BACKGROUND AND AIMS: Crop growth models (CGM) are a valuable tool for predicting crop performance in contrasting growing conditions and interpreting crop responses to future scenarios. Inaccuracies in the simulation of leaf area dynamics directly impact estimates of intercepted radiation, biomass production and transpiration demand by the crop, especially during the early stages when the canopy is not yet fully covering the soil. An empirical bell-shaped function of individual leaf area versus leaf position, combined with the response of leaf appearance to thermal time, is used in many CGMs to simulate total leaf area per axis and generate canopy leaf area index. This study proposes that an individual leaf area approach, based on predicting blade length and blade width of successive leaves, can make modelling of leaf area dynamics less empirical, while offering the flexibility to better simulate genotypic, and genotypic × environment interaction effects in sorghum (Sorghum bicolor (L.) Moench), maize (Zea mays L.), and pearl millet (Pennisteum americanum L.).METHODS: A generic model of leaf area by leaf position was developed using data on individual blade length and width compiled from numerous experiments over the period 1990-2022 that involved a broad range of genotypes of sorghum, maize, and pearl millet.KEY RESULTS: This study developed and tested a generic individual leaf size model for maize, sorghum and pearl millet, based on relationships quantifying length and width of successive leaves. Generic parameters of an expolinear-logistic model obtained across species and related to total leaf number (TLN) as appropriate, facilitated satisfactory predictive performance for blade length, width, and leaf area profiles. Genotypic-specific parameters improved model predictions in this study.CONCLUSIONS: Improvements in parameterisation of canopy development in CGM can enhance predictions of Genotype × Environment × Management (G×E×M) interactions to support identifying breeding targets for enhanced yield and strategies for sustainable crop management.