
Classification of the Non-Chernozem Zone regions of Russia by Agro-Climatic and Soil Indicators
Author(s) -
Elizaveta Vladimirovna Egorova,
Marta V. Semkiv,
Yu.T. Farinyuk,
Alexander S. Vasiliev
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/852/1/012028
Subject(s) - chernozem , agriculture , soil classification , environmental science , soil water , cluster (spacecraft) , physical geography , geography , soil science , computer science , archaeology , programming language
Agro-climatic and soil conditions are fundamental factors for effective agricultural production, which must be taken into account when determining the priority directions for the development of agriculture in the regions. The Non-Chernozem Zone of Russia includes regions, some of which are similar in agro-climatic and soil conditions, others are very different. To facilitate the task of setting targets, it is advisable to classify the regions. At the same time, to develop a classification, it is sufficient to use a certain number of indicators characterizing agro-climatic and soil conditions. When classifying the subjects of the Non-Chernozem Zone of Russia, two methods of cluster analysis were considered and described in the article: the method of hierarchical tree classification and K-Mean cluster based segmentation method. The results obtained by the two methods almost completely coincided, which indicates the objective existence of the identified groups of regions. The subjects of the Non-Chernozem Zone were divided into four groups; each significantly differs from another in terms of agro-climatic and soil indicators underlying the classification. The suggested classification can serve as a starting point in designing a state program for the development of agriculture, as well as the basis for further analysis of the efficiency of agriculture, while the labor intensity of work will be significantly reduced due to the compression of the information environment.