Forecasting potential yields under uncertainty using fuzzy cognitive maps

Abstract Background The aim of the study is identification factors influencing reduction potential maximum yield winter wheat in weather conditions dry farming European part Russia, Volgograd region. novelty work forecasting yields under uncertainty that allows to assess risks and threats can influence maximize yield. To solve this problem, tool for formalization, analysis modeling semi-structured systems processes Fuzzy Cognitive Maps (FCM) used. Results Based on disparate heterogeneous information about multitude external influences crop formation during plant photosynthesis, a model analyzing level target factor constructed an effective control impact scenario developed. This used identify factors, where each one them iteratively passes from initial value stable according chosen formula, based which, other are determined. Conclusions conclusions obtained as result confirm concept precision farming: quantity quality innovation agriculture depends ability apply it effectively field. Developed method predicting levels be not only future agricultural performance, but also estimate harvested yields.