Estimating the impact of BBF technology adoption in soybean cultivation: A probit regression model

In the present study, the economic impact of BBF technology on soybean cultivation has been assessed. This was based mainly on primary data collected through the personal interview method with the help of pre-tested schedules. Dharashiv district was purposively selected due to its high soybean production. A multistage sampling technique was employed for the selection of the district, tehsils and villages. The total sample size was 120, comprising 60 adopters and 60 non-adopters of soybean growers. A Probit regression model was fitted to assess the impact of BBF technology on adopters and non-adopters among soybean cultivators. Dharashiv falls under the dry zone of western Maharashtra. Given increasing climatic challenges, assessing the real-world impact of such innovations is essential. BBF is a “Four-in-One” planter that performs bed making, seed sowing, fertilizer application and weedicide spraying simultaneously, promoting efficient water use and enhanced crop productivity. Due to time and resource limitations, the study is confined to selected variables which are likely to affect the acceptance of BBF technology by farmers. Independent variables considered are age, level of education, source of information, annual income and family size. The Probit model is a method to perform regression for a binary outcome variable with two possibilities, such as adopters and non-adopters of BBF technology. The analysis indicated that annual income (significant at 1% level) and source of information (significant at 10% level) were the two major factors that significantly influenced the adoption of the technology. The result revealed that helping farmers improve their financial condition and providing them with better access to information can play a key role in encouraging the adoption of BBF technology in the study area.