Cloud model for Carbon Footprint assessment in crop growing and agri food value chains.
About the Pilot
AgriFootprint is a BIOINVEST-AGRO innovative pilot project, namely: a cloud-based model for the carbon footprint assessment in crop production and agro-food added value chains that contributes to the development of adaptive to climate change technologies for growing crop products, greening of technological processes of food production, improving product quality. The service meets demand among crop producers and create a new niche of eco-economic services. The idea of AgriFootprint project in the modern form of digital services for agricultural producers was formed in 2020. The prerequisite for the project creation was 20 years of experience in the use of biological products in cultivation of agricultural crops, as well as the development of a joint implementation project (JI UNFCC) on the assessment of sequestration for greenhouse gasses in the cultivation of grain crops, which passed international determination and is posted on the UN website.
HOW DID BIOINVEST-AGRO BECOME INVOLVED WITH EOSC DIH?
We learned about the cooperation opportunities of EOSC and EOSC DIH through NOSC-UA DIH
WHAT SERVICES WERE USED?
We needed support for :
- Consultancy and education in appropriate use of EOSC services
- Initial configuring of the requested services and support in its use
- Deployment support
- Support, consultancy and monitoring used EOSC services during the developing system exploitation
The EOSC Service Providers are: EGI ACE and OpenAire Nexus, more specifically:
- EGI Dynamic DNS Service
- EGI Cloud Compute
- EGI Cloud Container Compute
- EGI Data Transfer
- EGI Online Storage (relational DB like PostgreSQL/MySQL)
- Elastic Cloud Compute Cluster (EC3) (for Kubernetes or PaaS/SaaS)
- Deep training facility
ABOUT THE EXPERIENCE OF WORKING WITH EOSC DIH AND THE VALUE OF EOSC FOR THE PILOT:
The result of the pilot project implementation is the digital service that has been developed, tested and built as a tool for making agro-technological, organizational and management decisions towards sustainable carbon cycles and carbon farming.
- Repositories, CI/CD and infrastructure for algorithm testing purposes cluster created
- Modelling software developed for different approaches
- Testing and fine-grained tuning of modelling core performed with software
- Site and one commerce version of modelling core worked out
Despite the war in Ukraine EOSC DIH was maintained and continued to support us in our business operations at all stages of the pilot development, improving them to define, integrate and validate our service into the cloud infrastructure. We also had the opportunity to use the resources and services of the research infrastructure to raise awareness and improve our innovations in the EOSC community.
In perspective, development of AgriFootprint project implementation foresees also the usage of AI tools. It means that we are going to prepare datasets that covers most of cases in crops growing and use them to learn the core AI model engine (AgriModelAI sub-project with using Deep training facility), to validate the results and outcomes of the first generation of AgriModelAI engine exploitation, to involve scientists and farmers for improving the carbon footprint modeling approach which will be used in AgriModelAI.