Paper presented at the International Sustainability Transitions conference 2018 (12-14 june) Manchester, UK. The Dutch agrifood regime is grinding to a halt. International economic pressures force Dutch farmers to further scale up and intensify their businesses, while food scandals and calamities as well as many and varied negative environmental impacts have led to an all-time low societal acceptance of the agrifood regime as well as a host of legislative measures to stifle further growth. Such a situation, in which regime pressures increasingly undermine the regime, represents a strong call for transition of the Dutch agrifood system.At the same time, new business models emerge: new players arrive, new logistical pathways come to the fore and innovative consumer and farmer relationships – food co-operatives – are forged. In a sense, the transition is already under way (cf. Hermans et al., 2010), with new business models forming an important backbone. However, the way forward is still a matter of great uncertainty and controversy: How do new business models relate to reconfiguring the Dutch agrifood system? We explore the hypothesis that different transition pathways put specific demands on the role of new business models. We studied various new business models in the Dutch agrifood system and their relations to three different transition pathways. Our research combines future exploration (backcasting) and analysis of new business models. In this research, we approach this question from two angles. First, we introduce a transition-oriented business model concept, in order to effectively link new business models to transition. Then we shortly touch upon the transition pathway typology introduced by Geels et al. (2016) and describe three different transition pathways for the Dutch agrifood system. We report on XX business models in each of these transition pathways. The paper ends with a discussion of the role of business models for different types of transition pathways.
MULTIFILE
Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
DOCUMENT
New Dutch agrifood business models are emerging in response to economic, social and ecological pressures: new players arrive, new logistical pathways come to the fore and innovative consumer and farmer relationships – food coöperatives – are forged. How do new business models relate to reconfiguring the Dutch agrifood system? Our research combines future exploration (backcasting) and analysis of new business models. We developed three agrifood transition scenarios with various groups of stakeholders. For each scenario, we then analysed a specific, representative business model to explore the different roles of business models in agrifood transition. Business models in the “Added value in and with the countryside” already exist and occupy a niche in the market. However, a breakthrough of these business models require large-scale institutional and behavioural change. Business models in the “New products, specific markets” exist but are rare. They usually concern high-value specialist products that could result in widespread market change, but might require little institutional change. The “Sustainable production methods” most resembles the current system. Some associated business models become successful, but they have difficulty distinguishing themselves from conventional produce, which raises questions about whether business models are able to drive a transition in this direction. Thus, our results lend credence to the hypothesis that different transition pathways offer specific potential for and requirements of new business models.
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The European eel (Anguilla anguilla) is a delicacy fish and an integral part of the Dutch culinary history. However, the stock of adult eel has decreased significantly due to a precipitous recruitment of glass eel fall. This relates to multiple factors including obstacles in migration pathways, loss of habitat and chemical pollution. Consequently, Anguilla anguilla has become a critically endangered species and is protected under European legislation. One possible solution, explored on laboratory scale, is the captive reproduction of eels and growth of glass eel in aquaculture. A big challenge of this technique is the limiting aspect of possible nutrients for the eels in the larval stage, as the diet must be delivered in micrometric capsules (< 20 µm) with a high protein content. Such diets are not yet available on the market. Electrohydrodynamic atomization (EHDA) is a novel option to prepare a micro-diet suitable for eel larvae. EHDA is especially interesting for its narrow size distribution capabilities and for applications which require submicrometric sizes. This project aims to evaluate the use of EHDA to produce high protein content micrometric size capsules for feeding larval eels. If successful, this would assist in the captivity production of glass eel and to make the eel culture independent of wild catches, restoring the culinary market. The project will be conducted in two phases. Firstly, tests will be conducted to evaluate the necessary conditions of the capsules using EHDA. Subsequently, the obtained capsules will be tested as feed for eel larvae. The main objective is to favour the development of a more sustainable eel culture, regarding the possibilities of investigating the current fish in natura option and exchanging it for a captivity one.
In societies where physical activity levels are declining, stimulating sports participation in youth is vital. While sports offer numerous benefits, injuries in youth are at an all-time high with potential long-term consequences. Particularly, women football's popularity surge has led to a rise in knee injuries, notably anterior cruciate ligament (ACL) injuries, with severe long-term effects. Urgent societal attention is warranted, supported by media coverage and calls for action by professional players. This project aims to evaluate the potential of novel artificial intelligence-based technology to enhance player monitoring for injury risk, and to integrate these monitoring pathways into regular training practice. Its success may pave the way for broader applications across different sports and injuries. Implementation of results from lab-based research into practice is hindered by the lack of skills and technology needed to perform the required measurements. There is a critical need for non-invasive systems used during regular training practice and allowing longitudinal monitoring. Markerless motion capture technology has recently been developed and has created new potential for field-based data collection in sport settings. This technology eliminates the need for marker/sensor placement on the participant and can be employed on-site, capturing movement patterns during training. Since a common AI algorithm for data processing is used, minimal technical knowledge by the operator is required. The experienced PLAYSAFE consortium will exploit this technology to monitor 300 young female football players over the course of 1 season. The successful implementation of non-invasive monitoring of football players’ movement patterns during regular practice is the primary objective of this project. In addition, the study will generate key insights into risk factors associated with ACL injury. Through this approach, PLAYSAFE aims to reduce the burden of ACL injuries in female football players.
