Papers by Giovanni L'Abate
Standards to describe soil properties are well established, with many ISO specifications and a fe... more Standards to describe soil properties are well established, with many ISO specifications and a few international thesauri available for specific applications. Besides, in recent years, the European directive on “Infrastructure for Spatial Information in the European Community (INSPIRE)” has brought together most of the existing standards into a well defined model. However, the adoption of these standards so far has not reached the level of semantic interoperability, defined in the paper, which would facilitate the building of data services that reuse and combine data from different sources.
This paper reviews standards for describing soil data and reports on the work done withinthe EC funded agINFRA project to apply Linked Data technologies to existing standards anddata in order to improve the interoperability of soil datasets. The main result of this work istwofold. First, an RDF vocabulary for soil concepts based on the UML INSPIRE model waspublished. Second, a KOS (Knowledge Organization System) for soil data was publishedand mapped to existing relevant KOS, based on the analysis of the SISI database of theCREA of Italy. This work also has a methodological value, in that it proposes and appliesa methodology to standardize metadata used in local scientific databases, a very commonsituation in the scientific domain. Finally, this work aims at contributing towards a wideradoption of the INSPIRE directive, by providing an RDF version of it.
World Soils Book Series, 2013
There is a general consensus that the main factors causing desertification are soil, climate, veg... more There is a general consensus that the main factors causing desertification are soil, climate, vegetation and land management. The first problem that arises in assessing desertification risk is the integration of different data sets in the same model. At the moment, in Italy it is possible to find or to derive databases covering continuously all the national territory for land use, morphology, lithology, climatic data and vegetation indexes, although at different reference scales. On the contrary, the relevant soil information for the evaluation of desertification processes (i.e. soil depth, available water capacity, salinity, moisture regimes …) is referred to punctual observations. Our project focuses on a spatialization methodology applied to about 15,000 soil observations of the Italian National Soil Database, in order to integrate soil data with the other geographic databases. We generalized punctual soil data through the identification of the relationship between soils and their environmental pedo-genetic factors (morphological pattern and processes, lithology, land uses) in the different pedo-geographic contexts. The Land components of Land subsystems (1:250,000) are the geographic units used as base for the spatialization. The methodology estimates desertified, sensitive and vulnerable lands integrating soil characteristics to Landsat and Aster images, NDVI index, orthophotos, an original land cover database at 1:100,000 scale, DEM with 20m resolution, socio-economic databases, digital geological map at 1:500,000 scale, Soil regions, Land systems, Land subsystems and field surveys of local experts.
Cited By (since 1996): 4, Export Date: 11 January 2013, Source: Scopus, Language of Original Docu... more Cited By (since 1996): 4, Export Date: 11 January 2013, Source: Scopus, Language of Original Document: English, Correspondence Address: Costantini, E.A.C.; CRA, Istituto Sperimentale per lo Studio e la Difesa del Suolo, p.za M. D'Azeglio 30, 50121, Firenze, Italy; email: [email protected], References: PROVINCIALE, A., SIENA, D., (2005) Uso del suolo [Online], , HTTP://GEOSERVER.ETELNET.IT/METADATA, verified on 21.05.2005;
Agriculture, Ecosystems & Environment, 2011
Land use strongly influences soil properties and unsuitable practices lead to degradation of soil... more Land use strongly influences soil properties and unsuitable practices lead to degradation of soil and environmental quality. The aim of this study was to assess the impact of different land uses on some chemical properties of soils developed from Pliocene clays, within hilly environments of central and southern Italy. The areas investigated are located in Vicarello di Volterra (Pisa, Tuscany), S. Quirico d'Orcia (Siena, Tuscany) and Soveria Simeri (Catanzaro, Calabria). Within each area different land uses were compared, including a natural ecosystem (Mediterranean bush), a perennial grass or pasture and an intensive crop (wheat, as monoculture or in rotation). The soils were sampled at 0.0-0.1, 0.1-0.2 and 0.2-0.4 m depth and analysed for particle size, pH, bulk density, cation exchange capacity and exchangeable cations, total organic carbon (TOC) and humified carbon (HC) concentrations, organic carbon stock and total N. The stratification ratio of soil organic carbon was calculated to characterize soil organic carbon distribution with depth. At all sites, soil under Mediterranean bush contained the largest amounts of TOC (as both concentration and stock), HC, total N and exchangeable K, together with the highest cation exchange capacity and the lowest pH values. The decrease in soil OC stock with land use change from natural to agricultural ecosystem was 65-85% to 0.1 m depth, 55-82% to 0.2 m depth and 44-76% to 0.4 m depth, with the lowest decrements for perennial grass from S. Quirico and the highest decrement for continuous wheat from Soveria Simeri. Continuous wheat cropping, based on conventional tillage, proved to be the least sustainable land use. At Soveria Simeri, the organic carbon content under pasture was not significantly larger than under wheat cultivation, probably because of grazing mismanagement; however, organic carbon under pasture was more humified. At S. Quirico, the perennial grass resulted in a significant increase in soil organic carbon at the soil surface relative to the wheat cultivation, while at Vicarello no differences were observed between alfalfa/wheat rotation and perennial grass. Our results lead to the questioning of sustainability of intensive cereal farming and uncontrolled grazing in the considered environments, emphasizing the need for greater attention to conservative land managements.
Standards to describe soil properties are well established, with many ISO specifications and a fe... more Standards to describe soil properties are well established, with many ISO specifications and a few international thesauri available for specific applications. Besides, in recent years, the European directive on “Infrastructure for Spatial Information in the European Community (INSPIRE)” has brought together most of the existing standards into a well defined model. However, the adoption of these standards so far has not reached the level of semantic interoperability, defined in the paper, which would facilitate the building of data services that reuse and combine data from different sources.
This paper reviews standards for describing soil data and reports on the work done within the EC funded agINFRA project to apply Linked Data technologies to existing standards and data in order to improve the interoperability of soil datasets. The main result of this work is twofold. First, an RDF vocabulary for soil concepts based on the UML INSPIRE model was published. Second, a KOS (Knowledge Organization System) for soil data was published and mapped to existing relevant KOS, based on the analysis of the SISI database of the CREA of Italy. This work also has a methodological value, in that it proposes and applies a methodology to standardize metadata used in local scientific databases, a very common situation in the scientific domain. Finally, this work aims at contributing towards a wider adoption of the INSPIRE directive, by providing an RDF version of it.
agINFRA (www.aginfra.eu) is a project co-funded by the European Commission under its Seventh Fram... more agINFRA (www.aginfra.eu) is a project co-funded by the European Commission under its Seventh Framework Programme that tries to introduce the agricultural scientific communities into the vision of open and participatory data-intensive science. agINFRA aims to remove existing obstacles concerning the data sharing and open access to scientific information and agriculture‘ data as well as to improve the preparedness of agricultural scientific communities to face, manage and exploit the abundance of relevant data that is available and can support agricultural research.
The agricultural domain includes a wide variety of increasingly complex, multi-disciplinary topics. Subjects vary from plant science and horticulture to agricultural engineering and
agricultural economics to the environment generally and include an ever-growing array of interrelated research issues such as the linkages between climate change on the one hand and food security, or the loss of agro-biodiversity, or pressure on individual species on the other.
Scientists from all over the world are extensively researching those different subjects and
thereby consuming as well as producing large volumes of data.
The integration process of the services accessing those data requires a registry of all the existing systems, a challenge that has started since the beginning of the project (agINFRA started on the 15th of October 2011 and will last three years). Many of those systems will be efficiently and securely accessed through single web entry points by both end users and system/data
maintainers.
This contribution aims to demonstrate how the adoption of the Catania Science Gateway
Framework (www.catania-science-gateways.it) can have a key role during and also beyond the agINFRA project lifetime providing a unique environment able to deal with this heterogeneity of systems. This work will describe the Science Gateway (http://aginfra-sg.ct.infn.it/) developed by the INFN Dpt. of Catania and registered as a Service Provider of several Identity Federations, which together with the adoption of the CLEVER cloud middleware, can provide a unique interface able to seamlessly access the different services of the project. Among others, the integration and use of the WebGIS-enabled Italian Soil Information System (ISIS), developed by the Agrobiology and Pedology Research Centre of the Italian Agricultural Research Council, will be shown.
This very challenging target could be reached only thanks to the adoption of widely accepted standards such as SAGA and SAML that ensure the sustainability, reliability and scalability of the proposed architecture.
The assessment of class frequency in soil map legends is affected by uncertainty, especially at s... more The assessment of class frequency in soil map legends is affected by uncertainty, especially at small scales where generalization is greater. The aim of this study was to test the hypothesis that data mining techniques provide better estimation of class frequency than traditional deterministic pedology in a national soil map.
In the 1:5,000,000 map of Italian soil regions, the soil classes are the WRB reference soil groups (RSGs). Different data mining techniques, namely neural networks, random forests, boosted tree, classification and regression tree, and supported vector machine (SVM), were tested and the last one gave the best RSG predictions using selected auxiliary variables and 22,015 classified soil profiles. The five most frequent RSGs resulting from the two approaches were compared. The outcomes were validated with a Bayesian approach applied to a subset of 10% of geographically representative profiles, which were kept out before data processing. The validation provided the values of both positive and negative prediction abilities.
The most frequent classes were equally predicted by the two methods, which differed however from the forecast of the other classes. The Bayesian validation indicated that the SVM method was more reliable than the deterministic pedological approach and that both approaches were more confident in predicting the absence rather than the presence of a soil type.
The agINFRA project focuses on the production of interoperable data in agriculture, starting from... more The agINFRA project focuses on the production of interoperable data in agriculture, starting from the vocabularies and Knowledge Organization Systems (KOSs) used to describe and classify them. In this paper we report on our first steps in the direction of publishing agricultural Linked Open Data (LOD), focusing in particular on germplasm data and soil data, which are still widely missing from the LOD landscape, seemingly because information managers in this field are still not very familiar with LOD practices.
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Papers by Giovanni L'Abate
This paper reviews standards for describing soil data and reports on the work done withinthe EC funded agINFRA project to apply Linked Data technologies to existing standards anddata in order to improve the interoperability of soil datasets. The main result of this work istwofold. First, an RDF vocabulary for soil concepts based on the UML INSPIRE model waspublished. Second, a KOS (Knowledge Organization System) for soil data was publishedand mapped to existing relevant KOS, based on the analysis of the SISI database of theCREA of Italy. This work also has a methodological value, in that it proposes and appliesa methodology to standardize metadata used in local scientific databases, a very commonsituation in the scientific domain. Finally, this work aims at contributing towards a wideradoption of the INSPIRE directive, by providing an RDF version of it.
This paper reviews standards for describing soil data and reports on the work done within the EC funded agINFRA project to apply Linked Data technologies to existing standards and data in order to improve the interoperability of soil datasets. The main result of this work is twofold. First, an RDF vocabulary for soil concepts based on the UML INSPIRE model was published. Second, a KOS (Knowledge Organization System) for soil data was published and mapped to existing relevant KOS, based on the analysis of the SISI database of the CREA of Italy. This work also has a methodological value, in that it proposes and applies a methodology to standardize metadata used in local scientific databases, a very common situation in the scientific domain. Finally, this work aims at contributing towards a wider adoption of the INSPIRE directive, by providing an RDF version of it.
The agricultural domain includes a wide variety of increasingly complex, multi-disciplinary topics. Subjects vary from plant science and horticulture to agricultural engineering and
agricultural economics to the environment generally and include an ever-growing array of interrelated research issues such as the linkages between climate change on the one hand and food security, or the loss of agro-biodiversity, or pressure on individual species on the other.
Scientists from all over the world are extensively researching those different subjects and
thereby consuming as well as producing large volumes of data.
The integration process of the services accessing those data requires a registry of all the existing systems, a challenge that has started since the beginning of the project (agINFRA started on the 15th of October 2011 and will last three years). Many of those systems will be efficiently and securely accessed through single web entry points by both end users and system/data
maintainers.
This contribution aims to demonstrate how the adoption of the Catania Science Gateway
Framework (www.catania-science-gateways.it) can have a key role during and also beyond the agINFRA project lifetime providing a unique environment able to deal with this heterogeneity of systems. This work will describe the Science Gateway (http://aginfra-sg.ct.infn.it/) developed by the INFN Dpt. of Catania and registered as a Service Provider of several Identity Federations, which together with the adoption of the CLEVER cloud middleware, can provide a unique interface able to seamlessly access the different services of the project. Among others, the integration and use of the WebGIS-enabled Italian Soil Information System (ISIS), developed by the Agrobiology and Pedology Research Centre of the Italian Agricultural Research Council, will be shown.
This very challenging target could be reached only thanks to the adoption of widely accepted standards such as SAGA and SAML that ensure the sustainability, reliability and scalability of the proposed architecture.
In the 1:5,000,000 map of Italian soil regions, the soil classes are the WRB reference soil groups (RSGs). Different data mining techniques, namely neural networks, random forests, boosted tree, classification and regression tree, and supported vector machine (SVM), were tested and the last one gave the best RSG predictions using selected auxiliary variables and 22,015 classified soil profiles. The five most frequent RSGs resulting from the two approaches were compared. The outcomes were validated with a Bayesian approach applied to a subset of 10% of geographically representative profiles, which were kept out before data processing. The validation provided the values of both positive and negative prediction abilities.
The most frequent classes were equally predicted by the two methods, which differed however from the forecast of the other classes. The Bayesian validation indicated that the SVM method was more reliable than the deterministic pedological approach and that both approaches were more confident in predicting the absence rather than the presence of a soil type.
This paper reviews standards for describing soil data and reports on the work done withinthe EC funded agINFRA project to apply Linked Data technologies to existing standards anddata in order to improve the interoperability of soil datasets. The main result of this work istwofold. First, an RDF vocabulary for soil concepts based on the UML INSPIRE model waspublished. Second, a KOS (Knowledge Organization System) for soil data was publishedand mapped to existing relevant KOS, based on the analysis of the SISI database of theCREA of Italy. This work also has a methodological value, in that it proposes and appliesa methodology to standardize metadata used in local scientific databases, a very commonsituation in the scientific domain. Finally, this work aims at contributing towards a wideradoption of the INSPIRE directive, by providing an RDF version of it.
This paper reviews standards for describing soil data and reports on the work done within the EC funded agINFRA project to apply Linked Data technologies to existing standards and data in order to improve the interoperability of soil datasets. The main result of this work is twofold. First, an RDF vocabulary for soil concepts based on the UML INSPIRE model was published. Second, a KOS (Knowledge Organization System) for soil data was published and mapped to existing relevant KOS, based on the analysis of the SISI database of the CREA of Italy. This work also has a methodological value, in that it proposes and applies a methodology to standardize metadata used in local scientific databases, a very common situation in the scientific domain. Finally, this work aims at contributing towards a wider adoption of the INSPIRE directive, by providing an RDF version of it.
The agricultural domain includes a wide variety of increasingly complex, multi-disciplinary topics. Subjects vary from plant science and horticulture to agricultural engineering and
agricultural economics to the environment generally and include an ever-growing array of interrelated research issues such as the linkages between climate change on the one hand and food security, or the loss of agro-biodiversity, or pressure on individual species on the other.
Scientists from all over the world are extensively researching those different subjects and
thereby consuming as well as producing large volumes of data.
The integration process of the services accessing those data requires a registry of all the existing systems, a challenge that has started since the beginning of the project (agINFRA started on the 15th of October 2011 and will last three years). Many of those systems will be efficiently and securely accessed through single web entry points by both end users and system/data
maintainers.
This contribution aims to demonstrate how the adoption of the Catania Science Gateway
Framework (www.catania-science-gateways.it) can have a key role during and also beyond the agINFRA project lifetime providing a unique environment able to deal with this heterogeneity of systems. This work will describe the Science Gateway (http://aginfra-sg.ct.infn.it/) developed by the INFN Dpt. of Catania and registered as a Service Provider of several Identity Federations, which together with the adoption of the CLEVER cloud middleware, can provide a unique interface able to seamlessly access the different services of the project. Among others, the integration and use of the WebGIS-enabled Italian Soil Information System (ISIS), developed by the Agrobiology and Pedology Research Centre of the Italian Agricultural Research Council, will be shown.
This very challenging target could be reached only thanks to the adoption of widely accepted standards such as SAGA and SAML that ensure the sustainability, reliability and scalability of the proposed architecture.
In the 1:5,000,000 map of Italian soil regions, the soil classes are the WRB reference soil groups (RSGs). Different data mining techniques, namely neural networks, random forests, boosted tree, classification and regression tree, and supported vector machine (SVM), were tested and the last one gave the best RSG predictions using selected auxiliary variables and 22,015 classified soil profiles. The five most frequent RSGs resulting from the two approaches were compared. The outcomes were validated with a Bayesian approach applied to a subset of 10% of geographically representative profiles, which were kept out before data processing. The validation provided the values of both positive and negative prediction abilities.
The most frequent classes were equally predicted by the two methods, which differed however from the forecast of the other classes. The Bayesian validation indicated that the SVM method was more reliable than the deterministic pedological approach and that both approaches were more confident in predicting the absence rather than the presence of a soil type.
The awareness about the cultural value of soils has also been widened from the increasing collaboration between palaeopedologists, palaeontologists and archaeologists. Although there is still a lack of widespread organic work in Italy, an increasing number of pedologists have substantially amplified their sensibility towards the cultural values of the soil (L’Abate and Costantini, 2000).
The aim of this work was to create a database of the soil cultural heritage of Italy that would easily promote Italian soilsites in the web.
1. The agINFRA Linked Data layer Valeria Pesce Global Forum on Agricultural Research (GFAR) Giovanni L’Abate Consiglio per la Ricerca e la sperimentazione in Agricoltura Centro di ricerca per l’agrobiologia e la pedologia (CRA-ABP) Luca Matteis Koraljka Golub Research Data Alliance 4th Plenary Meeting 22-24 September 2014, Amsterdam Agricultural Data Interoperability Interest Group agINFRA project EC 7th framework program INFRA-2011-1.2.2 - Grant agr. no: 283770
2. agINFRA - Background • agINFRA: FP7 project EC 7th framework program INFRA-2011-1.2.2 - Grant agr. no: 283770 Objective: Promoting data sharing and development of trust in agricultural sciences • agINFRA Knowledge Fair co-located with the Agricultural Data Interoperability Interest group meeting at the RDA 4th Plenary Meeting • Types of data covered by agINFRA: bibliographic, educational, germplasm, soil
3. Interoperability 1 They are often both called vocabularies Metadata elements to describe individual “things” (entities, datums, series…) Aka metadata sets, metadata element sets, vocabularies Sets of values for (some of) the metadata elements Aka controlled vocabularies, authority data, value vocabularies, Knowledge Organization Systems (KOSs)
4. Various flavors of vocabularies Title Author(s) Abstract Subject(s) Publication date Publication place Type of document other features… Entity to be describedType? Bibliographic resource for describing bibliographic resources Metadata vocabulary Authority data KOS “Value vocabularies” Data of type Person Authority data Data of type Geographic location “Description vocabularies” Controlled list Concepts suitable for organizing by Topic Concepts suitable for organizing by Type for describing people Metadata vocabularyfor describing geographic places Ontology
5. Names of things URIs of things Links to other URIs Metadata vocabularies RDFs / OWL KOS SKOS Names of metadata elements URIs of classes and properties Links to other URIs Serialization into RDF Interoperability 2: RDF and Linked Data http://purl.org/dc/elements/1.1/contributorhttp://purl.org/ontology/bibo/editor“Editor” rdfs:subPropertyOf http://aims.fao.org/aos/agrovoc/c_6599 http://id.loc.gov/authorities/sh85113862#concept“Rice” skos:exactMatch
6. Names of metadata elements URIs of classes and properties Databases / tables / series Names of things URIs of things Links to other URIs http://vocabularies.aginfra.eu/soil#isObservedOnLocation • Then, other complex things like “URI de-referencing” and “content negotiation”… (some good triple store platforms do it out of the box) Interoperability 2: RDF and Linked Data http://purl.org/ontology/bibo/editor“Editor” https://aginfra- sg.ct.infn.it/rdf/cncp/resource/ObservedSoilSite/ 16.4CLcch1-1 http://vocabularies.aginfra.eu/soil#Obs ervedSoilProfile “Observed soil 16.4CLcch1” rdfs:type Serialization into RDF “Observed in location”
7. Tools used in agINFRA • For building and managing SKOS: the FAO VocBench • For publishing KOSs as Linked Data: SKOS loaded into Allegrograph • For building and publishing RDF vocabularies: Neologism • For publishing data as Linked Data: D2RQ from database to RDF > mapping to published classes and properties Links are provided in the last slide
8. Linked Data in agINFRA • Linked Data Vocabularies – Reference to existing relevant RDF vocabularies and SKOS – New RDF vocabularies only when not existing (e.g. soil ontology) – New KOS only when: • Not existing • Mapping needed between local concepts and published concepts • Extension needed • Linked Data datasets – Bibliographic data: AGRIS triple store – Germplasm data: • CAAS Linked Data API (presented later) • CRA triple store (presented later) – Soil data: CRA triple store (presented later)
9. agINFRA LOD vocabularies 9 Vocabularies. aginfra.eu agINFRA VocBench agINFRA Neologism http://202.73.13.50:55481/aginfra/ VEST Registry http://vocabularies.aginfra.eu http://vocabularies.aginfra.eu CIARD RING Existing vocabularies KOSs Metadata / ontologies New agINFRA vocabularies http://aims.fao.org/vest-registry http://ring.ciard.net TOOLS agINFRA shop CIARD directories http://202.45.139.84:100 35/catalogs/fao/repositori es/agINFRA Triple store
10. URLs of agINFRA Linked Data vocabulary platforms • agINFRA overview of vocabularies: http://vocabularies.aginfra.eu • New agINFRA Soil Vocabulary: http://vocabularies.aginfra.eu/soil# • VocBench instances: http://202.73.13.50:55481/aginfra/ http://artemide.art.uniroma2.it/vocbench2 • Allegrograph triple store of agINFRA KOSs: http://202.45.139.84:10035/catalogs/fao/repositories/ag INFRA
11. Namespaces of agINFRA new vocabularies • agINFRA Soil vocabulary: http://vocabularies.aginfra.eu/soil# • CRA Soil Terms: http://data.entecra.it/rdf/kos/soil/ or http://soilmaps.entecra.it/rdf/kos/soil/ • CRA Germplasm Terms: http://data.entecra.it/rdf/germplasm/soil/ or http://planta-res.entecra.it/rdf/kos/germplasm/ • agINFRA Resource Types Terms: http://aginfra.eu/voc/aginfra_doctypes/ • agINFRA Educational Resources Terms: http://aginfra.eu/voc/aginfra_eduterms/
12. Example 1: the Soil Terms KOS Rationale: • CRA had local lists of values for several soil properties • In most cases those values mapped conceptually with terms in published KOSs Local values published as new KOS with mappings to USDA Soil Taxonomy terms and/or WRB whenever possible
13. Soil Terms: starting from a database table Excel file
14. Table loaded into the VocBench as SKOS
15. Example 2: Resource types in AGRIS Starting from a table Concept Type of Concept Relationship among concepts Bibliography Top broadMatch http://purl.org/dc/dcmitype/Text Book Top NT Handbook/Manual broadMatch http://purl.org/dc/dcmitype/Text Conference BT Event broadMatch http://purl.org/dc/dcmitype/Event Dictionary Top broadMatch http://purl.org/dc/dcmitype/Text Directory Top relatedTerm http://purl.org/dc/dcmitype/Collection Drawing BT Image broadMatch http://purl.org/dc/dcmitype/Image Encyclopaedia Top broadMatch http://purl.org/dc/dcmitype/Text Event Top NT Conference exactMatch http://purl.org/dc/dcmitype/Event Extension Top Film Top broadMatch http://purl.org/dc/dcmitype/MovingImage Graphics BT Image broadMatch http://purl.org/dc/dcmitype/Image Handbook/Manual BT Book broadMatch http://purl.org/dc/dcmitype/Text Image Top NT Drawing, NT Graphics, NT MapsorAtlases exactMatch http://purl.org/dc/dcmitype/Image JournalArticle Top NT Preprint broadMatch http://purl.org/dc/dcmitype/Text Lit.Review Top broadMatch http://purl.org/dc/dcmitype/Text Manuscript Top broadMatch http://purl.org/dc/dcmitype/Text MapsorAtlases BT Image broadMatch http://purl.org/dc/dcmitype/Image News Top Non-Conventional Top NumericalData Top broadMatch http://purl.org/dc/dcmitype/Dataset Other Top Patent Top broadMatch http://purl.org/dc/dcmitype/Text Preprint BT JournalArticle broadMatch http://purl.org/dc/dcmitype/Text Report Top broadMatch http://purl.org/dc/dcmitype/Text Sound/Music Top broadMatch http://purl.org/dc/dcmitype/Sound Speech Top broadMatch http://purl.org/dc/dcmitype/Sound Standard Top broadMatch http://purl.org/dc/dcmitype/Text Summary Top broadMatch http://purl.org/dc/dcmitype/Text Thesaurus Top broadMatch http://purl.org/dc/dcmitype/Text Thesis Top broadMatch http://purl.org/dc/dcmitype/Text Website Top External mapping
16. AGRIS resource types in the VocBench http://aginfra.eu/voc/aginfra_doctypes/
17. Example 3: the Soil VocabularyStartingfromtheINSPIREUMLrepresentation
18. http://vocabularies.aginfra.eu/soil# INSPIRE data model agINFRA Soil Vocabulary RDF model
19. agINFRA LOD data 19 Germplasm data http://[CAAS-API-base-URL]/germplasm/rest https://aginfra-sg.ct.infn.it/rdf/cncp/ CIARD RING Existing datasets CAAS CRA New agINFRA datasets http://ring.ciard.net DATASETS agINFRA shop? CIARD directories Germplasm data Soil data API https://aginfra-sg.ct.infn.it/rdf/... ?? CRA Triple store CRA Triple store CKAN CKAN Dataverse AGRIS GLN
20. Namespaces of agINFRA partners’ Linked Data • Sustainability namespaces with the data owners • CRA data: data.entecra.it (presented later) – http://data.entecra.it/rdf/soil/ temporarily at https://aginfra-sg.ct.infn.it/rdf/cncp/ – http://data.entecra.it/rdf/germplasm/ temporarily at http://93.63.35.32:8080/d2rq/ • CAAS data (presented later) – API: http://www.cgris.net/pquery.asp • AGRIS data – http://agris.fao.org/aos/ 20
21. Useful links • agINFRA project: http://aginfra.eu • agINFRA vocabularies: http://vocabularies.aginfra.eu • Tools: – VocBench: http://aims.fao.org/tools/vocbench-2 – Neologism: http://neologism.deri.ie/ – Allegrograph: http://franz.com/agraph/allegrograph/ – D2RQ: http://d2rq.org/
22. The end Thank you for your attention Valeria Pesce Giovanni L’Abate Luca Matteis Koraljka Golub
2. Who needs soil data? Who are our clients? Soil information is needed also for validating subsidies to farmers Private sector like insurance companies (i.e. flood risk areas) Research and education institutions Environmental institutions Even forensic experts Renewable energy sector: solar or wind Outcomes of Group 5: Soil
3. Precision agriculture Fertilizer industry Modeling experts (i.e. climate change) The new Common Agriculture Policy (CAP) in the EU place particular attention to green economy and soil conservation Public at large in response to increased environmental awareness culture Soil clients (con.t)
4. National and International Global Soil Partnership Pillar 4: Enhance the quantity and quality of soil data and information: data collection (generation), analysis, validation, reporting, monitoring and integration with other disciplines Institutional framework
5. • There is a great wealth of existing information on soils at various levels IUSS, FAO, USDA NRCS, ISRIC, IIASA, etc • For missing data pedotransfer rules and functions may be used • Modelling is another trend in soil science • Web soil information system is becoming the norm in data distribution • Private sector should distribute the data they have on soils What is available
6. •Strengthen partnerships •Facilitate cooperation and processes for best utilization of Soil Research Data at Global level •Improve communication, dissemination and delivery •Create standard metadata structures •Promote global standards, harvest and or Harmonize data collection •Quality control quality assurance •Improve data infrastructure operation and platforms •Awareness, guidance and training •Monitor success •Land is a private property: how to overcome this handicap for the societal use for soil data information and dissemination Outcomes of Paris meeting
7. •Complexity of data collection and datasets •Use common terminology •Make use of extensive existing soil data and share/distribute •User friendly platforms •Facilitate collaboration •Interoperability between data holders and data users •Web soil survey data distribution •Endorse a professional culture for data sharing Outcomes of Paris meeting (con.t)
8. WG5 suggest: • There is interest to establish an “Interest Group on Soil” inside the RDA •Contact the Global Soil community through emails, listservs, blogs etc to gather the interest in such an IG or WG • Topics of discussion (data quality and interoperability and many more to be suggested by the community) • State of the art of data availability and gaps at various levels • Expand and invite the soil science community to join the IG •Links with existing soil communication entities (i.e. International Panel on Soil of GSP) •RDA should provide funding for attending meetings and/or organizing workshops
Soil Applications and Data
The webGIS application
About Soil Data
Publishing Soil Data as Linked Data
AGRIS Implementation (Germoplasm data)
Example of SQL code to access Soil Data
Soil Vocabularies
Models Integration
Publishing the agINFRA Soil Vocabulary
Building the SKOS
Linking Soil terms
agINFRA Soil Terms Vocabulary uses VocBench 2.1
About agINFRA Soil Terms Vocabulary
How agINFRA Vocabularies could improve GACS
agINFRA Repository uses AllegroGraph WebView 4.11
Our Vision
Standards
Capture Methods
Ethics and Intellectual Property
Access
Data Sharing and Reuse
The SISI webGIS application
Short-Term Storage and Data Management
Deposit and Long-Term Preservation
Resourcing
The Interoperability challenge
SISI compliancy with INSPIRE and IUSS models
agINFRA Soil Terms Vocabulary
Example of SQL code to access Soil Data
AGRIS Implementation (Germoplasm data)
Our Vision
Soil survey, due to the complexity of collected information is a time loosing activity with high costs. Besides the survey, soil sample analysis are also costly. This high survey and analytical costs may be reduced by the adoption of new instruments as spectroradiometers. Materials & methods
Survey of Mugan Valley, Azerbaijan was carried out in the last years by Institute of Soil Science and Agro-chemistry of The National Academy of Science of Azerbaijan, Genesis, geography and soils mapping.
A related database was set up adopting the one freely available at the internet site of National Center for Soil Mapping, Italy (CREA-ABP). Soil observations, analyzed and geo-referenced have been digitalized. Sampling and analytical procedures were performed in accordance with national and international standards. The field observation method was conducted to ISIS (L’Abate et al., 2013) and with the relative methodology (Costantini et al., 2007). Every genetic horizon were sampled and air dried.
In total 194 soil samples, out of 46 mini-pits have been collected and analyzed to determine pH, carbonates, texture, and Electric Conductibility with standard analytical procedures. Both wet and dry bare soil samples, and dry sieved samples were scanned with an ASD FieldSpec 3 spectroradiometer (350 to 2500 nm with a step of 1 nm) to set up a soil signatures digital library with a total of 1164 signatures.
The library was than used to:
1) perform a munsell color conversion model on colorimetry data
2) orientate the selection of significant samples to be analyze
The colorimetry model
The adopted scanning procedure was saving a 20 measure scan and repeating scanning up to 3 times. The best result was choose for splice correction using the ASD software VieSpecPro. Colorimetry data have been than exported to text files (CIE, 1964 illuminate D65: X, Y, Z, x, y,u', v'). The color conversion was performed adopting the Virtual Colour Atlas software (v. 2.1.0730) on CIE X, Y, Z data of the raw dry samples. To attribute the nearest Munsell soil colour the Atlas Sample CMC function was selected. Results have been compared with the manual attribution with Munsell soil colour charts.
The samples selection model
To perform the best reduced dataset to be further investigated on Organic Carbon and Nitrogen content, the signatures of the sieved dry samples were selected. K-mean Spectral analyses was performed both on different genetic horizons (A, B, C) and on the whole dataset. The two analyses allowed us to determine two different datasets with, respectively 60 and 45 samples with a total of 103 selected samples. Out of the unselected samples were randomly selected 45 to use in the validation procedure of prediction. Four different calibrations were performed both on the two datasets, on their sum of 103 signatures, and on the whole datasets that included 149 samples. Prediction models were produced for Carbonates content and Electric Conductibility.
The Learned lesson
The colorimetry performed model highlighted several aspects of adopting colorimetry data: on one side the Munsell Hue determination seam to be consistent while the Munsell Value appears overestimated for the class 2 and the Chroma generally underestimated. The automatic attribution of Munsell color reduces the survey time and consequently positively impacts on the relatives costs but, on the other side, must be considered the higher uncertainty of modeled data.
The samples selection model highlighted that it is possible to reduce the calibration dataset maintaining relatively good results on prediction models.
References
Costantini, E.A.C.; Fantappiè, M.; L’Abate, G. (2007) Linee guida dei metodi di rilevamento e informatizzazione dei dati pedologici
L’Abate, G.; Allegri, G.; Barbera, R.; Bruno, R.; Fargetta, M.; Costantini, E.A.C. (2013) The ISIS webgis application for online Italian soil data consultation. EFITA conference, Turin, 24th-26th June, 2013 8 pp.
Virtual Colour Systems. VirtualAtlas Virtual Colour Atlas. Online: http://www.vcsconsulting.co.uk/VirtualAtlas.htm
Guidelines of the methods of soil survey and data informatization
SISI Compliancy with INSPIRE and IUSS model
Sampling and analytical procedures
Monitored environments
Main Parameters
Geographical distribution of observations
Soil monitoring in decades
The soil samples archive
The spectral library
Collected metadata
Soil properties prediction of the main Reference Soil Groups
Open questions
CREA Open Data policy
Aknowledgments
Soil observations from surveys carried out between 1950 and 2013, analyzed and georeferenced have been computerized in the national database. The analyzed soil samples were physically stored and their placement has been computerized in order to enable further analysis with new instrumentation or for subsequent repetitions. Sampling and analytical procedures were performed in accordance with national and international standards.
At present there are in the database 7430 analyzed observations among soil profiles, minipits or auger holes, sampled in forest environments, 3445 on Shrub, and 808 on permanent grassland. Forest environments are described in 6 categories of vegetation and in some cases further detailed in 50 subcategories. For the main analyzes were stored textural analysis, pH, organic carbon, nitrogen, bulk density, effervescence. The created historical archive will allow national monitoring activities.
Some history: Guidelines of the methods of soil survey and data informatization (Italian) - On 2007 the CRA-ABP published the ”Linee guida dei metodi di rilevamento e informatizzazione dei dati pedologici” (Guidelines of the methods of soil survey and data informatization) a volume with an attached CD-rom “CNCP 3.0, Database for soil observations and pedological units storing, correlating and geoexploring”.
The CNCP 3.0 software - The software was developed according to the indication of the European Soil Bureau Handbook (Finke et al., 1999) and the ”Metodologie pedologiche” project (Costantini and D'Antonio, 2001). Was adopted as soil information system by several regional soil services. Can be considered as the Italian standard for soil database architecture.
About soil international standards - Technical Committee ISO/TC 190, Soil quality, Subcommittee SC 1, Evaluation of criteria, terminology and codification. ISO 11074:2005. Soil quality – Vocabulary Technical Committee ISO/TC 190, Soil quality, Subcommittee SC 1, Evaluation of criteria, terminology and codification. ISO 28258:2013(en) Soil quality — Digital exchange of soil-related data The adoption of soil international standards was developed to enhance interoperability of data for uses related to agriculture, agro-industry, food, forestry, natural and geological science....
by the Agriculture Research Council of Italy (Soil Information System of Italy, SISI). The available soil
geodatabases for the whole of Italy are those of the soil regions (1:5,000,000), subregions (1:1,000,000), and
systems (1:500,000), while the soil subsystems geodatabase (1:250,000) is available for most part of Italy.
Soil Applications and Data
The webGIS application
About Soil Data
Publishing Soil Data as Linked Data
AGRIS Implementation (Germoplasm data)
Example of SQL code to access Soil Data
Soil Vocabularies
Models Integration
Publishing the agINFRA Soil Vocabulary
Building the SKOS
Linking Soil terms
agINFRA Soil Terms Vocabulary uses VocBench 2.1
About agINFRA Soil Terms Vocabulary
How agINFRA Vocabularies could improve GACS
agINFRA Repository uses AllegroGraph WebView 4.11
Our Vision
Since agINFRA is an EC project, the partners agreed that INSPIRE should be adopted as the starting point for a LOD metadata vocabulary for soil data.
INSPIRE is a good starting point for both:
An RDF metadata vocabulary, as it defines entities and attributes / relationships;
The identification of KOSs that need to be published, as it defines “registers” of values.
11th - 13th of June, 2014
Athens, Greece
1 WP 5: Data Policies, Workflows, Interoperability & Integration
Exposing the soil and germplasm datasets as linked data6th agINFRA Project Meeting
11th - 13th of June, 2014
Athens, Greece
2 "Aims"Aims
3 "Exposure for germplasm and..."Exposure for germplasm and soil data
4 data integrationagINFRA Soil Vocabulary
5 https://aginfra-sg.ct.infn.it/rdf/
http://soilmaps.entecra.it/rdf/
http://planta-res.entecra.it/rdf/Publishing RDF for Soil and Germplasm data
6 "Publishing KOSs for Soil and..."Publishing KOSs for Soil and Germplasm data
7
8 "Linking Soil terms to existing..."Linking Soil terms to existing KOSs
9 "Importation in Vocbench" Importation in Vocbench
10 How to improve the webGIS "Italian Soil Information System"
It has been suggested to simplify the Identify procedure; to add soil information at other scales (1:1.000.000 and 1:250.000); to add metadata about adopted soil analysis standards in the soil profile and Soil derived profile description pages; to create other projects (Maps) with every soil feature produced by CRA-ABP in the years; to create a project (Map) of every catalogued soil map present in the Soil map Web Catalogue (see below).
How to improve the Soil map Web Catalogue (gLibrary Repository Component)
It has been suggested to better organize the folder preview (according to Scale, Region or Subject); to activate the thumbnails preview functionality (when available); to add search functionality as the current version allows; to populate the description page as the current version of the tool (with enlarged preview and active link of the source/download functionality); to map catalogued maps on a webgis project (see over); to adopt Standard English descriptors of the fields; to open the Browse functionality to the web after the beta version will be improved.
How to improve the Soil map App Catalogue (gLibrary Mobile Browsing App)
Several users refer problems with the download of the tool (not compatible with their Android version). Previous suggestions for the web Catalogue version are valid. It was expressed the interest of accessing maps based on the location of the mobile user in a map window (webgis functionalities).What could be improved (WP4: agINFRA Components)
Introduzione ai Biosamples e Geosamples
La politica sui dati secondo il CREA
La pedoteca del CREA
Oggetti digitali derivati: La libreria spettrale
Role of standard vocabularies to search for and describe Open Data, especially in the context of soil data infrastructures
agINFRA work on lifting the local values used in ISIS to published, linked vocabularies
agINFRA Soil Terms vs Agrovoc & NALT
Toward a real interoperability of soil data: SOIL.WRB