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TEAM 6: Open Data and Data Sharing in Agri-Food Chains in Africa

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at the Nairobi INSPIRE Hackathon 2019

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TEAM 6: Open Data and Data Sharing in Agri-Food Chains in Africa

  1. 1. Open Data and Data Sharing in Agri-Food Chains in Africa Raul Palma (PSNC) Nairobi, May 2019
  2. 2. Supporting project EU FP7, ICT CIP, 2014- 2017 EU FP7, ICT CIP, 2014- 2017 FOODIE aimed at building an open and interoperable cloud-based platform addressing among others the integration of data relevant to farming production including their geo-spatial dimension, as well as their publication as Linked data. SDI4Apps aimed at building a cloud- based framework with open API for data integration focusing on the development of six pilot apps, drawing along the lines of INSPIRE, Copernicus and GEOSS DataBio aims at showcasing the benefits of Big Data technologies in the raw material production from agriculture & others for the bioeconomy industry; deploying an interoperable platform on top of the existing partners’ infrastructure. DataBio aims at delivering solutions for big data mgmt., including i) the storage and querying of various big data sources; ii) the harmonization and integration of a large variety of data from many sources, using linked data as a federated layer Nairobi, May 2019
  3. 3. Objectives • To explore available data sources and data sharing practices between different stakeholders in the agri-food chain (farmers, service providers, advisors, food industry, machinery producers, etc.). • Goals: • To identify data catalogues, standards and data models that would work in Africa. • To identify existing data sharing practices, and how they can apply in Africa • To implement mechanisms for data sharing that can facilitate integration tasks • To implement interfaces for visualizing integrated data Nairobi, May 2019
  4. 4. Work carried out • Created team space folder [1] in gogle docs, including: • Work space document [2] • List of participants • Relevant literature • Folder for input documents (e.g., CVs) Nairobi, May 2019 [1] https://drive.google.com/drive/folders/1W2SDGHW9bZHRGKqgDrejkQ6xauSXzrF4?usp=sharing [2] https://docs.google.com/document/d/1QKM2uRUZMUq3aB-e7GspqSTpTZ3-g8kgI-JytpI2G4g/edit?usp=sharing
  5. 5. Working space document • In this document we collected information (with focus in Africa) about: • catalogues and other data sources • standards and models • sharing practices in agri-food. • proposed solutions for data sharing, • proposed interfaces for visualizing and exploiting data, particularly using as data source the integrated layer provided by the Linked Datasets. • ideas for use cases on how open and sharing data can benefit agri-food processes. Nairobi, May 2019
  6. 6. Catalogues and other data sources identified • https://www.wri.org/resources/data-sets/kenya-gis-data • http://www.fao.org/geonetwork/srv/en/main.home • https://datacatalog.worldbank.org/dataset/africa-development-indicators • https://datacatalog.worldbank.org/ • https://data.worldbank.org/ • http://datacatalogs.org/portal/open-data-for-africa • http://tourismdataforafrica.org/ • http://africaclimate.opendataforafrica.org/ • http://aih.opendataforafrica.org/ • http://dataportal.opendataforafrica.org/gqzdwxe/agriculture • http://ssa.foodsecurityportal.org/regional-sub-portal/sub-saharan-africa • https://dataafrica.io/, open-source climate, agriculture, poverty and health visualization engine • https://cocoacloud.org/ This data platform generates, translates and disseminates critical data – such as weather forecasts and location-specific advice – for farmers and industry in West African cocoa landscape. (Commercial) • Nairobi, May 2019
  7. 7. Standards and models used • Agri domain • FOODIE model and ontology • AIMS – Agricultural Information Management Standards (http://aims.fao.org) contains: • AGROVOC ontology / vocabulary / thesaurus • FOODON http://foodon.org) • 215 agri-food related ontologies on https://vest.agrisemantics.org/ • Cross-domain (relevant) • W3C Provenance Ontology https://www.w3.org/TR/prov-o/ • Open standard for creating a timestamp proof of any data, file, or process • Chainpoint https://chainpoint.org/ (JSON Schema) • OGC standards http://www.opengeospatial.org/standards • Messaging Standards: • EDIFACT (for the electronic interchange of structured data) https://www.unece.org/cefact/edifact/welcome.html • GS1 EPCIS https://www.gs1.org/standards/epcis: enables trading partners to share information about the physical movement and status of products as they travel through the supply chain – from business to business and to consumers • FOODEX2 https://www.efsa.europa.eu/en/data/data-standardisation: food classification and description system • Platform specific (models): • DKE Data Hub http://dke.my-agrirouter.com/wpdke/en/ • MuddyBoots https://en.muddyboots.com/ • Agriplace https://www.agriplace.com/ • Blockchain or Distributed Ledger, e. g. • http://origen-trail.com. • http://provenance.org • http://arc-net.io Nairobi, May 2019 DKE Data Hub FOODIE
  8. 8. Sharing practices • There are available different guidelines for data sharing, e.g., i) a coalition of associations from the EU agri-food chain launched a joint EU code of conduct on agricultural data sharing in Brussels (April 2018); ii) FAIR principles, which although is focused on research data, some of its principles may also apply in the agri-food sector. • The following list provides typical data sharing practices in agri-food sector, with focus in Africa: • Knowledge exchange in farmers’ cooperatives: • Farmers present their problems and get ideas from colleagues during regular meetings; • Agricultural extension staff from government agency take farmers’ problems and indigenous farm practices and give learned solutions/suggestions during training of cooperatives. • Peer-to-Peer model: paper or PDF-based system, allowing one actor to copy data from another – common in Africa • Casual conversations on the road, in homes and anywhere (during which farmers express problems, give and take climate and other agriculture-related information) • Radio broadcast (NGOs and government agencies talking to farmers; no interaction) • Facebook (e. g. by Nkulima ‘Young Farmer’, Kenya) • Mobile app (e. g. “Kenya launches 14 mobile apps to transform agriculture” https://www.scidev.net/sub- saharan-africa/agriculture/news/kenya-mobile-apps-transform-agriculture.html) • Online system - Ureport (http://www.ureport.ug/) by Ugandans to either provide information about banana bacterial wilt or request information or both, via SMS. • Research Data Alliance has multiple very active groups working on various components of agricultural data best practices (see here) . Perhaps the most interest here might be • Agrisemantics Working Group: 2 reports so far from this group are: i) Landscaping the Use of Semantics to Enhance the Interoperability of Agricultural Data; ii) A set of use cases and requirements Nairobi, May 2019
  9. 9. Proposed solutions for data sharing • Develop SSID-based tools (since most farmers afford basic mobile phones) in addition to smartphone apps (for others who own smartphones) that will help national meteorological agencies and agricultural development programs deliver timely to farmers in their jurisdictions weather history and forecasts, disaster alerts, market trends, and other relevant open datasets, both proactively and on demand. • Create a feature on online agricultural data platforms whereby farmers’ telephone numbers can be collected and text messages (SMS) sent to the numbers as critical data that will meet their immediate and later needs are made available on the platforms. • Establish (what I call) Farming Data Centres (FDCs) in communities. In a FDC, agents from meteorological services, agricultural development programs and other relevant service providers periodically meet with local farmers to exchange data in local languages. The centre also should have Internet-capable computers on which farmers can be helped to use portals and other online tools that host agri-food-quality standards, market data and other information helpful for producing/buying good quality products, deciding appropriate prices and reaching new markets. • Build capacity for community-based agri-food data generation and management Nairobi, May 2019
  10. 10. Proposed solutions for visualizing/exploiting integrated data • Using Map composition concept maps as objects for sharing • Exploratory visualisation • Metaphactory platform Nairobi, May 2019
  11. 11. Use cases ideas on how open and sharing data can benefit agri-food processes. • Early warning of pest/invasives for a region • Emergency response: There’s already a pest attack or other disaster and a farmer (or a group) sends descriptions (morphology, manner of attack, location, etc.) and seeks data toward addressing the challenge as promptly as possible, to also stall spread. • Training data for AIs should result in AI forecasts and recommendations that increase yields • Transparent authenticated product pipelines for higher prices (eg Fair trade/Organic) • Carbon credit/monetary reward for farmers who implement low carbon practices and prove gains with data Nairobi, May 2019
  12. 12. Results: application of linked data publication pipelines for data sharing and integration • Datasets input • Africa: Roads Inventory 2018: contains: highways, primary, secondary, tertiary and local roads. • Africa - Water Bodies: includes lakes, reservoir, and lagoons • Soil Maps for Kenya: subset of the FAO-UNESCO soil map of the world. • Crop Lands for Kenya: based on the dataset for size of agricultural fields in Kenya Nairobi, May 2019
  13. 13. Results: application of linked data publication pipelines for data sharing and integration • Models used • FOODIE ontology and extensions • Open Transport Map (OTM) ontology • Transformation into RDF • Creation of mapping specifications • Most input datasets were in shapefile format • Tool used: Geotriples • Some were in Json and CSV format • Tool used: R2MLProcessor • Loaded in Virtuoso triplestore • Linking • Geo-relations found via SPARQL queries • Equivalence relations is next step Nairobi, May 2019 R2MLProcessor
  14. 14. Results: application of linked data publication pipelines for data sharing and integration • Total number of triples generated: • 26,054,097 for Road Map (africa) • 11,330 for water bodies (africa) • 76,787 for crop lands (kenya) • 10,168 for soil maps (kenya) • Exploring the Linked data: • Sparql endpoint: https://www.foodie-cloud.org/sparql • Faceted search: http://www.foodie-cloud.org/fct/ Nairobi, May 2019
  15. 15. Results: Interfaces for visualizing the linked data sets • Example use cases: • Select crop lands based on LCCS (FAO) code • Filter fields by soil type in the area • Filter fields which are near water bodies Nairobi, May 2019 We can also visualize the Points of Interest (global dataset) http://app.hslayers.org/project-databio/africa

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