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Modeling on grassland NPP and understanding resource management at different scales in northern China
by Professor Xin Xiaoping, Chinese Academy of Agricultural Science

Declining local agricultural production as a part of ongoing grassland degradation and desertification is considered a severe problem with global climate change and anthropogenic disturbances in north China. Estimation of net primary production in Chinese grassland are critical for many applications such as raising stock, protecting environment, realizing sustainable development of grassland, learning global change and Carbon storage.

Modeling research on grassland NPP in different stages changed with the available data source. Climate-data-based stochastic model is developed to simulate the potential productivity grassland in hundred-year-scale. RS-based stochastic model are developed to simulate above ground biomass from the early 1980’s. Weather data is integrated with RS data to simulate monthly variability of grassland NPP in late 1990’s. The mentioned models are all work better in regional scale because of the data resolution. Mathematical models based on plant physiology and ecology is important in local scale but it needs abundant experimental data and long-term justification. A number of mathematical models were introduced into China such as EPIC, SPUR, GrassGro are introduced into China to simulate the ecological process of grassland-livestock system. 

The author applied CASA model to simulate monthly change of grassland NPP and livestock capacity at regional scale, which is important for administrative decision-making. As the development of household production pattern in pasturing area after the ensuring of grassland use right, decision-making of grassland utilization and livestock feeding at farm scale became more and more important.  Understanding grassland production systems and resource management  at local, landscape and regional scales through spatial-temporal modeling should be a priority field for the Sino-Australian cooperation in sustainable grassland management.