Dear Mr. Tiwari. Yes, I runned AMMI1 analyses.However, in my opinion, GGE is superior than AMMI1, because GGE isolate environmental (E) effects (Did not use aditive effect of E). Thus, the model of GGE consider only the main effect of Genotype plus Genotype Environment Interaction (G + GE). If you see articles that used AMMI1, you can see that the E effect correspond for the big part of the sum of square, i.e., you make inference about E and not about genotypes.Thus, I recommend use GGE analyses. In R plataform, you can performed mean vs. instability and ideal genotypes analyses to evaluate genotypes and representativeness vs. discriminating power to evaluate test location. As cited before, for each analyses, different scaling methods can change the inferences of analyses.

You can read the below articles about GGE and AMMI1 to understanding and make conclusions about:

Article 1: "GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data"

Article 2: "Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations".

You can use the Package "GGEBiplotGUI" to run GGE analyses. With GGE analyses, you can identify best genotypes and test locations. However, you have to be careful with Singular Value Partition, Data Centenring and Data Scaling Methods. I recommend reading the book "Crop variety trials data management and analysis" by Weikai Yan before the analyses.

Thanks Andrei. I will definitely try the suggested R function. What about AMMI model used in Agricolae? It also generate plots. Did you try this by any chnace?

Dear Mr. Tiwari. Yes, I runned AMMI1 analyses.However, in my opinion, GGE is superior than AMMI1, because GGE isolate environmental (E) effects (Did not use aditive effect of E). Thus, the model of GGE consider only the main effect of Genotype plus Genotype Environment Interaction (G + GE). If you see articles that used AMMI1, you can see that the E effect correspond for the big part of the sum of square, i.e., you make inference about E and not about genotypes.Thus, I recommend use GGE analyses. In R plataform, you can performed mean vs. instability and ideal genotypes analyses to evaluate genotypes and representativeness vs. discriminating power to evaluate test location. As cited before, for each analyses, different scaling methods can change the inferences of analyses.

You can read the below articles about GGE and AMMI1 to understanding and make conclusions about:

Article 1: "GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data"

Article 2: "Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations".

Thanks Andrei. Get back to you once I am done with analysis.

Thanks

Try this code using "Plantbreeding" Package

d1=read.table(file="clipboard",h=T)

attach(d1)

str(d1)

results

Thank you Manoj. I have tried plantbreeding package but I can see some more functions which I have not tried. Thanks

Dear Tiwari,

You have already used "plantbreeding" package. You can use different stability related packages to analyze different stability parameters. A short list is here

packages--------- stability parameters

• plantbreeding---- Regression coefficient (bi), deviation from regression
• agricolae---- AMMI Stability Value (ASV), Kang’sYield Stability (YSi)
• st4gi---- alpha and lambda (Tai model)
• stability---- Shukla variance (s2), Wricke's Ecovalence (W2), CV%

All packages and their codes are available online. you can try.

Dear Hasan

Thank you for sharing the package detail with their respective function. Nice one

Mr. Tiwari, you may try "phenability" as well as "stability" package in R.

Dear Mr. Tiwari, I hope all is going well. I have recently published a series of papers regarding Multi-Environment Trial Analysis. In my first paper ( Article Mean Performance and Stability in Multi-Environment Trials I...

Article Mean Performance and Stability in Multi-Environment Trials I...

Thank you Mr Tiago Olivoto for your reply and sharing the your package. I will definitely try this for my data analysis.