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The integrative capacity of crop models is of great value to identify in-silico optimal combinations of traits (ideotypes) and traits x cultu ral practices in targeted agro-environments. This approach becomes even more challenging when considering the multiple environ mental factors constituting future agro-climatic scenarios : increasing stress frequency and severity (e.g., heat, drought, wind, flooding); enhanced atmospheric C02 concentration (e-COzl, and also adoption of more sustainable and resilient cultural practices (agroecology) combining of productivity with ecosystem services. Several studies reported the weaknesses of crop models in predicting crop performance in response to climate change. While these limitations were until now mainly explained by poor simulation by crop models of physiological responses to heat and drought, crop model shortcomings of the representation of Carbon (C) source-sink relations and interactions, involving phenotypic plasticity of both source and sink, should explain this limitations and have received less attention (Chang and Zhu 2017). Recent studies reported a down-regulation of C source capacity (i.e. photosynthesis) in C3 crops under e-C02 by sink lim itation in the afternoon, involving low TPU levels (Triose Phosphate Utilization) (rice: (Fabre et al. 2019). (Fabre, in prep) indicated that high constitutive source-sink ratios increase photosynthesis under e-C02 • Finally, (Kikuchi et al. 2017) demonstrated in a FACE trial that rice plants with high adaptive plasticity of tillering and panicle size respond better to e-C02 • Although particularly relevant to C3 crops that respond strongly to e-C02 , this also applies to C4 crops when they are C-sink limited (Oszvald et al., 2018). Therefore, Carbon source-sink relationships and their physiological and morphological adaptability (feedbacks) are pivotai in predicting crop ideotypes in a climate change context. ln addition, the agro-ecological transition needs better crop models to design solutions for improving (1) crop energy and carbon use, (2) resilience under abiotic stresses, and (3) ecosystem services such as channeling assimilates into the roots/soil (4p1000 initiative: C sequestration and soil improvement). This implies to further model plantplant and/or plant-soil interactions and related impacts on C source-sink relationships and competitions for (light) resources. Crop models should indeed be able to predict trade-offs among several crop performance objectives such as multiple production purposes (e.g., grain and biomass), between potential productivity and adaptation, and between productivity and ecosystem services such as C sequestration into the soil. For this, we will provide examples of analytical and modeling concepts. More quantitative, extrapolatable evidence is needed to understand the importance of C source and sink traits, their adaptive plasticity as they interact during plant development, and their impact on crop performance under anticipated agro-climatic conditions. This requires a dialogue between experimental and modeling research for which we are presenting concepts here. Our laboratory focuses on rice (C3) and sorghum (C4) model cereals using crop models simulating sink- and source driven phenotypic plasticity, namely SAMARA (Kumar et al., 2016) and Ecomeristem (Larue et al., 2019). Sorne (experimental, modelling) preliminary resu lts will be shown but a broader dynamics is needed. Once further improved, the models will be used to (i) estimate in silico the prediction errors caused by ignoring source-sink feedbacks and plasticity; (ii) predict the potential of improved trait combinations and plasticity on the performance of future crops; and (iii) propose how existing, generic crop models should be improved and what type of data will be needed for that.