The aim of the study was to develop a methodology for determining homogeneous territorial zones for precision farming. In this study we took into account the national land use system which provides for the absence of private ownership of agricultural land. The algorithm for determining management-zones provides for: establishing zones of spatial heterogeneity; determining the presence of clusters and emissions; modelling the spatial distribution of soil quality indicators. It is recommended to use data from agrochemical soil studies which are conducted centrally every 4 years for each agricultural enterprise as input parameters. These data include: the humus content in the soil, the content of available phosphorus and potassium and soil pH. The data should be carefully examined using spatial statistics tools to provide a more accurate delineation of the management-zones boundaries. The developed technique makes it possible to determine fertile and marginal areas within each individual field and differentiate the use of fertilizers, taking into account the presence of intra-field heterogeneity. This will save from 2.5 to 21.8 kg P haE−1 and from 0.9 to 26.7 kg K haE−1 due to the redistribution of the fertilizer dose calculated for the planned yield, taking into account the identified site-specific management zones. The differentiated use of mineral fertilizers will increase the profitability of growing winter cereals by 2.2%, sugar beets by 1.3%, rapeseed by 1.1%, and malting barley by 0.8%.

The possibilities of using the geospatial analysis methods for visualizing land monitoring data and modelling the spatial distribution of the main agrochemical soil indicators are discussed in the article. The research was conducted within the limits of land use of RUP “Uchkhoz BGSHA” (Republic of Belarus, Mogilev region, Goretsky district). The total area of the surveyed territory was 3187.0 hectares. The geospatial analysis of the spatial distribution of humus, mobile phosphorus, mobile potassium and pHKCl was carried out using the Geostatistical Analyst module of the ArcGIS software. Semivariograms were used as the main tool for studying the structure of the spatial distribution of agrochemical indicators. The exponential function was identified as the best variogram model, the type of the circle was standard, the type and the number of sectors was 4 with a displacement of 450, and the lag was 200 metres. The interpolation accuracy was determined from the mean error (ME), mean square error (RMSE) and standard error (RMSS). The universal kriging method was used to perform the forecast and visualize the spatial distribution of agrochemical indicators. The multivariate analysis was performed using the functionality of the Raster Calculator tool, Principal Component analysis and Maximum Likelihood Classification. The search and determination of areas of sites with the most optimal agrochemical indicators were carried out by the multifactor analysis in the GIS environment. Calculation of the area of each circuit within the limits of working parcels was carried out using the utility "Zone Statistics".