Application of the cluster analysis and methods of multifactorial statistical modelling when studying factors of grain crops productivity growth

The most important condition for the effective development of grain farm is revealing factors providing productivity growth and gross grain yield. In this regard the methodological base allowing to do scientific research in the context of modern requirements and principles of achieving the target has essential value. The discussions concerning defining statistical interrelations and dependences, justification of application of these or those analytical practice and methods to define factors of formation and increase in grain and leguminous productivity crops aren't new in the scientific research. Various concepts found reflection in works of native and foreign authors. Rather wide base of theoretical and practical developments to study the factors of grain crops productivity growth and production of agricultural products doesn't cancel the requirements to improve the methodical maintenance of analytical monitoring. Moreover, the emergence of new economic conditions and need to increase the efficiency of functioning of the agrarian sector demand changes in the system of registration and analytical work, including development of new directions of the econometric analysis, search of necessary information and complex techniques of its processing. The consideration of these and many other questions, actualize the stated scientific direction. In the presented material the factors of grain crops productivity growth and also the conditions defining stability of dynamics of the production of agricultural products in the Oryol region are studied by means of general scientific and economical and statistical methods of the research. The clustering of the municipal districts of the region on indicators and criteria of growth of grain crops productivity and multifactorial modeling are carried out. The theoretical and practical importance of the results of the research is that they can form a base for further development of methodology of the stochastic factorial analysis in the context of studying of the indicators influencing grain crops productivity growth.