Extent of technology adoption
The adoption level of SWC technologies by farm women is elucidated in Table 2. It signifies that majority of women (55%) had a medium level of technology adoption followed by low adoption (34.17%). Whereas, very few respondents (10.83%) had high rate of adoption. These findings indicate that women farmers are reluctant in accepting a new technology. Supporting this view, a study in Burkina Faso revealed that women have less bargaining power than men which limits access and control over household resources by them which also influences the adoption of technology
(Theriault et al., 2017). In some developing countries, access to credit is gender biased where female-headed households are discriminated by credit institutions and they are unable to invest in yield raising technologies, leading to low adoption rate
(Mwangi and Kariuki, 2015).
Multiple regression analysis
To explore the nature of relationship on factors for adoption of soil water conservation measures, multiple regression analysis was done. The regression model was employed to establish relationship between dependent (adoption of SWC technologies) and independent variables (demographic and socio-economic factors) affecting women’s participation in SWC (Table 3). For this purpose, 17 explanatory variables were selected to explain the dependent variable. However, only six variables namely age (X
1), education (X
2), farming experience (X
3), risk bearing (X
8), social participation (X
9) and cost-effectiveness (X
16) influenced the dependent variable. The detailed explanation of each variable is given below:
Age
The output of regression model demonstrated that age of farm women had a negative significant association with technology adoption (P<.01) with the value of -4.81. This implies that with increasing age, women have shown decreasing interest in trial and adoption of new technology. The young farmers as compared to older community largely adopt the technologies due to increased exposure and educational levels. Few findings also revealed a negative relationship between age and technology adoption
(Berkowsky et al., 2018). However, some studies reported a positive relationship
(Chuang et al., 2020), few concluded that age has a positive as well as a negative impact on technology adoption
(Melesse, 2018). He also mentioned that older farmers have more experience compared to young farmers. Moreover, older farmers have more resources than young farmers that help in adoption of new technologies. On the contrary, the young farmers largely adopt the technologies due to their tech savvy behaviour
(Belcher, 2022).
Education
Another important variable is the education of women in terms of number of schooling years, which has positive significant impact on the adoption of technology with t-value of 5.42. Education level of household heads and training participation significantly affected farmers’ adoption decision
(Dilebo, 2017;
Kumari, 2023). Most studies found that better-educated farmers, regardless of gender, are more likely to adopt new technologies but women farmers with less education, less land access are less likely to adopt new technologies
(Quisumbing, 1995). Furthermore, farmers who had higher education level were more interested in adoption of high yielding variety in Ethopia
(Egge et al., 2012). It might be due to change in the knowledge, attitude and skills of farmers through higher level of education
(Choudhary et al., 2013).
Farming experience
The farm experience is an important determinant in deciding the level of adoption of SWC technologies. The t-value for this variable was observed negative (-12.65) at 1% level of significance. Contrary to this, gender differences in cassava production technology adoption were examined and found that the adoption level was 26% higher among male adopters than their female counterparts
(Obisesan, 2014). He concluded that adoption was significantly influenced by gender, participation in off-farm activities, distance to market, land area cultivated, years of farming experience, access to credit, cassava yield and level of education. Besides, greater experience of older farmers might have led to adoption of new technology
(Silva and Broekel, 2017).
Risk bearing
The farm women who were already adopting improved agricultural practices, vermi-composting, organic farming, new variety
etc., were able to bear risks of adoption of new technologies. Risk bearing of farm women had positive association with adoption (2.26). Risk-averse people are generally small farmers, who are resistant to adopt new technologies due to low income and less capital. They are relatively experienced in growing and are more satisfied with the use of current technology and less receptive to new technology
(Gwara et al., 2022).
Social participation
Participation in social institutes like SHG, FPO make women exposed to new avenues with more confidence
(Choudhary et al., 2013). While discussing, the problems related to soil erosion, water scarcity,
etc., women with no participation in social organization or local institutions showed less probability of adopting SWC measures than those women involved in discussions. Social participation in institutions has positive association with the adoption of technology with value of 2.60 at 5% level of significance. This suggests that women who take an active role in discussions, meetings and various community dialogues, have 14.2% higher likelihood of participation in SWC compared to those who are not engaged in any discussion. Traditional management practices and discussions within social institutions play a crucial role in fostering robust and cooperative social network within SWC practices
(Bekele and Drake, 2002). A study on similar lines revealed the connection of social network relations formed by cotton farmers based on geography and association makes information transfer, collective communication and decision-making as the main way of technology diffusion
(Ren et al., 2022).
Cost-effectiveness
The impact of cost-effectiveness on technology adoption is multifaceted and can be analysed from several perspectives,
i.
e. initial investment, scope for scalability, operational efficiency,
etc. The cost-effectiveness of SWC technology had a significant positive (1.77) association with its adoption at 5% level of probability. The two main factors that affect the adoption process are the availability and affordability of new agricultural technologies and farmers’ expectations of long-term profitability
(Silva and Broekel, 2017). Furthermore, some workers have described that the “relative advantage, compatibility, complexity, trialability and observability” of the innovation are key pillars in the adoption process
(Warner et al., 2019). The R2 value (0.97) in Table 3, expressed the idea that six variables jointly contributed toward 97% of the variation in the level of adoption.
Correlation analysis
Unlike, the regression analysis, correlation depicted that out of sixteen variables, eleven variables significantly affected the adoption of SWC technologies (Table 4). Age was found as negatively correlated (0.857) with adoption at 1% level of significance. The impact of age on the adoption of technology has contested explanations. Some findings revealed a negative relationship between age and technology adoption
(Berkowsky et al., 2018), while other researchers revealed a positive relationship
(Chuang et al., 2020). Education of farm women has a positive correlation with their adoption (
p<.01) with the value of 0.926. This relationship was revealed in other studies too
(Ha and Park, 2020). The main reason for this positive relationship might be the ability of education to change the knowledge, attitude and skills of a farmer.
Farming experience is negatively correlated with adoption (0.961 at
p<.01) indicating that women having rich experience in farming are less inclined towards the technology adoption. Farmers are adjusted to the old technologies and find hard to discontinue them. The long-term experience would facilitate the farmers in making the best option
(Senanayake and Rathnayaka, 2015). However, negative experiences with similar technologies will affect the adoption negatively. Thus, proper awareness about the technology introduced is a prominent issue in influencing its adoption.
The land holding significantly affected adoption (0.216 at 5% level), which means as the land holding increases, there are chances to experiment with new technology. There is a positive relationship between the size of farm and the adoption of joint cultivation of inorganic and improved maize varieties
(Ogada et al., 2014).
Risk bearing had a positive impact (0.895) on adoption at
p<.01 which proves that risk-taking attitude and behaviour of farm women prepare them to adopt the technology. Social participation and adoption are positively correlated at 1% level of significance with a value of 0.901 which interprets that women who are more involved in social gatherings are more adaptable to the new technology. Farm women who are part of farmer organizations mostly had access to new information, also promoted technology adoption as well
(Katungi and Kankwasa, 2010). Agro-advisory and weather advisory are positively correlated with adoption at 5% and 1% level of significance, as both extension advisories connects farm women with updates in agriculture and weather. In similar trend, it was also identified that availability and access to extension services are key aspects of technology adoption
(Mwangi and Kariuki, 2015). Skill development was correlated with adoption at 1% level of significance with the value of 0.601. It indicated that skill development training positively affects the adoption process
(Tayade and Chinchmalatpure, 2022). The ease of using new technology (0.200) and cost-effectiveness (0.622) positively influenced adoption at 5% and 1% level of significance respectively.
