Process-based crop models have become a central tool for agricultural systems analysis as they enable the explicit formulation and testing of how physiological and soil processes interact and explain emerging system behaviour (Jones et al., 2017). Owing to the multifaceted and multidimensional nature of crops and agroecosystems, the development of a unified model is often implausible and practically non-existent. Thereby, models are predominantly employed through the “fit-for-purpose” concept, wherein the contextualisation of scientific questions assumes an important role in the delineation of model boundaries, methods and processes (Hamilton et al., 2022). Moreover, the modularisation of well-established biological processes (i.e. photosynthesis) in the form of algorithms or sub-modules has become increasingly prevalent with the advent of programming paradigms. This trend has allowed for the representation of the same process with varying levels of physiological detail (Adam et al., 2011), a typical example can be given by the differing methodologies associated with radiation use efficiency and the Farquhar approach (Monteith et al., 1977; Farquhar et al., 1980).