INTRODUCTION Groundnut (Arachis hypogea) is one of the important oilseed crop due to its nutritional value and high consumption all over the world. India ranks second in groundnut production in the world. The NARS has developed many new technologies for increasing the yield of groundnut, but the adoption of these technologies by the farmers is far less than the anticipated. There is a huge Yield Gap between FLDs plot and farmers’ practice. There are several reasons that contribute for these yield gaps, non- adoption of recommended technologies is one of the major reason. The reasons for non-adoption may be due to low knowledge and other socio-economic constraints encountered by the farmers. Hence, awareness has to be created among farming community about the suitable technologies in groundnut cultivation. Adoption of proper pre- and post-harvest management technologies plays a crucial role for improving famers’ yields. Timely and proper adoption of the recommended technologies is important to control the incidence of pest and diseases and for obtaining the potential yield. The present study was conducted with the objectives to study the profile characteristics of the groundnut farmers, analyse the practice wise extent of adoption of pre- and post-harvest management technologies, and understand relationship between profile characteristics of groundnut farmers and their extent of adoption of pre- and post-harvest management technologies . METHODOLOGY The study was conducted in Mahabubnagar district of Telangana State. Two mandals from the district viz., Koilkonda and Mohammadabad were selected based on the highest area under groundnut. From each mandal, four villages were selected based on the area under groundnut and FLD conducted in the villages. Sixty respondents were selected from the eight villages by simple random sampling method. The variables were selected based on the discussion with the experts. A semi-structured schedule was developed for the study and data were collected by personal interview of the respondents. To measure the extent of adoption of recommended pre- and post-harvest technologies in groundnut, the important practices (15 items) were listed and responses of farmers were recorded on a three-point continuum. Based on the farmers’ response, a score of 3, 2 and 1was given for full adoption, partial adoption and low adoption, respectively. RESULTS AND DISCUSSION Socio-economic characteristics of farmers. The results (Table 1) indicated the distribution of the farmers according to their profile characteristics. More than half of the respondents belonged to middle age group (60.0%), where as most (26.7%) of the farmers were educated up to primary level. Majority (40.0%) of the farmers possessed small size land holdings and most (43.3%) of the famers belonged to lower- middle group of annual income. Majority (70.0%) of the farmers had medium farming experience, categorized under low training (63.3%). In case of irrigation potential, majority (48.3%) of farmers had bore well as a major source of irrigation, medium level of social participation (56.6%), medium input acquisition pattern (53.4%), medium information seeking behaviour (66.7%), medium achievement motivation (65.0%), medium risk orientation (55.0%), medium knowledge (60.0%), low market intelligence (46.6%) and medium adoption (56.7%). Extent of adoption of pre- and post-harvest technologies by the farmers. The results (Table 2) indicated that majority (56.7%) of the farmers belonged to medium level of adoption followed by low (25.0%) and high (18.3%) adoption level. The reason for medium to low adoption might be because of medium knowledge, medium risk taking ability, medium input acquisition pattern and no proper trainings received regarding management technologies, low awareness about the new technologies. The results were similar with the findings of Shasani et al. (2020); Hadiya et al. (2016). The results (Table 3) indicated that majority of the farmers fully adopted the practices such as land preparation (85.0%), followed by use of improved seeds (80.0%), fertilizers (71.7%), optimum time of harvesting (65.0%) and time of sowing (60.0%), dry pod threshing (58.3%), management of pest and diseases (53.3%) and weed management (50.0%). Farmers had proper knowledge and benefited by adoption of the practices in reducing the yield losses due to pests and diseases, producing quality produce due to optimum time of sowing and proper use of good quality seed and hence will continue to use the recommended practices each season. The practices which were partially adopted by the farmers were optimum spacing (60.0%), recommended seed rate (45.0%), water management (36.6%), weed management (31.6%), sun drying of groundnut pods (30.0%), optimum time of sowing (28.3%) and threshing (26.7%). Majority of the farmers had not -adopted any value addition in groundnut (91.6%) and sold their produce directly in the market, seed treatment (78.3%) and use of hermetic bags for storage of groundnut pods (63.3%). Few of the farmers had not adopted management of pests and diseases (31.7%), sun drying of groundnut pods (26.7%), optimum seed spacing (25.0%) and optimum seed rate (21.7%). Farmers were not aware of the technologies, lack of processing units for value addition in groundnut, poor knowledge about importance of the seed treatment, lack of proper trainings, majority of the farmers were not able to purchase the hermetic storage bags because of high cost. The results (Table 4) indicated that the age had negative and non-significant relationship with extent of adoption. The independent variables like education, size of landholding, annual income, farming experience, trainings undergone, irrigation potential, social participation, input acquisition pattern, information seeking behaviour, achievement motivation, risk taking ability, knowledge, market intelligence had positive and significant relationship with extent of adoption pre- and post- harvest management technologies of groundnut crop. The findings were in congruence with of that of Shasani et al. (2020); Meena et al. (2019); Kumar et al. (2019); Prasad et al. (2019); Gorfad (2018), Hadiya et al. (2016); Rai et al. (2016); Chand and Meena (2011).