ADAPTIVE LASSO ANALYSIS FOR GRAIN YIELDAND YIELD COMPONENTS IN TWO-ROWEDBARLEY UNDER RAINFED CONDITIONS
Yazarlar (3)
Suna Akkol Van Yüzüncü Yıl Üniversitesi, Türkiye
Diğdem Arpali Van Yüzüncü Yıl Üniversitesi, Türkiye
Prof. Dr. Mehmet YAĞMUR Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | COMPTES RENDUS DE L ACADEMIE BULGARE DES SCIENCES | ||
| Dergi ISSN | 1310-1331 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 09-2018 |
| Kabul Tarihi | 12-04-2026 | Yayınlanma Tarihi | – |
| Cilt / Sayı / Sayfa | 71 / 9 / 279–287 | DOI | 10.7546/CRABS.2018.09.17 |
| Özet |
| The goal of this study was to determine the yield components related to grain yield in order to improve barley yield under rainfed conditions of Turkey (Van). Stepwise and Adaptive Lasso methods were performed for selection of most significant yield components. As cohesion criteria to compare Stepwise and Adaptive Lasso methods, the adjusted coefficient of determination and Akaike Information Criterion were used. Results revealed that when there were dependencies between independent variables stepwise and Adaptive Lasso achieved the same results. It has been determined that spike number per m2 and grain weight per spike can be used as the most effective selection criteria for barley breeding studies due to their significant effects on grain yield. |
| Anahtar Kelimeler |
| Grain yield | Lasso regression | Shrinkage methods | Two-rowed barley | Variable selection |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 5 |
| Web of Science | 1 |