Crop wild relatives Digital Twin: where data flow meets computation to revolutionize agricultural research

While the human population is rapidly increasing and expected to reach 11 billion by the end of this century, global agricultural production is challenged due to climate change. To meet the UN Sustainable Development Goal targets and to bring about zero hunger, we need to boost our food production. For this, we need crops with higher yields, nutritional values, and the ability to resist diseases and adapt to changing environments. Untapped genetic resources to meet these goals are often harbored in crop wild relatives. Digital twinning represents a promising technology for the model-based identification and use of these resources by facilitating: 1) data flow and fusion from distributed data sources), 2) dynamic model updating, 3) automated model uncertainty analysis (validation against real-life data), and 4) provision of automated alerts for new genetic resources with predicted target genetic properties for plant breeders, conservation scientists and policymakers. Here, we showcase the importance of data flow to develop a digital twin that facilitates improving the nutritional quality of grasspea but can be transferable across traits and crops. Grasspea is a climate-smart crop that requires only residual moisture to complete its life cycle and is considered a lifesaver during severe droughts in tropical and subtropical regions. While it is protein-rich and could potentially help in beating protein malnutrition in the future, it also contains a neurotoxin that can cause paralysis of the lower limbs in adults and brain damage in children, if consumed over longer periods. Specific goals of our work include modelling geographic areas that present populations of grasspea wild relatives and land races with potential alleles to lower the toxicity content of grasspea to a non-harmful level.