Improving the methodology of on-farm land management in the direction of transition from the formation of work sites to the formation of management zones for the specific requirements of the agricultural producer upon implementation of precision farming is extremely important for the agricultural sector of the Belarusian economy. The article presents the results of applying the methods of geostatistical and multifactor geoinformation analysis for the formation of management zones within the limits of land use of RUE “Uchkhoz BGSHA” (Republic of Belarus, Mogilev region, Gorky district). The total area of the surveyed territory is 83420.1 hectares. The nature of the spatial distribution of data on the content of humus, mobile phosphorus and potassium in the soil as well as pH level was estimated using the tools of the Spatial Statistics module of ArcGIS version 10.5. The presence of reliable clustering of data on soil parameters was established, since the value of the global Moran index I ranged from 0.197827 to 0.360388, and the z-score in all cases exceeded 2.58. The universal kriging method turned out to be the most suitable for modeling the spatial distribution of soil pH data, while the empirical Bayesian kriging method is the most acceptable when modeling the spatial distribution of the content of humus, phosphorus, and potassium in the soil. The method of principal components and the simple summation of rasters using a calculator proved to be suitable for identifying management zones by a set of soil parameters (the discrepancy with the actual area was 16.56 and 16.24 ha, respectively).
Agromonitoring is one of the most important sources of obtaining up-to-date and timely information about the state of agricultural crops. It is possible to speed up and reduce the cost of its implementation process using remote sensing data (RSD) obtained with the help of unmanned aerial vehicles (UAVs). Possibility of using ultra-high-resolution remote sensing to determine productivity of Silphium perfoliatum biomass has been evaluated using Phantom-4ProV 2.0 UAV. The shooting was carried out in RGB mode, the shooting height was 50 m, the spatial resolution was 2.5 cm. Based on the results of the survey, a height map and orthomosaic were created, which were later used to assess productivity of plants. To obtain the plant height values, the difference between the vegetation cover heights obtained from the surface model raster and the minimum height determined within the raster has been calculated. The actual height of plants measured in the field was compared with the data obtained using the UAV, and after the biomass productivity calculated from the actual and predicted heights was determined. The determination coefficient for equation of paired linear regression between the actual and predicted values of productivity made 0.97, and the value of the average approximation error was 3.3 %. To verify the results obtained, 60 samples of biomass were taken in the field within the study area, with the length of the plants determined using a tape measure, and the sampling sites coordinated using GPS positioning. 13 vegetation indices have been determined using pixel-based calibrated orthomosaic and normalized RGB channels, four of which (ExG, VARI, WI, and EXGR) showed to be suitable for creating a predictive model of multiple linear regression, which allows estimating and predicting the productivity of Silphium perfoliatum biomass during stemming phase with an error not exceeding 2 %. The results of the study can be useful both in development of prediction methods and in the direct prediction of Silphium perfoliatum biomass and other forage crops productivity, in particular Helianthus annuus and Helianthus tuberosus.
The paper analyzes the directions of development of on-farm land management in the context of the formation of innovative approaches to land management, in particular, when introducing a precision farming system in agricultural production in Belarus.
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