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2017

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This work focuses on modelling soil water reserves using an Artificial Neural Net-work (ANN). Four model variants were established based on 843 records (verifiedthrough 268 measurements) of soil water content (SWC) measured at full-scale fieldsites located in Southwest Poland. It is revealed that commonly recorded climaticdata (precipitation and temperature) linked with SWC and field water capacity(FWC) are applicable in the ANN modelling. The basic model (utilising the meteoro-logical data) was the most suitable for soil profiles with thicknesses of 0–25 cm,while in profiles with thicknesses of 0–50 cm and 0–100 cm the comprehensiveANN model (linking climatic data, FWC and SWC) was the most appropriate. Fur-thermore, comparative studies of the measured and modelled data indicated theirstatistical convergence, thus providing support for the practical implementation ofthe proposed ANN modelling.