TERRITORIAL PATTERNS OF ROBOTICS IN DAIRY FARMING IN THE RUSSIAN FEDERATION: CLUSTER ANALYSIS OF THE INTRODUCTION OF MILKING ROBOTS AS A SOURCE OF BIG DATA

DOI 10.32651/262-59

Issue № 2, 2026, article № 8, pages 59-66

Section: DIGITALIZATION IN THE AGRO-INDUSTRIAL COMPLEX

Language: Russian

Original language title: ТЕРРИТОРИАЛЬНЫЕ ЗАКОНОМЕРНОСТИ РОБОТИЗАЦИИ МОЛОЧНОГО ЖИВОТНОВОДСТВА В РФ: КЛАСТЕРНЫЙ АНАЛИЗ ВНЕДРЕНИЯ ДОИЛЬНЫХ РОБОТОВ КАК ИСТОЧНИКА БОЛЬШИХ ДАННЫХ

Keywords: AGRICULTURE, ROBOTICS, TERRITORIAL PATTERNS, MILKING ROBOTICS, BIG DATA, CLUSTER ANALYSIS, ANIMAL PRODUCTIVITY

Abstract: The relevance of the study is due to the lack of detailed data on the robotization of agriculture in the Russian Federation, its rapid development as a factor in increasing efficiency and a source of big data, as well as the need to identify real territorial disproportions of these processes. The goal is to identify territorial patterns and form a typology of regions of the Russian Federation by the level of robotization (with a focus on milking robots), its relationship with livestock productivity and the potential for generating big data. The research methods include requests to the territorial offices of the Ministry of Agriculture and online screening for the number of robots, the use of cluster analysis (k-means) with preliminary z-standardization of variables and the use of Euclidean distance on a sample of 79 regions. The main results of the study revealed 4 stable clusters (silhouette = 0.69). The first ("Leaders") includes 18 regions with the highest productivity of ~7384 kg / year and an average number of robots of ~12.8 robots / region (Ryazan, Sverdlovsk regions), including regions with unrealized potential (Vladimir, Belgorod regions). The second cluster ("Outsiders") includes 11 regions of the North Caucasus Federal District and the Far Eastern Federal District with low productivity of ~2183 kg / year, almost zero robotization. The third cluster ("Regions with potential") includes 20 regions with high productivity ~5869 kg/year and moderate robotization ~4.3 robots (Tatarstan, Tyumen region), with many regions without robots but high payback potential (Tver, Bryansk region). The fourth cluster ("Largest group with minimal robotization") includes 30 regions with low productivity ~4467 kg/year and weak robotization ~0.6 robots, isolated "islands". Specific recommendations are given on the need to concentrate government support measures and investments on regions with high unrealized potential (clusters 1 and 3), where high productivity ensures a quick payback of milking robots and the formation of big data. In cluster 4, stimulate the introduction of robots in individual highly productive farms as "growth points". For cluster 2, the priority task is to increase the basic productivity of livestock farming. It is necessary to take into account the role of robotics as a key source of big data when forming programs for digitalization of the agro-industrial complex at the regional level.

Authors: Skvortsov Egor Artemovich, SHaibakova Liudmila Faritovna, Morozova Galina Mikhailovna, Albycheva Mariia Dmitrievna