Effective agricultural adaptation to climate change needs two pieces of information, the climatic risks posed on crop growth, and yield responses to the risks and associated mechanisms. Assessing the sensitivity and vulnerability of crop production to observed climate change is able to produce such information, facilitates the investment of the limited adaptation resources. Use the relationships between changes in rice yield and climatic variables and their spatial variations, we identified the sensitivity and vulnerability of China忆s rice production to observed climate change . The growing鄄season mean climatic variables exhibited significant changes during 1961-2007, indicating the possible climatic risks for rice growth. The increase in day time temperature was most widespread and obvious, suggesting increased risks of heat stresses. The relationships between rice yield and the climatic variables were significant in some rice areas, with the http: / / www. ecologica. cn largest percent of the rice area showed yield sensitivity to changes in diurnal temperature range. With a 1益 warming in growing鄄season temperature, 1 益 increase in diurnal temperature range, and a 10% decrease in radiation, much of the rice areas showed depressed yield to these changes. The area with yield vulnerability was largest to the change in radiation, and second largest to the change in diurnal temperature range. The combined effects of the observed trends of the three climatic variables caused significant change in roughly 30% of the rice areas, but with a small portion showed yield vulnerability. In addition, the negative effects were not pronounced in the principal rice areas, such as Yangtze River Basin, especially in northeast China, the observed climatic trends substantially increased rice yield during the past decades.
Agricultural land use and land cover change (Agri鄄LUCC) is one of the key issues among global change and sustainability studies. Year鄄on鄄year progress makes " agricultural land change冶 to be an emerging interdisciplinary science. As an effective tool for understanding the driver, process and consequence of Agri鄄LUCC, spatially鄄explicit land change models have successfully applied in representing agricultural landscapes and its possible developments across scales. Although several breakthroughs have been achieved by traditional land change modeling, there are still many crucial issues remain unsolved, especially the insufficient cognition on the complexity and dynamics of agricultural land systems. Recently, some researchers begin to combine agent鄄based models (ABM, one of the key tools for complex system studies) with land change models, bringing a new emergence of model series in the agricultural land change modeling community, which are called as Agri鄄ABM / LUCCs. Progress in this field can be summarized as: (1) Based on the complexity system theory, most of these models bring theoretical and methodological innovations in analyzing the complexity of agricultural land systems. (2) These models innovatively take land use decisions at individual level into consideration, based on which to recognize the role of decision makers bringing about changes, through their choices, on regional level landscapes. Such " modeling with stakeholders冶 underlines the role of farmers in agricultural transformation, facilitating the expression of diversified decisions on agricultural land use from heterogeneous farmers. (3) Agri鄄ABM / LUCC links " land change driving
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