Climate change is galvanising plant science and agricultural research to develop stress resilient crops, highlighting the importance of a holistic understanding of the complex signalling responses and growth tradeoffs in plants under various stresses (biotic and abiotic). Knowledge on these molecular interactions and processes is scattered across various sources, in diverse formats and levels of detail, and thus not easily accessible for downstream analysis and modelling. Knowledge graphs provide a powerful and flexible platform for integrating information from diverse datasets with complex relationships. Stress Knowledge Map (SKM) is a knowledge graph resource that integrates previously dispersed information on molecular interactions in A. thaliana and S. tuberosum into a single entrypoint for plant stress response investigations. The knowledge graph implementation of SKM, together with our library of analysis tools (SKM-tools) allows for the systematic generation of context specific formulations, providing a multi-purpose platform for complex analyses to gain new insights behind experimental observations, as well as the development of systems biology models. Given the frequently missing kinetics for such models, a Boolean graph formulation circumvents the need for exhaustive mechanistic details. Together with our Python package for Boolean and semi-quantitative modelling (BoolDog), we are exploring signalling dynamics underlying plant responses to stress, with the long term aim of developing a digital twin. We will describe the development and features of SKM, showcase examples of system-wide data contextualisation and hypothesis generation, and our forays into systematically generating a basis for a molecular digital twin in plants.