ical product or production opportunities. Such trajectories face the difficulty that they easily turn out to be a waste of time for some of the stakeholders as value contribution steps and value share principles are determined on concrete outcomes. A nice example of the power of “value chain-wide” open innovation is provided by the car industry where Tesla backwards and forwards integrates into the value chain, in an attempt to rethink production machinery, component production, end-product, transport, sales and aftersales, while interacting with customers and society on social media. Moreover, they build wholly owned links with an emerging house-hold energy production and management sector and prepare for next-gen transport concepts with autonomous driving. This spread of activities is leading to a redefinition of working principles in the sector and potentially even creates new industry structures. It drives on balancing major business risks with major business benefits, that for instance allow to bridge critical, yet non-value adding steps, deliver uncompromised, integrated offerings, and, most importantly, allow for unbiased communication across the business-web about shifts in demands, improvement options, or radical product or production changes, and thereby enabling an agile implementation of a disruptive business strategy.
In agriculture, open innovation takes place in two main clusters and is common between academia, R&D companies and breeders and between academia, farmers, processors and end-product producers. A next step would be to open up such interactions across the value chain, and in addition to include players that are outside the current scope and wish to rethink their sourcing, production and product range from a holistic starting point.
ES: Is the EU using its innovation capacity well, and, if not, what are roadblocks?
MC: The EC published in 2017 a report2 revealing that science in the EU stands at a relatively high level and that this does not show in the output of innovation when compared to e.g., the US. This may relate to how the innovation ecosystems in the EU function. For instance, to meaningfully contribute to the EU Green Deal, a rejuvenation of the agricultural ecosystem including academia, breeding and R&D companies, farm supply industry and farmers, is desirable: It is critical to recognize that agriculture is progressively turning into a business field where big data approaches can create the competitive edge. Integration of data coming from plant, microbial, soil, agronomy, robotization, machine learning, modelling and weather/climate disciplines combined with machine learning approaches, smart data queries and visualizations, may offer cutting edge insights. For academia, it enables data-based feedback loops with farmers and breeders and may prompt scientists to start or revisit experimental questions. Breeders may integrate outcomes from science, farming and down-stream producers, and develop evidence-based breeding strategies. Farmers instead may incorporate into their decision-making the best available information on nearly any aspect that influences productivity, farm sustainability, and product composition, residues and quality.
For this to be a success, an open innovation governance concept is needed that serves the interest of all stakeholders in the ecosystem and goes beyond the proposition of installing an EU-wide virtual predictive workflow as described in figure 3. It should be able to deal with pre-competitive and competitive big data information and activities and run on FAIR access and fair reward principles. Importantly, such a data platform will draw a significant resource, even if it succeeds to build on and interface with existing data platforms and initiatives. For instance, it would need an enterprise component with a robust 24/7 performance, as well as an R&D component provided by specialized research institutes that together cover the agricultural science field. Such a platform should ultimately derive its own sustainability by delivering better varieties and the 