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.
Productivity growth in smallholder agriculture is an important driver of rural economic development and poverty reduction. However, smallholder farmers often have limited access to information, which can be a serious constraint for increasing productivity. One potential mechanism to reduce information constraints is the public agricultural extension service, but its effectiveness has often been low in the past.
Background
Weather risk is a serious issue in the African small farm sector that will further increase due to climate change. Farmers typically react by using low amounts of agricultural inputs. Low input use can help to minimize financial loss in bad years, but is also associated with low average yield and income. Increasing small farm productivity and income is an important prerequisite for rural poverty reduction and food security. Crop insurance could incentivize farmers to increase their input use, but indemnity-based crop insurance programs are plagued by market failures.
Increasing investment and spending in agricultural innovation is not enough to meet Sustainable Development Goal (SDG) targets of ending poverty and hunger because the effectiveness of investments in low- and middle-income (LMI) countries is affected by the low quality of infrastructure and services provided, and by different norms and practices that create a considerable gap between financing known technical solutions and achieving the outcomes called for in the SDGs.
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.
The recent proliferation of mobile phones in rural Africa has also led to increased interest in mobile financial services (MFS), such as mobile money and mobile banking. Such services are often portrayed as promising tools to improve agricultural finance, especially among smallholders who are typically underserved by traditional banks. However, empirical evidence on the actual use of MFS for agricultural activities is thin. Here, we use nationally representative data from Kenya to analyze the use of mobile payments, mobile savings, and mobile credit among the farming population.
Mobile phone based money services have spread rapidly in many developing countries. We analyze micro level impacts using panel data from smallholder farmers in Kenya. Mobile money use has a large positive net impact on household income. One important pathway is through remittances, which contribute to income directly but also help to reduce risk and liquidity constraints, thus promoting agricultural commercialization. Mobile money users apply more purchased inputs, market a larger proportion of their output, and have higher farm profits.
This study analyse how agricultural extension can be made more effective in terms of increasing farmers’ adoption of pro-nutrition technologies, such as biofortified crops. In a randomised controlled trial with farmers in Kenya, the authors implemented several extension treatments and evaluated their effects on the adoption of beans biofortified with iron and zinc. Difference-in-difference estimates show that intensive agricultural training can increase technology adoption considerably.
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.