Economic Analysis of the Impact of Changes in Cropping Patterns in the Parbhani District of Maharashtra, India: A Markov Chain Approach - Serene Toppo; R.V. Chavan; S.V. Bharti; S. Sangeeta Kumari (Agricultural Sciences: Techniques and Innovations Vol. 1, 2025)
The cropping pattern plays a vital role in determining the level of agricultural production and reflects the agricultural economy of a region. A change in cropping pattern implies a change in the proportion of area under different crops. The present investigation examines structural changes in the cropping pattern in Parbhani district of Maharashtra, utilising secondary time-series data on crop areas from 2013-14 to 2022-23. Markov chain analysis is a stochastic process that describes transitions from one state to another in a chain-like process. By applying Markov Chain analysis, the study evaluates shifts in cropping patterns by analysing transitions in the area allocated to different crops over time to reveal significant trends through the Transition Probability Matrix (TPM). Soybean and jowar retained the largest area shares. Hence, the area under these crops was stable, while sugarcane and soybean emerged as major area gainers, often at the expense of wheat, maize, mung, and safflower. The findings highlight soybean and sugarcane’s economic viability and adaptability to local conditions. The sharp decline in traditional crops such as wheat and maize, despite their historical prominence in the region, signals a transformation in agricultural priorities. This trend points toward the need for policy intervention to address food security concerns. This analysis underscores the importance of crop selection for economic resilience and sustainable agricultural practices in the region.