DATA-BUS

In the coming decades, the biggest challenge for the agricultural sector will be to produce enough food for a growing population while minimizing the environmental impact of production, as outlined in the EU Green Deal and its Farm to Fork strategy. In this context, assessing the economic and environmental performance of agriculture has become even more important to provide information on the sustainability of the sector.

It has been shown that the use of farm-level data is critical because of the large heterogeneity in farming practices, farm size, specialization and location, which is reflected in highly heterogeneous environmental performance, especially in terms of greenhouse gas (GHG) emissions and energy consumption.

The accuracy of the microeconomic data available through the Farm Accountancy Information Network (FADN) could be improved through the automatic digitisation of field activities based on the collection and processing of farm data, mainly from proximal and remote sensors and farm machinery. Data from remote and proximal sensors are already widely used in research, while few studies have been conducted on machine-supplied data from sensors integrated in CANBUS and ISOBUS networks, which are an enabling technology for Agriculture 4.0. The information potential of these huge datasets can be increased by designing an appropriate post-processing method. In fact, the spatial and temporal resolution of machine data can make it possible to obtain a detailed description of agricultural activities and to calculate the exact economic and environmental costs of crops, not only at the farm level, but also at the level of individual field activities. In this direction, the project aims to support agricultural entrepreneurs in their transition to data-driven agriculture in order to increase their productivity and decision-making efficiency.