Integrating Taguchi design and machine learning models for trait stability and predicting forage quality in naturally occurring grass pea (Lathyrus spp.)

Creative Commons License

Çatal M. İ., Sancak C., Ali S. A., Aasım M.

BMC PLANT BIOLOGY, cilt.26, sa.1188, ss.1-15, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 1188
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1186/s12870-026-08991-z
  • Dergi Adı: BMC PLANT BIOLOGY
  • Derginin Tarandığı İndeksler: Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest), Scopus, Science Citation Index Expanded (SCI-EXPANDED), BIOSIS, EMBASE, MEDLINE, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1-15
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Ankara Üniversitesi Adresli: Evet

Özet

This study investigated the forage quality of four naturally occurring Lathyrus species (Lathyrus czeczottianus, L. pratensis, L. roseus, and L. rotundifolius subsp. miniatus), including one endemic taxon, collected from the Rize province of Turkey. Forage traits of two years were analyzed using the Taguchi design (TD) of experiment to determine trait stability and species-based differences and robustness. Machine learning models (Random forest – RF and Light gradient boosting – LightGBM) were applied to predict forage quality parameters. Results revealed L. pratensis as the most robust species for critical traits such as crude ash ratio, crude protein ratio, and K/(Ca + Mg) based on signal-to-noise ratio. The ML models showed high predictive accuracy for mineral and digestibility-related traits (R² > 0.97). The RF model exhibited superior predictive capacity for fiber traits compared to the LightGBM model. The combined use of TD and ML-based prediction demonstrates a potential framework for evaluating species-level differences, supporting data-driven breeding, optimizing forage quality, and sustainable livestock production in diverse environments. While ML models demonstrated promising predictive performance, future studies incorporating larger datasets and different cross-validation techniques are needed to enhance model robustness and improve the general applicability of these findings.