This paper draws lessons from selected country experiences of adaptation and innovation in pursuit of food security goals.
There are divergent views on what capacity development might mean in relation to agricultural biotechnology. The core of this debate is whether this should involve the development of human capital and research infrastructure, or whether it should encompass a wider range of activities which also include developing the capacity to use knowledge productively. This paper uses the innovation systems concept to shed light on this discussion, arguing that it is innovation capacity rather than science and technology capacity that has to be developed.
This study examines the role of public–private partnerships in international agricultural research. It is intended to provide policymakers, researchers, and business decisionmakers with an understanding of how such partnerships operate, how they promote the exchange of knowledge and technology, and how they contribute to poverty reduction.
This article adds to the literature about the impact of social networks on the adoption of modern seed technologies among smallholder farmers in developing countries. The analysis centers on the adoption of hybrid wheat and hybrid pearl millet in India. In the local context, both crops are cultivated mainly on a subsistence basis, and they provide examples of hybrid technologies at very different diffusion stages: while hybrid wheat was commercialized in India only in 2001, hybrid pearl millet was launched in 1965.
This book is the re-titled third edition of the widely used Agricultural Extension (van den Ban & Hawkins, 1988, 1996). Building on the previous editions,Communication for Rural Innovation maintains and adapts the insights and conceptual models of value today, while reflecting many new ideas, angles and modes of thinking concerning how agricultural extension is taught and carried through today.
Linking farmers to markets is widely viewed as a milestone towards promoting economic growth and poverty reduction. However, market and institutional imperfections along the supply chain thwart perfect vertical and spatial price transmission and prevent farmers and market actors from getting access to information, identifying business opportunities and allocating their resources efficiently. This acts as a barrier to market-led rural development and poverty reduction.
Tanzania has tremendous potential to support a thriving agribusiness sector. Agriculture is diverse and extensive, employing more than 80 percent of the population, and contributing about 28 percent of Gross Domestic Product, or GDP and 30 percent of export earnings. A wide range of agricultural commodities are produced in Tanzania, including fiber (sisal, cotton), beverages (coffee, tea), sugar, grains (a diverse range of cereals and legumes), horticulture (temperate and tropical fruits, vegetables and flowers) and edible oils.
The report introduces 30 young innovators, 21 featured with full stories, and nine other "innovators to watch". They come from countries including Barbados, Botswana, Cameroon, Côte d'Ivoire, Kenya, Nigeria, Uganda, Jamaica, Senegal, Tanzania. The publication presents a multidimensional picture of the emerging field of ICT entrepreneurship in agriculture in developing countries. It describes challenges but also successes already achieved. It contains advice for aspiring agtech entrepreneurs as well as recommendations from youth on how to support their ventures.
This paper comparatively analyzes the structure of agricultural policy development networks that connect organizations working on agricultural development, climate change and food security in fourteen smallholder farming communities across East Africa, West Africa and South Asia.
The objective of the study was to identify a viable trade-off between low data requirements and useful household-specific prioritizations of advisory messages. At three sites in Ethiopia, Kenya, and Tanzania independently, we collected experimental preference rankings from smallholder farmers for receiving information about different agricultural and livelihood practices. At each site, was identified socio-economic household variables that improved model-based predictions of individual farmers’ information preferences.