The African agricultural production and marketing faces significant huddles for it to address the needs of the continent and achieve food security in 2030. For ages, the production and supply chains have been heavily fragmented coupled with non -transparent, insufficient and non-communicating business networks which have diminished farmer’s productive capacity and wellbeing. From a food supply and marketing side, selfish minded individuals have capitalized in this space by creating processes and networks that are exploitative. Likewise, processes that deal with production and transactional data between actors and products is rarely integrated let alone shared. As such, the disconnection and lack of transparency and accountability complicates fair pricing and quality of products.

In order to address the above problems, Challenge 7 team intends to develop a blockchain technology and apply same in Africa agriculture in the COVID 19 and post COVID 19 era.

PARTICIPANTS

Challenge currently has 29 registered members and some who are not registered but have identified on the various social media platforms from across different continent of the world.

MEAN OF COMMUNICATION

Challenge 7 created various social media platform (Whatsapp, Email and Skype) where occasionally they meet to have meetings and decide steps to achieve the goal of the team. 

WEBINAR

Challenge 7 hosted a webinar on the 23rd September, 2020 and they were 11 participants who joined the webinar out of about 36 persons that indicated interest.

BLOCKCHAIN TECHNOLOGY

Participants were asked to collate challenges information from their country’s local farmers that will be address in the development of the technology. Participants from Nigeria, Cameroun, Zambia, Uganda and Kenya submitted the challenges faced by their farmers and same was communicated to Jika (Technology developer). The technology is in the development process and will be shared with participants next weeks for familiarity and pilot testing.

WAYFORWARD

The next step for the Challenge 7 is the presentation of the Blockchain technology by Jika for familiarity, testing before the field pilot testing across Africa.

Since the inception of map whiteboard challenge and the first draft release in summer, various improvements have been made to the software parts enabling whiteboard functionality. This document will summarize the developments since that time. The current working prototype of the integrated system runs at https://dih.bosc.lv/modeller/private/ and is available for authorized users. The system’s purpose is to provide the user the possibility to create system dynamics models with linked spatial information in a form of map composition for each graph or model.

Whiteboard library and server configuration

Server architecture

A docker based environment has been setup to run the components in production (see the diagram).

It consists of 2 mongo databases – one for the container application (Polirural modeller) and one for map whiteboard. Similarly we use 2 rest api endpoints named polirural-model-builder-api and pmb-whiteboard-server where the whiteboard-server supports both REST and Websocket protocols. At the top resides an nginx proxy to rule them all. 

The map whiteboard server connects to model-builder-api provided GRAPHQL endpoint to validate user sessions and get the current users info. This is used as an alternative to OAUTH2, but conceptually does the same thing.

Client architecture

On the client side we have an Angular9 based application, which incorporates a map window component based on HSLayers-ng. Since it runs Openlayers under the hood, we can integrate the Map whiteboard library using the underlying Openlayers map object.

Since Hslayers already provides feature drawing functionality we have added to map whiteboard library the possibility to turn off the drawing toolbar not to have duplicate user controls and call the whiteboard libs data synchronization methods directly from javascript using hooks in hslayers. Also switching the current edited layer on mapwhiteboard library from outside needed to be created since the application supports multiple editable vector layers which are done on hslayers side. 

Map composition preparation

The map composition creation is still done in the application outside map whiteboard lib with the help of hslayers-ng, because it supports multiple layer types and parameters. The json serialize map object or individual layer is then passed to map whiteboard client side code, which then sends it to the server. This happens at the moment when the user selects a ‘Model’ in the dropdown list on the top-left corner of the UI. The software then checks if a map composition is linked to the model and creates it in case it doesn’t exist. In case it already exists the composition is being queried from the map whiteboard server, and layers are populated on the map including the features. 

Recently we also developed an initial user rights model to keep track of who can access and edit the compositions.

The uploaded composition is stored on the harddrive, parsed and the title, description and filename of the composition is stored in Mongo database.

Downloading map composition file was developed (currently without embedded features)

Currently functionality for editing the composition supports:

• Add new layers either drawable vector or wms, arcgis or vector data loaded from external files
• Listing of editable layers parsed from composition file
• Requesting features for particular layers. Multiple editable layers are now supported
• Listing of my uploaded compositions for current user
• Granting access (owning) rights to the uploader of composition.

Still needs developing:

• Granting and removing access rights to additional users. Currently all users can see and edit the composition only they don’t appear in the list of compositions he/she owns.
• Renaming of layers.
• Deleting of composition (available to owner).
• Styling of features
• Activation of scratch layer
• Serialization of layers and composition on the map-whiteboard side. 

Features

Whenever a user draws a feature, they are propagated to all the clients currently using the same composition meaning they selected the same ‘Model’ in the dropdown. Features are now linked to particular composition and layer, where previously we had a single fixed editable layer per composition. The features are stored in mongo databases  and modification of them is done through CRUD operations on the Websockets API.

Listing of features is done for a specific editable layer by its title through REST API.

The report summarizes the outputs achieved in Challenge #4 of the COVID-19 INSPIRE Hackathon during the first part of the event.

• Number of registered participants: 28
• Challenge #1 Shared Workspace (complete information about activities in the challenge)
• Interconnection with pilot activities of Polirural project → 15 of the registered participants are involved in Polirural pilots and represent 8 of the 12 pilot regions

• Webinar – October 12, invitation post, presentation, 27 live attendees
• Development of testing scenarios 
• Clustering – testing various clustering methods, imputation of missing data and combination of non-hierarchical and hierarchical clustering

• Application (rural attractiveness index, clusters)
  • Development and removing errors
  • Collecting feedback to improve the application in the next phase of the hackathon
  • YouTube video tutorial

This mid-term report of Challenge #3 contains the most interesting and valuable achievements, which have been done so far.

• There are 19 registered participants in our challenge.
• A collaboration with challenges has been established or being negotiated:

• CHALLENGE #8 Digitalization of indigenous knowledge in African agriculture for fostering food security – through the incorporation into the African community around such a challenge, 
• CHALLENGE #13 Calculation of agro-climatic factors – potential source of information for forecasting regional food supplies – through the visualization and map sharing of calculated agro-climatic factors (ongoing),
• CHALLENGE #4 Rural Attractiveness Visualization – through the sharing of maps created in this challenge (negotiated).

• Our intention is based on map data sharing using three main open source components (see the figure below representing a schema of map data publishing):
  • Layer Manager (Layman) QGIS Plugin
  • HSLayers-NG
  • Digital Innovation Hub (particularly SmartAfriHub)

Thanks to the intensive use of the mentioned components, there is a rapid development of them, fixing bugs appeared, enhancing user experience and spreading information about such tools for map data publishing among users.

• • Solved issues regarding authentication of QGIS plugin
  • Layman plugin installer updated to be more intuitive
  • Plugin refactored to request Layman asynchronously
  • Czech localization added to the plugin
  • Resolved sharing from HSLayers client to social networks
  • HSLayers Layman errors notifications made more user friendly
  • Fixed bugs and adapted GUI of HSLayers drawing tool based on user feedback
  • Another minor bug fixes

• Creation of Covid-19 related maps and map compositions. See the examples below:
  • Map composition of Covid-19 cases and deaths (new and cumulative) on 1st October 2020 worldwide displayed in QGIS SW

• Map composition of Covid-19 cumulative cases from the previous example displayed in the HSLayers web client, i.e. map composition is stored on a HUB and visualized using a web browser

• Map composition on Covid-19 weekly cases/cured/deceased in the Czech Republic from 30th September (ongoing work)

• Components for metadata harvesting have been set up in Micka catalogue and tested (https://www.agrihub.cz/micka/). Map compositions and data layers had been harvested from the Czech National GeoPortal (https://geoportal.gov.cz/)
• A webinar covering Challenge 3 topics was broadcasted on the 1st of October, the webinar’s recording is available here: https://event.webinarjam.com/replay/59/nkpy0i5pumzcvyur50 
• Step-by-step video sequences on how to use Layman QGIS plugin (available here: https://youtu.be/8Z1Gube6nuc) for map compositions and how to use HSLayers-NG for online creation and visualization of map compositions (available here: https://youtu.be/JFAhBCYPaSU) were shooted
• Future plans:
  • Further development of the mentioned involved tools
  • Further incorporation of users in development of the tools
  • Spreading the information about our tools and our open source-based approach
  • Sharing more maps and information helping to established Citizens Science Network for Peer to Peer Maps Sharing
  • Fulfill cooperation with other challenges of Covid-19 INSPIRE Hackathon

The report summarizes the outputs achieved in Challenge #2 of the COVID-19 INSPIRE Hackathon during the first part of the event.

• Number of registered participants: 11
• Challenge # 2 Shared Worskpace 
• The final goals of the challenge:
  • Main goal:
    • To develop the advanced and much more practical version of Atlas of Regional Specialities and the e-shop and to think out the proper way to intertwine it finally and usefully to successfully promote both but especially the region itself.
    • The challenge aims to eliminate the above-mentioned problem situations by supporting regional primary producers and primary food processors (farmers, fruit and vegetable growers, butchers, etc.), collecting information about their products and making this information and products available to end customers.
  • Secondary goals
    • To present midterm results in the events organized in the hackathon.
    • To create an active group of co-workers (developers, testers, data providers, feedback providers, etc.).
    • To implement a special set of functionalities for further innovation of the Atlas.
    • To prepare a space for possible future steps on replication of such Atlas also for further regions with a special and similar local-regional specialities. 
    • To figure out and arrange the sustainability of the solution.
    • To promote the H2020 project LIVERUR and vice versa as the background for the e-commerce side of the Atlas being developed in cooperation between 2 Czech partners (one technical, second one pilot): WRLS and UHLAVA. 
• Brainstorming session on Jamboard during the webinar (20/10/2020) via QR code:

Background:

Problem situations related to Covid-19, such as restrictions or temporary interruptions of work, quarantine, restrictions on the number of people in stores or restrictions on sales time, can lead to a change in shopping habits. The result can be an oversupply of households leading to unequal distribution, a temporary shortage of certain foods, and rising prices.

The challenge proposal aims to eliminate the above-mentioned problem situations by supporting regional primary producers and primary food processors (farmers, fruit and vegetable growers, butchers, etc.), collecting information about their products and making this information and products available to end customers.

Conception:

One of the possible solutions is to connect regional information about farmers and their available products with various platforms for trading in these products (stone shop, e-shop, purchase from the yard). The Atlas of the Best Practices will be used as the basic technological platform, on the basis of which a pilot prototype of the Atlas of Regional Specialties will be created.

Atlas of Regional Specialities

The report summarizes the outputs achieved in Challenge #1 of the COVID-19 INSPIRE Hackathon during the first part of the event.

• Challenge #1 Shared Workspace (complete information about activities in the challenge)
• The final goals of the challenge:
  • Main goal:
    • To develop the advanced catalogue of educational materials (best practices, educational documents, application, services, other resources…) suitable for remote teaching/learning in the EBAG domain.
  • Secondary goals
    • To present results in the events organized in the hackathon.
    • To create an active group of co-workers (developers, testers, data providers, feedback providers, etc.).
    • To implement advanced techniques (such as searching similarities through clusters, links to controlled vocabularies, etc.) to enrich the catalogue.
    • To interconnected with contemporary trends such as Responsible Research and Innovation, FAIR data (findability, accessibility, interoperability, and reusability), Linked Open Data or international standards.
    • To figure out and arrange the sustainability of the solution.
• Brainstorming session on Jamboard

• Catalogue for EBAG Education

• • Table with data – the current version contains 65 items from 3 providers (Czechia, Tunisia)
  • The data model of the catalogue based on Dublin Core and other standards, respecting Linked Open Data approach and Responsible Research and Innovation principles

Initial attempts with categorized data (metadata) clustering for searching homogeneities and similarities