An Application of Linear Model in Small Area Estimationof Orange production in Fars province

Methods for small area estimation have been received great attention in recent years due to growing demand for reliable small area estimation that are needed in development planings, allocation of government funds and marking business decisions. The key question in small area estimation is how to obtain reliable estimations when sample size is small. When only a few observations(or even no observation) are available from a given small area, small sample sizes lead to undesirably large standard errors. The only possible solution to the estimation problem is to borrow strength from available data sets. This is accomplish by using appropriate linking models (included explicit and implicit models) to increas the effect of sample size for estimation. The generalized linear mixed models and the empirical best linear unbiased predictor, are extensively used to estimate reliable mean of small areas. In this article,first we introduce the small area estimation.Then, to obtain reliable small area estimations we introduce the Fay-Herriot model as a special case of the generalized linear mixed model. Finally, in an Simulation study we use Iran 1382 agricultural census data to estimate orange production in Fars cities (small areas) in the year 1382 based on Fay-Herriot model.