The document summarizes work exploring data sources and sharing practices in the African agri-food sector. It identifies several relevant data catalogues and standards. Common sharing practices include farmers' meetings, peer-to-peer sharing, and radio broadcasts. Proposed solutions to improve sharing include developing tools to deliver data via basic phones, establishing community data centers, and building capacity for local data generation and management. The document also outlines use cases where open data could benefit agriculture and results applying linked data techniques to integrate geospatial and agricultural datasets from Kenya.

1. Open Data and Data Sharing in Agri-Food Chains in
Africa
Raul Palma (PSNC)
Nairobi, May 2019

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. 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. 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. 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. 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. 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. 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. 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. Proposed solutions for
visualizing/exploiting integrated data
• Using Map composition concept maps as objects
for sharing
• Exploratory visualisation
• Metaphactory platform
Nairobi, May 2019

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. 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. 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. 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. 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