{"title":"DIGITIZATION OF AGRICULTURE IN KAZAKHSTAN: REGIONAL EXPERIENCE","authors":"S. Pashkov, G. Mazhitova","doi":"10.17072/2079-7877-2021-4-27-41","DOIUrl":null,"url":null,"abstract":"The article deals with digitization of agriculture as the determinative direction of crop production intensification in Kazakhstan. The main condition for the digitization to be implemented is the development of precision agriculture. The aim of our work is to study the process of agricultural digitization in Kazakhstan and particularly the North Kazakhstan region, which is the oldest territory of rainfed agriculture and the leading area in terms of crop production by value and in terms of economic fertility of rainfed land. We analyze the main provisions of state programs (‘Kazakhstan–2050’, ‘Digital Kazakhstan’) aimed at integrating geospatial technologies into farming and crop production, which is supposed to increase labor productivity in the industry. In the course of research, the peculiarities of using satellite imagery and shooting from unmanned aerial vehicles in precision agriculture were noted; factors that hinder the introduction of digital technologies in the development of agriculture were identified; SWOT analysis of environmental and economic conditions of the precision agriculture development in the region was carried out. The article proposes measures to be taken for further development of agricultural digitization in the region: both innovative (development of agrarian voltaics) and paternalistic ones (state stimulation of agrarian formations, etc.). The digitization of the agricultural sector will create econologically sustainable programmable system of farming based on high natural agricultural potential and advanced geospatial technologies resting on predictive analytics.","PeriodicalId":345845,"journal":{"name":"Географический вестник = Geographical bulletin","volume":"129 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Географический вестник = Geographical bulletin","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17072/2079-7877-2021-4-27-41","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The article deals with digitization of agriculture as the determinative direction of crop production intensification in Kazakhstan. The main condition for the digitization to be implemented is the development of precision agriculture. The aim of our work is to study the process of agricultural digitization in Kazakhstan and particularly the North Kazakhstan region, which is the oldest territory of rainfed agriculture and the leading area in terms of crop production by value and in terms of economic fertility of rainfed land. We analyze the main provisions of state programs (‘Kazakhstan–2050’, ‘Digital Kazakhstan’) aimed at integrating geospatial technologies into farming and crop production, which is supposed to increase labor productivity in the industry. In the course of research, the peculiarities of using satellite imagery and shooting from unmanned aerial vehicles in precision agriculture were noted; factors that hinder the introduction of digital technologies in the development of agriculture were identified; SWOT analysis of environmental and economic conditions of the precision agriculture development in the region was carried out. The article proposes measures to be taken for further development of agricultural digitization in the region: both innovative (development of agrarian voltaics) and paternalistic ones (state stimulation of agrarian formations, etc.). The digitization of the agricultural sector will create econologically sustainable programmable system of farming based on high natural agricultural potential and advanced geospatial technologies resting on predictive analytics.