Impacts of Climate variability on Agriculture in Kerala

The study revealed that southwest monsoon rainfall in Kerala has been declining while increasing in post monsoon season. The annual rainfall exhibits a cyclic trend of 40-60 years, with a significant decline in recent decades. The intensity of climatological droughts was increasing across the State of Kerala through it falls under heavy rainfall zone due to unimodal rainfall pattern. The moisture index across the State of Kerala was moving from B4 to B3 humid, indicating that the State was moving from wetness to dryness within the humid climate.The study confirms that a warming Kerala is real as maximum, minimum and mean temperatures and temperature ranges are increasing. The rate of increase in maximum temperature was high (1.46°C) across the high ranges, followed by the coastal belt (1.09°C) of Kerala while the rate of increase was relatively marginal (0.25°C) across the midlands. The rate of increase in temperature across the high ranges is probably high because of deforestation. It indicates that the highranges and coastal belts in Kerala are vulnerable to global warming and climate change when compared to midlands.Interestingly, the trend in annual rainfall is increasing at Pampadumpara (Idukki), while declining at Ambalavayal across the highranges. In the case of maximum temperature, it was showing increasing trend at Pampadumpara while declining trend at Ambalavayal. In the case of minimum temperature it is declining at Pampadumpara while increasing in Ambalavalal.The paddy productivity in Kerala during kharif / virippu is unlikely to decline due to increasing temperature on the basis of long term climate change, but likely to decline to a considerable extent due to prolonged monsoon season, followed by unusual summer rains as noticed in 2007-08 and 2010-11.All the plantation crops under study are vulnerable to climate variability such as floods and droughts rather than long term changes in temperature and rainfall.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

IA Critical Study of the Impact of Institutional Finance on Agriculture in Selected Villages of Ernakulam Revenue District

Vast changes have taken place in the field of institutional rural credit in India since the nationalisation of nineteen commercial banks in 1969. The supply of institutional finance to cultivators amounted to 63.2 percent of the total credit in 1981 compared to 31.2 percent in 1971. Insti tutionalisation of agricultural credit envisaged two objectives in general. One was to emancipate cultivators and farmers from the clutches of indigenous financiers and money lenders. The second was to make farmers financially capable of adopting the new technology or improved practices in agriculture so as to increase their agricultural production and thereby contributing to the development of agriculture in India. Though vast literature on Institutional Credi t and agriculture is available, no indepth and serious work examining thoroughly the cause of credit diversion has been undertaken so far. The present study is an attempt to fill up this gap. The study will be helpful to lending insti tutions, viz. Co-ope:r-atives, Commercial banks and various other insti tutional agencies in connection with their lending activity_ Also, the study will help government in .formulating proper policies that will insure a preferential treatment in favour of the most needy category of farmers and cultivators with respect to agricultural credit disbursement

Refinance operations of the national bank for agriculture and rural development-the kerala experience

NABARD has completed 14 years of operation.ln the light of its experiences and achievements, the performance ev.ilu.ilion of the National Bank need to be looked into.This could provide certain criteria for its strength and weakness which may help in consolidating the institution for better utilisation of its potentialities. It is also noteworthy that no evaluative study on the National Bank has been conducted in Kerala. The Major objective of this study is to evaluate the role of NABARD in catering to the long-term agricultural requirements of Kerala for 1982 to 1992.This is done by analysing the quantum and quality of NABARD's schematic refinance. The qualitative indices like (1) the efficiency of loan recovery, (2) the impact or financial viability of NABARD refinanced schemes, (3) the credit gap, (4) the commitment-disbursement gap, and (5) the imbalances in the NABARD refinance form the core of the study.Hypotheses were formulated inorder to study and analyse these qualitative indices. The study is presented in eight chapters

A novel Sigma–Delta based parallel analogue-to-residue converter

Animportant step in the residue number system(RNS) based signal processing is the conversion of signal into residue domain. Many implementations of this conversion have been proposed for various goals, and one of the implementations is by a direct conversion from an analogue input. A novel approach for analogue-to-residue conversion is proposed in this research using the most popular Sigma–Delta analogue-to-digital converter (SD-ADC). In this approach, the front end is the same as in traditional SD-ADC that uses Sigma–Delta (SD) modulator with appropriate dynamic range, but the filtering is doneby a filter implemented usingRNSarithmetic. Hence, the natural output of the filter is an RNS representation of the input signal. The resolution, conversion speed, hardware complexity and cost of implementation of the proposed SD based analogue-to-residue converter are compared with the existing analogue-to-residue converters based on Nyquist rate ADCs