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The natural grassland ecosystem of the Xilingol steppe has traditionally been the source of the most productive and highest quality agriculture in northern China. Unfortunately, the area is now experiencing degradation due to resource overuse. In an attempt to forecast grassland production and to sustain the ecosystem, we built a time-dependent simulation model of the ecosystem based on long-range weather forecasts (several weeks to several months). The model incorporated five state variables including aboveand belowground biomass, the amount of standing dead plant material, livestock (sheep) weight, and the amount of excrement per unit ground area. Within the model, solar light energy is fixed by grassland vegetation and flows through the other variables via a variety of organism-environment interactions. The model was written using a set of simultaneous differential equations and was numerically analyzed. The values of the time-dependent parameters controlling energy flow were determined based on data accumulated in experiments and field surveys executed at a grassland experimental station located in Xilingol, as well as by reference to related literature. We used daily meteorological data including air temperature and rainfall recorded at the Xilinhot Meteorological Observatory. Simulated results for several stocking densities coincided well with the data of aboveground plant biomass observed at the experimental station in 1990, 1993, and 1997. We obtained reasonable simulation results for five stocking densities, three air temperature patterns, and five rainfall patterns. When a month-long drought, which sometimes occurs in this area, was forecast by a local weather station, a decrease in grassland production was forecast by the model. Such forecasts will assist in the management of livestock, forage preservation, and grassland conservation. (C) 2011 Elsevier B.V. All rights reserved.