Singh Kuntal, McClean Colin J, Büker Patrick, Hartley Sue E, Hill Jane K
Department of Biology, University of York, York YO10 5DD, UK.
Environment Department, University of York, York YO10 5NG, UK.
Agric Syst. 2017 Sep;156:76-84. doi: 10.1016/j.agsy.2017.05.009.
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 (), maximum and minimum temperatures ( and ), 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). 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.
预计未来全球变暖将会加剧,这会对依赖自然降雨(即非灌溉)的雨养作物产生不利影响。鉴于许多在雨养条件下种植的作物维持着低收入农民的生计,突出雨养地区对气候变化的脆弱性对于预测粮食安全的潜在风险至关重要。在本文中,我们聚焦于印度,该国约50%的水稻是在雨养条件下种植的。我们运用统计模型(气候包络模型(CEMs)和增强回归树(BRTs)),以绘制在未来(2050年)气候变化预测情景下(IPCC RCPs 2.6和8.5,使用三种全球气候模型:BCC-CSM1.1、MIROC-ESM-CHEM和HadGEM2-ES),区域层面(~18×18千米网格分辨率)雨养水稻种植气候适宜性的变化情况。我们量化了夏季风期间水稻的出现情况(使用CEMs确定雨养水稻是否普遍种植)以及水稻种植范围(使用BRTs确定种植面积),这与影响水稻生长和产量的四个气候变量相关,即降水与蒸散量之比()、最高和最低温度(和)以及收获期间的总降雨量。我们的模型对水稻的出现情况和种植范围描述得很好(用于出现情况的CEMs,集成AUC = 0.92;用于种植范围的BRTs,皮尔逊相关系数r = 0.87)。是雨养水稻出现的最重要预测因子,且与雨养水稻面积呈正相关,但所有四个气候变量对于确定水稻种植范围都很重要。我们的模型预测,目前15%-40%的雨养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宜)。然而,我们的模型预测印度各地未来气候变化的影响差异很大:印度东部和北部是风险最高的地区,但印度中部和西部部分地区可能受益于降水量增加。因此,我们的CEM和BRT模型在风险最高的地区上达成了一致,但对于这些地区的风险程度,共识较少。我们的结果有助于确定在未来几十年中,低收入农民的生计和区域粮食安全可能因气候变化而受到威胁的地区。在这些地区使用更具抗旱性的水稻品种和更好的灌溉基础设施,可能有助于减轻这些影响,并降低依赖雨养种植的农民的脆弱性。