Management of huge amounts of data using qualitative and statistical modeling: an agricultural case study

Abstract

The approaches and methodologies used for agricultural data analysis and processing in general continuously evolve (e.g. specific statistical analysis methods and tools, such as SPSS, are used quite intensively to assist the researcher’s work). The basic idea in our research work is to provide an integrated environment, where various data analysis and modeling tools would be at the disposal of the researcher to be used in processing farming production problems and extracting adequate solutions. For this purpose, certain database management and qualitative modeling techniques have been used in conjunction, as an integrated computing environment, called AgroModel, and tested upon specific cattle breeding cases. Artificial intelligence and qualitative modeling techniques have been applied for a long period of time, with quite successful results in most of the cases.1 However in the field of agriculture there is still a need for further research work to be carried out. We decided to use and apply qualitative techniques describing the structure and performance of plants and animals within agricultural environments, in order to assist the agriculturist to manage easily complicated processes, associated in particular with cattle breeding, and provide the ability to extract and evaluate the most valuable information from a set of complicated with various factors quantitative data. The retrieval of all the relevant information on the control treatments in various agricultural cases could be considered as a quite important research material for interesting studies of the plant species or livestock breeds in various experimental environments.