Barley yield estimation performed by ANN integrated with the soil quality index modified by biogas waste application

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Alaboz P., Dengiz O., Demir S.

ZEMDIRBYSTE-AGRICULTURE, vol.108, no.3, pp.217-226, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 108 Issue: 3
  • Publication Date: 2021
  • Doi Number: 10.13080/z-a.2021.108.028
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.217-226
  • Keywords: biogas waste, minimum data set, artificial neural networks, soil quality, analytical hierarchical process, Akaike information criterion, PREDICTION
  • Ondokuz Mayıs University Affiliated: Yes


Today, the evaluation of soil quality and crop yield has become a critical issue in meeting the increasing population's food needs. The current study aims to analyse and predict the effect of biogas waste (BW) application on soil quality and barley yield. The yield of barley grown in the soil with 0 (B0), 10 (B1), 20 (B2), 30 (B3) and 40 (B4) t ha-1 BW applied and the physical, chemical and biological properties of the soil were examined. In determining the soil quality index (SQI), the analytic hierarchy process and linear combination technique were used, 27 soil indicators in the total data set (TDS) and 10 soil indicators were evaluated separately due to the minimum data set (MDS) created with a principal component analysis (PCA). The relationship between SQI values obtained based on application and barley yield was estimated by applying general regression equations and Levenberg-Marquardt training algorithm in artificial neural networks (ANN).