Stability Analysis of Australia’s Wool Exports- A Markov Chain Model

Soumya, P.; Yeledhalli, R. A.

doi:10.9734/jsrr/2021/v27i330372

Cited by 3 publications

(3 citation statements)

References 5 publications

(4 reference statements)

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“…The trade directions of exports of Indian cocoa products were estimated by computing the transitional probability matrix. Similarly Deepika et al, [6] and Swarnalatha et al, [7] studied the trade directions of exports using the transitional probability matrix. The major importing countries, viz., the USA, Indonesia, Nepal, Niger, UAE and The Netherlands were considered for analysis.…”

Section: Trade Directions Of Exports Of Indian Cocoa Productsmentioning

confidence: 99%

An Application of Markov Chain Analysis to Study the Indian Cocoa Products Export Performance

Abhilash,

Joshi

et al. 2024

AIR

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The paper attempts to quantify the changing structure of Indian cocoa product exports. Data for analysis was considered for a period of 10 years from 2013-14 to 2022-23. The growth rates of exports of Indian cocoa products to Nigeria, Indonesia, USA, UAE, The Netherlands and other countries have shown a positive trend. The Markov chain analysis was attempted through the linear programming method to assess the transition probabilities for the major cocoa markets. The results revealed that Nigeria, Nepal, UAE and other countries were stable markets for Indian cocoa export products. Whereas, Indonesia and The Netherlands were the least stable markets based on the magnitude of transition probabilities. The export share predictions for 2025-26 behaved stagnant while also increasing slightly for major destinations.

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Section: Trade Directions Of Exports Of Indian Cocoa Productsmentioning

confidence: 99%

An Application of Markov Chain Analysis to Study the Indian Cocoa Products Export Performance

Abhilash,

Joshi

et al. 2024

AIR

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“…Where Y=Area or production or yield variable for which growth rate is calculated [11] t=time variable taking 1, 2, 3…….,n. and here they are years…”

Section: Compound Annual Growth Ratementioning

confidence: 99%

Effect of Climate, Growth and Decomposition Analysis of Maize in India

Soumya

2022

IJECC

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Maize is third most important cereal crop in India after rice and wheat. Maize can be grown under various agro-climatic zones. Aims: To study the effect of temperature, rainfall and crop are on crop yield in maize in India. To study Compound Annual Growth Rate of area, production and productivity of maize in India. To study percentage contribution of area, yield and their interaction to maize production in India. Study Design: Only secondary data has been considered for the present study. Maize in India is considered for study purpose. Data Considered: All the data required for the study is obtained from Indiastat website. The impact of climatic variables on yield is studied by using data for a period of 28 year i.e., from 1992-93 to 2019-20. For calculating CAGR and for decomposition analysis data from 1950-51 to 2019-20 were considered. Methodology: Ordinary Least Squares method, Compound Annual Growth Rate(CAGR) and decomposition analysis were tools employed for the study. Results: For every 10c rise in temperature, the yield of maize decreased by 1.6 units. For every 1 mm increase in rainfall, the yield increased by 0.05 units and for every thousand hectares increase in temperature, the crop yield increased by nearly 1.37 units. Temperature, Rainfall, crop area and time explain 89 per cent of variation in maize yield. Highest and significant growth rate of 3.39 per cent per annum was observed in area during the period II i.e., from 1960-61 to 1969-70. Similarly, in the case of production and yield a highest and significant growth rate of 7.32 and 4.03 per cent respectively were observed during the period I i.e., 1950-51 to 1959-60. For the entire study period i.e., from 1950-51 to 2019-20 significant and positive growth rates of 1.26, 3.34, and 2.05 per cent per annum were observed in area, production and yield respectively. For the entire period, the contribution of interaction effect is high (58.30%) followed by yield (28.73%) and area effects(12.97%). The results revealed that area and yield effect contributes more to production than their interaction when data is considered for a shorter period (10 years) whereas the interaction effect contributes more to production if the period considered is of longer duration(70 years). Conclusion: Care should be taken by the government in the supply of hybrid maize seeds to farmers. Farmers should be educated about the effect of climatic variables on maize yield and they are also trained in following an improved package of practices.

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“…Millets, characterized by their resilience in diverse agro-climatic conditions and rich nutritional profiles, have been integral to the Indian agricultural landscape for centuries [13]. As the world acknowledges the significance of millets in addressing contemporary challenges such as food security, malnutrition, and sustainable agriculture, understanding the trade trends and competitiveness of millets in India becomes paramount [14]. The International Year of Millets serves as a catalyst for our exploration, underscoring the global significance of millets in achieving sustainable development goals.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis of Export Performance and Trade Competitiveness of Millets from India

Madhu, D. M.,

Gunadal,

Harshitha, H. C.

et al. 2024

JSRR

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The present study examines the trade performance, competitiveness and trade directions of millets. Indian millet market shedding light on their role in fostering a resilient and sustainable food system, aligning with global agendas for a healthier and more sustainable future. The secondary data collected from the ITC Trade Map, focusing on global and Indian millet exports and imports performance, the data spanning from 2013 to 2021. The analytical tools like Revealed Comparative Advantage (RCA), Markov Chain analysis, and Herfindahl-Hirschman Index (HHI) are employed. Ukraine, India, and Russia are major millet exporters, while Indonesia, Belgium, and Germany are significant importers in the world. The UAE, Nepal and Saudi Arabia are prominent destinations for Indian millet exports. The RCA index analysis revealed that Ukraine has the highest average RCA index value for millet exports, followed by India. The trade direction stability, with Nepal being the most stable importer. The HHI for global millet exports is 0.141, indicating moderate concentration while global millet imports, the HHI is 0.277, signifying high concentration. In the case of Indian millet exports, the HHI is 0.17, representing moderate concentration. Efforts should be made to boost the exports of millets from India, tapping into the potential of diverse markets and reducing dependence on a limited number of countries.

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Stability Analysis of Australia’s Wool Exports- A Markov Chain Model

Cited by 3 publications

References 5 publications

An Application of Markov Chain Analysis to Study the Indian Cocoa Products Export Performance

An Application of Markov Chain Analysis to Study the Indian Cocoa Products Export Performance

Effect of Climate, Growth and Decomposition Analysis of Maize in India

A Comprehensive Analysis of Export Performance and Trade Competitiveness of Millets from India

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