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The research study completely based upon the secondary sources of data.

The required data was procured from UNCOMTRADE data accessed through the World Bank’s World Integrated Trade Solution (WITS) software. Data related to composition of trade were based on Harmonized System coding (HS 1992) and HS two-digit level of classification has been considered for a period of 10 years i.e., from 2009 to 2018. As data on weight is not available for two-digit level classification, sum of all the four-digit level categories under that two-digit level are considered for the study.
Markov chain analysis
Markov chain analysis was employed to analyze the structural change in any system whose progress through time can be measured in terms of single outcome variable. In the present study, the dynamic nature of trade patterns of cereals from Australia studied using the Markov chain model.
Markov chain analysis involving developing a transitional probability matrix ‘P’, whose elements, Pij indicate the probability of exports switching from country ‘i’ to country ’j’ over time. The diagonal element Pij where i=j, measure the probability of a country retaining its market share or in other words, the loyalty of an importing country to a particular country’s exports.
            In the context of current application, structural change was treated as a random process with eight importing countries for cereals. The assumption was that the average export of cereals from a country amongst importing countries in any period depends only on the export in the previous period and this dependence is same for all the periods. This was algebraically expressed as

The values in the transition probability matrix will have different interpretations. The value of diagonal elements indicates the probability of retention of the previous year’s share, while values in the columns reveal probability of gain by a particular country from other countries, values in rows reveal probability that a country might lose to other countries in respect of a specific commodity exports.

RESULTS AND DISCUSSION

The results have been discussed under the following heads.
Transitional probability matrix of cereals exported from Australia
Transitional probability matrix for quantity of Cereals exported from Australia during the period 2009 to 2018 is presented in Table 1.  The major importing countries are China, Indonesia, Japan, Vietnam, Philippines, Republic of Korea, Malaysia, Yemen and the remaining importing countries are grouped under the category others.
China and Philippines retained 54.88 and 48.32 per cent of their original share respectively. Where as other countries retained 43.24 per cent of its original share. Indonesia and Vietnam lost 70.99 and 44.48 per cent of their share to other countries respectively. Whereas Republic of Korea lost all of its share to other countries. Japan lost 42.32 per cent of its share to Republic of Korea. Philippines lost 51.68 per cent of its share to China. Malaysia lost 76.86 per cent of its share to Indonesia.
The most stable markets are China and Philippines and least stable markets are Japan, Indonesia, Vietnam, Republic of Korea, Malaysia and Yemen for export of Cereals from Australia.
Projections for export of Cereals from Australia
Projections for export of Cereals to major importing countries from Australia for the period from 2019 to 2023 are presented in Table 2. The results suggest that the category “others” import major quantity of Cereals from Australia followed by China whereas quantity imported by others increased from 73.52 to 84.68 lakh tonnes and quantity imported by China decreased from 51.03 to 42.02 lakh tonnes for the years from 2019 to 2023.
The share of import increased from 2019 to 2023 for the countries Indonesia, Japan Malaysia and Yemen whereas as the share decreased for Vietnam, Philippines and Republic of Korea.
In conclusion, though Australia is one of the leading exporters in the world in case of cereals, the major importing country, China is posing threat by imposing 80.5 % tariff on barley imports from Australia. In order to maintain its export share there is a need to explore new markets for cereals. As the category Others have major share, there is scope for new countries emerging as new markets. This can be done by studying sanitary and phytosanitary measures of these countries.

Table 1: Transition probability matrix for export of Cereals (HS Code:10) from Australia for the period from 2009 to 2018

Table 2: Projected export of Cereals (HS Code: 10) to major importing countries from Australia: 2019 to 2023 (in lakh tonnes)

Note: The figures within the parentheses indicate percentages to total exports

Acknowledgements

Authors are thankful to Department of Agribusiness Management, College of Agriculture, University of Agricultural Sciences, for providing encouragement, constant support and giving valuable suggestions during whole research.

REFERENCES

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