Dry and Wet Spell Probability by Markov Chain Model for Agricultural Planning at Parbhani

Abstract

Rainfed agriculture plays and will continue to play a dominant role in providing food and livelihoods for an increasing world population. Rainfall analyses are helpful for proper crop planning under changing environment in any region. An attempt has been made to analyse 42 years of rainfall (1971–2012) at the Parbhani region in Maharashtra, India for forecasting the probable time of onset and withdrawal of monsoon, probability of dry spells by using Markov chain model and finally crop planning for the region. The successive dry weeks indicate the need of supplemental irrigations and moisture conservation practices whereas, successive wet weeks gives an idea of excessive runoff water availability for rainwater harvesting and to take up suitable measures to control soil erosion. The average annual rainfall at Parbhani was observed as 928.4 mm with coefficient of variation (CV) of 32.8%. The data on onset and withdrawal of rainy season indicated that the monsoon started effectively from 24^th SMW (11-17^th June) and remained active up to 43^rd SMW (22-28^th October). During rainy season the probability of occurrence of wet week was observed more than 35% during 23-24^th SMW (4^th June–17^th June) and average weekly rainfall ranged from 27.4 to 41.9 mm, this rain can be utilized for summer ploughing and initial seed bed preparations.. Results obtained through this analysis would be utilised for agricultural planning and mitigation of dry spells at the Parbhani region in Maharashtra, India.