Syrian agricultural sector is one of the most sectors which contribute to the
national economy, because it provides employment for about 50% of the Syrian
labor force. In addition to secure food and clothing to the citizens and the raw
materials f
or the national downstream industries and reduces the trade deficit.
The cotton crop is the most important strategic crop and where 18% of the
workforce are employed there, starting from the process of agriculture until the
delivery of the product to the consumers. In addition to, it is an export crop
which includes raw cotton ginned, spun and woven garments worth tens
billions of Syrian pounds. The cultivation of cotton is one of the most important
irrigated crop in Syria and the relative importance in the Syrian agriculture,
For these reasons, the study includes many agricultural aspects - productivity
also it depends on official statistical data analyzed and discussed according to
the approved scientific basis for such these kinds of studies. The econometrics
showed that production and area of cotton was decreased when the cost was
increased in spite of there were increases in researches which are done in the
fields of cotton.
We performed in this research forecast in the direction of the index
numbers for consumer prices for ( food- clothes and shoes –
education -health- transportation communications - housing water,
electricity, gas and other fuel oils), by using Mark
ov chains in
estimating with dependence on monthly data were taken from the
central bureau of statistics in Syria during the period (1/1/2010 ,
31/12/2011) , So results were analyzed by calculating the vector of
states probabilities in the moment 0 t and using it with matrix of
transition probabilities states transition probability for forecasting in the
vector of states probabilities on the long and short range for knowing
the direction at which the index numbers may behave in the future.
The most important results of the study were instability of the beam of
the transition probabilities (high low stability) during the prediction
period, as well as for the matrix of transition probabilities.
