Statistical research automation for farm’s differentiation

Russian State Agrarian University - Moscow Agricultural Academy named after K.A. Timiryazev, 49, Timiryazevskaya st., 127550, Moscow, Russia

^* Corresponding author: aeulianckin@rgau-msha.ru

Abstract

Agriculture is a priority sector of the Russian economy, ensuring the country's food security. Three categories of producers are engaged in the agricultural production: agricultural organizations, peasant farms (PFs) and personal subsidiary farms. The share of peasant farms in the structure of gross agricultural output for all categories of farms increased from 2% in 1998 to 14% in 2019, the volume of their production almost doubled. Their support and development will solve the problems of not only food, but also geopolitical security, by fixing the population in countryside and developing rural areas. An important task for agricultural policy aimed at the balanced development of agriculture is to study the differentiation of farms within the selected categories, and the use of information technologies will significantly reduce the time for processing the data. Within the framework of the paper, an approach has been developed to automate the statistical study of the differentiation of peasant farms using two programming languages: Python and R. The data of the reporting form 1-KFH “Information on the production activities of heads of peasant farms - individual entrepreneurs”. As a result, the indicators of variation for the regions of Russia were obtained and visualized, a grouping and cartogram of regions by the level of income of peasant farms per unit of land area was built.