I have computed GLMM using glmer in R. My response variable is species richness and my explanatory variable is grazing treatment (with three categories: cattle, sheep and ungrazed). In the model I have included site as a fixed variable and also a new object with the same number of variations as I have to attempt to account for underdispersal (obs):

model2<-glmer(VegRichness~Grazing+(1|Site)+(1|obs),family="poisson",data=veg.rich)

My output is below and the questions I have about it are:

How do I interpret the fixed effects section? Cattle grazing seems to be missing in the oputput, is this because it is somehow incorporated into the intercept?

> summary(model2)

Generalized linear mixed model fit by maximum likelihood (Laplace
  Approximation) [glmerMod]
 
Family: poisson  ( log ) 

Formula: VegRichness ~ Grazing + (1 | Site) + (1 | obs)
   Data: veg.rich


     AIC      BIC   logLik deviance df.resid 
    178.8    185.2    -84.4    168.8       22 

Scaled residuals: 
    
Min..........           1Q............           Median....       3Q.........        Max
 
-1.4936...      -0.5698.....       -0.1928...      0.4923...   1.3646 

Random effects:

 Groups  ... Name......        Variance..... Std.Dev.

 obs.........      (Intercept).. 0.00000....  0.0000 
 
 Site.........     (Intercept).. 0.03596....  0.1896
  
Number of obs: 27, groups:  obs, 27; Site, 3

Fixed effects:
                .......Estimate.... Std. Error..... z value... Pr(>|z|)    
(Intercept)............      3.55358.......    0.12309.......  28.869.....  < 2e-16 ***                                                                 
GrazingSheep......     0.01242......    0.07876........   0.158.......  0.87467    
GrazingUngrazed -0.27526.....    0.08503........  -3.237......  0.00121 ** 

---
Signif. codes:  0 x***x 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)....GrzngS

GrazingShe......................... -0.322       
GrzngUngrzd...................... -0.298...  0.466