Study of Potential Impacts of Climate Change on Food Production in Bangladesh

Abstract

The climate plays significant role in the agriculture of a country. The Rajshahi region is the driest part of the country in terms of rainfall. Rainfall is the most important weather parameter affecting non-irrigated crop areas. Water deficits and excess water are the greatest constraints for rain fed rice yields in this region. The daily rainfall data for 30 years during the period 1981-2010 of the station Rajshahi, Barisal and Comilla district in region are considered in this study. This study also considered weekly rainfall for 2.5mm and 5mm. The daily data were reduced in the weekly form and the drought index has been calculated using the probability of Markov chain model. Drought is temporary but complex feature of the climate system. Agriculture drought is mainly concerned with inadequacy of rainfall. Markov chain model have been used to evaluate probabilities of getting a sequence of wet-dry weeks over this region. An index best on the parameters of this model has been suggested for agriculture drought measurement in this region. The results indicate that the drought index for rainfall 2.5mm of rain for Rajshahi district annual is Mild, pre-kharif are Mild, Occasional, and Moderate, kharif is Occasional and Rabi is Chronic. The drought index for rainfall 5mm of rain for Rajshahi district annual is Mild, pre-kharif are Mild, Occasional and Moderate, kharif is Occasional and Rabi is Chronic. The drought index for rainfall 2.5mm of rain for Barisal district annual is Mild, pre-kharif are Mild, Occasional and Mild, kharif is Occasional and Rabi is Chronic. The drought index for rainfall 5mm of rain for Barisal district annual is Mild, pre-kharif is Occasional, kharif is Occasional and Rabi is Chronic. The drought index for rainfall 2.5mm of rain for Comilla district annual is Mild, pre-kharif are Mild, Occasional, Mild and Moderate, kharif is Occasional and Rabi is Chronic. The drought index for rainfall 5 mm of rain for Comilla district annual is Occasional, and Mild, pre-kharif are Mild, Occasional and Mild, kharif is Occasional and Rabi is Chronic. As a result, failure to rains and the occurrences of drought during any particular growing season lead to severe food shortages. A Markov chain model is established to fit daily rainfall data for the various aspects of rainfall occurrence patterns and could be mathematically derive from the Markov Chain by maximum likelihood estimate and these were also established to fit the observed data .The distribution of the number of success is asymptotically normal. The rainfall probability was not found to very much during rabi (November- February) season. But much more variation of rainfall probabilities was observed during both kharif (June-October) and pre-kharif (March- May) seasons. Obviously, one would presume conditions variation in these probabilities also yearly, seasonally and annually. Based on these findings and using chi-square test, it could be concluded that the model fit was good. This study investigates the methods to obtain estimates of the conditional probabilities, the probability of success and its probability distribution to describe the yearly, seasonal and annual variability. The limited data set the results are quite good and the model is doing a reasonably good job of daily rainfall. The probability can be used for both instantaneous and climate time scale retrievals. As the distribution of number of success asymptotically normal, it is playing a vital role for important decisions such as disaster prevention preparedness strategy. This study will contribute toward a better understanding of the climatology of drought in major monsoon region of the world. The findings of this study will be helpful for every researcher.