Digital Agriculture Technology to Achieve data to Build User-friendly Sustainability indicators - 2020SCNF4L

The challenges prescribed by the EU Green Deal and Farm2Fork strategies with a growing population are pushing farmers to increase the productivity and the efficiency of their practices. In this context, the assessment of the agricultural economic and environmental performances is crucial for increasing the sector’s sustainability. Farm performances must be carefully monitored so that farmers can intervene with appropriate actions. However, farmers usually do not have the right tools to monitor their economic and environmental field practices and a rough evaluation of their farms are mostly based on publicly available data (e.g., FADN-RICA). Indeed, these kinds of data do not have the level of accuracy for representing in detail the actual use of machinery, input (seeds, agrochemicals, water, etc.), and labour; thus, leading farmers to possible inaccurate evaluations. The accuracy of field-level data could be deeply enhanced with an automatic digitalization of the field activities exploiting the data already available in most the farms where the major and underrated contribution is due to machine data available in present agricultural machinery. The goal of the project is to proficiently use of farm data to improve farm management, budgeting and environmental performance. The project will start with the analysis of stakeholders’ acceptance of advanced digital tracing tools, review of the technology embedded in present machines and today use of data coming from such machines. These activities will be carried out with a working group composed of researchers, associations and technology developers and users. Throughout the project, data from a large fleet of machines dedicated to producing few fully mechanized-crops will be recorded. This task will consider two whole crop cycles, an extensive agricultural area, and a very high spatial and temporal resolutions of data. The obtained highly heterogenous and huge dataset (which can full-fledged Big Data) will be analysed using developing domain-specific data mining methods to finely outline farm activities and, all the employed resources for each specific field activity, such as fuel, machines, time, labour, input, and output (i.e., yield). Consequently, the exact cost and the environmental impact of each specific crop production could be profitably retrieved. From these results, synthetic indexes presented in dashboards will be developed to provide an overall economic and environmental performance of the farm and to ease the fruition of the data-driven information and to support farmers in the decision-making processes. Finally, considering that a major obstacle in adopting digital tools in farms is the limited analytical skills of farmers, a range of dissemination activities will be addressed to stakeholders in order to: to support them in the proficient use of the developed tools and to make them aware about the operational inefficiencies will be increased with the efficiency of crop productions.