The dynamic nature of climate and its impacts on agriculture is rendering most of the existing adaptation and coping strategies unsupportive in many regions.
Globalization, urbanization and new market demands - together with ever-increasing quality and safety requirements - are putting significantly greater pressures on agrifood stakeholders in the world. The ability to respond to new challenges and opportunities is important not just for producers but also for industries in developing countries. This paper aims to present what "innovation response capacity" entails, especially for natural resourcebased industries in a developing country context.
This article investigates determinants and impacts of cooperative organization, using the example of smallholder banana farmers in Kenya. Farmer groups are inclusive of the poor, although wealthier households are more likely to join. Employing propensity score matching, we find positive income effects for active group members. Yet price advantages of collective marketing are small, and high-value market potentials have not yet been tapped. Beyond prices, farmer groups function as important catalysts for innovation adoption through promoting efficient information flows.
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.
Enhancing the diversity of agricultural production systems is increasingly recognized as a potential
means to sustainably provide diversified food for rural communities in developing countries, hence
ensuring their nutritional security. However, empirical evidences connecting farm production
diversity and farm-households’ dietary diversity are scarce. Using comprehensive datasets of
market-oriented smallholder farm households from Indonesia and Kenya, and subsistence farmers
Sustainable intensification of agriculture will have to build on various innovations, but synergies between different types of technologies are not yet sufficiently understood. We use representative data from small farms in Kenya and propensity score matching to compare effects of input-intensive technologies and natural resource management practices on household income. When adopted in combination, positive income effects tend to be larger than when individual technologies are adopted alone.
Supermarkets and high-value exports are currently gaining ground in the agri-food systems of many developing countries. While recent research has analyzed income effects in the small farm sector, impacts on farming efficiency have hardly been studied. Using a survey of Kenyan vegetable growers and a stochastic frontier approach, we show that participation in supermarket channels increases mean technical efficiency by 19%. This gain is bigger at lower levels of efficiency, suggesting the potential for positive income distribution effects.
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.