The livestock grazing system is one of the most important human natural coupling systems on the earth. More than one quarter of the global land surface is managed grazing grasslands, and the intensification of animal production and grazing systems is likely to continue worldwide. Managing the grassland grazing system towards a sustainable future is therefore an important issue for herders, grassland managers and policy makers. This requires dynamic monitoring and assessment of the grazing system, which consist of the complexities plant growth, livestock dynamics, plant-herbivore interactions and grazing management. Leaf Area Index (LAI) is commonly used as a proxy for grassland condition. However, current studies all focus on the year round aggregated LAI change or seasonal variation rather than the specific grazing-led LAI defoliation for each pixel, which is the important indicator for quantifying grassland grazing activities.
Three main components in this research will be explored: a new growth function under grazing considering both the growth and senescence of grass with an estimation algorithm; the employment of a LUE-VMP model to estimate Net Primary Productivity (NPP) for improved LAI validation; and a first attempt in building an agent-based model (ABMGG) integrated with patch-specific grazing information for the assessment of various grazing management strategies. In addition, the error propagation and uncertainty in the model will be further discussed, which will further improve the credibility of this research.