Info

47,852

Total

326,289

6,632,118

330,484

8,049,118

491,988 11,315,138

403,716

10,995,281

Source: USDA, Tropical Products — World Markets and Trade, various issues of Circular Series. Note

Source: USDA, Tropical Products — World Markets and Trade, various issues of Circular Series. Note

Supply sources

Sri Lanka is the only regular supplier of cinnamon bark and leaf oils. With the exception of 1990, when both oils were in short supply, production (as reflected in exports) has remained constant for bark oil, with a slight downward trend for leaf oil. Since internal consumption is small, the production levels are not much greater than exports. Madagascar and the Seychelles have been intermittent suppliers of leaf oil on a very minor scale in the past. India produces a very small quantity of leaf oil for domestic use (Spices Board, 1992).

Most cassia oil in international trade is of Chinese origin. There is believed to be a significant domestic consumption of cassia oil in the country. So, total annual production may be in excess of 500 t. Small quantitites of cassia oil are produced in Indonesia and Vietnam.

Quality and price

There is no international standard for cinnamon bark oil, however, the higher the cin-namaldehyde content the higher the price. (In the US and EOA, standard specifies an aldehyde content of 55-78%.)

International (ISO) standards exist for cinnamon leaf and cassia oils. For cinnamon leaf oil, the minimum eugenol content required for the oil is specified in terms of total phenol content. Oil from the Seychelles is preferred because of its high eugenol content (ca 90%). In practice, Sri Lanka now accounts for almost all of the oil in international trade and the standard specifies a 75-85% phenol content and a maximum level of 5% cinnamaldehyde. Physico-chemical requirements are also given. The US and FMA monograph, which replaces the old EOA standard, specifies the eugenol content of cinnamon leaf oil in terms of its solubility in potassium hydroxide (80—88%). For cassia oil, cinnamaldehyde is the major constituent and a minimum content of 80% is specified in the ISO standard.

Cinnamon bark oil is considerably more expensive than the leaf oil and probably the most highly priced of all essential oils. During 1992 it was being offered at around US$385/kg, largely reflecting the high raw material cost. In 1993 and early 1994 dealers in London were only quoting prices on request. Cinnamon leaf oil, in contrast, has been in the range of US$6.50—7.50/kg during the past three years. The price fell gradually from US$7.50/kg in early 1991 to US$6.50/kg in mid-1993. In late 1993, it had risen again to US$7.30/kg and in early 1994 it was US$8.25/kg. Although it is a comparatively low priced oil, it is still more expensive than clove leaf oil as a source of eugenol (which was approximately US$2.70/kg in early 1994).

Cassia oil, too, has remained fairly level in price over the last few years. During early 1991 to mid-1993, it fetched US$33-35/kg. The price then fell slightly and in early 1994 it was about US$29/kg. These prices are significantly lower than those that prevailed in the early and mid 1980s when there was a shortage of cassia bark in the People's Republic of China. Any appreciable rise in price above the US$30—35/kg level is likely to encourage end-users to blend cheaply available synthetic cinnamaldehyde with natural cassia oil.

Future Prospects

Like all other spices cinnamon and cassia are also subject to vagaries of the market. This could have adverse effects on the cinnamon growers of the developing countries. However, there are also favourable trends, which could be exploited. There is a strong growing market in the Middle East as well as in the Asia Pacific region, where a strong preference for specific flavours exists. The introduction of new snack foods is dependent to a large extent on the difference of flavours. Another factor, which is encouraging for developing countries, is the increase in global travel. This leads people to experiment with different flavours and spicy food. It has also given rise to the increase in ethnic restaurants. The importance of cinnamon is predominantly in the food-processing sector.

The future prospects for cinnamon and cassia can be studied in terms of two major influencing factors: supply and demand. Cinnamon and cassia production is influenced by national as well as international factors. While demand is influenced by many factors including the overall economic development, supply is influenced not only by economic factors but also by agro-climatic, biotic and abiotic stress factors in the growing regions. The product of commerce comes from perennial tree crops, hence responses to price changes get reflected in the form of altened supply after many years. Thus, there are a multitude of factors which are to be considered when forecasting the future of cinnamon and cassia. The kind of data available to us do not permit sophisticated forecasting models which may give correct and reliable predictions. What we have is only the historic data for area, production and export. A suitable model, which can give a reasonable prediction with these data, has been identified and fitted and this must be seen as a step on the road towards a more sophisticated modelling analysis based on superior data once if they become available.

Model identification

A variety of statistical forecasting techniques are available, ranging from very simple to very sophisticated. All of them try to capture the statistical distributions in the data provided and quantitatively present the future uncertainty. Lack of quality data forced us to choose methodologies, which forecast the future by fitting quantitative models to statistical patterns from historic data for several years. Therefore, univariated methodologies based solely on the history of the variable (one at a time) were tried. There are three such models:

Simple moving average models Exponential smoothing models Box-Jenkins models.

To identify the right model the data have been explored first.

Exploring the data

The time series data on production and export were plotted/graphed to select an appropriate model. The characteristics observed in the time series data for cinnamon are:

1. There is an overall positive trend (i.e. the trend cycle accounts for over 90%).

2. Non-seasonal in nature.

3. The time series is non-stationary in both mean and variance.

The classical decomposition of the time series data also revealed the fact that the trend cycle accounted for about 95% and above, while the irregularity accounted for the rest. Thus the forecasting model should account for trend, non-seasonality and also the non-stationary factor. Though (Box-Jenkins) models can be used, the models of exponential smoothing were more suitable, as these models were built upon clear-cut features like level, trend and seasonality.

Model selection

In order to identify a suitable model, the data was subjected to autocorrelation and partial autocorrelation analysis. The outcome of the analysis for both production and export separately indicated that AR (auto regression) (1) model was the suitable one. The AR (1) model is identical with exponential smoothing (Box and Jenkins, 1976). Hence exponential smoothing models were selected and tried.

The exponential smoothing, as its name suggests, extracts the level, trend and seasonal index by constructing smoothed estimates of these features, weighing recent data more heavily. It adapts to changing structure, but minimises the effects of outliers and noises. Three major exponential smoothing models are available:

a. Simple exponential smoothing b. Holt exponential smoothing c. Winters exponential smoothing

Finally, the Holt exponential smoothing model was selected as the best and the forecasting was done for variables in production and export.

308 M.S. Madan and S. Kannan The model

Holt's (1957) exponential smoothing model uses a smoothed estimate of the trend as well as the level to produce forecasts. The forecasting equation is:

The current smoothed level is added to the linearly extended current smoothed trend as the forecast into the indefinite future.

Where,

m

forecast lead time

Yt

observed value at time t

St

smoothed level at end of time t

Tt

smoothed trend at end of time t

y

smoothing parameter for trend

a

smoothing parameter for level of series

Equation (2) shows how the updated value of the smoothed level is computed as the weighted average of new data (first term) and the best estimate of the new level based on old data (second term). In much the same way, equation (3) combines old and new estimates of the one period change of the level, thus defining the current linear (local) trend.

Demand

On the demand side, apart from the increased imports from the traditional importing countries like the US, Mexico, Germany, and the Netherlands, import demand has also increased significantly in the newly emerging markets like the Middle East where the increase in demand for the product is not strong enough to create a demand driven force, but is enough to sustain the present level of production and export mainly for cassia. India, the largest consumer of spices in the world, is also increasing its import of cinnamon and cassia. In light of tariff-free trade among countries of the world, import is bound to increase not only in developed country markets but also in spice producing, developing and under developed countries. But in all these newly emerging markets imports are dominated by cassia. In recognising the increasing demand for 'cinnamon buns' in the US fast food diet, it is considered that this trend is likely to continue. Further, invasion of this western food culture into many emerging economies, along with an increase in income levels, is bound to accelerate the demand for fast food the world over. The growing awareness about the natural flavours and colours among health conscious consumers will also change demand.

In the absence of consolidated data for world imports, the data on exports were taken as the net imported quantity of the world market, as one country's export becomes another country's imports. Future demand for the commodity is forecasted using the model identified. The equation fitted for demand is:

Table 12.21 The forecasted world production and export of cinnamon (canella) up to 2005—06

Year

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