387", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:06Z", "type": "Journal Article", "created": "2020-04-18", "title": "RootPainter: deep learning segmentation of biological images with corrective annotation", "description": "<p>We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semi-automatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model.</p>", "keywords": ["Buildings and machinery", "0301 basic medicine", "phenotyping", "root nodule", "biopore", "interactive machine learning", "Research", "segmentation", "deep learning", "rhizotron", "Breeding and genetics", "Machine Learning", "Soil", "03 medical and health sciences", "Deep Learning", "GUI", "Farm nutrient management", "Image Processing", " Computer-Assisted", "Neural Networks", " Computer"]}, "links": [{"href": "https://www.biorxiv.org/content/10.1101/2020.04.16.044461v1.full.pdf"}, {"href": "https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.18387"}, {"href": "https://doi.org/10.1111/nph.18387"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/New%20Phytologist", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/nph.18387", "name": "item", "description": "10.1111/nph.18387", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/nph.18387"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-18T00:00:00Z"}}, {"id": "10.1128/msystems.00226-20", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:12Z", "type": "Journal Article", "created": "2020-04-06", "title": "Tracking the Dairy Microbiota from Farm Bulk Tank to Skimmed Milk Powder", "description": "<p>Microorganisms can enter and persist in dairy at several stages of the processing chain. Detection of microorganisms within dairy food processing is currently a time-consuming and often inaccurate process. This study provides evidence that high-throughput sequencing can be used as an effective tool to accurately identify microorganisms along the processing chain. In addition, it demonstrates that the populations of microbes change from raw milk to the end product. Routine implementation of high-throughput sequencing would elucidate the factors that influence population dynamics. This will enable a manufacturer to adopt control measures specific to each stage of processing and respond in an effective manner, which would ultimately lead to increased food safety and quality.</p>", "keywords": ["2. Zero hunger", "0301 basic medicine", "metagenomics", "0303 health sciences", "whole-milk silo", "collection tanker", "bulk tank milk", "Microbiology", "QR1-502", "3. Good health", "03 medical and health sciences", "skimmed milk silo", "skimmed milk powder", "microbiota", "dairy", "processing", "16S rRNA gene amplicon sequencing", "Research Article"]}, "links": [{"href": "https://journals.asm.org/doi/pdf/10.1128/mSystems.00226-20"}, {"href": "https://doi.org/10.1128/msystems.00226-20"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/mSystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1128/msystems.00226-20", "name": "item", "description": "10.1128/msystems.00226-20", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1128/msystems.00226-20"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-28T00:00:00Z"}}, {"id": "10.1146/annurev-food-052720-010751", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:17Z", "type": "Journal Article", "created": "2021-10-22", "title": "Next-generation food research: Use of meta-omic approaches for characterizing microbial communities along the food chain", "description": "<p> Microorganisms exist along the food chain and impact the quality and safety of foods in both positive and negative ways. Identifying and understanding the behavior of these microbial communities enable the implementation of preventative or corrective measures in public health and food industry settings. Current culture-dependent microbial analyses are time-consuming and target only specific subsets of microbes. However, the greater use of culture-independent meta-omic approaches has the potential to facilitate a thorough characterization of the microbial communities along the food chain. Indeed, these methods have shown potential in contributing to outbreak investigation, ensuring food authenticity, assessing the spread ofantimicrobial resistance, tracking microbial dynamics during fermentation and processing, and uncovering the factors along the food chain that impact food quality and safety. This review examines the community-based approaches, and particularly the application of sequencing-based meta-omics strategies, for characterizing microbial communities along the food chain. </p>", "keywords": ["0301 basic medicine", "2. Zero hunger", "0303 health sciences", "High-throughput sequencing", "Food Chain", "Food microbiome", "Microbiota", "high-throughput sequencing", "Meta-omic approaches", "food processing environment", "3. Good health", "meta-omic approaches", "03 medical and health sciences", "food-processing environment", "food microbiome; food-processing environment; high-throughput sequencing; meta-omic approaches; Fermentation; Food Industry; Food Chain; Microbiota", "food microbiome", "Fermentation", "Food Industry", "Food-processing environment"]}, "links": [{"href": "https://www.annualreviews.org/doi/pdf/10.1146/annurev-food-052720-010751"}, {"href": "https://doi.org/10.1146/annurev-food-052720-010751"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Annual%20Review%20of%20Food%20Science%20and%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1146/annurev-food-052720-010751", "name": "item", "description": "10.1146/annurev-food-052720-010751", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1146/annurev-food-052720-010751"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-25T00:00:00Z"}}, {"id": "10.1186/s12302-024-00873-1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:22Z", "type": "Journal Article", "created": "2024-03-11", "title": "SWAT\u2009+\u2009input data preparation in a scripted workflow: SWATprepR", "description": "Abstract<p>Input data collection, quality assurance and preparation are central but time_consuming steps in environmental modeling. Errors due to manual processing of model input data can result in an incorrect representation of an environmental system and may consequently lead to implausible model simulations. Correct input data preparation and thorough quality check at an early stage of the model setup procedure are essential to build confidence in model simulation results. Typically, in environmental model applications, many steps in the input data preparation phase have to be repeated with the inflow of new, additional or corrected data. In this study, we selected the widely used SWAT\uffe2\uff80\uff89+\uffe2\uff80\uff89ecohydrological model as an illustrative example to investigate challenges related to input data preparation. To assist in these tasks, we developed an R package named SWATprepR, which provides functions for typical and repeating SWAT\uffe2\uff80\uff89+\uffe2\uff80\uff89model input data preparation tasks. The package supports the preparation of weather input files, atmospheric deposition, soil parameters, crop rotations, and observed (control or calibration) data, to name a few, presently with focus on European applications. The SWATprepR functions are integrated in R script workflows and can help SWAT\uffe2\uff80\uff89+\uffe2\uff80\uff89modelers to avoid repetitive tasks, secure reproducibility and transparently document the data processing steps. Application of the package is illustrated with a test case of a SWAT\uffe2\uff80\uff89+\uffe2\uff80\uff89model for a small catchment in central Poland.</p", "keywords": ["Environmental sciences", "SWAT\u2009+\u2009model", "Environmental law", "R package", "0208 environmental biotechnology", "0207 environmental engineering", "GE1-350", "02 engineering and technology", "Input data processing", "K3581-3598", "Reproducibility", "Workflow"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1186/s12302-024-00873-1.pdf"}, {"href": "https://doi.org/10.1186/s12302-024-00873-1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Sciences%20Europe", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1186/s12302-024-00873-1", "name": "item", "description": "10.1186/s12302-024-00873-1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1186/s12302-024-00873-1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-11T00:00:00Z"}}, {"id": "10.1186/s40168-021-01131-9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:23Z", "type": "Journal Article", "created": "2021-10-14", "title": "Microbial colonization and resistome dynamics in food processing environments of a newly opened pork cutting industry during 1.5 years of activity", "description": "AbstractBackground<p>The microorganisms that inhabit food processing environments (FPE) can strongly influence the associated food quality and safety. In particular, the possibility that FPE may act as a reservoir of antibiotic-resistant microorganisms, and a hotspot for the transmission of antibiotic resistance genes (ARGs) is a concern in meat processing plants. Here, we monitor microbial succession and resistome dynamics relating to FPE through a detailed analysis of a newly opened pork cutting plant over 1.5 years of activity.</p>Results<p>We identified a relatively restricted principal microbiota dominated byPseudomonasduring the first 2 months, while a higher taxonomic diversity, an increased representation of other taxa (e.g.,Acinetobacter,Psychrobacter), and a certain degree of microbiome specialization on different surfaces was recorded later on. An increase in total abundance, alpha diversity, and \uffce\uffb2-dispersion of ARGs, which were predominantly assigned toAcinetobacterand associated with resistance to certain antimicrobials frequently used on pig farms of the region, was detected over time. Moreover, a sharp increase in the occurrence of extended-spectrum \uffce\uffb2-lactamase-producingEnterobacteriaceaeand vancomycin-resistantEnterococcaceaewas observed when cutting activities started. ARGs associated with resistance to \uffce\uffb2-lactams, tetracyclines, aminoglycosides, and sulphonamides frequently co-occurred, and mobile genetic elements (i.e., plasmids, integrons) and lateral gene transfer events were mainly detected at the later sampling times in drains.</p>Conclusions<p>The observations made suggest that pig carcasses were a source of resistant bacteria that then colonized FPE and that drains, together with some food-contact surfaces, such as equipment and table surfaces, represented a reservoir for the spread of ARGs in the meat processing facility.</p>", "keywords": ["0301 basic medicine", "2. Zero hunger", "0303 health sciences", "Bacteria", "Food Handling", "Swine", "Tecnolog\u00eda de los alimentos", "Research", "QR100-130", "610", "Food processing environments", "Antimicrobial resistance", "Gen\u00e9tica", "630", "Anti-Bacterial Agents", "Microbial ecology", "Red Meat", "03 medical and health sciences", "Genes", " Bacterial", "Pork Meat", "Animals", "Metagenomics"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1186/s40168-021-01131-9.pdf"}, {"href": "https://doi.org/10.1186/s40168-021-01131-9"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Microbiome", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1186/s40168-021-01131-9", "name": "item", "description": "10.1186/s40168-021-01131-9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1186/s40168-021-01131-9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-14T00:00:00Z"}}, {"id": "10.15454/q0xvvd", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:42Z", "type": "Dataset", "title": "CIRCASA DELIVERABLE D2.3: \u201cSynthesis report on knowledge demands and needs of stakeholders\u201d", "description": "In this report, we examine knowledge gaps identified by stakeholders. In this way, the find-ings support the creation of an international strategic research agenda for SOC, a central envisioned outcome of the CIRCASA project.", "keywords": ["Earth and Environmental Science", "Hardware_PERFORMANCEANDRELIABILITY", "Farming Systems and Practices", "Soils and soil sciences", "Agricultural Sciences", "Climate", "Earth and Environmental Sciences", "Soil Sciences", "Food and food processing", "Agricultural and Food Process Engineering", "Agriculture", " Forestry", " Horticulture", "Geosciences", "Farming Systems"]}, "links": [{"href": "https://doi.org/10.15454/q0xvvd"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/q0xvvd", "name": "item", "description": "10.15454/q0xvvd", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/q0xvvd"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.15454/syp4pe", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:42Z", "type": "Dataset", "title": "CIRCASA DELIVERABLE D2.2: \u201cAssessing barriers and solutions to the implementation of SOC sequestration options\u201d", "description": "Report on the key barriers and solutions for the implementation of SOC sequestration options across different geographic zones and what knowledge gaps need to be filled to overcome barriers.", "keywords": ["Earth and Environmental Science", "Farming Systems and Practices", "Soils and soil sciences", "Agricultural Sciences", "Climate", "Earth and Environmental Sciences", "Soil Sciences", "Food and food processing", "Agricultural and Food Process Engineering", "Agriculture", " Forestry", " Horticulture", "Geosciences", "Farming Systems"]}, "links": [{"href": "https://doi.org/10.15454/syp4pe"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/syp4pe", "name": "item", "description": "10.15454/syp4pe", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/syp4pe"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.3389/fsufs.2020.00115", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:51Z", "type": "Journal Article", "created": "2020-08-21", "title": "A Decision Support Model for Assessing the Water Regulation and Purification Potential of Agricultural Soils Across Europe", "description": "Water regulation and purification (WR) function is defined as \u201cthe capacity of the soil to remove harmful compounds and the capacity of the soil to receive, store and conduct water for subsequent use and to prevent droughts, flooding and erosion.\u201d It is a crucial function that society expects agricultural soils to deliver, contributing to quality water supply for human needs and in particular for ensuring food security. The complexity of processes involved and the intricate tradeoff with other necessary soil functions requires decision support tools for best management of WR function. However, the effects of farm and soil management practices on the delivery of the WR function has not been fully addressed by decision support tools for farmers. This work aimed to develop a decision support model for the management of the WR function performed by agricultural soils. The specific objectives of this paper were (i) to construct a qualitative decision support model to assess the water regulation and purification capacity of agricultural soils at field level, to (ii) conduct sensitivity analysis of the model; and (iii) to validate the model with independent empirical data. The developed decision support model for WR is a hierarchical qualitative model with 5 levels and has 27 basic attributes describing the soil (S), environment (E), and management (M) attributes of the field site to be assessed. The WR model is composed of 3 sub-models concerning (1) soil water storage, (2) P and sediment loss in runoff, and (3) N leaching in percolating water. The WR decision support model was validated using a representative dataset of 94 field sites from across Europe and had an overall accuracy of 75% when compared to the empirically derived values across these sites. This highly accurate, reliable, and useful decision support model for assessing the capacity of agricultural soils to perform the WR function can be used by farmers and advisors help manage and protect their soil resources for the future. This model has also been incorporated into the Soil Navigator decision support tool which provides simultaneous assessment of the WR function and other important soil functions for agriculture.", "keywords": ["2. Zero hunger", "decision support tool", "Nutrition. Foods and food supply", "food security", "04 agricultural and veterinary sciences", "soil functions", "TP368-456", "15. Life on land", "water quality", "01 natural sciences", "INCREASE", "Food processing and manufacture", "6. Clean water", "climate change", "13. Climate action", "EXTREME EVENTS", "water regulation", "11. Sustainability", "MANAGEMENT", "0401 agriculture", " forestry", " and fisheries", "TX341-641", "water purification", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.3389/fsufs.2020.00115"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Sustainable%20Food%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fsufs.2020.00115", "name": "item", "description": "10.3389/fsufs.2020.00115", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fsufs.2020.00115"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-21T00:00:00Z"}}, {"id": "10.3390/rs13122261", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:08Z", "type": "Journal Article", "created": "2021-06-09", "title": "DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images", "description": "<p>The form of a remote sensing index is generally empirically defined, whether by choosing specific reflectance bands, equation forms or its coefficients. These spectral indices are used as preprocessing stage before object detection/classification. But no study seems to search for the best form through function approximation in order to optimize the classification and/or segmentation. The objective of this study is to develop a method to find the optimal index, using a statistical approach by gradient descent on different forms of generic equations. From six wavebands images, five equations have been tested, namely: linear, linear ratio, polynomial, universal function approximator and dense morphological. Few techniques in signal processing and image analysis are also deployed within a deep-learning framework. Performances of standard indices and DeepIndices were evaluated using two metrics, the dice (similar to f1-score) and the mean intersection over union (mIoU) scores. The study focuses on a specific multispectral camera used in near-field acquisition of soil and vegetation surfaces. These DeepIndices are built and compared to 89 common vegetation indices using the same vegetation dataset and metrics. As an illustration the most used index for vegetation, NDVI (Normalized Difference Vegetation Indices) offers a mIoU score of 63.98% whereas our best models gives an analytic solution to reconstruct an index with a mIoU of 82.19%. This difference is significant enough to improve the segmentation and robustness of the index from various external factors, as well as the shape of detected elements.</p>", "keywords": ["multi-spectral", "[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing", "multispectral", "Science", "0211 other engineering and technologies", "[SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "02 engineering and technology", "Spectral indice", "Deep-learning", "image; precision agriculture; spectral indices; multi-spectral; deep-learning; vegetation segmentation", "deep-learning", "[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing", "[SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "[SDV.BV]Life Sciences [q-bio]/Vegetal Biology", "[SDV.BV] Life Sciences [q-bio]/Vegetal Biology", "image", "precision agriculture", "Precision agriculture", "Vegetation segmentation", "Multi-spectral", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "004", "Image", "vegetation segmentation", "spectral indices", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/12/2261/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/12/2261/pdf"}, {"href": "https://doi.org/10.3390/rs13122261"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13122261", "name": "item", "description": "10.3390/rs13122261", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13122261"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-06-09T00:00:00Z"}}, {"id": "10.3390/rs8110938", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:09Z", "type": "Journal Article", "created": "2016-11-11", "title": "Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples", "description": "<p>This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency\uffe2\uff80\uff99s (ESA) Sen2Cor algorithm, the platform processes ESA\uffe2\uff80\uff99s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).</p>", "keywords": ["550", "reflectance", "t\u00e9l\u00e9d\u00e9tection", "Science", "0211 other engineering and technologies", "02 engineering and technology", "7. Clean energy", "remote sensing", "Traitement du signal et de l'image", "atmospheric correction", "remote sensing;sentinel-2;atmospheric correction;Sen2Cor;LAI;broadband HDRF", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing", "9. Industry and infrastructure", "sentinel-2", "Q", "Signal and Image processing", "04 agricultural and veterinary sciences", "broadband HDRF", "620", "LAI", "atmosph\u00e8re", "Sen2Cor", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "donn\u00e9e satellitaire"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/8/11/938/pdf"}, {"href": "https://doi.org/10.3390/rs8110938"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs8110938", "name": "item", "description": "10.3390/rs8110938", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs8110938"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-11-11T00:00:00Z"}}, {"id": "10.3389/fsufs.2024.1410205", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:52Z", "type": "Journal Article", "created": "2024-08-26", "title": "Building a solid foundation: advancing evidence synthesis in agri-food systems science", "description": "<p>Enhancing the reliability of literature reviews and evidence synthesis is crucial for advancing the transformation of agriculture and food (agri-food) systems as well as for informed decisions and policy making. In this perspective, we argue that evidence syntheses in the field of agri-food systems research often suffer from a suite of methodological limitations that substantially increase the risk of bias, i.e., publication and selection bias, resulting in unreliable and potentially flawed conclusions and, consequently, poor decisions (e.g., policy direction, investment, research foci). We assessed 926 articles from the Collaboration for Environmental Evidence Database of Evidence Reviews (CEEDER) and recent examples from agri-food systems research to support our reasoning. The analysis of articles from CEEDER (n\uffe2\uff80\uff89=\uffe2\uff80\uff89926) specifically indicates poor quality (Red) in measures to minimize subjectivity during critical appraisal (98% of all reviews), application of the eligibility criteria (97%), cross-checking of extracted data by more than one reviewer (97%), critical appraisal of studies (88%), establishment of an a priori method/protocol (86%), and transparent reporting of eligibility decisions (65%). Additionally, deficiencies (Amber) were found in most articles (&amp;gt;50%) regarding the investigation and discussion of variability in study findings (89%), comprehensiveness of the search (78%), definition of eligibility criteria (72%), search approach (64%), reporting of extracted data for each study (59%), consideration and discussion of the limitations of the synthesis (56%), documentation of data extraction (54%) and regarding the statistical approach (52%). To enhance the quality of evidence synthesis in agri-food science, review authors should use tried-and-tested methodologies and publish peer-reviewed a priori protocols. Training in evidence synthesis methods should be scaled, with universities playing a crucial role. It is the shared duty of research authors, training providers, supervisors, reviewers, and editors to ensure that rigorous and robust evidence syntheses are made available to decision-makers. We argue that all these actors should be cognizant of these common mistakes to avoid publishing unreliable syntheses. Only by thinking as a community can we ensure that reliable evidence is provided to support appropriate decision-making in agri-food systems science.</p", "keywords": ["Agricultura--Aspectes econ\u00f2mics", "bias", "330", "systematic reviews", "610", "Ressenyes sistem\u00e0tiques (Investigaci\u00f3 m\u00e8dica)", "01 natural sciences", "Food processing and manufacture", "12. Responsible consumption", "03 medical and health sciences", "0302 clinical medicine", "Blas", "TX341-641", "Agri-food systems", "reproducibility", "0105 earth and related environmental sciences", "Agriculture--Economic aspects", "2. Zero hunger", "Nutrition. Foods and food supply", "Sustainable agriculture", "agri-food systems", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria", "evidence synthesis", "Agriculture", "Systematic reviews", "TP368-456", "Nutrition--Environmental aspects", "Reproducibility", "sustainable agriculture", "Evidence synthesis", "Evidence syntheses"], "contacts": [{"organization": "Pierre Ellssel, Georg K\u00fcstner, Magdalena Kaczorowska-Dolowy, Eduardo V\u00e1zquez, Claudia Di Bene, Honghong Li, Honghong Li, Diego Brizuela-Torres, Diego Brizuela-Torres, Elansurya Elangovan Vennila, Jos\u00e9 Luis Vicente-Vicente, Jos\u00e9 Luis Vicente-Vicente, Daniel Itzamna Avila-Ortega, Daniel Itzamna Avila-Ortega,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3389/fsufs.2024.1410205"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Sustainable%20Food%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fsufs.2024.1410205", "name": "item", "description": "10.3389/fsufs.2024.1410205", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fsufs.2024.1410205"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-26T00:00:00Z"}}, {"id": "10.3389/fpls.2020.00889", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:50Z", "type": "Journal Article", "created": "2020-06-23", "title": "An Optimized in situ Quantification Method of Leaf H2O2 Unveils Interaction Dynamics of Pathogenic and Beneficial Bacteria in Wheat", "description": "Hydrogen peroxide (H2O2) functions as an important signaling molecule in plants during biotic interactions. However, the extent to which H2O2 accumulates during these interactions and its implications in the development of disease symptoms is unclear. In this work, we provide a step-by-step optimized protocol for in situ quantification of relative H2O2 concentrations in wheat leaves infected with the pathogenic bacterium Pseudomonas syringae pv. atrofaciens (Psa), either alone or in the presence of the beneficial bacterium Herbaspirillum seropedicae (RAM10). This protocol involved the use of 3-3'diaminobenzidine (DAB) staining method combined with image processing to conduct deconvolution and downstream analysis of the digitalized leaf image. The application of a linear regression model allowed to relate the intensity of the pixels resulting from DAB staining with a given concentration of H2O2. Decreasing H2O2 accumulation patterns were detected at increasing distances from the site of pathogen infection, and H2O2 concentrations were different depending on the bacterial combinations tested. Notably, Psa-challenged plants in presence of RAM10 accumulated less H2O2 in the leaf and showed reduced necrotic symptoms, pointing to a potential role of RAM10 in reducing pathogen-triggered H2O2 levels in young wheat plants.", "keywords": ["biotic interactions", "0301 basic medicine", "0303 health sciences", "03 medical and health sciences", "color deconvolution", "hydrogen peroxide (H2O2)", "Plant culture", "Plant Science", "3-3\u2032diaminobenzidine (DAB)", "image processing", "SB1-1110"]}, "links": [{"href": "https://doi.org/10.3389/fpls.2020.00889"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fpls.2020.00889", "name": "item", "description": "10.3389/fpls.2020.00889", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fpls.2020.00889"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-23T00:00:00Z"}}, {"id": "10.3389/fsufs.2022.937802", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:51Z", "type": "Journal Article", "created": "2022-08-04", "title": "European Union's policymaking on sustainable waste management and circularity in agroecosystems: The potential for innovative interactions between science and decision-making", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The European Economic Community (EEC) and later the European Union (EU) have issued policies on waste during the last 50 years. This paper aims at analyzing EEC and EU's policymaking on waste management and circularity in agroecosystems as compared with other sectors of waste management (e.g., municipal, industrial, construction waste, etc.). Even if founded on the same general principles, and especially the precautionary principle, policymaking on waste and by-product management in agroecosystems differs from waste management in other sectors. In particular, agricultural waste management has been excluded from the European Waste Framework Directive, from its start in 1975 to this day. The issue of waste and by-products in agroecosystems has been addressed in multiple Directives and Regulations, historically aiming at reducing the potential negative impacts of residual organic matter application in agriculture. In the last decade, the swiftly growing interest for circular economy has triggered a breakthrough in traditional waste management, potentially affecting all economic sectors and enforcing systemic perspectives rather than more conventional \u201csilo\u201d approaches. Circularity in agroecosystems should thus become a major subject of EU's policymaking, but may suffer from its lack of a general framework, contrary to waste in other sectors. Moreover, agricultural valorization of urban residual organic streams may face several roadblocks in between differing legislations for agroecosystems and for \u201cnon-agricultural\u201d systems. A systemic approach of the question of residual matter in agroecosystems, backing a strong policymaking framework for the sector, would be necessary in this context. Science-policymaking interactions are necessary to tackle these issues and should take innovative forms to address their complexity. Policy briefs, Policy Labs and the new European Commission Scientific Advice Mechanism represent existing innovative tools to take the topic of policymaking for sustainable waste management and circularity in agroecosystems forward.</p></article>", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "organic waste agricultural waste by-product valorization waste management circular economy bioeconomy policymaking European Union", "330", "Nutrition. Foods and food supply", "circular economy", "05 social sciences", "TP368-456", "01 natural sciences", "7. Clean energy", "Food processing and manufacture", "12. Responsible consumption", "by-product valorization", "13. Climate action", "[SDE]Environmental Sciences", "0502 economics and business", "11. Sustainability", "policymaking", "waste management", "TX341-641", "European Union", "agricultural waste", "bioeconomy", "organic waste", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.3389/fsufs.2022.937802"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Sustainable%20Food%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fsufs.2022.937802", "name": "item", "description": "10.3389/fsufs.2022.937802", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fsufs.2022.937802"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-04T00:00:00Z"}}, {"id": "10.3390/agronomy11030427", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:54Z", "type": "Journal Article", "created": "2021-02-26", "title": "Differing Phosphorus Crop Availability of Aluminium and Calcium Precipitated Dairy Processing Sludge Potential Recycled Alternatives to Mineral Phosphorus Fertiliser", "description": "<p>The European dairy industry generates large volumes of wastewater from milk and dairy food processing. Removal of phosphorus (P) by complexing with metal (e.g., aluminium, calcium) cations in P rich sludge is a potential P source for agricultural reuse and P recycling. However, there is a significant knowledge gap concerning the plant availability of this complexed P in comparison to conventional mineral P fertiliser. The current absence of information on plant P bioavailability of dairy processing sludge (DPS) limits the ability of farmers and nutrient management advisors to incorporate it correctly into fertiliser programmes. The present study examined the most common types of dairy sludge\uffe2\uff80\uff94(1) aluminium-precipitated sludge (\uffe2\uff80\uff9cAl-DPS\uffe2\uff80\uff9d) and (2) calcium-precipitated lime-stabilised sludge (\uffe2\uff80\uff9cCa-DPS\uffe2\uff80\uff9d) at field scale to assess P availability in grassland versus mineral P fertiliser over a growing season. The experimental design was a randomised complete block with five replications. Crop yield and P uptake were assessed for 4 harvests. The initial soil test P was at a low level and the experimental treatments were super phosphate at 15, 30, 40, 50 and 60 kg P ha\uffe2\uff88\uff921, two dairy sludge applied at 40 kg P ha\uffe2\uff88\uff921 (comparison was made with mineral P at same application rate) and a zero P control applied in a single application at the beginning of the growing season. Results showed a significant positive slope in the relationship between P uptake response and mineral P application rate indicating the suitability of the experimental site for P availability assessment. The P bioavailability of Al- and Ca-DPS varied greatly between treatments. The P fertiliser replacement value based on the 1st harvest was 50 and 16% increased to 109 and 31% cumulatively over the four harvests for Al- and Ca-DPS, respectively. The Al concentration in Al-DPS did not limit P bioavailability, but low P bioavailability from Ca-DPS can be associated with its high Ca content that can lead to formation of low soluble Ca-P compounds at alkaline pH conditions with a high Ca/P ratio. These findings show that P availability from dairy sludge can be quite different depending on treatment process. Consequently, it is critical to have P availability information as well as total P content available to ensure the application rate meets crop requirements without creating environmental risk by over application.</p>", "keywords": ["fertiliser replacement value", "2. Zero hunger", "soil test P", "S", "dairy processing sludge", "phosphorus bioavailability", "Agriculture", "04 agricultural and veterinary sciences", "crop yield", "01 natural sciences", "6. Clean water", "12. Responsible consumption", "Earth and Environmental Sciences", "N use efficiency", "0401 agriculture", " forestry", " and fisheries", "grassland", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/3/427/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/3/427/pdf"}, {"href": "https://doi.org/10.3390/agronomy11030427"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy11030427", "name": "item", "description": "10.3390/agronomy11030427", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy11030427"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-26T00:00:00Z"}}, {"id": "10.3390/app12010341", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:56Z", "type": "Journal Article", "created": "2021-12-31", "title": "Replacing Mineral Fertilisers for Bio-Based Fertilisers in Potato Growing on Sandy Soil: A Case Study", "description": "<p>The refinement level of bio-based fertilisers (BBFs) can influence environmental and agronomic performance. This study analyses the environmental and agronomic effect of different BBFs on potato growing in sandy soil. A less refined product (liquid fraction of digestate (LFD)), two refined products (ammonium sulphate (AS) and potassium concentrate (KC)), and mineral fertilizer (MF) are compared by conducting: (i) a nitrogen (N) incubation experiment where the N release rate of the BBFs is determined, (ii) a greenhouse gas emission experiment where N2O, CO2, and CH4 emissions after BBF application are measured, (iii) a pot experiment where the nutrient fertiliser replacement value (NFRV) of the BBF is calculated, and (iv) a full-scale field trial where the potato quality and quantity and the remaining N residues in the soil after harvest are assessed. The N release rate and the NFRV of AS (142 \uffc2\uffb1 19% and 1.13, respectively) was higher compared with the LFD (113 \uffc2\uffb1 24% and 1.04) and MF (105 \uffc2\uffb1 16% and 1.00). Lowest N2O emissions were observed after the application of the less refined product (0.02 \uffc2\uffb1 0.01 per 100 g N applied) and highest for MF urea (0.11 \uffc2\uffb1 0.02 per 100 g N applied). In the full-scale field trial, no significant difference in potato yield was observed in the plots that received manure in combination with BBF or MF. This study showed that all three BBFs can safely be used in potato growing on sandy soils. However, the adoption of BBFs can be stimulated by (i) solving the practical issues that occurred during the application of LFD, (ii) making sure BBFs are on the list of RENURE materials so they can legally replace mineral fertiliser, and (iii) reducing the surplus of slurry manure to stimulate the use and fair pricing of BBF products.</p>", "keywords": ["Agriculture and Food Sciences", "Technology", "QH301-705.5", "QC1-999", "NITROUS-OXIDE EMISSIONS", "environmental impact", "Environmental impact", "agricultural circularity", "Biology (General)", "agricultural circularity; sustainable agriculture; environmental impact; manure processing; GHG emissions; fertiliser replacement value", "QD1-999", "manure processing", "fertiliser replacement value", "2. Zero hunger", "Fertiliser replacement value", "MANURE", "Agricultural circularity", "T", "Physics", "Sustainable agriculture", "04 agricultural and veterinary sciences", "Engineering (General). Civil engineering (General)", "6. Clean water", "sustainable agriculture", "GHG emissions", "Chemistry", "13. Climate action", "Earth and Environmental Sciences", "RESIDUES", "0401 agriculture", " forestry", " and fisheries", "TA1-2040", "Manure processing", "NITRATE"]}, "links": [{"href": "http://www.mdpi.com/2076-3417/12/1/341/pdf"}, {"href": "https://www.mdpi.com/2076-3417/12/1/341/pdf"}, {"href": "https://doi.org/10.3390/app12010341"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/app12010341", "name": "item", "description": "10.3390/app12010341", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/app12010341"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-30T00:00:00Z"}}, {"id": "10.3390/bioengineering4020055", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:56Z", "type": "Journal Article", "created": "2017-06-12", "title": "Recent Advances and Challenges towards Sustainable Polyhydroxyalkanoate (PHA) Production", "description": "<p>Sustainable biofuels, biomaterials, and fine chemicals production is a critical matter that research teams around the globe are focusing on nowadays. Polyhydroxyalkanoates represent one of the biomaterials of the future due to their physicochemical properties, biodegradability, and biocompatibility. Designing efficient and economic bioprocesses, combined with the respective social and environmental benefits, has brought together scientists from different backgrounds highlighting the multidisciplinary character of such a venture. In the current review, challenges and opportunities regarding polyhydroxyalkanoate production are presented and discussed, covering key steps of their overall production process by applying pure and mixed culture biotechnology, from raw bioprocess development to downstream processing.</p>", "keywords": ["0106 biological sciences", "0301 basic medicine", "Technology", "Renewable feedstock", "QH301-705.5", "biopolymers", "Review", "7. Clean energy", "01 natural sciences", "12. Responsible consumption", "mixed microbial consortia", "03 medical and health sciences", "Biopolymers", "/dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy; name=SDG 7 - Affordable and Clean Energy", "renewable feedstock", "Pure cultures", "pure cultures", "enrichment strategy", "Biology (General)", "Synthetic biology", "Polyhydroxyalkanoates", "T", "polyhydroxyalkanoates", "Mixed microbial consortia", "downstream processing", "Downstream processing", "13. Climate action", "Enrichment strategy", "synthetic biology"]}, "links": [{"href": "http://www.mdpi.com/2306-5354/4/2/55/pdf"}, {"href": "https://doi.org/10.3390/bioengineering4020055"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bioengineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/bioengineering4020055", "name": "item", "description": "10.3390/bioengineering4020055", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/bioengineering4020055"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-06-11T00:00:00Z"}}, {"id": "10.3390/rs10050761", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:06Z", "type": "Journal Article", "created": "2018-05-15", "title": "Unsupervised Classification Algorithm for Early Weed Detection in Row-Crops by Combining Spatial and Spectral Information", "description": "<p>In agriculture, reducing herbicide use is a challenge to reduce health and environmental risks while maintaining production yield and quality. Site-specific weed management is a promising way to reach this objective but requires efficient weed detection methods. In this paper, an automatic image processing has been developed to discriminate between crop and weed pixels combining spatial and spectral information extracted from four-band multispectral images. Image data was captured at 3 m above ground, with a camera (multiSPEC 4C, AIRINOV, Paris) mounted on a pole kept manually. For each image, the field of view was approximately 4 m \uffc3\uff97 3 m and the resolution was 6 mm/pix. The row crop arrangement was first used to discriminate between some crop and weed pixels depending on their location inside or outside of crop rows. Then, these pixels were used to automatically build the training dataset concerning the multispectral features of crop and weed pixel classes. For each image, a specific training dataset was used by a supervised classifier (Support Vector Machine) to classify pixels that cannot be correctly discriminated using only the initial spatial approach. Finally, inter-row pixels were classified as weed and in-row pixels were classified as crop or weed depending on their spectral characteristics. The method was assessed on 14 images captured on maize and sugar beet fields. The contribution of the spatial, spectral and combined information was studied with respect to the classification quality. Our results show the better ability of the spatial and spectral combination algorithm to detect weeds between and within crop rows. They demonstrate the improvement of the weed detection rate and the improvement of its robustness. On all images, the mean value of the weed detection rate was 89% for spatial and spectral combination method, 79% for spatial method, and 75% for spectral method. Moreover, our work shows that the plant in-line sowing can be used to design an automatic image processing and classification algorithm to detect weed without requiring any manual data selection and labelling. Since the method required crop row identification, the method is suitable for wide-row crops and high spatial resolution images (at least 6 mm/pix).</p>", "keywords": ["[SDV.SA]Life Sciences [q-bio]/Agricultural sciences", "2. Zero hunger", "[SDV.SA] Life Sciences [q-bio]/Agricultural sciences", "[SDV]Life Sciences [q-bio]", "weed detection", "SVM", "04 agricultural and veterinary sciences", "spatial information", "15. Life on land", "630", "6. Clean water", "image processing", "[SDV] Life Sciences [q-bio]", "multispectral information", "automatic training data set generation", "automatic training dataset generation", "0401 agriculture", " forestry", " and fisheries", "weed detection;image processing;spatial information;multispectral information;automatic training data set generation", "weed detection; image processing; spatial information; multispectral information; automatic training data set generation; SVM"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/5/761/pdf"}, {"href": "https://doi.org/10.3390/rs10050761"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs10050761", "name": "item", "description": "10.3390/rs10050761", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10050761"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-05-15T00:00:00Z"}}, {"id": "10.48550/arxiv.2112.03814", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:27Z", "type": "Report", "created": "2022-05-16", "title": "A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images", "description": "Open Access12 pages, ICIAP 2021", "keywords": ["FOS: Computer and information sciences", "computer vision", " semantic segmentation", " aerial images", " incremental learning", "Computer Vision and Pattern Recognition (cs.CV)", "Image and Video Processing (eess.IV)", "Computer Science - Computer Vision and Pattern Recognition", "FOS: Electrical engineering", " electronic engineering", " information engineering", "0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "02 engineering and technology", "Electrical Engineering and Systems Science - Image and Video Processing"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2962571/1/ICIAP_2021_arnaudo_contrastive_distillation_camera_ready.pdf"}, {"href": "https://iris.polito.it/bitstream/11583/2962571/3/978-3-031-06430-2_62.pdf"}, {"href": "https://link.springer.com/content/pdf/10.1007/978-3-031-06430-2"}, {"href": "https://link.springer.com/content/pdf/10.1007/978-3-031-06430-2_62"}, {"href": "https://doi.org/10.48550/arxiv.2112.03814"}, {"rel": "self", "type": "application/geo+json", "title": "10.48550/arxiv.2112.03814", "name": "item", "description": "10.48550/arxiv.2112.03814", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.48550/arxiv.2112.03814"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5220/0009169301030110", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:55Z", "type": "Journal Article", "created": "2020-03-19", "title": "Two-step Multi-spectral Registration Via Key-point Detector and Gradient Similarity: Application to Agronomic Scenes for Proxy-sensing", "description": "The potential of multi-spectral images is growing rapidly in precision agriculture, and is currently based on the use of multi-sensor cameras. However, their development usually concerns aerial applications and their parameters are optimized for high altitudes acquisition by drone (UAV \u2248 50 meters) to ensure surface coverage and reduce technical problems. With the recent emergence of terrestrial robots (UGV), their use is diverted for nearby agronomic applications. Making it possible to explore new agronomic applications, maximizing specific traits extraction (spectral index, shape, texture \u2026) which requires high spatial resolution. The problem with these cameras is that all sensors are not aligned and the manufacturers\u2019 methods are not suitable for close-field acquisition, resulting in offsets between spectral images and degrading the quality of extractable informations. We therefore need a solution to accurately align images in such condition. In this study we propose a two-steps method applied to the six-bands Airphen multi-sensor camera with (i) affine correction using pre-calibrated matrix at different heights, the closest transformation can be selected via internal GPS and (ii) perspective correction to refine the previous one, using key-points matching between enhanced gradients of each spectral bands. Nine types of key-point detection algorithms (ORB, GFTT, AGAST, FAST, AKAZE, KAZE, BRISK, SURF, MSER) with three different modalities of parameters were evaluated on their speed and performances, we also defined the best reference spectra on each of them. The results show that GFTT is the most suitable methods for key-point extraction using our enhanced gradients, and the best spectral reference was identified to be the band centered on 570 nm for this one. Without any treatment the initial error is about 62 px, with our method, the remaining residual error is less than 1 px, where the manufacturer\u2019s involves distortions and loss of information with an estimated residual error of approximately 12 px", "keywords": ["03 medical and health sciences", "0302 clinical medicine", "Registration", "Registration", " Multi-spectral imagery", " Precision farming", " Feature descriptor", "0202 electrical engineering", " electronic engineering", " information engineering", "[SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "Precision farming", "Feature descriptor", "Multi-spectral imagery", "02 engineering and technology", "15. Life on land", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing"]}, "links": [{"href": "https://doi.org/10.5220/0009169301030110"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%2015th%20International%20Joint%20Conference%20on%20Computer%20Vision%2C%20Imaging%20and%20Computer%20Graphics%20Theory%20and%20Applications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5220/0009169301030110", "name": "item", "description": "10.5220/0009169301030110", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5220/0009169301030110"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10451/47259", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:24:49Z", "type": "Journal Article", "created": "2020-06-23", "title": "An Optimized in situ Quantification Method of Leaf H2O2 Unveils Interaction Dynamics of Pathogenic and Beneficial Bacteria in Wheat", "description": "Hydrogen peroxide (H2O2) functions as an important signaling molecule in plants during biotic interactions. However, the extent to which H2O2 accumulates during these interactions and its implications in the development of disease symptoms is unclear. In this work, we provide a step-by-step optimized protocol for in situ quantification of relative H2O2 concentrations in wheat leaves infected with the pathogenic bacterium Pseudomonas syringae pv. atrofaciens (Psa), either alone or in the presence of the beneficial bacterium Herbaspirillum seropedicae (RAM10). This protocol involved the use of 3-3'diaminobenzidine (DAB) staining method combined with image processing to conduct deconvolution and downstream analysis of the digitalized leaf image. The application of a linear regression model allowed to relate the intensity of the pixels resulting from DAB staining with a given concentration of H2O2. Decreasing H2O2 accumulation patterns were detected at increasing distances from the site of pathogen infection, and H2O2 concentrations were different depending on the bacterial combinations tested. Notably, Psa-challenged plants in presence of RAM10 accumulated less H2O2 in the leaf and showed reduced necrotic symptoms, pointing to a potential role of RAM10 in reducing pathogen-triggered H2O2 levels in young wheat plants.", "keywords": ["biotic interactions", "0301 basic medicine", "0303 health sciences", "03 medical and health sciences", "color deconvolution", "hydrogen peroxide (H2O2)", "Plant culture", "Plant Science", "3-3\u2032diaminobenzidine (DAB)", "image processing", "SB1-1110"]}, "links": [{"href": "https://repositorio.ulisboa.pt/bitstream/10451/47259/1/Carril%20et%20al%20Front%20Plant%20Sci%202020.pdf"}, {"href": "https://doi.org/10451/47259"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10451/47259", "name": "item", "description": "10451/47259", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10451/47259"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-23T00:00:00Z"}}, {"id": "10.5281/zenodo.11200024", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:22:14Z", "type": "Dataset", "title": "Segmentation of particulate organic matter in X-ray Computed Tomography images of soil aggregates with deep convolutional networks", "description": "unspecifiedThis is a dataset that accompanies the paper entitled \u2018Segmentation of particulate organic matter in X-ray Computed Tomography images of soil aggregates with deep convolutional networks\u2019 by Oliveira, A.B., Bordonal, R.O., Peixinho, A.Z., Carvalho, J.L.N., Ferreira, T.R. The files will be publicly accessible when the paper is published.", "keywords": ["Soil carbon stability", "Image processing", "Interactive machine learning", "Deep learning", "Synchrotron"], "contacts": [{"organization": "Oliveira, Aline, Bordonal, Ricardo, Peixinho, Alan, Carvalho, Jo\u00e3o Luis, Ferreira, Talita,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.11200024"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.11200024", "name": "item", "description": "10.5281/zenodo.11200024", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.11200024"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.13972269", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:22:30Z", "type": "Report", "created": "2024-10-22", "title": "Soil Data Assimilation Guidance", "description": "The Soil Data Assimiliation Guidance wiki is a collaborative effort to collect and describe hands-on good practices on data assimilation in the soil domain, with a focus on Europe. The term Soil data assimilation\u00a0has been chosen by the autors as an alternative to the terms harmonisation and standardisation, which already have a specific meaning in the soil domain:    standardisation; aligning soil data to a common model, using common codelists.  harmonisation; transforming results from observations and measurements to values as if all results for a property are measured using the same procedure, by applying so called Pedotransfer Functions (PTF).   The process of assimilation also aims to capture additional aspects, such as finding, downloading and using the data. Most of these aspects are also well described as part of the\u00a0FAIR principles.  The \u201cInfrastructure for Spatial Information in the European Community\u201d (INSPIRE) directive has been and is an important effort for standardisation in the environmental data domain, therefore this wiki has many links to INSPIRE sources. Because INSPIRE adopts industry standards, this wiki does reference common standards from ISO (International Organization for Standardization), Open Geospatial Consortium, Global Soils Partnership, IANA (Internet Assigned Numbers Authority) and W3C (World Wide Web Consortium), giving it a global relevance.  This guide was initiatied by ISRIC - World Soil Information and Wageningen Environmental Research as part of the European Joint Program on Soil (EJP Soil) with funding from the European Union\u2019s horizon 2020 research and innovation programme under grant agreement No. 862695.", "keywords": ["EJP Soil", "Data processing", "Soil sciences", "OGC", "INSPIRE", "FAIR", "Data science"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13972269"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13972269", "name": "item", "description": "10.5281/zenodo.13972269", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13972269"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-22T00:00:00Z"}}, {"id": "10.5281/zenodo.13972270", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:22:30Z", "type": "Report", "created": "2024-10-22", "title": "Soil Data Assimilation Guidance", "description": "The Soil Data Assimiliation Guidance wiki is a collaborative effort to collect and describe hands-on good practices on data assimilation in the soil domain, with a focus on Europe. The term Soil data assimilation\u00a0has been chosen by the autors as an alternative to the terms harmonisation and standardisation, which already have a specific meaning in the soil domain:    standardisation; aligning soil data to a common model, using common codelists.  harmonisation; transforming results from observations and measurements to values as if all results for a property are measured using the same procedure, by applying so called Pedotransfer Functions (PTF).   The process of assimilation also aims to capture additional aspects, such as finding, downloading and using the data. Most of these aspects are also well described as part of the\u00a0FAIR principles.  The \u201cInfrastructure for Spatial Information in the European Community\u201d (INSPIRE) directive has been and is an important effort for standardisation in the environmental data domain, therefore this wiki has many links to INSPIRE sources. Because INSPIRE adopts industry standards, this wiki does reference common standards from ISO (International Organization for Standardization), Open Geospatial Consortium, Global Soils Partnership, IANA (Internet Assigned Numbers Authority) and W3C (World Wide Web Consortium), giving it a global relevance.  This guide was initiatied by ISRIC - World Soil Information and Wageningen Environmental Research as part of the European Joint Program on Soil (EJP Soil) with funding from the European Union\u2019s horizon 2020 research and innovation programme under grant agreement No. 862695.", "keywords": ["EJP Soil", "Data processing", "Soil sciences", "OGC", "INSPIRE", "FAIR", "Data science"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13972270"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13972270", "name": "item", "description": "10.5281/zenodo.13972270", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13972270"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-22T00:00:00Z"}}, {"id": "10.5281/zenodo.15043864", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:22:55Z", "type": "Report", "title": "Post-processing & interactive visualisation of optimisation results. Deliverable D5.2 of the EU Horizon 2020 project OPTAIN", "description": "Deliverable report D5.2 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756)  Summary\u00a0Multi-objective optimisation is a powerful approach for generating a set of Pareto optimal design alternatives that decision-makers can evaluate in order to select the most-suitable configuration. In practice, however, selecting from a large number of Pareto optimal solutions can be daunting. The objective of this report is to enable researchers and stakeholders to assess the optimisation outputs produced in OPTAINs previous Task 5.2 in a structured manner, to render the results tangible and understandable, and to maximise their use for the subsequent stakeholder consultation.  This report describes the tool ParetoPick-R, including how to run it, its data input requirements and the processes it employs. ParetoPick-R allows (1) to make the complex optimisation outputs understandable through various intuitive visualisation techniques, including for the links between the objective space and the decision space of Natural/Small Water Retention Measures (NSWRM) implementation plans. (2) It implements a methodology for reducing the high number of solutions from the previous optimisation to a manageable number while reducing information loss, and (3) allows to perform an Analytical Hierarchy Process for stakeholders to assign priorities based on pairwise preferences in a structured manner.  This report is useful for researchers and stakeholders from OPTAIN and beyond working with complex optimisation problems who want to analyse their results in\u00a0a structured and meaningful way and render them actionable.", "keywords": ["CoMOLA", "combination", "SWAT+", "NSWRM", "post-processing", "H2020", "OPTAIN", "interactive visualisation", "stakeholder support", "R tool", "multi-objective optimization", "allocation", "Pareto solutions", "Analytical Hierarchy Process", "pareto pruning", "clustering"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15043864"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15043864", "name": "item", "description": "10.5281/zenodo.15043864", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15043864"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-18T00:00:00Z"}}, {"id": "20.500.11820/03f81a44-477a-4a8c-b34d-85892c85bd6f", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:28Z", "type": "Journal Article", "created": "2017-10-13", "title": "An Explicit Structural Model of Root Hair and Soil Interactions Parameterised by Synchrotron X-ray Computed Tomography", "description": "The rhizosphere is a zone of fundamental importance for understanding the dynamics of nutrient acquisition by plant roots. The canonical difficulty of experimentally investigating the rhizosphere led long ago to the adoption of mathematical models, the most sophisticated of which now incorporate explicit representations of root hairs and rhizosphere soil. Mathematical upscaling regimes, such as homogenisation, offer the possibility of incorporating into larger-scale models the important mechanistic processes occurring at the rhizosphere scale. However, we lack concrete descriptions of all the features required to fully parameterise models at the rhizosphere scale. By combining synchrotron X-ray computed tomography (SRXCT) and a novel root growth assay, we derive a three-dimensional description of rhizosphere soil structure suitable for use in multi-scale modelling frameworks. We describe an approach to mitigate sub-optimal root hair detection via structural root hair growth modelling. The growth model is explicitly parameterised with SRXCT data and simulates three-dimensional root hair ideotypes in silico, which are suitable for both ideotypic analysis and parameterisation of 3D geometry in mathematical models. The study considers different hypothetical conditions governing root hair interactions with soil matrices, with their respective effects on hair morphology being compared between idealised and image-derived soil/root geometries. The studies in idealised geometries suggest that packing arrangement of soil affects hair tortuosity more than the particle diameter. Results in field-derived soil suggest that hair access to poorly mobile nutrients is particularly sensitive to the physical interaction between the growing hairs and the phase of the soil in which soil water is present (i.e. the hydrated textural phase). The general trends in fluid-coincident hair length with distance from the root, and their dependence on hair/soil interaction mechanisms, are conserved across Cartesian and cylindrical geometries.", "keywords": ["Plant biology", "2. Zero hunger", "0301 basic medicine", "0303 health sciences", "X-ray CT", "Biomedical imaging and signal processing", "Mathematical Concepts", "15. Life on land", "Models", " Biological", "Plant Roots", "root hairs", "Soil", "03 medical and health sciences", "Imaging", " Three-Dimensional", "in silico", "structural modelling", "synchrotron", "Rhizosphere", "Original Article", "Computer Simulation", "rhizosphere", "Tomography", " X-Ray Computed", "Synchrotrons"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/s11538-017-0350-x.pdf"}, {"href": "https://doi.org/20.500.11820/03f81a44-477a-4a8c-b34d-85892c85bd6f"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bulletin%20of%20Mathematical%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11820/03f81a44-477a-4a8c-b34d-85892c85bd6f", "name": "item", "description": "20.500.11820/03f81a44-477a-4a8c-b34d-85892c85bd6f", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11820/03f81a44-477a-4a8c-b34d-85892c85bd6f"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-13T00:00:00Z"}}, {"id": "10.5281/zenodo.4482322", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:27Z", "type": "Other", "title": "Video", "description": "Instruction video for recreating research results.", "keywords": ["Image processing", "Tutorial"], "contacts": [{"organization": "Bojana, Ivo\u0161evi\u0107", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4482322"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4482322", "name": "item", "description": "10.5281/zenodo.4482322", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4482322"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-30T00:00:00Z"}}, {"id": "10.5281/zenodo.4482323", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:27Z", "type": "Other", "title": "Video", "description": "Instruction video for recreating research results.", "keywords": ["Image processing", "Tutorial"], "contacts": [{"organization": "Bojana, Ivo\u0161evi\u0107", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4482323"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4482323", "name": "item", "description": "10.5281/zenodo.4482323", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4482323"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-30T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=processing&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=processing&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=processing&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=processing&offset=50", "hreflang": "en-US"}], "numberMatched": 73, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-07-26T05:10:34.906167Z"}