Аннотация:At the end of the twentieth century in Russia, an expanded program was developed for monitoring the agroecological consequences of applying high doses of fertilizers and using broad-spectrum agrochemicals. Methodological approaches were unified to improve the zonal systems of agriculture, create highly efficient and environmentally balanced agrocenoses, maintain soil fertility and make full use of natural and climatic resources. The parameters of the state and dynamics of soil cover properties were divided into three groups, which allowed us to carry out early diagnostics on the basis of seasonal dynamics, enabling reflection on long-term changes in soil properties. For basic research, it was proposed to use the main experiments of the Geographical Network of Long-Term Experiments with Fertilizers (Geoset), as they were laid in typical climatic zones, featured agrochemicals with a regular long-term impact on agroecosystems and all technological processes were carried out there at a high level in accordance with the zonal farming systems. The main requirement for long-term experiments was the maximum reduction of the initial spatial heterogeneity of standard agrochemical properties of the soils, so studies were conducted on small plots of 50–100 m2. Special methods of statistical processing were developed for the dissemination of long-term experimental data on large agricultural landscape units, which allowed the spatial heterogeneity of the parameters studied to be characterized. Processing the data from long-term and short-term experiments included in the “AgroGeos” database allowed researchers to establish that the growing conditions of plants change mainly under the influence of the micro- and meso-relief, the influence of which can even exceed the zonal changes. Currently, on the basis of the databases created, a concept is being developed for the targeted management of fertility indicators, agroecosystem stability, the possibility of using information technologies in monitoring systems and forecasting crop yields and crop quality. To implement this concept, highly objective models and yield forecast maps are being constructed on the basis of combined information on landscape, climatic, soil and agrochemical data using several GISs.