"International Journal of Geography, Geology and Environment"

Materials and Methods: Price data were sourced from the AGMARKNET website, a reliable platform for agricultural market information. To ensure the robustness of the analysis, the data series were subjected to tests for stationarity and homogeneity, which are prerequisites for time-series analysis. The Granger causality test was employed to evaluate the interdependence between state markets. This method determines whether historical price data from one market can predict price trends in another. The null hypothesis, stating that one time series does not Granger-cause another, was tested using F-statistics. A p-value below 0.05 led to the rejection of the null hypothesis, indicating a significant causal relationship, while a p-value above 0.05 supported the null hypothesis.

Conclusion: This study highlights the integrated nature of India’s cotton markets and underscores Odisha’s critical role in price transmission. The results provide valuable insights for policymakers and stakeholders to develop strategies for market stabilization and enhanced forecasting. By leveraging these causal relationships, stakeholders can promote efficient trade and mitigate the risks associated with price volatility in the cotton sector. Strengthening market linkages and improving price forecasting mechanisms will contribute to a more stable and efficient cotton economy in India.