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.
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.
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.
Familiar mixed dairy sheep farm is the most widespread system in the Mediterranean basin, in Latin America and in developing countries (85%). There is a strong lack of technological adoption in packages of feeding and land use in small-scale farms. To increase competitiveness, it would be of great interest to deepen the knowledge of how innovation was selected, adopted, and spread. The objective of this research was to select strategic feeding and land use technologies in familiar mixed dairy sheep systems and later assess dairy sheep farms in Spain.
The government of Rwanda is promoting agricultural intensification focused on the production of a small number of targeted commodities as a central strategy to pursue the joint policy goals of economic growth, food security and livelihood development. The dominant approach to increase the productive capacity of the land, crops and animal resources has been through large-scale land consolidation, soil fertility management, and the intensive use of biotechnology and external inputs.
Even prior to COVID, there was a considerable push for food system transformation to achieve better nutrition and health as well as environmental and climate change outcomes. Recent years have seen a large number of high visibility and influential publications on food system transformation. Literature is emerging questioning the utility and scope of these analyses, particularly in terms of trade-offs among multiple objectives.
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.
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.