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.

中文翻译：

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绘制印度雨养水稻种植气候变化带来的区域风险图


预计未来全球变暖将会加剧，这将对依赖自然降雨（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支持低收入农民的生计，因此必须强调雨养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，我们重点关注印度，该国大约 50% 的水稻是在雨养条件下种植的，我们采用统计模型（气候包络模型 (CEM) 和增强回归树 (BRT)）来绘制雨养水稻气候适宜性的变化预计未来（2050 年）气候变化（IPCC RCP 2.6 和 8.5，使用三种 GCM：BCC-CSM1.1、MIROC-ESM-CHEM 和 HadGEM2-ES）下的区域种植（约 18 × 18 km 细胞分辨率） 。我们根据影响水稻生长和产量的四个气候变量（即降水比），量化了夏季季风期间水稻的发生率（无论是否普遍种植雨养水稻）和水稻范围（种植面积，使用 BRT）。收获期间的蒸散量 (PER)、最高和最低温度（Tmax 和 Tmin）以及总降雨量。我们的模型很好地描述了水稻的发生和范围（发生的 CEM，整体 AUC = 0.92；范围的 BRT，Pearson r = 0.87）。 PER是雨养稻发生最重要的预测因子，它与雨养稻面积正相关，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，目前雨养水稻种植区的 15%–40% 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存在很大差异：印度东部和北部是面临最大风险的地区，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 模型在风险最高的地点上达成了一致，但对这些地点的风险程度却缺乏共识。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。在这些地区使用更抗旱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，并降低依赖雨养作物的农民的脆弱性。