ffer a positive economic perspective.
As discussed above, there are lasting merits in improving yield and in reducing input requirements to lower non-fossil based GHG emissions. But as a way forward, targets are ideally derived from Life Cycle Analysis (LCA) metrics that have predictive value for both productivity and sustainability, and that take into account location-specific priorities. While waiting for a proper LCA-based benchmark to become publicly available, targets may develop already around requirements for soil, water and nutrients, and, most simply said, translate LCA parameters into actionable breeding and agronomy targets so that progress becomes measurable and communicable.
Personally, I would expect that yield stability becomes in future the key concern for farmers. Climate change and increased weather volatility will alter the condition of the fields, lead to an accelerated migration of diseases and pest, and, equally important, put an unprecedented stress on crops to handle volatile changes with greater extremities in e.g., sunlight, temperature, wind, humidity and water. Both the volatility and the greater extremities pose challenges to breeders, as the solution asks for a sophisticated and unprecedented control of e.g., stomata, roots, pigments and wax-layers.
It appears to me that in breeding it will be critical to leverage maximally in-depth insights about how genetics and biological processes determine the decision-making in the different crops when grown in different geographies and under different conditions, and to derive from there predictive breeding starting points. Along the same lines I see that the speed with which diseases and pest geographically migrate, is a multiple faster than today’s breeding can address. Also, here there is a benefit from deploying in depth know-how on the biology and genetic make-up of pests, diseases and crops to derive smart breeding starting points. In this respect it would be great if state-of-art biological information would be presented in a format that is better accessible for use in breeding, and that breeders could make use of the most advanced new breeding technologies such as gene editing, as these would enable the development of the best performing varieties in the shortest time frame possible.
Figure 3. A win-win proposition for public and private innovators: an EU-wide virtual public-private breeding workflow with growing scope and predictive value by integrating outcomes from science and breeding in a data format of use for both parties. Public science may integrate past and current outcomes from e.g., genetics, biology, environmental conditions and soil, whereas private breeders may integrate outcomes and cultivation conditions from breeding experiments using such data. Artificial Intelligence approaches and smart queries and visualizations will offer evidence-based innovation starting points for both scientists and breeders. Progressive standards in e.g., variety testing could be used to set direction and focus. Critical will be a governance concept safeguarding public and private interests, and a self-supporting financing scheme to develop and maintain such platform.
ES: What other types of innovation do we need?
The agricultural value chains are operated by multiple stakeholders in an open market setting and tend to be locked in scope and dimension. The arrival of new products can’t be automatically handled and often will need risk investments or phasing out of less promising activities. With the current state of know-how in biology, chemistry, breeding and refining, together with the societal pressure to move to renewable solutions, it is very possible that an open innovation approach across the value chain from academia to end-product producers, kicks-off demand for innovative plant-derived products for new applications that require sufficiently high volumes and offer fair economics.
The challenge is to develop brainstorming sessions where one brings together the right combination of expertise to develop and characterize meaningful, yet hypothet