Final report of the Evidence-based Food System Design project (EFSD). This research project aimed at building a data-driven mapping of the Amsterdam Metropolitan Food System, as an evidence base for vision and scenario development, policymaking and other initiatives aimed at transitioning to a more sustainable regional food system.
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This study theorizes on the sociomateriality of food in authority-building processes of partial organizations by exploring alternative food networks (AFNs). Through the construction of arenas for food provisioning, AFNs represent grassroots collectives that deliberately differentiate their practices from mainstream forms of food provisioning. Based on a sequential mixed-methods analysis of 24 AFNs, where an inductive chronological analysis is followed by a qualitative comparative analysis (QCA), we found that the entanglements between participants’ food provisioning practices and food itself shape how authority emerges in AFNs. Food generates biological, physiological and social struggles for AFN participants who, in turn, respond by embracing or avoiding them. As an outcome, most AFNs tend to bureaucratize over time according to four identified patterns while a few idiosyncratically build a more shared basis of authority. We conclude that the sociomateriality of food plays an important yet indirect role in understanding why and how food provisioning arenas re-organize and forge their forms of authority over time. Pascucci, S., Dentoni, D., Clements, J., Poldner, K., & Gartner, W. B. (2021). Forging Forms of Authority through the Sociomateriality of Food in Partial Organizations. Organization Studies, 42(2), 301-326. https://doi.org/10.1177/0170840620980232
During times of high activity by predators and competitors, herbivores may be forced to forage in patches of low‐quality food. However, the relative importance in determining where and what herbivores forage still remains unclear, especially for small‐ and intermediate‐sized herbivores. Our objective was to test the relative importance of predator and competitor activity, and forage quality and quantity on the proportion of time spent in a vegetation type and the proportion of time spent foraging by the intermediate‐sized herbivore European hare (Lepus europaeus). We studied red fox (Vulpes vulpes) as a predator species and European rabbit (Oryctolagus cuniculus) as a competitor. We investigated the time spent at a location and foraging time of hare using GPS with accelerometers. Forage quality and quantity were analyzed based on hand‐plucked samples of a selection of the locally most important plant species in the diet of hare. Predator activity and competitor activity were investigated using a network of camera traps. Hares spent a higher proportion of time in vegetation types that contained a higher percentage of fibers (i.e., NDF). Besides, hares spent a higher proportion of time in vegetation types that contained relatively low food quantity and quality of forage (i.e., high percentage of fibers) during days that foxes (Vulpes vulpes) were more active. Also during days that rabbits (Oryctolagus cuniculus) were more active, hares spent a higher proportion of time foraging in vegetation types that contained a relatively low quality of forage. Although predation risk affected space use and foraging behavior, and competition affected foraging behavior, our study shows that food quality and quantity more strongly affected space use and foraging behavior than predation risk or competition. It seems that we need to reconsider the relative importance of the landscape of food in a world of fear and competition.
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Flying insects like dragonflies, flies, bumblebees are able to couple hovering ability with the ability for a quick transition to forward flight. Therefore, they inspire us to investigate the application of swarms of flapping-wing mini-drones in horticulture. The production and trading of agricultural/horticultural goods account for the 9% of the Dutch gross domestic product. A significant part of the horticultural products are grown in greenhouses whose extension is becoming larger year by year. Swarms of bio-inspired mini-drones can be used in applications such as monitoring and control: the analysis of the data collected enables the greenhouse growers to achieve the optimal conditions for the plants health and thus a high productivity. Moreover, the bio-inspired mini-drones can detect eventual pest onset at plant level that leads to a strong reduction of chemicals utilization and an improvement of the food quality. The realization of these mini-drones is a multidisciplinary challenge as it requires a cross-domain collaboration between biologists, entomologists and engineers with expertise in robotics, mechanics, aerodynamics, electronics, etc. Moreover a co-creation based collaboration will be established with all the stakeholders involved. With this approach we can integrate technical and social-economic aspects and facilitate the adoption of this new technology that will make the Dutch horticulture industry more resilient and sustainable.
The consortium would like to contribute to structural reduction of post-harvest and food losses and food quality improvement in Kenyan avocado and dairy value chains via the application of technical solutions and tools as well as improved chain governance competences in those food chains. The consortium has four types of partners: 1. Universities (2 Kenyan, 4 Dutch), 2. Private sector actors in those chains, 3. Organisations supporting those chains, and 4. Associate partners which support category 1 to 3 partners through co-financing, advice and reflection. The FORQLAB project targets two areas in Kenya for both commodities, a relatively well-developed chain in the central highlands and a less-develop chain in Western-Kenya. The approach is business to business and the selected regions have great potential for uptake of successful chain innovations as outcome of research results. The results are scalable for other fresh and processed product chains via a living lab network approach. The project consists of 5 work packages (WPs): 1. Inventory , status quo and inception, 2. Applied research, 3. Dissemination of research outputs through living lab networks, 4. Translation of project output in curricula and trainings, and 5. Communication among partners and WPs. The applied research will be implemented in cooperation with all partners, whereby students of the consortium universities will conduct most of the field studies and all other partners support and interact depending on the WPs. The expected outcomes are: two knowledge exchange platforms (Living Labs) supported with hands on sustainable food waste reduction implementation plans (agenda strategy); overview and proposals for ready ICT and other tech solutions; communication and teaching materials for universities and TVETs; action perspectives; and knowledge transfer and uptake.
Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
