ANOVA using count data I'd like to perform a two-way ANOVA on count data. I was told to first fit a GLM and then do the ANOVA.  My first problem is that 
fit1 <- aov(glm(Branches~Accession*Location, data=branches, family=quasipoisson))
summary(fit1)

and
fit2 <- glm(Branches~Accession*Location, data=branches, family=quasipoisson)
Anova(fit2, test="F")

don't result in the same p-values.  What is the mistake here? Which is the right way of doing this, or is it wrong to do an ANOVA following a GLM anyway?
My second problem is that I don't know how I can do a post hoc test. For example, when I do a Tukey's test, should I use the ANOVA model of the GLM or the GLM itself?
 A: I don't know how aov handles glm objects, but the documentation for aov mentions only lm objects.
My advice, then, is to not use aov, but just use car::Anova directly to produce an analysis of deviance table.  Another option is emmeans::joint_tests.
For post-hoc testing, I recommend the emmeans package, since it explicitly lists supported model objects.
if(!require(car)){install.packages("car")}
if(!require(emmeans)){install.packages("emmeans")}

set.seed(1234)

Accession = factor(rep(c("1", "2", "3"), 1, each=6))
Location  = factor(rep(c("A", "B"), 9))

Branches  = as.numeric(Accession) * 2 +
           as.numeric(Location) + rnorm(length(Accession), 0, 1)

Branches = round(Branches)

branches = data.frame(Accession, Location, Branches) 

str(branches)

# # #

fit1 <- glm(Branches~Accession*Location, data=branches, family=quasipoisson)

summary(fit1)

library(car)

Anova(fit2)

   ### Analysis of Deviance Table (Type II tests)
   ### 
   ### Response: Branches
   ###                   LR Chisq Df Pr(>Chisq)    
   ### Accession            36.153  2  1.411e-08 ***
   ### Location              5.912  1    0.01504 *  
   ### Accession:Location    1.841  2    0.39837    
   ### ---
   ### Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


library(emmeans) 

marginal = emmeans(fit1, ~ Accession)

pairs(marginal)

   ### NOTE: Results may be misleading due to involvement in interactions
   ###
   ###  contrast   estimate        SE  df z.ratio p.value
   ###  1 - 2    -0.3389399 0.1363010 Inf  -2.487  0.0345
   ###  1 - 3    -0.7680533 0.1260328 Inf  -6.094  <.0001
   ###  2 - 3    -0.4291135 0.1128866 Inf  -3.801  0.0004
   ### 
   ### Results are averaged over the levels of: Location 
   ### Results are given on the log (not the response) scale. 
   ### P value adjustment: tukey method for comparing a family of 3 estimates 

