Markov Chain Model Probability of Dry Wet Weeks and Statistical Analysis of Weekly Rainfall for Agricultural Planning at Jabalpur

摘要

Among different weather variables, rainfall is the major determinant and deciding factor for success or failure for success or failure of rain fed agriculture. For successful agricultural management and planning of soil water conservation measures, information about occurrence of dry and wet periods along with onset of and withdrawal of rainy season is important and will have dominating effect of on the crop yield. In this study Markov chain model has been employed to find the initial and conditionalprobability of occurrence of dry and wet weeks and onset of and with drawl of rainy season and weekly analysis of Agra region. The successive dry weeks hints for need of supplemental irrigation and moisture conservation practices whereas successive wetweeks gives an idea of excessive runoff water availability for rain water harvesting and to take up suitable measures to control soil erosion. The data onset and with drawal monsoon indicate that rainy season starts effectively from 27th SMW (02-08 July)and remain active up to41 SMW (08-14 Oct). During rainy season the probability of occurrence of wet week is more than 50% expect during 37th to 41 th SMW.