Export of Maize from India: Destinations and Competitiveness

2020 ◽  
pp. 437-443
Keyword(s):  
Markov Chain ◽  
Moderate Degree ◽  
Markov Chain Analysis ◽  
Chain Analysis ◽  
Export Competitiveness ◽  
Nominal Protection

The study examined the direction of trade of maize by Markov chain analysis and showed that Bangladesh was the most stable market of Indian maize as it could retain its share of 90.07 percent, Indonesia, Malaysia and Vietnam with 63.44, 62.33 and 45.92 percent retention were found to be major destinations for Indian maize exports. The most unstable markets among the maize importing countries were Taiwan and UAE with the zero percent retention. The analysis of export competitiveness revealed that the Indian maize has a moderate degree of competitiveness as Nominal Protection Coefficients during all the years studied ranged between 0.5 and 1.0.

Export Competitiveness of Indian Mango: Determinants and Empirical Evidences

2021 ◽  
Keyword(s):  
Markov Chain ◽  
Comparative Advantage ◽  
Policy Recommendations ◽  
Markov Chain Analysis ◽  
Revealed Comparative Advantage ◽  
Chain Analysis ◽  
Export Competitiveness ◽  
Probability Matrices ◽  
The Uk ◽  
Market Requirement

The main objective of the present study was to work out the export competitiveness of Indian mango and policy recommendations for improvement in its export and the destinations as well. To achieve the objectives, various tools such as net terms of the trade revealed comparative advantage, and Markov chain analysis was applied to mango export data from 1999-2019. The estimations of net terms of trade and revealed comparative advantage discovered that India has a comparative advantage for exporting mango. It is evident from the transitional probability matrices developed through Markov chain analysis that Qatar was the most loyal buyer for Indian mango during this period. The attention should be focused on the market requirement and specifications of Qatar, Bangladesh, UAE and the UK as they were the most stable buyers of Indian mango.

scholarly journals Assessing Changes in Soil Erosion Rates: A Markov Chain Analysis

1999 ◽  
Vol 31 (3) ◽  
pp. 611-622 ◽  
Author(s):  
Rhonda Skaggs ◽  
Soumen Ghosh
Keyword(s):  
Markov Chain ◽  
Soil Erosion ◽  
Great Plains ◽  
Markov Chain Analysis ◽  
Conservation Policies ◽  
Erosion Rates ◽  
National Resources Inventory ◽  
Long Run ◽  
Chain Analysis ◽  
Soil Erosion Rates

AbstractMarkov chain analysis (one-step and long-run) is applied to the National Resources Inventory (NRI) database to evaluate changes in wind-based soil erosion rates over time. The research compares changes in soil erosion rates between NRI sample sites with and without applied conservation practices for a random sample of Great Plains counties. No significant differences between sites are found for half of the counties evaluated. The effectiveness and efficiency of conservation policies are thus questioned in light of these research results.

Markov chain analysis of Leading Ones problem

2017 ◽  
Vol 22 (4) ◽  
pp. 443-448 ◽  
Author(s):  
Yifei Du ◽  
Kenji Aoki ◽  
Makoto Sakamoto ◽  
Hiroshi Furutani ◽  
Kunihito Yamamori
Keyword(s):  
Markov Chain ◽  
Markov Chain Analysis ◽  
Chain Analysis

262 Markov Chain Analysis of Time Dependent Changes in AMR Using the 2011 ISHLT pAMR Classification

2012 ◽  
Vol 31 (4) ◽  
pp. S95
Author(s):  
G. Snow ◽  
J. Stehlik ◽  
E.H. Hammond ◽  
K. Brunisholz ◽  
E. Gilbert ◽  
...  
Keyword(s):  
Markov Chain ◽  
Time Dependent ◽  
Markov Chain Analysis ◽  
Chain Analysis
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