An Investigation on the Relationship Between Yield and Canopy Components in Wheat (Triticum aestivum)


Ozkan M. M., ADAK M. S., KOCABAŞ Z.

JOURNAL OF AGRICULTURAL SCIENCES-TARIM BILIMLERI DERGISI, cilt.14, sa.2, ss.148-153, 2008 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 2
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1501/tarimbil_0000000516
  • Dergi Adı: JOURNAL OF AGRICULTURAL SCIENCES-TARIM BILIMLERI DERGISI
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.148-153
  • Anahtar Kelimeler: Gerek-79, wheat cv., yield components, canopy components, canonical correlation, canonical variables
  • Ankara Üniversitesi Adresli: Evet

Özet

The purpose of this study is to investigate to which extent yield components are related to canopy components in Gerek-79 cv. of bread wheat. The data on biological yield, grain yield, 1000-grain weight, harvest index, fertile spikelet number, spike number, spike length and plant height were used. The canonical correlation analysis was fulfilled using the biological yield, grain yield, 1000-grain weight, fertile spikelet number and harvest index as the first set, called yield components, and spike number, spike length and plant height as the second set, called canopy components. The results of the canonical analysis showed that a high canonical correlation of 0.923 was achieved between yield and canopy components. The largest contribution to the first canonical variable for yield component was made by biological yield. The first canonical variable for the canopy component was most affected by spike length. The computed squared multiple correlation coefficients confirmed that the first canonical variable for canopy components had a substantial predictive power for biological yield (0.730) but much less for the other yield components. The squared multiple correlations also showed that the first canonical variable for yield components was a better predictor of canopy components, being 0.784 to predict plant height.