Problems and Solutions of Field Scale Agro-ecological Data Acquisition and Data Interpretations in Agroinformatical Domain

Agricultural production is an activity of high economic and environmental risk, nowadays especially. Handling of widening scientific knowledge and up-to-date technological appliances needs rethinking of foregoing theory and practice; according to some researchers, total paradigm change is needed. The “sustainable development” theory of Agenda 21 made up by the Rio Summit (1992) means long-term tasks for all branches of economy, so as for agriculture. As a result, the change of land use planned from economic and environmental points of view would realign the overall view of the Carpathian Basin for centuries in a territory where the ratio of agricultural regions is one of the highest (72%) in Europe. Risks of decisions refer to any period or area can be decreased by having more and more data and analysed information. Agricultural activity, as an open ecological system, has a hardly elaborative data requirement even by the present information technological devices [12] About half of the input data interfering the results are changed during a fragment of the phenological cycle, that can not be described exactly by such widely used ecological models as CERES [25] or CROPWAT [44] during particularly extreme changes validated for Central-European model border conditions. These results in more data need in space and time, which has properly been satisfied so far by the development of digital sensors, but the practice is not able to keep up with the manipulation integration and interpretation of the collected data. Similar process occurs on the field of data mining technology performed on Internet, where effective solution of information production still has to be waited for. Seemingly, this causes contradiction, the decision maker uses smaller and smaller part of the information available, but this data amount – in absolute sense – grows at a highly increased rate in time. During many decisions the incomplete interpretation assumes the reliability of data or their interval and value of uncertainty are not known. As a result, decision makers have no numerical risk values actualised according to the information built up from the data, thus the farmer dares to undertake lower and lower risk level in the smaller decision space. How one can escape from the trap of this lack of data and data dumping that sometimes comes up at the same time in destructive cases? The key element of the decision-making process is improvement of the interpretation techniques that must be optimised during the whole data flow.