Increased crop failure due to climate change: assessing adaptation options using models and socio-economic data for wheat in China
Increased crop failure due to climate change: assessing adaptation options using models and socio-economic data for wheat in China
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DOI：
10.1088/1748-9326/5/3/034012
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发表时间：
2010-07-01
影响因子：
6.7
通讯作者：
Collins, Mathew
中科院分区：
文献类型：
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作者：
Challinor, Andrew J.;Simelton, Elisabeth S.;Collins, Mathew
在一系列条件下预测作物生产力和评估适应方案的工具是优先考虑适应投资的重要组成部分。本文以东北地区春小麦为例，提出了考虑生物物理过程、内在不确定性和适应性的作物生产力集合预测。一种平行的“脆弱性指数”方法使用定量的社会经济数据来解释农民的自主适应。模拟结果显示，在气候变化的影响下，由于极端高温和缺水情况的增加，农作物失收率正在上升。作物失收率随平均温度的升高而增加，最大失收率的增加大于中位数失收率的增加。结果表明，通过社会经济措施（如加大投资）或生物物理措施（如作物的耐旱性或耐热性），可以实现显著的适应。结果还表明，随着平均温度和相关极端事件数量的上升，适应变得越来越必要。这些结果以及本研究的局限性还为将气候与作物模型、社会经济分析和作物品种试验数据联系起来的研究指明了方向，以便优先考虑能力建设、植物育种和生物技术等选项。
Tools for projecting crop productivity under a range of conditions, and assessing adaptation options, are an important part of the endeavour to prioritize investment in adaptation. We present ensemble projections of crop productivity that account for biophysical processes, inherent uncertainty and adaptation, using spring wheat in Northeast China as a case study. A parallel 'vulnerability index' approach uses quantitative socio-economic data to account for autonomous farmer adaptation.The simulations show crop failure rates increasing under climate change, due to increasing extremes of both heat and water stress. Crop failure rates increase with mean temperature, with increases in maximum failure rates being greater than those in median failure rates. The results suggest that significant adaptation is possible through either socio-economic measures such as greater investment, or biophysical measures such as drought or heat tolerance in crops. The results also show that adaptation becomes increasingly necessitated as mean temperature and the associated number of extremes rise. The results, and the limitations of this study, also suggest directions for research for linking climate and crop models, socio-economic analyses and crop variety trial data in order to prioritize options such as capacity building, plant breeding and biotechnology.