Aim of the project
To develop climate smart cattle farming systems reducing GHG and ammonia emissions while maintaining the social-economic outlook of the farm business.
FarmSustainaBl will apply a holistic approach for decreasing the GHG emissions derived from intensive livestock farming by optimizing the livestock production. For doing this, the consortium will (1) monitor the animal feed; (2) the animal behaviour and characteristics; and (3) the stable environment. A web based platform will be developed that will collect and analyse all the aforementioned data for providing recommendations to the livestock farming stakeholders (farmers, consultants etc) in order to take management decisions for reducing GHG emissions.
What: FarmSustainaBl ambition is to provide a reduction in GHG intensity of animal production systems in Europe by approaching three main pillars: one pillar relates to actual GHG reduction techniques, but it relies heavily on modelling and optimization in livestock management together with a blockchain component for preserving and maintaining the consumer and authority trust in the authenticity of produce from sustainable livestock agriculture. Precision Farming (PF) uses new technologies to handle and manage farm information. Precision Livestock Farming (PLF) technologies enable continuous, automatic monitoring of animal welfare, health, production and environmental impact in real-time.
Why: Farming livestock – cattle, sheep, goats, pigs and chickens – contributes around 6 billion tonnes of greenhouse gases (carbon dioxide, methane and nitrous oxide) to the atmosphere each year. While estimates vary, this could represent up to 18% of global emissions and at the same time, the consumption of meat, milk and eggs is projected to grow 70% by 2050.
Where: The research is conducted in five leading research institutes and SMEs located in Greece, Denmark and Romania
Main project activities
In order to achieve this the consortium will monitor the animal feed, behaviour and characteristics as well as the stable environment. The data will be collected and analysed by a web based platform and it will provide recommendations to the livestock farming stakeholders (farmers, consultant etc.) in order to take management decision for reducing GHG emissions. Specifically, IoT devices will be installed in the farm for monitoring key parameters of the stable environment (temperature, humidity, gas sensors (NOx, COx, CH4, NH3, etc.), and the animal (accelerometer, motion sensor, weight sensor, etc.) and the feed (flow sensor, weight sensor, humidity sensor etc.).
Concept and approach
A web based platform will be developed that will collect and analyse all the aforementioned data for providing recommendations to the livestock farming stakeholders (farmers, consultants etc) in order to take management decisions for reducing GHG emissions. In addition, BlockChain Technology (BCT) will be used in the platform for developing different features such as data protection, data privacy, data sharing, traceability and smart contracts among the livestock farming stakeholders. Specifically, the smart contracts feature of the platform will help livestock farming stakeholders to have contracts with better prices due to decreased GHG emissions.
Coordinated by: Mr. Zisis Tsiropoulos, Agricultural & Environmental Solutions PC, AGENSO (Greece)
- GREECE: Agricultural and Environmental Solutions and Agricultural University of Athens
- ROMANIA: BEAM INNOVATION and BEIA Consult International
- DENMARK: University of Southern Denmark
- The FarmSustainaBl project started on 1 January 2020 and runs until 31 December 2022.
- Website: https://www.farmsustainabl.eu/
- Twitter: @farmsustainabl
- Facebook: Farmsustainabl Project
- LinkedIn: FarmSustainaBl
Presentations and posters
- Project presentation
- Roll up banner
- Kick-off meeting Pitch
- Kick-off meeting Presentation
- Kick-off meeting November 2020
- Modelling and Simulation For Decision Support In Precision Livestock Farming
- Decision support platform for intelligent and sustainable farming
- Machine Learning algorithms for air pollutants forecasting
- Data-driven Decision Support Tools for Reducing GHG Emissions from Livestock Production Systems: Overview and Challenges