The indirect approach is mainly used to assess the forest-growing potential of lands in the forestry of Ukraine. For these purposes, a comparative ecological (forest typological) method is used to assess soil conditions according to their forest growth effect. The species composition of the forest stand and its productivity are the main indicators of the forest growth effect. The undoubted advantages of this method are high forestry value, low labour intensity and cost, and the main disadvantages are the subjectivity of determining the types of forest conditions (especially derivatives and artificial plantations), its insufficient environmental sensitivity, and the difficulty of applying to places where there is no forest vegetation. The aim of the study was to quantify the quality of forest land and develop markers of the forest-growing potential of soils. The studies were carried out by synthesizing two methodological approaches – forest typological as the leading method for assessing the potential of habitats and direct study of soils (field and analytical). The package of markers was developed to assess forest potential of soils based on the establishment of a correlation between the productivity of forest stands (height, quality class) and soil indicators (thickness of the humus part of the profile, pH, content of clay particles, humus, total and exchange forms of N, P, K, Ca, Mg). The package of markers depends on the soil type and consists of the following soil indicators: the content of particles of physical clay (d<0.01 mm), the thickness of the humus part, as well as the content of humus, N, Ca, K, and Mg. A gradual increase in their quantitative values leads to increase in the forest productivity of the soil and, as a result, the productivity of the forest stand.

The purpose of the article is to consider the features of the anthropogenic impact on agricultural land in the Republic of Kazakhstan (RK). The article deals with the current problem of agricultural land degradation, and desertification in Kazakhstan, where one of the causes is the anthropogenic activities of the population and the aridity of the country's climate. Most of the territory of Kazakhstan is located in conditions highly vulnerable to anthropogenic desertification when in combination with natural factors of desertification the ecological situation is sharply deteriorating. Another cause of land degradation is urbanization and intensive degradation of desert habitats — unsystematic road network, regulation of river flows, illegal cutting of saxaul for firewood for sale. As a result of urbanization and intensive agricultural development of the foothill strip in the south and east of the country, the natural vegetation cover is highly disturbed. Roads, pipelines, and power lines, which are being laid at an increasing rate, have a great impact on the fauna. The area of land occupied by mining enterprises is steadily growing. In the last 10 years, the areas of oil and gas extraction, development of uranium ores, etc. in Western Kazakhstan, the Eastern Caspian Sea region, the Betpakdala desert, etc. have been sharply increasing. As a result of insufficiently thought-out land management from water erosion 5.6 million hectares of arable land was affected and grain yield was reduced by 20–30%. Degradation of agricultural lands, including pastures, is 30–50% and higher % in 9 of 14 regions of Kazakhstan. The use of the existing model of agricultural development of raw materials leads to inefficient economic development and constantly increasing pressure on ecosystems. The soil in Kazakhstan is very vulnerable, as it is comprehensively affected by various anthropogenic factors that lead to the constant deterioration of its quality. Soils near the Aral Sea and the Caspian Sea are subjected to the most intensive degradation, as wind erosion is widespread there.

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".