Site Regression Biplot Analysis for Matching New Improved Lentil Genotypes into Target Environments

Document Type : Research Paper

Abstract

Abstract
The evaluation of the yield stability of genotypes and environment is of prime concern to plant breeders. Therefore, a comprehensive analysis of the structure of the GE interaction is needed. The objective of this investigation was to evaluate the use of sites regression (SREG) GGE methodology to stratify the pe × environment (GE) interaction in lentil. Yield data of 10 genotypes of lentil tested across 10 rain-fed environments during 2007 and 2008 growing seasons, using randomized complete block design with four replications, were analyzed. The location (L) explained 56 and 77% of the total (G + L + GL) variation for the first and second year, respectively. According to polygon view of biplot, Gonbad, Shirvan and Gachsaran with wining genotype G9, Ilam with wining genotype G5 and Kermanshah with wining genotype G8 were detected in the first year; and Gonbad and Ilam with wining genotype G5; Gachsaran with wining genotype G9; Kermanshah with wining genotype G2 and Shirvan with wining genotype G3 were detected in the second year. In the first year, genotypes G1 and G9 and in the second year genotypes G8 and G9 were the most favorable genotypes based on average tester coordinate biplot. Gachsaran location was more representative of the overall locations and more powerful to discriminate genotypes than the unfavorable ones. In conclusion, G9 (ILL6199) was found to be the most stable and higher yielding genotype which may be recommended for commercial release in semi-arid areas of Iran.
 

Keywords

Article Title [Persian]

تجزیه بایپلات رگرسیون مکانی برای اختصاص ارقام جدید پیشرفته عدس به محیطهای هدف

Abstract [Persian]

چکیده
ارزیابی پایداری عملکرد ژنوتیپ‌ها و محیط‌ها چالش اولیه اصلاح‌کنندگان نبات است. بنابراین، تحلیل جامعی از ساختار اثر متقابل ژنوتیپ×محیط مورد نیاز است. هدف از این تحقیق، ارزیابی استفاده از رگرسیون مکانی (SREG) روش GGE بای‌پلات برای گروه‌بندی اثر متقابل ژنوتیپ×محیط در عدس بود. داده‌های عملکرد 10 ژنوتیپ (G) عدس مورد آزمایش در 10 محیط طی سال‌های زراعی 2007 و 2008، با استفاده از طرح بلوک­های کامل تصادفی با چهار تکرار، مورد تجزیه و تحلیل قرار گرفت. مکان (L) در سال اول و سال دوم به‌ترتیب 56 و 77 درصد از تغییرات کل (G + L + GL) را توجیه کرد. براساس نمودار چندضلعی بای‌پلات، در سال اول اجرای آزمایش، ژنوتیپ شماره 9 در گنبد، شیروان و گچساران، ژنوتیپ شماره 5 در ایلام و ژنوتیپ شماره 8 در کرمانشاه به عنوان ژنوتیپ‌های برنده شناخته شدند و در سال دوم اجرای آزمایش، ژنوتیپ شماره 5 در گنبد و ایلام، ژنوتیپ شماره 9 در گچساران، ژنوتیپ شماره 2 در کرمانشاه و ژنوتیپ شماره 3 در شیروان به عنوان ژنوتیپ‌های برنده شناخته شدند. براساس نمودار مختصات محیط متوسط، در سال اول، ژنوتیپ‌های شماره 1، 9 و 3 و در سال دوم ژنوتیپ‌های شماره 8 و 9 مطلوب‌ترین ژنوتیپ‌ها بودند. منطقه گچساران بیشتر از سایر مناطق (مکان‌ها) نماینده محیط متوسط بود و از قدرت تمایز بالایی برای تشخیص ژنوتیپ‌های ایده‌آل و نامناسب برخوردار بود. در نتیجه ژنوتیپ شماره 9 (ILL 6199) به عنوان پایدارترین و پرمحصول‌ترین ژنوتیپ انتخاب شد که  می­تواند برای عرضه تجاری در مناطق نیمه خشک ایران توصیه شود.
 
 

Keywords [Persian]

  • آزمایش‌های چند مکانی
  • اثر متقابل ژنوتیپ×محیط
  • تجزیه بای‌پلات ژنوتیپ و ژنوتیپ×محیط

References

Annicchiarico P, 2002. Defining adaptation strategies and yield stability targets in breeding programmes. Pp. 365-383. In Kang MS (Ed.), Quantitative Genetics, Genomics, and Plant Breeding, Wallingford, UK, CABI.
Becker HC and Leon J, 1988. Stability analysis in plant breeding. Plant Breeding 101: 1-23.
Burgueno J, Crossa J and Vargas M, 2001. SAS programs for graphing GE and GGE biplots. Biometrics and Statistics Unit, CIMMYT.
Cooper M, Stucker RE, DeLacy IH and Harch BD, 1997. Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Science 37: 1168-1176.
Crossa J and Cornelius PL, 1997. Site regression and shifted multiplicative model clustering of cultivar trials sites under heterogeneity of error variances. Crop Science 37: 406-415.
Dehghani H, Ebadi A and Yousefi A, 2006. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agronomy Journal 98: 388-393.
Dehghani H, Sabaghnia N and Moghaddam M, 2009. Interpretation of genotype-by-environment interaction for late maize hybrids’ grain yield using a biplot method. Turkish Journal of Agriculture and Forestry 33: 139-148.
Ebadi-Segherloo A, Sabaghpour SH, Dehghani H and Kamrani M, 2010. Screening of superior chickpea genotypes for various environments of Iran using genotype plus genotype × environment (GGE) biplot analysis. Journal of Plant Breeding and Crop Science 2: 286-292.
Ebdon JS and Gauch HG, 2002. Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: I. Interpretation of genotype × environment interaction.   Crop Science 42: 489-496.
Erskine W, 2009. Global production, supply and demand. Pp. 4-13 In: Erskine W, Muehlbauer F, Sarker A and Sharma B (Eds.),  The Lentil: Botany, Production and Uses. 480 pp, CABI. UK.
FAOSTAT, 2010. Data stat year 2010. Food Agriculture Organization, verified 2 Feb. 2012. Rome, Italy. (http://faostat.fao.org/)
Flores F, Moreno MT and Cubero JI, 1998. A comparison of univariate and multivariate methods to analyze environments. Field Crops Research 56: 271-286.
Gauch HG and Zobel RW, 1996. AMMI analysis of yield trials. Pp. 85-122. In: Kang MS and Gauch HG (Eds.), Genotype-by-Environment Interac­tion. CRC Press, Boca Raton, FL.
Gauch HG and Zobel RW, 1997. Identifying mega-environments and tar­geting genotypes. Crop Science 37: 311-326.
Gollob HF, 1968. A statistical model which combines features of factor ana­lytic and analysis of variance techniques. Psychometrika 33: 73-115.
Kang MS, and Pham HN, 1991. Simultaneous selection for high yielding and stable crop genotypes. Agronomy Journal 83: 161-165.
Kang MS, 2002. Quantitative Genetics, Genomics and Plant Breeding. 432 pp. CABI, UK.
Sabaghnia N, Dehghani H and Sbaghpour H, 2006. Nonparametric methods for interpreting genotype by environment interaction of lentil genotypes. Crop Science 46: 1100-1106.
Sabaghnia N, Dehghani H and Sabaghpour SH, 2008. Graphic analysis of genotype × environment interaction of lentil yield in Iran. Agronomy Journal 100: 760-764.
Sarker A, Aydogan A, Chandra S, Kharrat M and Sabaghpour S, 2009. Genetic enhancement for yield and yield stability. Pp. 102-120 In: Erskine W, Muehlbauer F, Sarker A and Sharma B (Eds.), The Lentil: Botany, Production and Uses. 480 p. CABI, UK.
Yan W, 2001. GGE biplot– A Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal 93: 1111-1118.
Yan W, 2002. Singular value partitioning in biplot analysis of multievironment trial data. Agronomy Journal 94: 990-996.
Yan W, Hunt LA, 2002. Biplot analysis of diallel data. Crop Science 42: 21-30.
Yan W and Kang MS, 2003. GGE Biplot Analysis: A Graphical Tool for Breed­ers, Geneticists, and Agronomists. CRC Press, Boca Raton, FL.
Yan W, Rajcan I, 2002. Biplot evaluation of test sites and trait relations of soybean in Ontario. Crop Science 42: 11-20.
Yan W, Hunt LA, Sheng Q, and Szlavnics Z, 2000. Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science 40: 597-605.
Yan W, Kang MS, Ma B, Woods S and Cornelius PL, 2007. GGE biplot vs. AMMI analysis of genotype by environment data. Crop Science 47: 643-655.
Journal of Plant Physiology and Breeding
Volume 3, Issue 2 - Serial Number 2
December 2013
Pages 51-65

Files

Share

How to cite

Statistics