Farmers can also walk in the farm areas while tracking the farmland with Global Positioning System device to capture the total area .In 2005, the World Economic Forum announced 500 million peripheral devices are linked to the Internet, 8 billion are connected presently, and it is predicted to be 1 trillion in 2030 . These technological devices have facilitated agriculture and the art of agriculture with a prediction of yield, enabling farmers to take appropriate storage measures . Furthermore, agriculture technology is on its way to revolutionize the farming industry, and farmers must prepare themselves to embrace the new future .According to Verspagen , technology is of no use to agriculture economic development if it is unknown to the people.Channels such as the extension officers , Community farm associations,Farmer-to-Farmer interactions , agriculture institutions in the country, etc.,must be operational in the dissemination of technical information.Conley et al. stated new users might also learn the technological features from others while other factors such as education, ebb and flow tray farm size, etc. also play an essential role in technology enactment .
Also, the outline of agricultural policies and provisions made for a particular technology defines its acceptance .Moreover, many perceive technology usage as a decree to their freedom,while others refrain from it due to cultural or religious beliefs. Yet, the millennial farmers feel easy in this virtual agricultural environment , while long-standing farmers can accept technological innovations if productivity increases whereas labor reduce .Technology inclusion in the agriculture industry is a milestone in affirming food security, . In the research conducted by Kansanga et al. on “Traditional agriculture in transition: examining the impacts of agricultural modernization on smallholder farming in Ghana under the new Green Revolution,” the study confirmed the significance of using technology to enhance modern-day agriculture productivities. The pre-data-preparation showed that the participants in this study were youth between age 17 and 35 years and are fairly educated, of which males are the majority.The adequacy of each variable to be included was assessed for its appropriateness in factor analysis using the Keiser-Meyer-Olkin test . The observed value was 0.768, statistically significant, ebb and flow trays considering a threshold of 0.60stated by . To successfully classify the variables into five constructs, the researchers used Cattel’s scree plot and the percentage of variation criterion described by . Figure 1 below shows the scree plot having eigenvalues on the vertical and the number of constructs extracted on the horizontal .The number of components with eigenvalues greater than 1 is selected .
The 5 constructs extracted can explain 66.88% of the variance.The measure internal consistency of the items was assessed Cronbach’s alpha,CR, and AVE and the outcome displayed in Table 1 above. All values reported are above the threshold, which established that the content validity and reliability of the items are satisfactory.The factor loadings for the constructs give a statistically significant percentage of variation explained and describe that technology implementation has the greatest of variance explained, 18.37%, followed by the economic development with 16.85%; participants motivation accounted for 12.69% and government policy having 10.56. Except for H2 and H3, all other hypotheses were significant at a 95% confidence level, as shown in Table 4. The inferential relationship between technology implementation and youth farming was positive, meaning that the ease of applying technology in agriculture tends to boost the youth to go into farming. Again, the positive relationships indicate that if appropriate governmental policies with incentives are laid down for farmers,young educated people will move into agricultural farming. Also, the negative the moderating effect of knowledge on technology implementation in farming implies that when the youth have enough knowledge on the application and performance of the technology in farming, their attitude of risk perception on farming decreases.In Figure 2, the latent variables are marked with oval shapes, whereas the rectangles are the measurement items, and the circles labeled e1 to e23 are the unobserved variations in the model.