Digital Agriculture

“Precision Agriculture is a management strategy that gathers, processes, and analyses temporal, spatial, and individual data, and combines it with other information to support management decisions according to the estimated variability for the improved resource-use efficiency, productivity, quality, profitability, and sustainability of agricultural production”. The International Society of Precision Agriculture adopted this definition in 2019. Digital technologies are useful tools that can support farmers by improving efficiency, enabling better decisions, but they are not new to agriculture. Precision agriculture (PA) was born in the late 1980s with the use of global positioning system (GPS) guidance, yield mapping, and proximal and remote sensing systems for monitoring variations of soil and crop parameters within the field and linking them to variable rate technologies (VRT) to drive precise agronomic practices. The next step for PA is to exploit the potential of the data collected to provide adapted decisions. Large amounts of data generated by remote and proximal sensors must be able to aggregate and extract useful and intelligible information from stakeholders through the application of machine learning (ML) and artificial intelligence (IA) algorithms. This step is mandatory to link this data in a decision support systems (DSS) in order to understand field variability and promote practices for site-specific management. The ability to use technology to convert accurate data into usable knowledge to guide and support complex decision-making processes that will distinguish digital agriculture (DA) from PA, allow for the transition "from precision to decision". A key solution is the promotion of agricultural services that can produce clear and rapid decisions for the farmer using farm management software and ICT (information and communication technology) applications. The agricultural sector still faces a series of challenges before it can enter the DA era. These range from the cost of technological equipment, to the lack of broadband infrastructure in agricultural areas, to the intergenerational "electronic transition" and the collection and management of big data. Farmers will not invest in the technology without public funding; on the contrary, they will do so if they see the value deriving from the use of technology. Unfortunately, there is a lack of case studies that clearly describe the development of decision methodologies, and highlight the added value for the agricultural sector. The aim of this Special Issue is to promote the publication of case studies describing tools that digitally collect, store, analyze, and share electronic data and/or information along the agricultural value chain.

Dr. Alessandro Matese
Guest Editor

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