Vulnerability Assessment Of Regional Agriculture To Climate Change

The regional agricultural vulnerability to climate change was assessed taking the Ningxia HuiAutonomous Region as a case study. According to the IPCC on the definition of vulnerability, using fieldsurveys and expert recommendations, by analysing the relevance of meteorological observation data aswell as agriculture and other socio-economic statistical data of all cities and counties in Ningxia from1995 to 2003, indicators of the sensitivity of the Ningxia regional agricultural production and adaptationcapacity of the agricultural system to climate change were determined, and agricultural vulnerability toclimate change in Ningxia was completed combining Artificial Neural Network (ANN); By usingclimate change scenarios data under A2 and B2 scenarios from regional climate model system PRECISand socio-economic scenario data, the assessment of agricultural vulnerability to climate change in thefuture was completed by ANN. Thereby, the aim of the study was to find the characteristics ofgeographical distribution of agricultural vulnerability to climate change in Ningxiao In the process ofvulnerability, the indicator selection and vulnerability weight identifying were improved, in order toreduce the human impact, and provide a basis for the formulation of the strategy to vulnerability toclimate change and regional responses to climate change.The conclusions were drawn as follows:1. Through 1995～2003 meteorological observation data from the Meteorological Bureau of Ningxiaand Ningxia 1995～2003 socio-economic statistics, considering indicators of sensitivity and adaptabilityto climate change, the assessment of Ningxia agricultural vulnerability to climate change from 1995 to2003 was completed. The results showed that: from the time distribution, agricultural vulnerability toclimate change on the overall downward trend from 1995 to 2003. But in 2000 the region's agriculturevulnerability to climate change is relatively high; From the spatial distribution, Ningxia Agriculturalvulnerability to climate change to the overall showed a trend: the South Mountain is higher than CentralArid Zone, and the North Irrigation District is the lowest. However, vulnerability of Shizuishan andYinchuan were higher than other cities and counties in the North Irrigation Areas.2. In this study, on the basis of progress of assessment of agricultural vulnerability to climatechange, through field surveys, the Experts recommendation, SPSS and ANN, the indicator system ofNingxia regional agricultural vulnerability to climate change was created. The results showed that theindicator system can objectively reflect agricultural vulnerability to climate change in Ningxia.3. By studying Ningxia's A2 and B2 scenarios of future climate scenarios data from 2011 to 2030obtained from the regional climate PRECIS model, and data of the Ningxia's A2, B2 scenarios for thefuture socio-economic scenario in 2020 and 2030, assessment of agricultural vulnerability to climatechange in 2020 and 2030 was completed. On the whole, under the climate and socio-economic scenarios,agricultural vulnerability to climate change increased in varying degrees in Ningxia. However,vulnerability under conditions of A2 scenario was higher than B2 scenario conditions. In the futureclimate and socio-economic conditions, to 2020, under conditions of A2 scenario, on the whole, the degree of agricultural vulnerability was higher than the B2 scenario condition, but it contrasted in theSouthern Area; To 2030, under the conditions of A2 scenario the degree of agriculture vulnerability washigher than the B2 scenario conditions.4. Under A2 scenario conditions, agricultural vulnerability to climate change in Ningxia, on thewhole, was the highest in 2030, in 2020 it followed and in 2000 it was the smallest; But agriculturalvulnerability in Guyuan was relatively high in 2000. The agricultural vulnerability in Ningxia overallshowed an upward trend under the A2 scenario conditions; under B2 scenario, the degree of agriculturalvulnerability of the North Irrigation and South Mountain were highest in 2000, in 2020 it followed, andit was lowest in 2030; But in the Central Arid Zone, it was the highest in 2020, in 2030 it followed and itwas the lowest in 2000.