تجزیه پایداری عملکرد دانه لاین ها و ارقام جو دیم با استفاده از مدل GGE biplot

محورهای موضوعی : بوم شناسی گیاهان زراعی

فرهاد آهک پز ¹ , فرزاد آهک پز ²

1 - عضو هیأت علمی موسسه تحقیقات کشاورزی دیم کشور، مراغه، ایران.
2 - عضو هیأت علمی دانشگاه آزاد اسلامی، واحد میاندوآب، گروه کشاورزی، میاندوآب، ایران.

تاریخ دریافت : 1392/02/08 تاریخ پذیرش : 1392/11/15 تاریخ انتشار : 1392/11/01

کلید واژه: عملکرد, پایداری, جو دیم, GGE بای پلات,

چکیده مقاله :

این تحقیق به منظور بررسی پایداری عملکرد دانه و تعیین ژنوتیپ‌های پرمحصول و سازگار در قالب آزمایش‌های یکنواخت ناحیه ای جو، در ایستگاه‌های تحقیقاتی مناطق سردسیر و معتدل دیم شامل مراغه، سرارود، اردیبل، ارومیه، کردستان (قاملو) و زنجان انجام گرفت. در این بررسی تعداد 10 لاین جو به همراه دو رقم شاهد (محلی و آبیدر) در قالب طرح بلوک‌های کامل تصادفی در چهار تکرار طی سه سال زراعی (88-1385) مورد مطالعه و ارزیابی قرار گرفتند. تجزیه مرکب (3 سال و 6 مکان) نشان داد که اثرات ساده سال، مکان و ژنوتیپ، اثر متقابل مکان× ژنوتیپ و اثر سه جانبه سال× مکان× ژنوتیپ بر عملکرد دانه از نظر آماری معنی‌دار هستند. بیشترین وکمترین عملکرد دانه به ترتیب مربوط به ایستگاه سرارود (2549) کیلوگرم در هکتار و ایستگاه زنجان (1578) کیلوگرم در هکتار بوده و لاین شماره 9 با متوسط عملکرد دانه 2061 کیلوگرم در هکتار بیشترین عملکرد دانه را در بین ژنوتیپ های آزمایشی داشت. برای مطالعه اثر متقابل ژنوتیپ در محیط از روش GGE بای پلات استفاده شد. بر اساس نمودار چند ضلعی مربوط به ژنوتیپ‌ها، ژنوتیپ شماره 1 بیشترین عملکرد را در اردبیل و ژنوتیپ شماره 5 بیشترین عملکرد را در زنجان، مراغه و ارومیه داشته و ژنوتیپ شماره 3 و شماره 4 به ترتیب در قاملو و سرارود برتر بودند. بر اساس نمودار محیط های ایده آل فرضی، محیط ارومیه به این محیط نزدیک تر بود و بر اساس نمودار ژنوتیپ ایده آل فرضی و نمودار بای پلات ژنوتیپ ها و محیط‌ها، ژنوتیپ شماره 5 باتوجه به معیار پایداری مورد استفاده، پایداری مطلوب‌تری را نشان داد و این ژنوتیپ به عنوان ژنوتیپ برتر از نظر پر محصولی و پایداری عملکرد شناسایی شد.

چکیده انگلیسی:

This study was conducted to determine grain yield stability of rainfed promising barley genotypes in six Iranian rainfed research stations (Maragheh, Sararood, Uromieh, Ghamloo, Zanjan and Ardabil) under cold and semi-cold conditions during three cropping seasons (2006-2009). The studied genotypes were 10 promising barley  lines  along  with  two  checks  (one  local  variety  and  newly  released  variety  of  Abidar).  In  each environment, the experiment was arranged in randomized complete block design (RCBD) with four replications. Results of combined ANOVA showed that location, year, genotype, interaction of location and genotype, and interaction of year, location and genotype had significant effect on grain yield. The genotypes showed the highest and the lowest grain yield in Sararood (2549 Kg/ha) and Zanjan (1578 Kg/ha) stations, respectively. Line No.9 produced the highest grain yield (2061 Kg/ha). To study the interaction of genotype and environment, GGE  biplot  method  was  used.  Based  on  the  polygonal  graphs  related  to  genotypes,  genotype  No.1  (G1) produced the highest yield in Ardabil and genotype No.5 (G5) showed the highest yield in Zanjan, Maragheh and Uromieh. Besides, genotype 3 (G3) and genotype 4 (G4) produced the highest yield in Ghamloo and Sararood stations, respectively. Based on the theoretical ideal environments graph, Uromieh was closer to the ideal environment. Regarding to the imaginary ideal genotype graph and biplot of genotypes and environments, genotype No.5 was identified as the best one due to its higher yield and better yield stability

منابع و مأخذ:

Agaee M, Rajabi R., and Ansari Y(2010) Evaluation of grain yield stability and two-steps screening  for drought stress tolerance in barley genotypes. Iranian Journal of Crop Sciences 12 (3) 305-317.

Blanche SB, and Myers GO (2006) Identifying discriminating locations for cultivar selection in Louisiana. Crop Science 46: 946–949.

Chand N, Vishvakarma SR, Verma OP, Kumar M (2008) Phenotypic stability of elite barley lines over hetrogeneous environments. Barley Genetics Newsletter 38: 14-17.

Cornelius PL, and Crossa J (1999) Prediction assessment of shrinkage estimators of multiplicative models for multi-environment cultivar trials. Crop Science  39: 98-1009.

Crossa J, Cornelius PL, and Yan W (2002) Biplots of linear-bilinear models for studying crossover genotype × environment interaction. Crop Science  42: 136-144.

Crossa J, Fox PN, Pfeiffer WH, Rajaram S, and Gauch HG (1991) AMMI adjustment for statistical analysis of an international wheat yield trial. Theoretical and Applied Genetics 81:27-37.

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.

Dehghanpour Z, Karimizadeh R., Dehghani H, and Sabaghnia N (2007) Determination of adaptability and stability of seed yield of foreign earl maturity corn hybrids. Iranian Journal of Agricultural  Sciences 38: 249-257 (In Farsi).

Dimitrios B, Christos G,  Jesus R., and Eva B (2008) Separation of cotton cultivar testing sites based on representativeness and discriminating ability using GGE Biplots. Agronomy Journal 100: 1230–1236.

Fallconer DS (1981) Introduction to Quantitatiive Genetics. 2nd ed Longman Press. London, UK.

Fan XM, Kang MS, Chen H, Zhang Y, Tan J, and Xu C (2007) Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agronomy Journal 99: 220–228.

FAO (2008) Production Yearbook, Rome.

Gabriel KR. (1971) The biplot graphic display of matrices with application to principal component analysis. Biometrika 58: 453–467.

Gauch HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Science 46: 1488-1500.

Jamshidi Moghaddam M, and Pourdad S (2012) Evaluation of Seed Yield Adaptability of Spring Safflower Genotypes Using Nonparametric Parameters and GGE Biplot Method in Rain-fed Conditions. Seed and Plant Improvement Journal Vol. 29-1 No 1.

Javidfar F, Alizadeh B, Amirioghan H, and Sabbagnia N (2011) A study of genotype by environment interaction in oilseed rape genotypes, using GGE Biplot method. Iranian Journal of Field Crop Science 41: 771-779 (in Persian).

Kaya Y, Akcura M. & Taner S (2006) GGE-biplot analysis of multi-environment yield trials in bread

wheat. Turkish Journal Agriculture Foresty 30:325-337.

Koocheki A, Sorkhi B, Eslam Zade Hesari M (2012) Yield Stability of Barley Elite Genotypes in Cold Regions of Iran Using GGE biplot. Seed and Plant Improvement Journal Vol 28-1 No 4.

Laffont JL, Hanafi M, and Wright K(2007) Numerical and graphical measures to facilitate the interpretation of GGE Biplots. Crop Science 47: 990–996.

Letta T, D’Egidio MG, and Abinasa M (2008) Analysis of multi-environment yield trials in durum wheat based on GGE-biplot electronic resource. Journal of Food, Agriculture and Environment 6(2): 217-221.

Milomirka madic A, Paunovic A, and Knezevic D (2005) Correlation and path coefficient analysis for yield and yield components in winter barley. Acta Agric. Serbica 20: 3-9.

Mohammadi R, Armion M, Zadhassan E, Eskandari M (2014) Analysis of genotype × environment interaction for grain yield in rainfed durum wheat. Journal of dryland agriculture of Iran Vol.1, No 4.

Mohammadi R, Amri A, and Ansari Y (2009) Biplot Analysis of rainfed barley multi environment trials in Iran. Agronomy Journal 101: 789–796.

Mohammadi R, Haghparast R, Amri A, and Ceccarelli S (2010) Yield stability of rainfed durum wheat and GGE biplot analysis of multi environment trials. Crop and Pasture Science 61: 92–101.

Perkins JM, and Jinks JL (1971) Environmental and genotype environment components of variability.III. Multiple line and crosses. Heredity 23: 339-356.

Pourdad  S, and Jamshidi Moghaddam M (2013) Study on Genotype× Environment Interaction Through GGE biplot for Seed Yield in Spring Rapeseed (Brassica Napus L.) in Rain-Fed Condition. Journal of Crop breeding Vol. 5 No. 12.

Roozeboom KL, Schapaugh TW, Tuinstra MR, Vanderlip RL, and Milliken G (2008) Testing wheat in variable environments: Genotype, environment, interaction effects, and grouping test locations. Crop Science 48:317-330.

Roy D (2000) Plant breeding analysis and exploitation of variation. Alpha Science International ltd. UK.

Sabaghnia N, Dehghani H & Sabaghpour SH (2008) Graphic analysis of genotype × environment

interaction for lentil (Lens culinaris Medik) yield in Iran. Agronomy Journal 100: 760–764.

Samonte S O P B, Wilson LT, McClung AM, and Medley JC (2005) Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analysis. Crop Science 45: 2414–2424.

Voltas J, lopez-corles H, and Borras G (2005) Use of biplot analysis and factorial regression for the investigation of superior genotypes in multi-environment trials. European Journal of Agronomy 22: 309-324.

Yan W, Fregeau-Reid JA,  Pageau D, Martin RA, Mitchell fetch JW, Etienne M, Rowsell J, Scott P, Price M, De Haan B, Cummiskey A, Lajeunesse J, Durand J, and Sparry E (2010) Identifying essential test locations for oat breeding in eastern Canada. Crop Science 50: 504-515.

Yan W, Kang MS, Ma B, Woods S, & Cornelius P L (2007) GGE biplot vs. AMMI analysis of

Genotype by environment data. Crop Science 47: 643–655.

Yan W (2001a) 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 (2002c) Singular-value partitioning in biplot analysis of multi-environment trial data. Agronomy Journal 94: 990–996.

Yan W, and Kang MS(2003) GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. CRC Press, Boca Raton, FL, USA.

Yan W, Cornelius PL, Crossa J, and Hunt L A (2001b) Two types of GGE biplots for analyzing multi environment trial data. Crop Science 41: 656–663.

Yan W, and Hunt LA (2002a) Biplot analysis of diallel data. Crop Science 42: 21– 30.

Yan W, and Tinker NA(2006) Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623–645.

Yan W, and Rajcan I (2002b) Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Science 42: 11–20.