Role Of Climate Change In Agriculture

ANALYZING THE ROLE OF CLIMATE CHANGE IN AGGRAVATING
THE AGRARIAN CRISISUSING DELPHI ADAPTED
INDUCED LINKED FRM (DAILFRM)
A.Victor Devadoss1, D.Ajay2, S.Santhoshkumar3
1Head & Associate Professor, Department of Mathematics, Loyola College, Chennai, India.
Email: [e-mail]
2School of Advanced Sciences, VIT University, Vellore, Tamilnadu, India.
Email:[e-mail]
3Department of Mathematics, Loyola College, Chennai, India.

Abstract:This paper introduces a new model called Delphi Adapted Induced Linked FRM by extending Delphi Adapting technique to the existing ILFRM model and the model is applied to analyze the role of climate change in aggravating the crisis in agriculture in India. This model works on the opinion of experts

While on one hand it faced serious challenges with the onset of neoliberal policies, on the other hand it was hard hit by failure in monsoon and depletion of ground water. The view that Indian agriculture is facing huge threats due to changes in climate has been consolidated by several studies. They also predict that this scenario will continue as there has been no sign of decrease in its harmful effects. The important observed climate related factors like increase in average temperature, decreased water availability, decrease/variability in annual rainfall, flooding, droughts, changing patterns of precipitation, higher air and soil temperature, etc suggest us that the vulnerability of Indian agriculture is increasing.It is evident that climate change has harmful effect on Indian agriculture but the direct relation between climate change and the agrarian crisis is something that needs to be established. Finding this relationship is of utmost importance to understand and to address this crisis. Therefore, in this paper, Delphi Adapted Induced Linked Fuzzy Relational Maps model is employed to analyze the role of climate change in aggravating the agrarian

Let M be the relational matrix. Let us find a hidden pattern when D1 is switched on. We pass the stage vector C1 through the connection matrix M. A particular attribute, say, D1 is kept in ON state and all other components are kept in OFF stage. LetC1°M yields, C1’. To convert to signal function, choose the first two highest values to ON state and other values to OFF state with 1 and 0 respectively. We make each component of C1’ vector pass through M repeatedly for each positive entry 1. Then choose that vector which contains the maximum number of 1’s, that which causes maximum attributes to ON state and call it, say, C2. Suppose that there are two vectors with maximum number of 1’s in ON state, we choose the first vector. Repeat the same procedure for C2 until we get a fixed point or a limit cycle. We do this process to give due importance to each vector separately as one vector induces another or many more vectors into ON state. We get the hidden pattern. We observe a pattern that leads one cause to another and may end up one vector or a