Within the pilot project Smart Dairy Farming, companies, research institutes and farmers together to develop innovative resources in the field of animal health, fertility and nutrition. This means farmers can extend the life of their cows. The concrete result consists of sensors, indicators, decision models and advisory products that help in making the right choices when caring for cows. Proper care contributes to better health and longer life expectancy of the animals. Thus, this project contributes to the sustainability of the chain.

On the internet is a wealth of data freely available: Websites and Social Media provide information on using experiences of consumers and their questions about products. Open Data from public and private sources provide insight into the expected supply of products. Horticulture companies make little use of this wealth, precisely at a time when market information is of such importance. Two factors hindering the efficient use of data. First, the necessary investments and knowledge go beyond the capacities of individual companies.

Currently, communications infrastructure such as zigbee, wifi or internet connexions are needed by sensors to automated the collection of data. Sometimes, dataloggers are needed as additional equipment. This technology present some limitations in security aspects interactions a data representation. These limitations force the use of manual measures with a higher error. The aim of this project is the development of a low cost data reading which solves these limitations. This is a versatile, reliable and secure system which it does not require network installations.

Objectives The principal objective of this project is to design and develop mobile and web-based applications in order to enhance crop variety selection activities (real-time data accessibility, data analytics, and decision making). More specific objectives are to: 1.Identify and examine various crop management and performance data sources and data format. 2.Collect data in a centralized format (i.e.

IT-AGRO project is led by the company AIROD Technologies and 8 partners Midi-Pyrénées and Aquitaine, to develop an ISOBUS-ISO-XML console open and interoperable, including a precision positioning function and the ability to develop agronomic applications. This console will be supported by a suitcase to facilitate the management of the ISOBUS interface. The first steps will be referred to the tillage using RAZOL manufacturer tools. This project with a duration of 3 years will make the promotion of the technology in the service of precision farming and tillage.

Open standards and interoperability are key issues in information management for Precision Farming. A collaboration of heterogeneous user groups, from different backgrounds with the need to access common spatial data sets, could benefit with an extensible and open structure with data that can be created and maintained throughout different systems and located in different physical locations (web, machinery, office, etc.). The past GeoWebAgri project have identified the components for an Spatial Data Infrastructure targeting to Precision Farming.

Ignoring the inherited spatial variation in soil properties with traditional sampling methods leads to poor crop management, yield loss and excess use of input. The proposed system of FarmFuse addresses these issues in 2 ways: (a) utilising a new and innovative on-line multi-sensor platform for measuring key soil properties at an appropriate resolution. (b) Integrating this improved soil data with other information such as vehicle-borne sensing of crop growth, weather data, soil conductivity and yield maps, to develop algorithms to determine rules for variable rate application.

The overall aim of the GeoWebAgri-project is to analyse and develop an ICT infrastructure for handling geospatial data and knowledge both in agricultural machines and farm management information systems (FMIS) and promote the introduction of this technology in European software and automation products for agriculture. The technology is studied particularly in the context of spatial data infrastructures (SDI) for the planning and operation of precision agriculture in arable farming. This overall objective can be divided into four specific objectives.