A MARKOV CHAINS FOR STOCHASTIC MODELLING OF LAND USE CHANGE: A FRAMEWORK FOR ECONOMIC AND AGRICULTURAL POLICYMAKERS

Abstract

The dynamic patterns of land use and land cover play a crucial role in shaping the agricultural, economic, and social landscapes of a region. In Karnataka, a rapidly developing state in India, changes in land use have become increasingly significant due to expanding urbanization, agricultural intensification, deforestation, and infrastructural growth. This study employs Markov Chain Analysis (MCA), a robust stochastic modelling technique, to analyze and predict land use transitions in Karnataka over 40 years. The ability of Markov Chains to estimate transition probabilities from one land use category to another makes it an ideal tool for understanding temporal trends and anticipating future land cover changes.

The primary aim of this study is to model and quantify the dynamics of land use change in Karnataka using Markov Chain Analysis. The objectives include: (1) identifying the major transitions among land use categories over time; (2) computing transition probability matrices to quantify the likelihood of shifts between these categories; and (3) projecting future land use scenarios based on historical trends to inform sustainable land management strategies.

The study’s impact spans academia, industry, and policy. For sectors like agriculture, real estate, and infrastructure, it provides actionable insights into land availability and development trends. Policymakers can use the findings to guide zoning, conservation efforts, and long-term planning, ensuring a balanced approach to development and environmental protection. Overall, the research serves as a strategic tool for sustainable regional planning in Karnataka.