Atıf İçin Kopyala
Demirel F., Eren B., Yilmaz A., Türkoğlu A., Haliloğlu K., Niedbała G., ...Daha Fazla
AGRONOMY, cilt.13, sa.6, ss.1-13, 2023 (SCI-Expanded)
-
Yayın Türü:
Makale / Tam Makale
-
Cilt numarası:
13
Sayı:
6
-
Basım Tarihi:
2023
-
Doi Numarası:
10.3390/agronomy13061438
-
Dergi Adı:
AGRONOMY
-
Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, CAB Abstracts, Food Science & Technology Abstracts, Directory of Open Access Journals
-
Sayfa Sayıları:
ss.1-13
-
Atatürk Üniversitesi Adresli:
Evet
Özet
Genetic information obtained from ancestral species of wheat and other registered wheat has brought about critical research, especially in wheat breeding, and shown great potential for the development of advanced breeding techniques. The purpose of this study was to determine correlations between some morphological traits of various wheat (Triticum spp.) species and to demonstrate the application of MARS and CHAID algorithms to wheat-derived data sets. Relationships among several morphological traits of wheat were investigated using a total of 26 different wheat genotypes. MARS and CHAID data mining methods were compared for grain yield prediction from different traits using cross-validation. In addition, an optimal CHAID tree structure with minimum RMSE was obtained and cross-validated with nine terminal nodes. Based on the smallest RMSE of the cross-validation, the eight-element MARS model was found to be the best model for grain yield prediction. The MARS algorithm proved superior to CHAID in grain yield prediction and accounted for 95.7% of the variation in grain yield among wheats. CHAID and MARS analyses on wheat grain yield were performed for the first time in this research. In this context, we showed how MARS and CHAID algorithms can help wheat breeders describe complex interaction effects more precisely. With the data mining methodology demonstrated in this study, breeders can predict which wheat traits are beneficial for increasing grain yield. The adaption of MARS and CHAID algorithms should benefit breeding research.