Challenge #13: Mid-term Report

The main goal of the challenge is to calculate agro-climatic factors of a selected area of interest. The second task for this challenge is to analyse inner uncertainty of climatic data provided by the Copernicus ERA5 Land dataset and compare this dataset to the dataset produced by in-situ sensors in pilot localities. 

During the first period of the Challenge No.13 discussion about agro-climatic factors and their selection for forecasting regional food supplies was done. The selected agro-climatic factors are following:

  • frost-free periods, growing degree units, heat stress units, number of (optimal) growing degree days from temperature.
  • nitrogen application window from soil temperature.
  • accumulated solar radiation from incident sunlight.
  • water balance from precipitation, evapotranspiration, and runoff data.

Pilot area of interest was selected and datasets from Copernicus ERA5-Land database was downloaded. The pilot area of interest will be Pilsen region, Czech Republic. For evaluation and comparison of global data versus local in-situ sensors, two small localities were selected. Previous sensor campaigns were organized in these 2 pilot localities. First locality is in southern part of Czech-Moravian Highlands – Kojcice. Second one is in Northern Moravia – Trisce.

We have collected sensor data from locality Kojcice between spring 2016 – winter 2018 from soil sensors and meteostation. We have data from locality Trsice between spring 2015 – autumn 2018 from meteostation. Corresponding phenomena will be collected form the ERA5-Land dataset for both localities. 

First period was mainly oriented on selection of phenomena and factors, selecting area of interest and data collection from global and local perspective.

The team for the Challenge No. 13 consists from 15 members, where mainly EU states are presented, partly Africa and one member from the US. 

Challenge #11: Mid-term Report

Covid19 pandemic and mainly subsequent restrictions was and still is a test for the food supply chain in order to provide enough food to the market to the end customers. Especially in crises a good decision can be made only with enough information.The yield potential is highly prone to the seasonal effects such as drought, floods, optimal amount of rainfall, duration of insolation, temperature and others which can make the predictions highly different from the reality. The goal of challenges is to design methods of monitoring yield and climatic conditions during the season, which can influence negatively or positively yield in the season. 

As the reference layers we use the yield production zones calculated from 2013 – 2019 with the trends during the season of 2018. Temporal trends or events will be analysed on the basis of comparison of yield trends with Sentinel 2 EVI (Enhanced Vegetation Index) Index which is very close to NDVI index. The reason why we have decided to choose EVI index instead of NDVI is that EVI index does not suffer from saturation effect.

Figure 1: Map of yield potential delineated from multi-temporal Landsat imagery

Our aim is to monitor phyto phenology of wheat in Rostenice farm, where we have available data of yield potential, field boundaries geometry and inter alia, the information on the type of crop grown in the field.


EVI will be calculated for every image where it is not too cloudy. The difference Δ between EVI values in two consecutive images will be calculated using following formula:


Figure 2: Formula to calculate normalized difference between two EVI layers

where v1 and v2 are EVI values of the same pixel and t1 and t2 stands for time in days.We know from previous experience that the difference cannot be negative. The size of the difference value shows how much had the crop (wheat) changed in reality. This information is very useful for measuring the effect of any issue or condition during the season. Thanks to this difference information we will understand better the individual phases in crop growth.

Another way to compare the EVI values is to calculate integral for the whole season. Mathematical formula to calculate integral (SUM) is depicted below:

Figure 3: Formula to calculate integral for the whole season

where again v1 and v2 are EVI values of the same pixel and t1 and t2 stands for time in days. Thanks to integral value we can see where EVI was mostly above the average and which places were under the average most of the season compared to other places in the image.

Last way to compare EVI values will be by simple statistics within chosen fields. When comparing the mean value of some fields, we will learn which field has higher yield potential. We will also compare deviation in order to validate our metrics.

Challenge #8: Mid-term Report

The figure 1 outlines the challenge #8 concept and selected two pathways. The concept encompasses 4 phases; what indigenous knowledge to collect, how to collect, how to evaluate the quality of the knowledge,  and finally how to record and share the indigenous knowledge.  So far we have decided to put effort on two pathways but we are still looking for additional ideas for pathways. Pathway 1 is addressed on grain storage systems in Africa. Pathway 2 is about mapping out soil microbes coverage, quality and quantity in African continent.

Figure 1: The concept of challenge #8

The team of challenge 8 consists of 31members from 17 countries.  Most of the team members are from Africa continent.  The team uses emails, WhatsApp and Skype for communication. WhatsApp is the most active channel having 13 team members. Skype group has also gathered 13 members.  

By 19.10.2020, there have been 3 team meetings: 1 pcs for general, 1 pcs for pathway 1 and 1 pcs for pathway 2.  The discussion, ideation, knowledge transfer could have been more active and vivid, but we are still in a ramp-up phase of the project.  The first results to come will hopefully and activate participants into dialogue.  

The kick-off webinar was planned to be on week 40, but it was necessary to postpone to 23.10.2020 (week 43) due to one presenter’s personal reason. 

Pathway 1 needs support from the challenge 6 expert Peter Horak,  and Pathway 2 from the challenge 3 expert Pavel Hajek. The co-operation between challenges enriches and enables more ambitious deliverables and increases all participants’ know-how and skills. 

At the moment there are no particular risks ahead.  The next tasks are to ideate additional pathways, pilot the 4-step concept and constantly assess the progress. To enhance team dialogue is one of the core tasks. 

Challenge #7: Mid-term Report

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.


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.


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. 


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.


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.


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.

RDA 16th Plenary Meeting – Costa Rica (Virtual)

The 16th Plenary meeting of the Research Data Alliance will take place 9-12 November 2020.

With the theme “Knowledge Ecology“, the event is co-organised by CONARE Costa Rica, RDA United States and Research Data Canada.

In light of the ongoing COVID-19 pandemic, RDA has been contemplating the feasibility of holding its face-to-face plenary in Costa Rica in November. Taking into consideration the health and well-being of our community, the anticipation of continued travel and budget constraints, and the success of RDA’s most recently held Virtual Plenary 15, the organizing committee has decided to move forward in planning another virtual meeting (VP16). VP16 will provide attendees the opportunity to remotely attend plenary sessions, participate in multiple breakout sessions, attend poster sessions and collaborate with attendees.

On behalf of the Plenary 16 organizing committee, thank you in advance for your support and understanding. Although an in-person event may still be able to take place in Costa Rica, it is too soon to make that decision and if it does move forward, we expect a more simplified meeting format than our typical plenary event, with a much smaller audience. Details on an in-person event will be shared with the community as they become available. In the interim, if you have any questions/concerns, please contact RDA.

RDA 16th Plenary Meeting – Registration

Webinar Replay on Using the Atlas of Best Practice to Fight COVID-19 in Rural Areas

Agenda of the webinar:

  • Welcome from the moderator
  • Hackathon introduction and quick overview of 3 challenges: similarities and differences
  • Atlas of the Best Practices (the Enabling project) – common technical platform for presentation of outputs of individual Challenges
  • Ch. 2 presentation
    • goals, the Atlas prototype – Regional Specialities, Expected outputs, brainstorming
  • Ch. 6 presentation
    • goals, the Atlas prototype – Polirural, Expected outputs, brainstorming
  • Ch. 12 presentation
    • goals, the Atlas prototype – Social Enterprises, Expected outputs, brainstorming
  • general Q&A
  • Conclusion

You will hear from 

EUXDAT Webinar Series – Pilot’s Session 2

The EUXDAT Webinar Series continues with the Pilot’s Session 2 following the Pilot’s Sessoin 1. In case you missed it, you can replay the webinar right now!

Please, helps us to get your opinion on the EUXDAT filling in the questionnare.

Webinar Agenda:

EUXDAT relevance towards Achieving Climate Neutrality. Karel  Charvát (CoO)

Climate action will be  at the heart of the European Green Deal – an ambitious package of measures ranging from ambitiously cutting greenhouse gas emissions, to investing in cutting-edge research and innovation, to preserving Europe’s natural environment. We will explain, how EUXDAT can help manage weather information and how can help to agriculture in mittigation on climatic changes and also how reduce negative influence of Agriculture on clime

Agro-Climatic Zones Scenario. Karel Jedlička, Pavel Hájek (WirelessInfo)

The Agro-climatic classification system allows user to calculate a time and spatial distribution of agroclimatic variables such as Forst dates (Last frost dates/First spring dates), Annual/Seasonal Evapotranspiration and precipitation, Soil temperature, Solar radiation, Growing degree units, Heat stress units or Number of days with optimal growing temperatures based on historical data of ERA5-Land dataset.

Field Accessibility Tool and its Components. Marcela Doubková (PESSL Instruments)

WORKFLOW is a service for clients that provides them information on field accessibility for their machinery for upcoming 3-5 days. The service is based on iMETOS soil moisture data (from Pessl Instruments), soil moisture forecasted data (Meteoblue), DEM available from Shuttle Radar Topographic Mission (SRTM) as well as state-of-the-art Sentinel-2 data. The output information informs user about field accessibility ranging from ‘not accessible’ to ‘easy accessible with heavy machine’.

Discussion and feedback analysis, Karel Charvát (CoO), Jorge Lopez (ATOS)

Challenge #5: Mid-term Report

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 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. 


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.

Challenge #4: Mid-term Report

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