Dry and Wet Spell Probability by Markov Chain Model over Adilabad District of Telangana

Abstract

This research uses the Markov Chain Model to analyze dry and wet spells in Telangana, India’s Adilabad district. The research intends to provide Received: 17 Jul 2024 Revised: 30 Jul 2024 Accepted: 21 Aug 2024 significant insights for agricultural planning in the region, which is critical considering that agriculture is the foundation of Indian economy. The study emphasizes the need to understand and effectively exploit natural resources, particularly rainfall, for the improvement and sustainability of rainfed agriculture. According to the calculation, there is a 70% chance of two consecutive wet weeks throughout the monsoon season (24th SMW to 40th SMW), resulting in about 17 weeks of monsoon rain in the Adilabad area. This enables the successful development of short-duration crops like rice. According to the study, the Markov Chain Model is effective for simulating the long-term frequency behavior of wet or dry spells, providing a comprehensive understanding of rainfall patterns and their impact on agriculture in the Adilabad District. Agriculture is the backbone of the Indian economy, and it is important to ensure food and nutrition security. Rainfall is the most essential climatic component for farmers using rainfed agriculture (Waniet al., 2017). Understanding the amount and timing of rainfall is crucial for crop planning. Unfavorable weather conditions can disrupt the equilibrium and have major effects on lives and food production systems (Wakjira et al., 2021). Rainfed agriculture is an important source of agricultural productivity. Crop yield, particularly in rainfed settings, is determined by rainfall patterns (Habib et al., 2022). It is viable to enhance farm productivity by altering cropping patterns and agronomic procedures based on the weather. (Liliane and Charles., 2020) Many researchers have investigated the likely behavior of rainfall. (Salehyan.,2014). The study of wet and dry spells helps to characterize command area crops, plan cropping systems, and construct conservation structures (Srinivasarao et al., 2020). The Markov Chain Model has been widely used to investigate the distribution of spells and other aspects of rain events.The appropriate understanding and efficient use of natural resources, particularly rainfall, are critical for the improvement and sustainability of agriculture in rainfed areas (Gao et al., 2020). The Markov Chain Model has shown to be effective in describing the long-term frequency behavior of wet or dry spells. The annual and seasonal rainfall study gives a broad overview of the region’s rainfall pattern, whereas the weekly rainfall analysis is especially valuable for agricultural planning. The Markov chain probability model has been widely used to predict the long-term frequency of rainy and dry spells (Victor and Sastri, 1979). Another part of crop planning is the forward and backward accumulation of rainfall to predict the start and end of the rainy season using precipitation data. Numerous studies have been conducted to examine daily, weekly, monthly, seasonal, and annual rainfall data for location-specific agricultural planning, and crop planning in particular.

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