Adam A.M.Adam M.Asch F.Boote K.J.Chimonyo V.G.P.Christina M.Corbeels M.Couëdel A.De Freitas M.Della Chiesa T.Ewert F.Falconnier G.N.Fuchs K.Gaiser T.Giller K.E.Hoogenboom G.Huth N.Jibrin J.M.Justes E.Kamara A.Y.Kermah M.Khongdee N.Koomson E.Kraus D.Lana M.Laub M.Lusiana B.Marohn C.Moreno-Cadena P.Nendel C.Pansak W.Pavan W.Fils Pierre J.Poffenbarger H.J.Eyshi Rezaei E.Ruane A.C.Salmerón M.Scheer C.Seidel S.J.Simwaka P.Singh U.Six J.Srivastava A.K.van Noordwijk M.Volk J.Yu J.Cadisch G.Mahidol University2026-05-042026-05-042026-06-01Field Crops Research Vol.343 (2026)03784290https://repository.li.mahidol.ac.th/handle/123456789/116538Context: The global shift toward sustainable agriculture has increased interest in using process-based models to design and optimize intercropping systems. However, these models differ fundamentally in how they represent trade-offs (competition for light, water, and nutrients) and synergies (facilitation, complementarity) in resource sharing between species. This conceptual variation creates uncertainty in model selection and application, particularly since most models were originally developed for monoculture and subsequently adapted for intercropping. Objective: In this study, we describe how current crop models represent interspecies resource-sharing mechanisms, analyse their structural differences, and synthesize findings from existing validation studies to understand how their structural differences affect model performance. The models examined include APSIM (APSIM-Canopy, APSIM-Micromet, APSIM-Strip, APSIM-Alternating, APSIM-APSwim, APSIM-SoilArbitrator), DayCent, DSSAT-Mixed, DSSAT-MPI, LandscapeDNDC, LUCIA, MONICA, SIMPLACE Lintul5-Intercrop, STICS-Big-Leaf, STICS-Multilayer, and WaNuLCAS. Methods: Through the Agricultural Model Intercomparison and Improvement Project (AgMIP) platform, we engaged with model developers and expert users to collect detailed information on how crop models represent intercropping systems using structured interviews and questionnaires. We then developed a framework that groups models by their core conceptual approaches to simulating resource sharing. Finally, we synthesize findings from existing quantitative validation studies to connect conceptual intercomparison to predictive performance across different intercrop characteristics and environments. Results and Conclusions: Our analysis identifies six distinct conceptual approaches for simulating light sharing and four for belowground resource (water and nutrient) competition. Intercrop models show greater structural divergence in canopy than in belowground representation. Furthermore, competitive trade-offs (light, water, and nutrients) are widely represented while facilitative and other complex processes like N₂-fixation, plasticity (shoot and root), microclimate effects or hydraulic lift are often simplified or omitted. Our analysis of model validation studies reveals a critical trade-off: structurally complex models often perform well in simulating intercropping when calibration is done on sole crops, whereas simpler models require extensive intercrop-specific calibration to achieve better prediction performance. This distinction is vital for model application in data-scarce environments, as more complex architectures can leverage existing sole-crop data to effectively simulate intercrop systems. The classification of the structural resource capture differences in combination with the evaluated model performance analysis allowed us to devise an evidence-based model selection criteria framework useful also for for non-specialist, while the unique detailed description provided are highly valuable for the model research community. Significance: This study establishes a conceptual framework that provides the necessary foundation for a meaningful quantitative intercomparison of intercrop models, as structural understanding enables the interpretation of numerical differences in model outputs. It also guides hypothesis testing, model choice, priorities in model development and improvements.Agricultural and Biological SciencesArticleSCOPUS10.1016/j.fcr.2026.1104912-s2.0-10503712359518726852