The concept of an innovation system is used to understand how innovation contributes to economic growth. However, innovation systems do not evolve evenly in different parts of the world. This paper contributes to the ongoing debate on the emergence of innovation systems in the context of developing countries. It uses the Rwandan case, where agriculture is a dominant socio-economic sector with high innovation potential. It explores how stakeholder interactions and policies contribute to the emergence of an agriculture innovation system in Rwanda.
A central concern about achieving global food security is reconfiguring agri-food systems towards sustainability. However, historically-informed trajectories of agri-food system development remain resistant to a change in direction. Through a systematic literature review, the authors identify three research domains exploring this phenomenon and six explanations of resistance: embedded nature of technologies, misaligned institutional settings, individual attitudes, political economy factors, infrastructural rigidities, research and innovation priorities.
The Newsletter of the Tropical Agriculture Platform (TAP) provides regular updates on activities by TAP and its partners, on the projects and on upcoming events. This issue specifically refers to the period from August to October 2021.
Two major agricultural transformations are currently being promoted worldwide: digitalisation and ecologisation, that include different practices such as organic farming and sustainable intensification. In literature and in societal debates, these two transformations are sometimes described as antagonistic and sometimes as convergent but are rarely studied together. Using an innovation system approach, this paper discusses how diverse ecologisation pathways grasp digitalisation in the French agricultural sector; and do not discriminate against organic farming.
CONTEXT
Big data applications in agriculture evolve fast, as more experience, applications, good practices and computational power become available. Actual solutions to real-life problems are scarce. What characterizes the adoption of big data problems to solutions and to what extent is there a match between them?
OBJECTIVE
We aim to assess the conditions of the adoption of big data technologies in agricultural applications, based on the investigation of twelve real-life practical use cases in the precision agriculture and livestock domain.
METHODS
A fragmented digital agriculture ecosystem has been linked to the slow scale-out of digital platforms and other digital technology solutions for agriculture. This has undermined the prospects of digitalizing agriculture and increasing sectoral outcomes in sub-Saharan African countries. We conceptualized an aggregator platform for digital services in agriculture as a special form of digital platforms that can enhance the value and usage of digital technologies at the industry level. Little is known about how such a platform can create value as a new service ecology in agriculture.
Animal-source foods (ASF), such as fish, provide a critical source of nutrients for dietary quality and optimal growth of children. In sub-Saharan Africa, children often consume monotonous cereal-based diets, a key determinate of malnutrition such as stunting. Identifying existing sources of ASF for children’s diets will inform the development of nutritious food systems for vulnerable groups.
Fish is a key source of income, food, and nutrition in Zambia, although unlike in the past, capture fisheries no longer meet the national demand for fish. Supply shortfalls created an opportunity to develop the aquaculture sector in Zambia, which is now one of the largest producers of farmed fish (Tilapia spp.) on the continent. In its present form, the aquaculture sector exhibits a dichotomy.
Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of themost challenging problems in agriculture. If the diseases are not identified in the early stages, then they may ad-versely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many re-searchers have presented different state-of-the-art systems based on Deep Learning and Machine Learningapproaches. However, most of these systems either use millions of training parameters or have low classificationaccuracies.
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.