The study was conducted at Visakhapatnam district of Andhra Pradesh on FLD in blackgram by Scientists of district Agricultural Advisory and Transfer of Technology Centre of ANGRAU. Blackgram crop was sown as rice fallow in Rabi season during 2018-19 and 2019-20 in an area of 4229 ha and 4950 ha also it was found in an increase in yield of 32.7 per cent and 47.3 per cent in FLD field, respectively. Thus, it was concluded that front line demonstrations conducted under the close supervision of scientists is one of the most important tools of extension to demonstrate crop management practices at farmers’ field. FLDs motivate farmers to adopt scientific practices. So, FLDs are to be designed, supervised with extension activities viz., training programmes, demonstration, field days etc. to increase yields and profits of farmers.
A field experiments was conducted during 2015 and 2016 to study the effect of green manures and phosphorus levels in blackgram crop at Agricultural College Farm, Bapatla. The experiment was conducted in split- split plot design on sandy clay loam soil with three main treatments three subtreatments to Kharif rice and three sub-sub treatments to Rabi crop. The treatments consisted of Dhaincha green manure crop, sunnhemp green manure crop and without green manure as main plot treatments and three phosphorus levels to rice crop comprising of 45 kg P2O5 ha-1, 60 kg P2O5 ha-1 and 75 kg P2O5 ha-1 as sub- plot treatments and are replicated thrice. The Rabi experiment was laid out on the same site in a split-split plot design without disturbing the soil for succeeding blackgram crop and each of the Kharif plot was divided into three sub-sub plots to receive three levels of phosphorus (No P, 50% RDP and 100% RDP) to each plot. Yield and economics of blackgram which received Dhaincha green manure incorporation with 75 kg P2O5 ha-1 to Kharif rice crop and 100% RDP to Rabi blackgram was recorded significantly higher and it was on a par with sunnhemp green manure incorporation with 75 kg P2O5 ha-1 to Kharif rice crop and 100% RDP to Rabi blackgram.
Agriculture is slowly becoming digital. The adoption of Artificial Intelligence (AI) and Machine Learning (ML) both in terms of agricultural products and in-field farming techniques are increasing. Artificial Intelligence in agriculture is emerging in three major areas, namely agricultural robotics, soil and crop monitoring and predictive analytics. The use of sensors and soil sampling techniques are increasing day by day which helps in gathering of data. In turn, this data is stored in farm management system which is better processed and analysed. Thus, the data available along with other related data paves a way to successfully deploy AI in agriculture. AI in agriculture is emergingin cognitive computing and it has all the scope to become the most disruptive technology in agriculture services as it is able to understand, learn and respond to different situations (based on learning) to increase efficiency. The areas where the use of cognitive solutions can benefit agriculture are growth driven by IOT, image-based insight generation, identification of optimal mix for agronomic products, health monitoring of crops and automation techniques in irrigation and enabling farmers. In addition, the drone based solutions have significant impact in terms of productivity gains, coping with adverse weather conditions, yield management and precision farming.The emergence of new age technologies like Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are creating an ecosystem for smart farming. Fusion of all this technology is enabling farmers achieve higher average yield and better price control.
A self-help group (SHG) is a voluntarily association of people with common goal. The concept of “self-help groups appears to be a good substitute strategy to involve people in the improvement process. Empowerment of women through self-help groups has benefited not only the individual women but also her family and community as a whole. The study was carried out in the Jorhat district of Assam. Four blocks were selected from national rural livelihood mission (NRLM) initiative blocks. Thus, all total 16 self-help groups were selected from these blocks for the present study. Ten members from 16 SHG’s, total 160 members were selected using simple random sampling for the study. Majority of 54.4 per cent of the members belonged to young aged category i.e. 27 – 39 years. A large majority ( 95.00 % ) of the respondents were married and 37.50 per cent of the respondents had education upto high school level. A majority of the respondents (56.30 %) belonged to the OBC category, 68.10 per cent belonged to nuclear family and 62.50 per cent belonged to small family size. Majority of the respondents ( 48.8 % ) were marginal i.e. having 3 – 7.5 bigha of land holdings. Majority of the respondent’s family occupation (41.70 %) was cultivation and 56.30 per cent had medium income level and 91.00 per cent were member of one organization. Majority of the respondents (91.25 %) possessed television and 40.00 per cent attended training programme during the year 2017, 91.87 per cent source of information were extension officers, 88.75 per cent had contact with community resource person. A high percentage ( 62.50 %) of members of self-help group were completely dependent on bank namely Gramin Vikash Bank for their entrepreneurship development.
