SavedAdd your journal

Journals / Turkish Journal Of Field Crops / 2016 / Cilt: 21 - Sayı: 1

EVALUATION OF GENOTYPE BY ENVIRONMENT INTERACTION FOR GRAIN YIELD IN DURUM WHEAT USING NON-PARAMETRIC STABILITY STATISTICS

Yuksel KAYA
Musa TURKOZ
Pages
51–59
Publication Date
2016-06-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, this study showed that the crossing block of TNDWBP should be enriched by germplasm being capable of dynamic stability, wide adaptation and higher yielding.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:If genotype (G) ranks change from one environment (E) to another, genotype by environment interactions (GEI) reflects the need for testing Gs in numerous Es in order to obtain reliable results.
📊
Key Findings & Data:The aim of this study was to compare 16 non-parametric stability statistics (NPSSs) for GEI on grain yields of 15 durum wheat genotypes, consisting of 11 advanced lines selected from Turkish National Durum Wheat Breeding Program (TNDWBP) and four checks, tested in 12 rain-fed environments during the 2 cropping seasons (2009–2010 and 2010-2011) in Turkey.
💡
Conclusion & Impact:As a result, this study showed that the crossing block of TNDWBP should be enriched by germplasm being capable of dynamic stability, wide adaptation and higher yielding.
🔬Design:Research Article
📍Scope:2009–2010 Verileri
⚙️Variables:Durum Wheat (T. durum L.) · Grain Yield · Genotype by Environment Interaction

Abstract

If genotype (G) ranks change from one environment (E) to another, genotype by environment interactions (GEI) reflects the need for testing Gs in numerous Es in order to obtain reliable results. The aim of this study was to compare 16 non-parametric stability statistics (NPSSs) for GEI on grain yields of 15 durum wheat genotypes, consisting of 11 advanced lines selected from Turkish National Durum Wheat Breeding Program (TNDWBP) and four checks, tested in 12 rain-fed environments during the 2 cropping seasons (2009–2010 and 2010-2011) in Turkey. The combined ANOVA indicated that G, E and GEI effects were significant for grain yield. According to analyses of NPSSs, the highest in ranking (TOP), percentage of adaptability (PA), rank mean (RM) and yield-stability (YS) statistics were positively associated with grain yield and therefore characterized under the dynamic concept of stability. In addition, spearman rank correlation analysis revealed that only TOP, PA, RM and YS statistics would be useful for simultaneous selection for high grain yield and stability. Based on the 16 NPSSs used in this study, Dumlupinar cultivar (G15) was both the most stable and one of the highest yielding ones. On the other hand, G5 and G7 were the most stable ones among the advanced lines tested, but their yield performances were lower. As a result, this study showed that the crossing block of TNDWBP should be enriched by germplasm being capable of dynamic stability, wide adaptation and higher yielding.

🔖

Cite This Article

KAYA, Y., TURKOZ, M. (2016). EVALUATION OF GENOTYPE BY ENVIRONMENT INTERACTION FOR GRAIN YIELD IN DURUM WHEAT USING NON-PARAMETRIC STABILITY STATISTICS. Turkish Journal Of Field Crops, Cilt: 21 - Sayı: 1, 51–59. https://ajindex.com/articles/180789

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (17)

  1. Akcura, M., Y. Kaya and S. Taner. 2009. Evaluation of durum wheat genotypes using parametric and nonparametric stability statistics. Turk. J. Field Crops, 14(2): 111-122.
  2. Becker, H.C. 1981. Correlations among some statistical measures of phenotypic stability. Euphytica, 30: 835-840.
  3. Becker, H.C. and J. Leon. 1988. Stability analysis in plant breeding. Plant Breed. 101: 1-23.
  4. Duarte, B.J. and M.J.O. Zimmermann. 1995. Correlation among yield stability parameters in common bean. Crop Sci. 35: 905-912.
  5. Ebdon, J.S. and H.G. Gauch. 2002. Additive main effects and multiplicative interaction analysis of National Turfgrass performance trials: II. Genotype recommendation. Crop Sci. 42: 497-506.
  6. Flores, E., M.T. Moreno and J.I. Cubero. 1998. A comparison of univariate and multivariate methods to analyze environments. Field Crop Res. 56: 271-286.
  7. Fox, P.N., B. Skovmand, B.K. Thompson, H.J. Braun and R. Cormier. 1990. Yield and adaptation of hexaploid spring triticale. Euphytica, 47: 57-64.
  8. Habash, D.Z., Z. Kehel and M. Nachit. 2009. Genomic approaches for designing durum wheat ready for climate change with a focus on drought. J Exp Bot 60(10): 2805- 2815. Huehn, M. 1979. Beitrage zur Erfassung der phanotypischen Stabilitat. EDV Med. Biol 10:112-117.
  9. Huehn, M. 1990. Nonparametric measures of phenotypic stability: I. Theory. Euphytica, 47: 189-194.
  10. Huehn, M. 1996. Non-parametric analysis of genotype x environment interactions by ranks. In: Genotype by Environment Interaction, ed. Kang, M.S. and Gauch J.H.G., 235-271,
  11. CRC. FL. Hussein, M.A., A. Bjornstad and A.H. Aastveit. 2000. SASG x ESTAB: A SAS program for computing genotype x environment stability statistics. Agron J 92: 454-459.
  12. Kang, M.S. 2002. Genotype-Environment Interaction: Progress and Prospects. In: Quantitative genetics, genomics and plant breeding. ed. Kang, M.S. 221-243.
  13. CAB. Kang, S.M. and R. Magari. 1995. STABLE: A BASIC program for calculating stability and yield-stability statistics. Agron J 87: 276-277.
  14. Ketata, H. 1988. Genotype x environment interaction. Proceeding of the workshop on biometrical techniques for cereal breeders. 16-32,
  15. ICARDA, Aleppo, Syria. Langer, S., K.J. Frey and T. Baily. 1979. Association of different stability models in wheat. Euphytica, 28: 17-24.
  16. Lipkovich, I.A. and E.P. Smith. 2002. Biplot and Singular Value Decomposition Macros for Excel©. J Stat Softw 7(5):1-15.
  17. Lu, H.S. 1995. PC-SAS Program for estimating Huhn’s nonparametric stability statistics. Agron J 87: 888-891. Moh