Abstract <p>The research study was performed to compare the suitability of two mathematical methods for the procedure of forecasting the winter rye yield within abandoned lands. The training set for adjusting the forecast models (regression and neural network analyses) was the results of long-term monitoring of the winter rye yield on an agroecological transect on the terminal moraine hill near Tver. The regression equations and perceptrons obtained for different climatic conditions, describing the dependence of rye yield on landscape conditions, were used to forecast its productivity on the abandoned lands located in the same region. The forecasts developed based on regression and neural network models differ only in details. Maps of the forecast winter rye yield, created based on various mathematical approaches, indicate the suitability of the studied area for growing this crop, which forecast average weighted yield varies from 1.52 to 2.61 t/ha. The optimal locations for growing rye crops were found, based on the maps created with the use of the forecast data, across the moraine fluvioglacial plain landscapes, while the areas unsuitable for this crop growth are located within the outwash plain and the Volga River valley. Therefore, combining the regression and neural network analyses should be recommended to produce the reliable and informative forecast models for landscape-level adaptive farming across the Non-Black Earth Region. Creating the winter-rye yield forecast maps for the abandoned lands with the use of the archival data is a tool essential for methodology and applied practice experience since this procedure allows us to assess the land suitability for growing the crop and, thus, answer the question on the reasonableness for carrying out the expensive expeditionary research.</p>