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Quality, efficiency and transparency of farming are crucial for successful EU economic performance and sustainability of its natural environment. There are 12 million Agricultural holdings in the EU member states and about 40 % of the land area is farmed for crops and livestock. The European Bio-Economy sector has a market size of €1.5 trillion and employs more than 22 million people. This highlights the importance of modern and efficient farming practices and raises needs for addressing a number of challenges like: cost-effectiveness improvement to face global competition; efficient use of new agricultural knowledge; increasing demand for reports and documentation to comply with CAP schemes and European and national requirements; minimization of environmental impacts and adjusting to environmental legislation.The overall objective of the project is to set-up and pilot AgroIT platform to be tested in different parts of Europe (Denmark, Poland, FYR of Macedonia and Romania).

AgroIT platform will integrate key applications and services that will address the challenges presented above. More specifically the project will• Select mobile applications for inclusion in ArgoIT user interface for simple and efficient input of data during farmers' daily activities (into database of ERP system of farms).• Integrate services and applications for wireless data collection from sensors, monitoring systems and other devices into the platform.• Provide advanced decision support for farming through implementation of decision support system and cloud based.• Integrate all of the above into a working AgroIT platform, based on open standards (enabling the inclusion of additional applications and services after the end of the project thus achieving sustainability of the platform) and pilot test it.AgroIT project brings together partner experts from farming consultancy, software industry, agriculture and HEI for the duration of 31 months.

Acronym
AgroIT
Website
Address
SI
Scientific discipline(s)
Geographic coverage
Type
Data science categories
Data science categories