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 eco鄄efficiency which combines both economic and environmental performance is an effective tool for agricultural sustainability analysis. Taking China忆 s 30 provinces as case regions, We applied a SBM model of DEA to measure the agricultural eco鄄efficiency from 1998 to 2009. The SBM model of DEA is non鄄radical, non鄄oriented and
The normalized difference vegetation index (NVDI) , linearly correlated with density distribution of vegetation, is widely used for investigating macro-scale vegetation coverage and thus vegetation changes at short and long time scales. We calculated the vegetation coverage and NDVI greater than 0. 1 by the Landsat TM satellite images on Sept. 26, 1987
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