\section{Introduction}

Brazilian citrus fields are responsible for about $53\%$ of 
the worldwide  orange juice production 
and for $80\%$ of the concentrated form.
Citrus producers, industry and scientists are constantly aiming for 
higher productivity,  control of the production process and 
capacity.
Such targets are threaded by various diseases among which is the  \msc{} (\MSC{}). 
This disease affects commercial varieties 
on \textit{limoeiro Cravo}, 
which represents the majority of the commercial fields causing reduction in size, weight and 
number of fruits, combined with rapid decline and death of the trees.
The suspicion  it was caused by a virus transmited by a efficient flying vector  \cite{bassanezi:03}
has recently being confirmed.

In a citrus fields the trees are usually arranged in a grid with possibly different 
distances between rows and columns.
There is an interest in assessing spatial patterns of the disease.
Methods for characterizing the pattern as aggregated, regular or random are
currently used. However such methods are note design to quantify the 
effects of spatial covariates since they do not assume an
explicitly model  relating such covariates with the presence of the disease.
One alternative investigated here is the adoption of an autologistic model
which relates the probability of a unit become diseased given the status of 
neighboring plants in space and/or time, taken as covariates and therefore having a
associated coefficient parameter.
The regular arrangement favors for the adoption of autoregressive models for the 
analysis which allows for the detection of usual covariate effects as well as
the assessment of spatial effects. The latter are particularly 
useful for the description and hypothesis tests on the patterns of the disease,
which may suggests propagation mechanisms and control strategies.
For instance, for binary data such as presence/absence of the disease 
the autologistic model describes the probability of a tree become infected 
given the status of the neighboring trees.
The model parameters has an straightforward interpretation, incorporating explicitly the dependence structure.
In agricultural applications the model has being initially adopted the study the incident of 
 \textit{Phytophthora} in bell pepper~\cite{gumpertz:97}.
Here we further explore the model considering the particular aspects of the \MSC.
The model is presented in Section~\ref{met} and 
Section\ref{aplic} reports the analysis of data collected at 11 different time points
in a field with presence of \MSC. The conclusions and discussion are presented in  Section~\ref{conc}.

