Technological influence was a great support for judgment-making in various fields, especially in agriculture. Agriculture production has been on the rise over recent years due to a lack of knowledge of agriculture and ecological shifts. The main goal of this system is to accomplish farmers in e-Agriculture of their wakefulness, usage, and observation. The study used a technique of numerical study design to collect data from farmers for their e-commerce awareness The data gathered indicate there is less understanding that there is a need for help for e-agriculture.
Classical innovation adoption models implicitly assume homogenous information flow across farmers, which is often not realistic. As a result, selection bias in adoption parameters may occur. We focus on tissue culture (TC) banana technology that was introduced in Kenya more than 10 years ago. Up till now, adoption rates have remained relatively low.
Classical innovation adoption models implicitly assume homogenous information flow across farmers, which is often not realistic. As a result, selection bias in adoption parameters may occur. We focus on tissue culture (TC) banana technology that was introduced in Kenya more than 10 years ago. Up till now, adoption rates have remained relatively low.
Most micro-level studies on the impact of agricultural technologies build on cross-section data, which can lead to unreliable impact estimates. Here, we use panel data covering two time periods to estimate the impact of tissue culture (TC) banana technology in the Kenyan small farm sector. TC banana is an interesting case, because previous impact studies showed mixed results. We combine propensity score matching with a difference-in-difference estimator to control for selection bias and account for temporal impact variability.