INTRODUCTION The ratio of Extension workers to Farmers is low at 1:1162 at national level as against recommended ratio of 1:750. In the past 20 years, extension has changed from technology-focused, public services-dominated, transfer of technology approaches to a much broader scope with many different actors from the private and civil society sectors (Sulaiman and Davis 2012). Many new service providers and institutional arrangements in agricultural extension have emerged over the last two decades. These include private extension agencies, input agencies, agri-business firms, farmers’ organisations, producer cooperatives, financial agencies involved in rural credit delivery, and consultancy services (Sulaiman and Davis 2012). Different roles of Agri. Input Dealer(s) in providing Extension and advisory services: •Delivering and checking relevant and timely Extension and Agro advisory services to the farmers •Report to the company agents/agriculture officer any serious pest and disease problems and natural calamities prevailing in his area and also unusual/inappropriate agro-advisory services •Visiting the farmers field on fixed day •To observe field operations, conditions of crops •To enquire the problems and suggest appropriate measures •To make a note to what extent recommendations of agro-advisory services have been adopted and reasons for non-adoption •To detect the incidence of pests, diseases and natural disorders Provide credit based agro-advisory services to the farmers •Report to the department of agriculture special achievements of farmers by using agro advisory services •Evaluate the agro-advisory services feasibility in field conditions •Ensuring quality, low cost agro-advisory services to the farmers •Works out how to convince farmers to adopt the recommendations on agro advisory services made during the training sessions •Motivating the farmers to adopt new and additional recommended agro advisory services •Conduct demonstrations/campaign/seminars on productive technologies related to different crops to farmers •Arranging for company agents to visit the field in order to get solution when he is unable to get satisfactory solution during field visits/training programs •Hold the meeting with the farmers interest groups and rural institutions to discuss location specific problems of farmers MATERIALS AND METHODS Telangana state was chosen for the study. The present study was undertaken in Khammam, Karimnagar, Rangareddy districts (1 district for each zone) covering all the 3 zones of Telangana state. All these 3 districts were randomly selected from the state of Telangana. A total number of 90 farmers (30 from each district, six each from the five Agri. Input Dealer(s) with three years of interaction) were selected randomly. Data were collected from the respondents by using a pre-tested interview schedule and analysed using Statistical Package for Social Sciences (SPSS 20). RESULTS AND DISCUSSION Age. The data in Table 1 revealed that majority of the farmers 82.22 per cent belonged to middle age group (35 to 55 years) in the sample area. While, 12.22 per cent belonged to the young age group (upto 35 years) and very less 5.56 per cent belonged to old age group (above 55 years). From the above findings it can be concluded that the majority of the farmers belonged to middle age group. Education. From the Table 1 it was evident that majority of the farmers had medium level of education (81.11%) followed by high (10.00%) and low (8.89%). From the Table 1 it was evident that nearly half of the farmers (46.8 percent) studied upto PUC followed by high school (21.1%), graduates (13.3%), agricultural graduates (10%), no school education (4.4%), primary school (4.4%). None of the respondents reported for middle school (5-7) and any others (Technical course).The probable reason for this might be due to the availability of formal schooling and college facilities in the study area. These results were similar to the findings of Saravanan (1999); Parouha (2014). Family details. It is operationalized as the family type, family size, Number of dependents of the farmer. From the Table 2 it was evident that majority of the farmers belonged to nuclear families (86.67%) followed by joint families (13.33%). The results were similar to the findings of Rout (2004); Kumari (2012). From the Table 2 it was evident that majority of farmers come under medium family group (82.22%) followed by large (11.11%) and small (6.67%) From the Table 2 it was evident that the number of dependents in the family of farmer fall under medium category (77.78%) followed by high (14.44%) and low (7.8%). From the Table 1 it was evident that majority of farmers fall under medium family profile category (73.33%), followed by high (16.67%) and low (10%). Farm size. From the Table 1 it was evident that 61.11 percent farmers had medium land holdings, followed by 23.33 percent semi-medium,11.11 percent large,2.2 percent small and marginal land holdings. The medium farm size enabled the farmers in the area to avail better services from AIDs. Farming experience. From the Table 1 it was evident that majority of farmers fall under medium level of experience group (60%) followed by high (21.11%) and low (18.89%). The probable reason might be that farming is their primary occupation from years. The result was in agreement with the Kumari (2012). From the Table 3 it was evident that in kharif season 10 percent farmers grown paddy, 16.67 percent farmers grown Paddy and Chilli, 15.55 percent farmers grown Chilli and Cotton, 16.67 percent farmers grown Paddy and Cotton, 8.89 percent farmers grown Paddy and vegetables, 11.11 percent farmers grown Sugar cane, 11.11 percent farmers grown Paddy, Chilli and Cotton, 5.55 percent farmers grown Chilli, cotton and sugarcane, 4.44 percent farmers grown vegetables. In rabi season 38.89 percent farmers grown maize, 16.67 percent farmers grown ground nut, 40.00 percent farmers grown maize and ground nut, 4.44 percent farmers grown vegetables. In addition to these perennial fruit trees like mango was cultivated by some farmers. The probable reason might be that the climactic conditions are favourable to grow the particular crops. Annual income. From the Table 1 it was evident that 85.56 percent farmers had medium annual income, followed by high (12.22 %) and low (2.2%). The possible reason might be due to their capacity to take risk of accepting the recommended practices and services offered by AIDs. The result was in agreement with the Parouha (2014) Socio-political participation. From the Table 1 it was evident that majority of farmers fall under low degree of socio-political participation (42.2%) and medium (28.9%) and high (28.9%). The probable reason for the kind of result might be that socio-political participation is considered as prestigious. The findings were similar to Vinayak Nayak (2014). Degree of contacts with Agri. Input Dealers. From the Table 1 it was evident that majority of farmers fall under medium degree of contacts with Agri. Input Dealers (62.22%) followed by low (23.3%) and high (14.45%). The probable reason for the kind of result might be that getting agricultural information and inputs from AIDs is very easy. Information seeking behaviour From the Table 4 it was evident that 75.56 percent farmers had medium Information seeking behaviour from Informal sources followed by high (14.44 percent) and low (10 percent). From the Table 4 it was evident that 74.44 percent farmers had medium Information seeking behaviour from formal sources followed by high (14.44 percent) and low (11.11 percent). From the Table 4 it was evident that 66.67 percent farmers had medium Information seeking behaviour from mass media followed by low (20 percent) and high(13.33 percent). From the Table 1 it was evident that 75.56 percent farmers had medium Information seeking behaviour followed by high (14.44 percent) and low (10 percent). The results were partially in consistent with Androulidakis et al. (2002). Credibility on Agri. Input Dealer(s). From the table 1 it was evident that 73.33 percent farmers had medium credibility on AIDs, followed by 15.56 percent had high and 11.11 percent had low credibility on AIDs. The probable reason might be because farmers avail services from AIDs from years and are in touch with them more frequently. The results were in consistent with Singh and Narain (2008a). Input acquisition behaviour. From the Table 1 it was evident that 82.22 percent farmers had medium Input acquisition behaviour, followed by 17.80 percent had low and 0.00 percent had high Input acquisition behaviour. The criteria for going to AIDs was because they were the prime source of farm information to farmers. The findings were similar to Androulidakis et al. (2002); Singh and Narain (2008a). Cosmopoliteness. From the Table 1 it was evident that 68.89 percent had medium cosmopoliteness followed by 30 percent high and 1.11 percent had low cosmopoliteness. The results were partially in consistent with Shashidhar (2004). Risk management behaviour. From the Table 1 it was evident that 75.56 percent had medium risk management behaviour followed by 15.55 percent high and 8.89 percent had low risk management behaviour. The probable reason may be the higher education, enough farming experience and annual income of respondents. The findings were similar to Vedamurthy (2002). From the Table 5 it was evident that 67.78 percent AIDs fall under medium effectiveness category, 16.67 percent fall under less effectiveness category, 15.55 percent fall under high effectiveness category. The result was similar to the findings of Borah (2019).