AgriFoodTech start-ups are coming to be seen as relevant players in the debate around and reality of the transformation of food systems, especially in view of emerging or already-established novel technologies (such as Artificial Intelligence, Sensors, Precision Fermentation, Robotics, Nanotechnologies, Genomics) that constitute Agriculture 4.0 and Food 4.0. However, so far, there have only been limited studies of this phenomena, which are scattered across disciplines, with no comprehensive overview of the state of the art and outlook for future research.
This collection of posters from the TAP-AIS project illustrates key achievements of the project towards strengthening national agricultural innovation systems (AIS) in Africa (Burkina Faso, Eritrea, Malawi, Rwanda, Senegal), Latin America (Colombia), Asia and the Pacific (Cambodia, Lao PDR, Pakistan). For each of these nine countries, and for their respective regions, the posters provide: i) thematic focus and context; ii) constraints in the AIS; iii) capacity development interventions; iv) outcomes; v) the way forward.
Mission-oriented agricultural innovation systems (MAIS) are becoming more prevalent in view of tackling the challenges of agri-food systems transformation. In this perspective, we argue that the politics of MAIS requires more comprehensive and considerable attention in the field, given the contested and deeply normative nature of the direction of innovations in agri-food systems transformation. Literature from development studies, policy sciences, and transition studies is reviewed to inform the perspective.
The OECD InDeF team developed a portfolio approach to innovation. A portfolio approach takes a balcony view on innovation which helps organizations align innovation processes, resources and performance with organizational objectives and enables them to track innovation with a view to scaling. Coached by the OECD team, Enabel colleagues in Benin, Morocco and Palestine piloted this portfolio approach by reviewing their current innovation supporting activities and investments against a set of key criteria.
Droughts are causing severe damages to tropical countries worldwide. Although water abundant, their resilience to water shortages during dry periods is often low. As there is little knowledge about tropical drought characteristics, reliable methodologies to evaluate drought risk in data scarce tropical regions are needed.
Digitization in agriculture is rapidly advancing further on. New technologies and solutions were developed and get invented which ease farmers’ daily life, help them and their partners to gain knowledge about farming processes and environmental interrelations. This knowledge leads to better decisions and contributes to increased farm productivity, resource efficiency, and environmental health. Along with numerous advantages, some negative aspects and dependencies risk seamless workflow of agricultural production.
This article extends social science research on big data and data platforms through a focus on agriculture, which has received relatively less attention than other sectors like health. In this paper, I use a responsible innovation framework to move attention to the social and ethical dimensions of big data “upstream,” to decision-making in the very selection of agricultural data and the building of its infrastructures.
Cotton, a major crop worldwide, is harvested in mechanized production systems once at the end of the growing season. To facilitate harvest and maximize fiber quality, the plants are typically defoliated when about 60% of the cotton bolls are open. Due to non-uniform maturation, the bolls that have opened early expose their fiber to weather until harvest, commonly for weeks, degrading fiber quality. Furthermore, high capacity harvesting machines are heavy, potentially compacting the soil that in turn reduces hydraulic conductivity in the wheel tracks and reducing yield.
Equipping agricultural extension and advisory services with nutrition knowledge, competencies and skills is essential to promote nutrition-sensitive agriculture. This report presents the results of an assessment of capacity within agricultural extension and advisory services, undertaken in Telangana State, India, with the global capacity needs assessment (GCNA) methodology developed by FAO and GFRAS. The methodology is available online at https://doi.org/10.4060/cb2069en
Accurate and operational indicators of the start of growing season (SOS) are critical for crop modeling, famine early warning, and agricultural management in the developing world. Erroneous SOS estimates–late, or early, relative to actual planting dates–can lead to inaccurate crop production and food-availability forecasts. Adapting rainfed agriculture to climate change requires improved harmonization of planting with the onset of rains, and the rising ubiquity of mobile phones in east Africa enables real-time monitoring of this important agricultural decision.