Fitted value versus probability for logistic regression Dependent variable
I have a dependent value in the range of [0,1].  Meaning 0 and 1, and all values in between are included.  Therefore this is a proportional value such as for instance the percentage of land a farmer fertilizes.
Model
The model I am currently focusing on is a logistic model.

*

*However, as an output, I would like to see how my dependent variable is predicted by the model (to compare the real values with the estimated values).

However, a logistic regression normally gives as an output "the probability".  As a result, I am now a little bit confused.
My model =
out <- glm(cbind(fertilized, total_land-fertilized) ~ X-variables,
       family=binomial(cloglog), data=Alldata)

To predict the estimated percentage of fertilized land I use
Alldata$estimated_fertilized<-predict(out,data=newdata,type="response"))

Is this correct?  Or does this line give me the probability instead of the predicted percentage?  If not correct, what should I do to get what I want?
UPDATE
Given the fact that there are questions on the correctness of the chosen model, I provide some additional information:
Distribution of the dependent variables (which is a proportion for 0-1, 0 and 1 included).

 A: It is in fact fine to use logistic regression to summarize observed proportions lying in the range of [0-1] inclusive.
In the past, such approaches were discredited when the data were in fact hierarchical and the goal of the analysis was to summarize individual level exposures which were aggregated up to a cluster level. In this particular case, it is incorrect to apply logistic regression because of ecological fallacy and non-collapsibility of the odds ratio as a measure of association. 
The logistic regression estimating equations are appropriate to apply to any analysis where the linear model for the log of the mean minus the log of one minus the mean is appropriate (the logit link) and when the variance of the proportion is equal to the proportion times one minus the proportion (binomial variance assumption). It turns out the latter is a rather stringent requirement, so typically analysts use a more flexible variance estimator like a quasibinomial likelihood equation, or generalized estimating equations.
A problem with logistic regression (and its variants) is that it is not clear how you will validate the model. If you summarize predictive accuracy with mean squared error--a valid approach for many reasons--a non-linear least squares (NLS) estimator for the logit curve should be used instead. NLS will find the optimal S-shaped curve(s) that summarize association(s) with model predictors by minimizing the sum of squared differences from the predicted response surface. Alternately, if the desire is to apply some threshold based on a linear combination of covariates to classify subsets of fields which were over or under fertilized, linear discriminant analysis will provide superior classifications. A logistic model can be suboptimal according to a large number of predictive metrics.
So ultimately, it is not the structure of the data that should determine the analysis, but the question the analyst is trying to assess.
