Modelling the Yield and Estimating the Energy Properties of Miscanthus x Giganteus in Different Harvest Periods

Author:

Brandić Ivan1ORCID,Voća Neven1ORCID,Leto Josip2,Bilandžija Nikola3

Affiliation:

1. Department of Sustainable Technologies and Renewable Energy Sources, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, Croatia

2. Department of Field Crops, Forage and Grassland, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, Croatia

3. Department of Mechanization and Autonomous Systems in Agriculture, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, Croatia

Abstract

This research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of Miscanthus x giganteus (MxG), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over three years in two different geographical areas, ANN regression models were used to estimate the lower heating value (LHV) and yield of MxG. The models showed high predictive accuracy, achieving R2 values of 0.85 for LHV and 0.95 for yield, with corresponding RMSEs of 0.13 and 2.22. A significant correlation affecting yield was found between plant height and number of shoots. In addition, a sensitivity analysis of the ANN models showed the influence of both categorical and continuous input variables on the predictions. These results highlight the role of MxG as a sustainable biomass energy source and provide insights for optimizing biomass production, influencing energy policy, and contributing to advances in renewable energy and global energy sustainability efforts.

Funder

Croatian Science Foundation

Publisher

MDPI AG

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