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My experimental design is GRBD's (generalized randomized block design) with split plot (strips 1&2).

I made my model with the lmerTest package to check the effects of g_diversity and t_diversity on the response variable decomposed_weight:

m3b<-lmerTest::lmer(decomposed_weight ~ 
                  g_diversity:t_diversity+
                  g_diversity+t_diversity+strip+block+
                  (1|block/stt_plot/repetition)+
                  (1|g_diversity:t_diversity:strip:block)+
                  (1|g_diversity:t_diversity:block)+
                  (1|g_diversity:strip:block)+
                  (1|t_diversity:strip:block)+
                  (1|g_diversity:block)+
                  (1|t_diversity:block)+
                  (1|strip:block),
                data=Dt, na.action=na.omit, REML = FALSE, 
                control = lmerControl(optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))

The anova shows significant effect for the interactions- g_diversity:t_diversity.

When I'm running post-hoc test for the interactions I'm getting different results from the lsmeans and the difflsmeans commands: enter image description here
enter image description here I would like to know-

Why the tests provides different results?

Which of the tests fits to my situation? (if any)

Thanks!

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Except for rounding, the reported estimates, standard errors, t ratios, and degrees of freedom are exactly the same. The reason the p values are different is right there in the annotations: "P value adjustment: tukey method for a family of 4 estimates." The p values from difflsmeans are not adjusted.

In most cases, I think it's wise to report the adjusted results. It guards against making too many type I errors.

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