Hi Erika,
based solely on the past price values, you can simply use some forecast algo in PA (f.ex. Triple Exponential Smoothing). Having a finer granularity (e.g. per week or at least month) would be better to achieve more meaningful results. To be honest, with just one value per year, you won't really be able to perceive any seasonal characteristics and extract meaningful insights from it.
Another possibility, if you had other KPIs that would be used to explain the increase or decrease in rice price, would be to use e.g. Linear Regression. For example, suppose you know that the rice price on a determined month has a considerable correlation with the amount of rice produced last month, the price last month and the planned amount of rice to be produced this month, then you could use those 3 inputs as a param (considering you have already run a forecast on the rice production for the next month 
), something like Price[k] = a.Price[k-1] + b.Production[k-1] + c.Production[k] + d. The algo would give you a, b, c and d, so that you can apply it to the future.
The problem is that you could only really run that for one month, since the second month calculation would depend on that 1st month. AFAIK, PA is not ready yet for recursive algos (I might be wrong). I'd go probably with custom R code for that.
Just some ideas.
Best,
Henrique.