Abstract
The main paradigm common to Precision Agriculture (PA) and Industry 4.0 (I4.0) concerns the so-called Knowledge Management 4.0 (KM4.0), which is based on the need to use integrated information systems (IIF) to manage all areas of any system productive. Here 'raw data' are transformed into 'information' only when they are profitably used within a decision-making process, which leads to the need to design an IIF just starting from the need to satisfy some decisional requirements (infologic approach) and not from the simple need of collecting data (datalogic approach). To better understand the quality and the reliability of the information required to match a decision-making process, the concept of 'Macrodomain of Prevailing Interest' (MPI) is then introduced. An MPI identifies a predominant point of view by which a system can be analysed according to a prevailing purpose. The quality of the analysis depends on the cognitive level and its related methodological approaches permitted by the knowledge maturity reached by the MPI itself. Four main MPIs are finally described: 1) Physical Chemical, 2) Biological Ecological, 3) Productive Hierarchical, and 4) Economic Social. The reliability of the information-And consequently its related tolerance-Tends to decrease moving from the 1st till the 4th MPI. Some practical examples are then discussed to clarify this concept in relation to the use of positioning systems, the application of yield mapping and the management of slurry facilities in animal faming systems. The above considerations show the need to provide new operational indications for the certification of the reliability of information on the entire decision-making chain' in order to highlight: 1) objectives of intervention and related decision-making strategies, striving-where possible-To provide criteria of measurability for the objectives; 2) minimum degree of efficacy allowed; 3) list of information necessary for the decision-making process with their degree of reliability required (global tolerance); 4) for each information: specify the requirements for measuring equipment (in terms of accuracy and precision) and for the related inference engines; 5) the test modes in controllable environments for each measuring equipment; 6) the validation modes of the most relevant interpretative procedures.