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Sericulture is considered as main employment and income generating activity in Karnataka. The Technical Service Centres (TSCs) are playing an incredible role of extension for wellbeing of sericulture farmers through their extension activities. Technical Service Centers are the front line units and they motivate the farmers to take up mulberry cultivation and silkworm rearing. The TSC is the distinctive extension system established at grass root level, which are easy accessible to farmers and helps to disseminate knowledge and make for them adopt the scientific technologies of mulberry cultivation and silkworm rearing. All the findings of the study help to refinement and reformation of extension methodologies of TSCs. There is a scope for extension system for diffusion of sericulture technologies to the non-sericulture growing areas and to become agriculture as a profitable sector and none of the research studies was conducted on sericulture extension system (Technical Service Centres). The present study on Impact of Technical Service Centres on Sericulture Farmers in Karnataka State of India was carried out during 2017. The Karnataka state was contributing 35.00 per cent of silk production in India. The Ramanagara and Mandya districts were selected because these district having highest number of TSCs in Bangalore and Mysore division respectively. Ex-post-facto research design used for the study. Mandya, Malavalli and K.R Pet taluks from Mandya district on the other hand Ramanagara, Channapatna and Kanakapura taluks were purposively selected for the study. Above the taluks were selected based on top 3 taluks in TSCs in district. The four TSCs from each taluk leads to twelve from each district, Totally 24 TSCs were selected for the study. Ten sericulture farmers under each TSC, collectively 240, were selected by using random sampling method. The age, education, farming experience, annual income, size of land holding, family status, cropping pattern, mass media utilization, social participation, , information consultancy, credit acquisition and utilization, scientific orientation, management orientation , risk orientation and achievement motivation were xvii considered as independent variables and attitude, direct and indirect changes, knowledge and adoption were considered as dependent variables for the study. For the variables attitude and knowledge were studied by constructing scale and test respectively. A well structured interview schedule was used for data collection. The statistical tools like frequency, percentage, mean, standard deviation, correlation, multiple regression and Garret ranking were used for data analysis. The detailed analysis of the profile of sericulture beneficiary farmers of TSCs indicated that majority of them were middle aged with having education high school and middle school and 23-30 years of experience in sericulture. It also found that majority of them had medium annual income and most of the farmers belonged to small farmers and marginal farmers. Most of the sericulture farmers had medium family status (4-6 members), Mulberry+Coconut+Ragi cropping pattern, medium mass media utilization, high social participation, medium information consultancy, medium scientific orientation, medium management orientation, high risk orientation and high achievement motivation. The dependent variable attitude of sericulture beneficiary farmers towards activities of TSCs was measured by constructing Likert Scale with 21 statements comprises both positive and negative statements. The study found that more than half of the sericulture farmers had medium favourable attitude towards activities of TSCs followed by high favourable attitude and low favourable attitude. In case of knowledge, most of sericulture farmers had medium knowledge on recommended technologies of sericulture. Only just more than one fifth (11.25 %) of the sericulture farmers had low knowledge on recommended technologies of sericulture. With respect to adoption of recommended technologies, majority of the sericulture farmers belonged to medium adoption category followed by high adoption and low adoption category. Before interventions of TSC, majority of the sericulture farmers had low mulberry yield succeeded by medium mulberry yield, after interventions of TSC, of the sericulture farmers had high mulberry yield followed by medium mulberry yield and low mulberry yield. Correlation coefficient (r) values of independent variables such as education, Mass media utilization, information consultancy and Scientific orientation were positively significant at 0.01 level of significance. Whereas, experience in sericulture social participation and Achievement motivation were positively significant at 0.05 level of significance. Independent variables such as education, Experience in Sericulture, Mass media utilization, Information consultancy and Achievement motivation were positively significant at 0.01 level of significance. Whereas, cropping pattern, Social participation and scientific orientation were positively significant at 0.05 level of significance. The major constraints of TSCs as perceived by the sericulture farmers were Discrimination in identification of farmers for benefits followed by misuse of funds as second rank, Poor information dissemination about scientific technologies as third rank, Delay in subsidy transfer to the beneficiary account as fourth rank, xviii Selling of subsidised inputs to grainage centres as fifth rank, Lack of consultancy services about mulberry cultivation and silkworm rearing, less demand driven technologies and finally lack of need based trainings for mulberry cultivation silkworm rearing. The major suggestions given by sericulture farmers for improvement of functioning of TSCs were Transparency in selection of farmers to benefit extension services followed by Online transfer of monetary benefits to beneficiary farmers, Providing insurance to sericulture as like Crop insurance, Appointment of technically sound and skilful extension officials, Effective consultancy services in sericulture by TSC officials, Generation demand driven technologies and Unbiasness in the selection of farmers for training .