In this paper, we describe the effects of environment (temperature, drought, heat stress) and management (harvest timing and N fertility) on plant cell wall structure and the digestibility of cell wall and forage, and second propose approaches to model forage digestibility as a function of environmental and management conditions. Forage scientists have equated fiber to the cell wall component (CWC), which is typically measured by the neutral detergent fiber (NDF) method. However, this method does not define the digestibility of the NDF because of varying degrees of lignification of the cell wall fraction. Furthermore, NDF is poorly correlated with total forage digestibility. Rising temperature is known to increase the cell wall fraction and reduce cell wall digestibility of recently expanded leaves and stems across a range of species, whether tropical or cool-season types. Species differ in the baseline beginning CWC and digestibility, but seem to be affected similarly by temperature, and all show an aging/maturation effect on top of the temperature effect. These temperature effects also exist in annual crops such as maize. For every 1°C increase, forage digestibility decreases 0.3 to 0.7 percentage units. Effects of water deficit are small, and often increase digestibility because of slowed maturation, lower NDF, and accumulation of soluble assimilate. Elevated CO₂ has little effect on NDF and digestibility. Stems have higher CWC and lower digestibility than leaves; therefore, leaf-stem ratio caused by differences in species and crop maturation is important. Existing forage crop models such as the CROPGRO-Perennial Forage model can predict non-structural carbohydrates, CP, leaf-stem ratio, and tissue age of the herbage produced. Prediction of forage digestibility is lacking, but present outputs may be useful in predicting digestibility. In this talk, we present conceptual approaches and plans to model effects of temperature, drought, tissue maturation, and harvest management on cell wall and tissue digestibility, based on literature information and on inverse solving against data sets on CP and in vitro digestibility. Our goal is to achieve a forage modeling tool that predicts production as well as nutritive value.