مطالعه اثر متقابل ژنوتيپ و محيط در ژنوتيپ‌هاي ذرت به روش اثرات اصلي جمع‌پذير و اثرات متقابل ضرب‌پذير و روش GGE باي‌پلات

Genotype - Environment Interaction Study in Corn Genotypes Using additive main effects and multiplicative interaction method and GGE- biplot Method

ﺑﺮﻫﻢ ﮐﻨﺶ ژﻧﻮﺗﯿﭗ و ﻣﺤﯿﻂ ﯾﮑﯽ از ﻣﻬﻢ ﺗﺮﯾﻦ ﻋﺎﻣﻞ ﻫﺎي اﯾﺠﺎدﮐﻨﻨﺪه ﻣﺤﺪودﯾﺖ در ﺑﺮﻧﺎﻣﻪ ﻫﺎي اﺻﻼﺣﯽ ﻣﺤﺴـﻮب ﻣﯽ ﺷﻮد. ﻫﺪف از ﭘﮋوﻫﺶ ﺣﺎﺿﺮ ﺗﺠﺰﯾﻪ ﺑﺮﻫﻢ ﮐﻨﺶ ژﻧﻮﺗﯿﭗ و ﻣﺤﯿﻂ ﺑﺮاي ﻋﻤﻠﮑـﺮد داﻧـﻪ ذرت داﻧـﻪ اي در ﺷـﺮاﯾﻂ آب و ﻫـﻮاﯾﯽ ﻣﺘﻔﺎوت ﺟﻬﺖ ﺷﻨﺎﺳﺎﯾﯽ ارﻗﺎم ﺳﺎزﮔﺎر و ﭘﺎﯾﺪار ﺑﺮاي ﻣﺤﯿﻂ ﻫﺎي ﻣﻮرد ﻣﻄﺎﻟﻌﻪ ﺑﺮ اﺳﺎس ﻣﺪل اﺛﺮات اﺻﻠﯽ اﻓﺰاﯾﺸﯽ و اﺛﺮات ﻣﺘﻘﺎﺑـﻞ ﺿﺮب ﭘﺬﯾﺮ و روش ﮔﺮاﻓﯿﮑﯽ GGE ﺑﺎي ﭘﻼت ﺑﻮد. ﻣﻮاد و روش ﻫﺎ: ﺗﻌﺪاد 12 ژﻧﻮﺗﯿﭗ ذرت داﻧﻪ اي ﺑﻪ ﻣﻨﻈﻮر ﻣﻄﺎﻟﻌﻪ ﭘﺎﯾﺪاري و ﺳﺎزﮔﺎري در ﭼﻬﺎر ﻣﺤـﯿﻂ )اراك، ﺑﯿﺮﺟﻨـﺪ، ﺷـﯿﺮاز و ﮐﺮج( ﻣﻮرد ارزﯾﺎﺑﯽ ﻗﺮار ﮔﺮﻓﺘﻨﺪ. آزﻣﺎﯾﺶ در ﻗﺎﻟﺐ ﻃﺮح ﺑﻠﻮك ﻫﺎي ﮐﺎﻣﻞ ﺗﺼﺎدﻓﯽ ﺑﺎ ﺳﻪ ﺗﮑﺮار در ﺳﺎل زراﻋﯽ 1395 اﻧﺠﺎم ﺷﺪ. از روش ﻫﺎي ﺗﺠﺰﯾﻪ ﭘﺎﯾﺪاري اﻣﯽ و GGE ﺑﺎي ﭘﻼت ﺟﻬﺖ ﺗﺠﺰﯾﻪ وﺗﺤﻠﯿﻞ ﺑﺮﻫﻢ ﮐﻨﺶ ژﻧﻮﺗﯿﭗ و ﻣﺤﯿﻂ ﻋﻤﻠﮑﺮد داﻧﻪ اﺳﺘﻔﺎده ﮔﺮدﯾﺪ. ﯾﺎﻓﺘﻪ ﻫﺎ: ﻧﺘﺎﯾﺞ ﺣﺎﺻﻞ از ﺗﺠﺰﯾﻪ وارﯾﺎﻧﺲ اﻣﯽ ﺣﺎﮐﯽ از ﻣﻌﻨﯽ دار ﺑﻮدن اﺛـﺮ ﻣﺤـﯿﻂ و اﺛـﺮ ژﻧﻮﺗﯿـﭗ در ﺳـﻄﺢ اﺣﺘﻤـﺎل 1 درﺻـﺪ و ﺑﺮﻫﻢ ﮐﻨﺶ ژﻧﻮﺗﯿﭗ ﺑﺎ ﻣﺤﯿﻂ در ﺳﻄﺢ اﺣﺘﻤﺎل 5 درﺻﺪ ﺑﻮد. ﺗﺠﺰﯾﻪ اﺛﺮات ﺿﺮب ﭘﺬﯾﺮ ﻧﺸـﺎن داد ﮐـﻪ ﺗﻨﻬـﺎ ﻣﺆﻟﻔـﻪ اول ﺑـﺮﻫﻢ ﮐـﻨﺶ ژﻧﻮﺗﯿﭗ ﺑﺎ ﻣﺤﯿﻂ در ﺳﻄﺢ اﺣﺘﻤﺎل 1 درﺻﺪ ﻣﻌﻨﯽ دار اﺳﺖ و ﺑﻪ ﺗﻨﻬﺎﯾﯽ ﺣﺪود 63 درﺻﺪ از وارﯾﺎﻧﺲ ﺑﺮﻫﻢ ﮐﻨﺶ را ﺗﺒﯿﯿﻦ ﻣﯽ ﻧﻤﺎﯾـﺪ. ﺑﺎي ﭘﻼت ﺣﺎﺻﻞ از ﻣﯿﺎﻧﮕﯿﻦ ﻋﻤﻠﮑﺮد داﻧﻪ ﺑﺮاي ژﻧﻮﺗﯿﭗ ﻫﺎ و ﻣﺤﯿﻂ ﻫﺎ و اوﻟﯿﻦ ﻣﺆﻟﻔﻪ اﺻﻠﯽ ﺑﺮﻫﻢ ﮐﻨﺶ ﻣﺆﯾﺪ ﺑﺮﺗـﺮي ژﻧﻮﺗﯿـﭗ 6 ﺑـﻪ دﻟﯿﻞ دارا ﺑﻮدن ﻋﻤﻠﮑﺮد داﻧﻪ و ﭘﺎﯾﺪاري ﺑﺎﻻ ﻧﺴﺒﺖ ﺑﻪ ﺳﺎﯾﺮ ژﻧﻮﺗﯿﭗ ﻫﺎي ﺗﺤﺖ ﺑﺮرﺳﯽ ﺑﻮد. ﻧﺘﺎﯾﺞ ﺑﻪ دﺳـﺖ آﻣـﺪه از روش ﮔﺮاﻓﯿﮑـﯽ GGE ﺑﺎي ﭘﻼت ﻧﺸﺎن داد ﮐﻪ ﻣﺆﻟﻔﻪ اﺻﻠﯽ اول و دوم ﺑﻪ ﺗﺮﺗﯿﺐ 66/97 و 20/57 درﺻﺪ و ﻣﺠﻤﻮﻋﺎً 87/53 درﺻﺪ از ﺗﻐﯿـﯿــﺮات ﮐﻞ ﻣﻮﺟﻮد در داده ﻫﺎي ﻋﻤﻠﮑﺮد داﻧﻪ را ﺗﻮﺟﯿﻪ ﻣﯽ ﮐﻨﺪ. ﺑﺎي ﭘﻼت ﺑﺮرﺳﯽ و ﻣﻘﺎﯾﺴﻪ ﻣﺤﯿﻂ ﻫﺎ ﻧﺸﺎن دﻫﻨﺪه واﮐﻨﺶ ﻣﺸﺎﺑﻪ ﻣﺤﯿﻂ ﻫﺎي اراك، ﺑﯿﺮﺟﻨﺪ و ﺷﯿﺮاز از ﻧﻈﺮ رﺗﺒﻪ ﻋﻤﻠﮑﺮد داﻧﻪ ژﻧﻮﺗﯿﭗ ﻫﺎ ﺑﻮد. ﻧﻤﻮدار ﺑﺎي ﭘﻼت ﭼﻨﺪ ﺿﻠﻌﯽ ﻣﺤﯿﻂ ﻫـﺎي ﺗﺤـﺖ ﺑﺮرﺳـﯽ را در ﯾـﮏ اﺑـﺮ ﻣﺤﯿﻂ ﻗﺮار داد ﮐﻪ در آن ﺗﻨﻬﺎ ژﻧﻮﺗﯿﭗ 6 ﭘﺎﯾﺪار ﺑﻮد. ﺑﺮ اﺳﺎس ﺑﺎي ﭘﻼت ژﻧﻮﺗﯿﭗ اﯾﺪه آل ﻓﺮﺿﯽ ژﻧﻮﺗﯿـﭗ 6 از ﻟﺤـﺎظ ﻫـﺮ دو ﺟﻨﺒـﻪ ﭘﺎﯾﺪاري و ﻣﯿﺎﻧﮕﯿﻦ ﻋﻤﻠﮑﺮد داﻧﻪ ﻣﻄﻠﻮب ﺗﺮ از ﺑﻘﯿﻪ ژﻧﻮﺗﯿﭗ ﻫﺎ ﺑﻮد و ﺳﺎزﮔﺎري ﻋﻤﻮﻣﯽ زﯾﺎدي در ﻫﻤﻪ ﻣﺤﯿﻂ ﻫﺎي ﺗﺤـﺖ ﻣﻄﺎﻟﻌـﻪ از ﺧﻮد ﻧﺸﺎن داد. ﻧﺘﯿﺠﻪ ﮔﯿﺮي: ﻧﺘﺎﯾﺞ اﯾﻦ ﻣﻄﺎﻟﻌﻪ ﺑﺎ اﺳﺘﻔﺎده از روش ﻫﺎي ﭼﻨﺪﻣﺘﻐﯿﺮه ﻣﺆﯾﺪ ﺗﺄﺛﯿﺮ ﺑﺴـﺰاي اﺛـﺮ ﻣﺤـﯿﻂ ﺑـﺮ ﻋﻤﻠﮑـﺮد داﻧـﻪ در ذرت ﺑـﻮد. ژﻧﻮﺗﯿﭗ ﻫﺎي ﻣﻮرد ﺑﺮرﺳﯽ ﺗﻨﻮع ژﻧﺘﯿﮑﯽ ﻗﺎﺑﻞ ﻣﻼﺣﻈﻪ اي را ﻧﺸﺎن دادﻧﺪ. اﯾﻦ ﺗﻨﻮع ژﻧﺘﯿﮑﯽ ﻣﯿﺎن ژﻧﻮﺗﯿﭗ ﻫﺎ ﺑﻪ اﻧﺪازه اي ﺑﻮد ﮐﻪ ﺗﻘﺮﯾﺒـﺎً دو برابر عامل محيط در توجيه واريانس كل نقش داشت. نتايج حاصل از هر دو روش ژنوتيپ 6 (KSC704) را به‌عنوان ژنوتيپ پايدار معرفي كردند. اين ژنوتيپ در هر چهار محيط تحت مطالعه بهترين ژنوتيپ بود.

The genotype- environment interaction is one of the most important factors causing constraints in breeding programs. The purpose of this study was genotype- environment interaction analyze for grain yield of corn in different weather conditions to identify adapted and stable cultivars for the studied environments based on the additive main effects and multiplicative interaction method and GGE biplot graphical method. Materials and Methods: Twelve maize genotypes were evaluated for stability and adaptation in four environments (Arak, Birjand, Shiraz and Karaj). Experiment was conducted in a randomized complete block design with three replications in 2016. Stability analysis of AMMI and GGE biplot methods were used to analyze the effect of interaction between genotype and environment for grain yield. Results: The results of ANOVA showed that the effect of environment and effect of genotype were significant at 1% probability level and the effect of genotype- environment interaction at the 5% probability level. The multiplicative effects analysis showed that only the first component of the genotype- environment interaction was significant at 1% probability level and alone accounts for about 63% of the genotype- environment interaction variance. Biplot was obtained from mean grain yield for genotypes and environments, and the first main component of the interaction effect was the superiority of genotype 6 due to its grain yield and high stability compared to other genotypes under study. The results obtained from the graphical GGE bipolar method showed that the main components of the first and second factors were 66.97% and 57.5%, respectively, and a total of 87.53% of the total variation in the grain yield data. Biplot analysis and comparison of the environments indicated a similar reaction in Arak, Birjand and Shiraz environments in terms of grain yield of genotypes. The polygon biplot comprised the environments under study in a mega-environment where only genotype 6 was stable. Based on the ideal genotype biplot, genotype 6 in terms of both stability and grain yield was more favorable than other genotypes and showed a high degree of adaptation in all studied environments. Conclusion: The results of this study, using multivariate methods, showed that the effect of environment on grain yield in corn was significant. The genotypes showed significant genetic variation. The genetic variation between genotypes was so large that it was roughly twice the environmental factor in justifying the variance of the total. The results of both methods indicated genotype 6 (KSC704) as a stable genotype. This genotype was the best genotype in all four studied environments.