Forest Landscape Models (FLMs) project responses to different climate, disturbance, and management scenarios and can inform decision-making that shapes ecosystems. However, use of FLM outputs by decision makers can be hampered by a lack of transparency and credibility in the calibration of modeled processes. Landscape modelers typically use fine-scaled (i.e., plot- or stand-level) information to calibrate the growth functions central to FLMs, but methods vary widely and are often poorly documented. We suggest best practices for calibration and assessment of tree growth in FLMs adapted from prior guidelines to increase rigor in ecological models and their application. Our proposed best practices include: (1) evaluating available information, (2) articulating assumptions, (3) accounting for scale, (4) formalizing model assessment stages, (5) grounding parameter ranges within empirical bounds, (6) considering parameter sensitivity, (7) verifying and corroborating output, (8) making iterative improvements, and (9) delivering sufficient documentation. We illustrate our approach across five case studies that involve a diversity of FLM designs centred on the tree-species, age-cohort structure available within the LANscape DIsturbance and Succession (LANDIS-II) modeling framework. We suggest that these best practices are applicable to many FLM platforms and provide the enhanced transparency essential for wider scientific acceptance of FLM projections.