Mapping regional risks from climate change for rainfed rice cultivation in India.
Mapping regional risks from climate change for rainfed rice cultivation in India.
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DOI：
10.1016/j.agsy.2017.05.009
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发表时间：
2017-09
影响因子：
6.6
通讯作者：
Hill JK
中科院分区：
文献类型：
--
作者：
Singh K;McClean CJ;Büker P;Hartley SE;Hill JK
据预测，未来全球变暖将加剧，对依赖自然降雨（即非灌溉）的作物造成不利后果。鉴于在干旱条件下种植的许多作物支持低收入农民的生计，必须强调干旱地区对气候变化的脆弱性，以预测粮食安全的潜在风险。在本文中，我们重点介绍了印度，那里约50%的水稻是在干旱条件下种植的，我们使用统计模型（气候包络模型（CEMs）和增强回归树（BRT）），以绘制区域一级水稻种植气候适宜性的变化（~ 18 × 18 km单元分辨率），根据预测的未来（2050年）气候变化（IPCC RCPs 2.6和8.5，使用三种GCM：BCC-CSM 1.1、MIROC-ESM-CHEM和HadGEM 2-ES）。我们量化发生的水稻（是否普遍种植，使用CEMs）和水稻的范围（种植面积，使用BRT）在夏季季风的四个气候变量，影响水稻生长和产量，即降水蒸散量（PER），最高和最低温度（Tmax和Tmin），总降雨量在收获期间。我们的模型很好地描述了水稻的发生和程度（发生的CEMS，集合AUC = 0.92;程度的BRT，Pearson r = 0.87）。PER是水稻发生的最重要的预测因子，它与水稻种植面积呈正相关，但所有四个气候变量都是决定水稻种植范围的重要因素。我们的模型预测，目前15%-40%的水稻种植区将面临风险（即气候适宜性下降或变得完全不适合）。然而，我们的模型预测印度各地在未来气候变化的影响方面存在相当大的差异：印度东部和北方是风险最大的地区，但印度中部和西部的部分地区可能会受益于降水量的增加。因此，我们的CEM和BRT模型在风险最大的地点上达成一致，但在这些地点的风险程度上达成的共识较少。我们的研究结果有助于确定低收入农民的生计和区域粮食安全在未来几十年可能受到气候变化威胁的地点。在这些地区使用抗旱能力更强的水稻品种和更好的灌溉基础设施，可能有助于减少这些影响，并降低依赖旱作物种植的农民的脆弱性。统计模型成功地绘制了印度水稻枯萎病的发生和范围。季风降雨量和温度是水稻种植的重要预测因子。预计到2050年，15%-40%的水稻种植区的气候适宜性将下降。抗旱的新作物品种和更好的灌溉基础设施可以保护这些地区脆弱的农民。
Global warming is predicted to increase in the future, with detrimental consequences for rainfed crops that are dependent on natural rainfall (i.e. non-irrigated). Given that many crops grown under rainfed conditions support the livelihoods of low-income farmers, it is important to highlight the vulnerability of rainfed areas to climate change in order to anticipate potential risks to food security. In this paper, we focus on India, where ~ 50% of rice is grown under rainfed conditions, and we employ statistical models (climate envelope models (CEMs) and boosted regression trees (BRTs)) to map changes in climate suitability for rainfed rice cultivation at a regional level (~ 18 × 18 km cell resolution) under projected future (2050) climate change (IPCC RCPs 2.6 and 8.5, using three GCMs: BCC-CSM1.1, MIROC-ESM-CHEM, and HadGEM2-ES). We quantify the occurrence of rice (whether or not rainfed rice is commonly grown, using CEMs) and rice extent (area under cultivation, using BRTs) during the summer monsoon in relation to four climate variables that affect rice growth and yield namely ratio of precipitation to evapotranspiration (PER), maximum and minimum temperatures (Tmax and Tmin), and total rainfall during harvesting. Our models described the occurrence and extent of rice very well (CEMs for occurrence, ensemble AUC = 0.92; BRTs for extent, Pearson's r = 0.87). PER was the most important predictor of rainfed rice occurrence, and it was positively related to rainfed rice area, but all four climate variables were important for determining the extent of rice cultivation. Our models project that 15%–40% of current rainfed rice growing areas will be at risk (i.e. decline in climate suitability or become completely unsuitable). However, our models project considerable variation across India in the impact of future climate change: eastern and northern India are the locations most at risk, but parts of central and western India may benefit from increased precipitation. Hence our CEM and BRT models agree on the locations most at risk, but there is less consensus about the degree of risk at these locations. Our results help to identify locations where livelihoods of low-income farmers and regional food security may be threatened in the next few decades by climate changes. The use of more drought-resilient rice varieties and better irrigation infrastructure in these regions may help to reduce these impacts and reduce the vulnerability of farmers dependent on rainfed cropping. Statistical models successfully map the occurrence and extent of rainfed rice in India. Monsoon rainfall and temperature are important predictors of rainfed rice cultivation. Climate suitability is projected to decline in 15%–40% of rainfed rice locations by 2050. Drought-resistant new crop varieties and better irrigation infrastructure may protect vulnerable farmers in these locations.
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影响因子：
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