凌霄霞 张作林 翟景秋 叶树春 黄见良,*



气候变化对中国水稻生产的影响研究进展

凌霄霞1张作林1翟景秋2叶树春3黄见良1,*

1农业部长江中游作物生理生态与耕作重点实验室/ 华中农业大学植物科学技术学院, 湖北武汉 430070;2中国人民解放军31010部队, 北京 100081;3广东省云浮市气象局, 广东云浮 527300

水稻生产系统是响应气候变化最敏感的农业生态系统之一, 本文综述了当前和未来气候变化对我国水稻生产的影响。气候变化背景下, 我国水稻生长季的热量资源增多, 辐射资源减少, 降水不均一性加大。高温热害、干旱、暴雨和洪涝灾害发生更频繁, 这可能降低水、热资源的有效性。气候变化使我国单季稻和双季稻潜在种植边界显著北移, 导致单季稻、早稻和晚稻的主要生育期缩短。基于统计模型和水稻生长模型的研究结果表明, 如果不考虑品种改良和栽培技术的进步, 气候变化使单季稻、早稻和晚稻产量下降, 但不同稻作区和方法间存在差异。我国水稻生产重心北移、实测生育期延长和产量增加的变化趋势, 反映了水稻生产系统通过种植分布调整、品种改良和技术改进来适应气候变化的能力。未来气候变化将进一步导致水稻生育期缩短和产量下降, 对我国水稻生产和粮食安全带来严峻挑战。仍需加强气候变化影响机制的研究及其在影响评估中的应用, 减小影响评估的不确定性并增加其系统性, 为制定有效的应对策略提供可靠的理论支持。

气候变暖; 种植北界; 稻作制度; 生育期; 产量

政府间气候变化专门委员会第五次评估报告(IPCC_AR5)指出, 1880—2012年全球地表平均温度升高约0.85℃, 过去3个10年历史时期全球地表温度已连续上升。气候系统的变化已对全球粮食生产造成了普遍影响, 未来气候变化严重影响作物产量的风险也可能增长[1]。

水稻是中国最主要的口粮作物, 我国65%以上的人口以稻米为主食[2]。据统计, 2012—2016年我国水稻年均播种面积为3.023×107hm2, 占粮食作物年均播种面积(11.245×107hm2)的26.9%; 水稻年均总产为2.059×108t, 占粮食年均总产(6.072×108t)的33.9%[3]。虽然过去30年来我国各县市水稻产量翻倍增长, 但近期有一半以上的县市出现了水稻增产停滞现象[4], 这可能与温度和太阳辐射等气候变化有关[5]。因此, 科学评估气候变化对水稻生产的影响并制定有效的应对策略比以往更显重要, 为突破水稻产量瓶颈提供气候影响的理论支持。

统计模型和作物生长模型是评估气候变化对农业生产影响中最常见且有效的方法[6-9]。因此, 本文主要对2000年以来基于这两类模型的水稻生产影响评估研究进行综述, 以期为气候变化对农业影响评估等工作提供参考。

1 气候变化对水稻生长环境的影响

1.1 气候资源变化特征

1980—2008年全球水稻生长季气温明显升高, 65%国家的增幅已超过年际变化的标准差, 中国部分稻区的增幅甚至大于年际变化标准差的2倍[10]。1961—2010年, 我国水稻生长季的最低气温和平均气温分别升高0.61℃和0.47℃, 气温日较差则降低0.38℃[11]。气温变化特征在水稻种植区、稻作类型和生育阶段间存在明显差异。总体而言, 北方稻区的升温幅度大于南方[12]; 早稻生长季平均气温和最高气温的增温速率大于晚稻[13]。长江中下游地区早稻和晚稻生殖生长期的增温趋势显著高于营养生长期, 单季稻则相反[14]。除气温的升高, 稻田水温也呈增加趋势, 但升高幅度较气温小[15]。与1960s相比, 2000s中国稻作区≥10℃总有效积温平均增加9.4%, 东北和西南稻区的增加幅度大于中部和南部[16]。

从辐射资源来看, 主要稻作区2000s的日照时数比1960s减少11.9%[16], 太阳总辐射量降低9.4%[11], 这一现象在长江中下游单季稻生长季尤为明显[14]。从降水情况来看, 降水总量的长期变化趋势并不明显, 但平均降水强度增加约3.2%[11,16]。Ye等[17]研究表明, 气候变化降低了南方单、双季稻生产可利用的水热资源有效性, 这意味着当前气候变化对我国水稻生产的不利影响可能被低估。热量资源增加、辐射资源减少而降水量的时空不均一性加大, 这一系列变化对优化我国稻作制度的空间分布、提高水稻生产资源利用效率提出了新的挑战。

1.2 农业气象灾害变化特征

我国水稻生产所遭受的农业气象灾害种类多、地域性强、时期明显, 其中高温热害和低温冷害是最主要的气象灾害[18]。东北单季稻和南方晚稻抽穗开花期发生低温冷害的风险最大, 而高温热害则在长江流域单季稻孕穗期至灌浆期、南方早稻抽穗开花期风险最大[19]。1960—2009年间, 我国长江流域单季稻和南方早稻抽穗扬花期的高温胁迫积温每年增加0.12℃; 东北、长江流域、云贵高原单季稻和南方晚稻抽穗扬花期的低温胁迫积温每年减少0.21℃[19]。

据农业气象灾害观测数据显示, 与前10年相比, 2000—2009年南方早稻孕穗期至成熟期发生高温胁迫的频次增加6~15次, 东南晚稻移栽期和孕穗期的高温胁迫增加14次和24次; 湖南和广西早稻移栽期发生低温冷害的频次增加59次, 单季稻和晚稻孕穗期至成熟期的低温冷害增加15~42次; 冷害的发生还表现出延迟型冷害减少而障碍型冷害增多的特征[20]。在干旱和洪涝灾害变化方面, 早稻、晚稻和单季稻抽穗期之后发生干旱的频次增加最多, 而单季稻和晚稻孕穗期遭受洪涝灾害的频次增加更显著[20]。为应对水稻生产当前所面临的灾变环境, 需加强防灾减灾技术的创新和应用。

2 气候变化对水稻生产的影响

评估正在发生的气候变化对水稻生产的影响, 有利于客观评价气候变化背景下水稻生产所面临的挑战, 为提出应对气候变化对策提供理论参考[10,21]。

2.1 对水稻种植制度的影响

气候变暖导致热量资源增多, 有利于扩大农作物潜在种植面积, 增加粮食生产总能力。1980—2010年间, 气候变化使我国水稻适宜种植面积的比例增加约4个百分点, 东北地区增加幅度最大[22]。黑龙江省水稻潜在种植区随2000℃ d等值线北移约4个纬度[23], 实际集中种植区北移约1个纬度[24]。雨养条件下, 中国单季稻可种植北界到达黑龙江漠河县北部, 灌溉条件下, 单季稻可种植北界则可达我国最北端[25]。南方双季稻潜在种植边界北移34~60 km, 部分稻-麦两熟区可满足早、晚双季稻的光热需求[26-27]。气候变暖对我国北方稻区种植边界的影响较南方稻区明显。在气候适宜性方面, 双季稻低适宜种植面积有所减少, 中、高适宜种植面积有所增加[28]。

近60年来, 我国水稻实际种植重心和产量重心分别向东北迁移约2个和3个纬度, 水稻种植面积的扩张和位置迁移与气温变化趋势高度一致[22,29-30]。这说明气候变化是驱动我国水稻种植区域调整的重要因素, 同时也体现了我国水稻生产快速适应气候变化的能力[31]。

2.2 对水稻生育进程的影响

生育期观测数据的趋势分析表明, 近30年来我国水稻播种和移栽期提前[14,32-35], 单季稻成熟期推后, 早、晚稻成熟期提前[14,33-35]。此外, 我国单季稻营养生长期、生殖生长期和全生育期延长[14,33-34,36], 晚稻主要生育阶段呈缩短趋势[14,32-33,37], 早稻生育期的变化并没有一致结论。水稻生育期的变化主要受气候、品种和栽培管理等因素影响。不考虑品种熟期变化和管理措施调整的情况下, 气候变暖可导致作物物候期提前和生育期缩短[38]。我国水稻营养生长期、生殖生长期和全生育期因气候变暖而分别缩短0.4~2.8 d 10 yr–1、0.1~1.3 d 10 yr–1和2.9~4.1 d 10 yr–1(或2.0~3.6 d ℃–1、1.1 d ℃–1和3.6~5.5 d ℃–1), 营养生长期的缩短比生殖生长期明显[32,36-37]。除温度外, 光周期、CO2浓度和非生物逆境等因素也可调节水稻的生长发育速度[33,39-40], 但在评估气候变化对水稻生育期的影响时, 很少考虑这些因素的作用。

在适应气候变化过程中, 农民为充分利用热量资源或为避免单季稻在高温时间段抽穗扬花, 往往提早播种或改种生育期较长的品种[36,41], 这补偿了气候变化的不利影响, 使观测到的单季稻生育期延长。对晚稻而言, 为躲避成熟期低温而种植短生育期品种则可能加速生育期的缩短[32]。另有研究表明, 水稻成熟期受其分布地区、种植模式和移栽时间的影响比受温度的影响更大[34], 非气候因素对水稻生育期的影响可能大于气候因素[14,33,42]。

2.3 对水稻产量的影响

气候变化对水稻产量的影响是最受关注的内容, 前人主要研究了气候变化的影响趋势和程度、气候与非气候因素的贡献、关键气候因素及影响机制等。

2.3.1 气候变化对水稻产量的影响 水稻生产是个复杂的自然-社会系统, 产量的长期变化同时掺杂了气候变化和人为因素信号。总体而言, 1980—2010年我国单季稻、早稻和晚稻的实测单产每10年增加0.69 (0.37~1.07) t hm–2(表1)。单就气候因素的影响而言, 近几十年的气候变化对我国水稻产量造成了不利影响。基于水稻生长模型的评估表明(表1), 1980—2010年气候平均态的变化使我国水稻单产减少0.25 (0.01~0.56) t hm–210 yr–1, 1961—2010年间则造成水稻单产减少约12.0% (11.5%~12.4%)。在气候变化过程中, 改种生育期长或者灌浆期长的品种可提高水稻产量[36-37], 种植抗逆性强的品种或提高栽培管理水平则降低了水稻产量的年际波动性[43]。品种改良和合理施肥等措施对水稻产量的正效应甚至超过了气候变化的负效应[44-46]。可见, 气候变化虽然严重制约了水稻产量的增长, 但我国水稻生产系统已通过适宜的方式来积极应对这种不利影响, 使水稻产量稳步提高。然而, 未来气候变化仍将严重制约技术进步对粮食生产的贡献[39], 增加农业技术创新的难度。

气候变化因素对我国水稻生产的影响又与地区和稻作类型有关。基于统计模型与生长模型的结果表明(表1), 在气候长期变化影响下, 华北、华东、华中(长江中下游单季稻)和西南(四川盆地单季稻)地区水稻、南方双季稻减产显著, 长江中下游晚稻、东北和云贵高原单季稻产量有所增加。极端天气是造成产量损失的另一重要原因, 其对水稻产量的影响可能大于气候要素的长期变化和年际波动[47-48]。我国近30年的极端温度胁迫导致全国灌溉稻产量损失约6.1%, 四川盆地单季稻、长江中下游单季稻、南方早稻因此造成的产量损失显著上升[49]。此外, 气候资源的合理配置有利于提高水稻产量和光、温资源利用效率[50], 资源配置不合理的年份则可造成严重的产量损失[51]。另有研究表明, 气溶胶浓度影响入射的太阳总辐射以及散射辐射所占的比例, 重度大气污染对水稻产量将造成不利影响[52-53]。与不利的大气环境相反, 大气CO2浓度升高有利于水稻增产[54], 且晚稻产量对CO2浓度升高的响应大于早稻和单季稻[44-45]。CO2浓度升高的增产效应在很大程度上减少了气候变化造成的产量损失, 近30年来甚至基本补偿了气候变化造成的减产(表1)。

表1 当前气候变化对中国水稻产量的影响

(续表1)

稻作类型Rice system研究区域Region研究时段Period变化趋势Change trend评估方法Method参考文献Reference 统计模型aStatistical modela(t hm–210 yr–1)作物模型bCrop modelb(t hm–210 yr–1) 单季稻Single rice东北Northeast China1980–20080.59% yr–1—Statistical model[94] 单季稻Single rice云贵高原Yunnan-Guizhou Plateau1980–20080.34% yr–1—Statistical model[94] 单季稻Single rice四川盆地Sichuan Basin1980–2008–0.29% yr–1—Statistical model[94]

a统计模型列是基于统计模型对历史水稻产量实测数据的分析结果, 斜体数值为实测产量随时间的变化趋势, 其他数值为实测产量对气候变化的响应;b作物模型列是基于水稻生长模型, 将品种和管理参数设为定值得到的模拟产量的变化趋势或变化百分率, ( )中的值为考虑CO2浓度升高的模拟结果, 其他数值是将CO2浓度设为定值的模拟结果。

aThe analysis results of historical observed rice yields based on statistical model were listed in the column of statistical model, values in italic represent for the trends of observed yields, and other values represent for the response of observed yields to climate change;bThe trends or percent changes of the simulated yields derived from rice growth model with constant parameters of variety and management were listed in the column of crop model, values in ( ) represent for the simulations with elevated CO2concentration, and other values represent for the simulations with constant CO2concentration.

2.3.2 影响水稻产量的关键气候因素 影响水稻产量的关键气候因素, 是制定气候变化应对策略的重要依据。然而, 因研究区域气候的复杂性、气候要素的自相关性以及稻作类型等原因, 使该问题尚未得出统一结论。研究表明, 热带地区水稻产量下降的主要原因是最低气温的升高[55], 而中国部分稻作区的水稻产量却与温度呈正相关[56-57]。在温度较低的华北地区, 气温日较差减小则是水稻产量下降的首要原因[11]。另有研究认为, 我国水稻产量对太阳辐射的长期变化趋势比温度更敏感[54,56,58], 而作物产量的年际波动则更多地由降水量和太阳辐射变异以及温度胁迫解释[12,18,59-60]。此外, 温度、辐射和降水量等气候要素存在自相关性, 忽略该问题得出的结论可能是错误的[12,56,61], 影响产量变化的关键因素或许不应归结为单个气候要素[59,62]。

2.3.3 气候变化影响水稻产量的机制 目前, 水稻响应气候变化的机制研究主要集中在高温、干旱等非生物逆境方面[63]。气候变暖一方面缩短水稻生育期, 另一方面造成光合作用减少和呼吸作用增加[64]。水稻孕穗期高温主要影响花器官发育, 如影响颖花分化和退化、缩短颖花长度、抑制花药充实[65-66]; 抽穗扬花期高温主要伤害正在开放的颖花, 影响花粉活力、数量以及颖花授粉受精过程, 增加空、秕粒率[67-70]; 灌浆结实期高温使灌浆过程提早结束, 造成粒长和粒宽减小、粒重下降[67,71]。白天高温造成水稻产量降低最突出的原因是结实率下降, 夜间高温对结实率、每穗颖花数、粒重和生物量的影响相当[64]。水稻遭受低温胁迫时, 因生殖生长期绒毡层变厚和营养失衡而使花粉失去育性, 还可能导致籽粒败育[72]。弱光逆境则降低了植株净光合速率, 使干物质生产和积累速度减慢, 干物质分配到穗部的比例下降[73]。干旱胁迫下, 叶片气孔导度的下降使植株蒸腾速率减慢, 胞间CO2通量的减小则限制了光合作用。蒸腾速率的下降又减少了植株对营养物质的吸收、升高了冠层温度, 进一步导致呼吸消耗增多以及存储器官建成的时间缩短[63]。大气CO2浓度升高时, 叶片气孔导度和密度均呈下降趋势, 造成蒸腾作用降低。但此时冠层光合作用的增加将促进有机物累积[74], 且地下部干物质的增加幅度比地上部更显著[75]。当多种非生物逆境同时发生时, 对植物的影响往往不是单因子影响效应的简单叠加, 需要在复杂环境条件下研究其影响机制[74-77]。

3 未来气候变化对水稻生产的影响

3.1 对水稻生产的有利影响

与2000s相比, 预计2030s、2050s、2070s我国水稻生长季日均温分别增加0.8~2.7℃、1.7~3.4℃、2.3~4.1℃[78]。我国一年两熟带和一年三熟带的潜在边界将持续北移[79-80], 21世纪末三熟制占种植制度总面积的潜在比例最大可达到75.0%[81]。未来单季稻和双季稻潜在种植边界也将继续北移。与1961—1990年相比, 2080s我国单季稻和双季稻可扩种面积约为5.0×105hm2和6.2×106hm2 [82]。热量资源增多使作物潜在生长季延长, 大大增加了水稻生长季节弹性[15,79], 有利于水稻生产灵活地制定应对气候变化策略。

3.2 对水稻生产的不利影响

IPCC第五次评估报告指出, 气候变化和极端气候事件对作物产量的不利影响比较普遍[1]。若未来气温升高1~3℃, 我国水稻生育期缩短的概率为100%[83]。当温度升高1.5℃和2.0℃时, 我国双季稻的生育期将缩短4%~8%和6%~10%, 单季稻的生育期约缩短2%[84]。一项集合网格作物模型、单点作物模型、统计模型和观测试验的研究表明, 气温每升高1℃可能导致全球水稻产量平均下降3.2%[85]。到21世纪末, 温度持续上升可能使全球水稻产量减少3.3%~10.8% (表2)。未来气候变化造成我国水稻产量变化幅度为−40.2%~6.3%, 平均减产10.7%, 且空间差异明显(表2)。若考虑CO2浓度升高对产量的影响, 其对气候变化造成的减产有一定补偿作用(表2)。但这种补偿作用在某些情景和地区仍无法抵消增温幅度过高的负效应, 也不能降低水稻产量的年际变率[82-83,86]。此外, 降水和温度变率增大可能导致低产年出现频次增多, 减产幅度增加[82,87]。水稻产量减少和不稳定性增加最明显的区域是四川盆地、长江流域和黄淮海平原, 这些地区或将成为水稻响应未来气候变化的高敏感区[82]。研究还表明, 若能采用合理的应对策略, 可以有效减缓气候变化对水稻产量的不利影响(表2)。未来可以从培育强抗逆性品种和高效利用CO2浓度品种、优化栽培管理和抗逆栽培技术、调整播期和种植面积等方面, 加强水稻生产应对气候变化措施的研究。

值得注意的是, 越来越多的影响评估关注了极端天气事件的变化及其对水稻生产的可能影响[84,88-90]。2000s到2050s, 全球水稻生殖生长期遭受极端高温胁迫的面积将由8%增加到27%[91]。我国水稻生产遭受高温胁迫的概率、强度和面积也将增加, 这可能抵消热量资源增多及低温危害减少带来的正效应[92-94]。若温度升高1.5℃和2.0℃, 热胁迫可能导致我国水稻产量分别下降2%和5%[84]。四川盆地和长江中下游流域或将成为高温热害高发区, 东北、云贵高原和华东稻区经历严重低温危害的风险比其他地区大[89,94]。未来降水变率增加则可能导致季节性干旱和暴雨发生频次增多[95], 在江苏等东部地区, 极端降水事件对水稻产量的影响可能比极端温度事件更显著[88]。此外, 气温升高导致参考作物蒸散量普遍增加, 我国西南地区将经历湿润指数明显减小的干旱化过程[79]。

4 问题与展望

气候变化已导致我国水稻生长季气候条件的改变, 对水稻种植面积、气候适宜性、生长发育、产量等造成一定影响。现有的评估工作是在当前科学认知和技术水平上的有益尝试, 未来还有许多亟待解决的问题需要进一步深入探索。

4.1 加强气候变化影响机制的研究及其在影响评估中的应用

水稻响应气候变化的机制是气候变化影响评估的重要理论基础。前人主要研究了高、低温胁迫和干旱胁迫等极端天气事件的影响, 对增温、CO2浓度升高等气候平均态变化的影响研究较少; 对水稻光合作用、白天蒸腾等生理过程的研究较多, 对夜间蒸腾等其他生理生化过程的研究还比较薄弱; 对单因子胁迫的影响机制研究较多, 对多因子胁迫、非生物逆境与高CO2浓度互作等复杂环境的影响机制研究较少[63,76-77]。更值得注意的是, 基于作物响应气候变化机制来改进生理生态模型的研究远远滞后于机制研究本身, 需要设计专门的田间试验并将试验结果与模型改进紧密联系起来[63]。重点关注叶片光合模型参数在环境变化中的适应性[96]、植株氮素动态的响应等[97]受环境变化影响较大的生理生态过程, 使水稻响应气候变化的机制研究在区域尺度的影响评估中发挥更充分的作用。

4.2 减小气候变化影响评估的不确定性

当前气候变化农业影响评估的结果还存在较大的不确定性, 阻碍了应对气候变化策略的科学制定[59,98]。目前处理不确定性的方法主要有敏感性分析、模型对比、集合模拟和概率风险评估等[83,98], 这些方法对减少影响评估的不确定性以及客观认识气候变化的影响仍显不足。未来迫切需要发展适应非生物逆境的作物生长模型、减小排放情景的不确定性以及改进影响评估方法来获得更可靠的预估结果。

4.3 改进气候变化影响评估的方法和技术

应用统计模型进行评估时需注意非气候因素的影响及其与气候因素的互作、气候要素的自相关性以及选择合适的时空研究尺度等[62]。基于作物生长模型的评估则需注意模型参数不稳定性、与气候模式的空间匹配性、建模机制不完善等问题。此外, 多方法融合也是改进气候变化影响评估方法的重要发展方向[99], 如统计模型、作物生长模型与观测试验的集合评估[85], 作物生长模型与社会-经济模型的组合应用[100], 作物生长模型与卫星遥感、无人机监测及作物表型观测相结合等, 有助于提高评估结果的可信度和系统性。

4.4 注重气候变化对水稻生产影响评估的系统性

将农业生产系统作为有机整体来全面评估气候变化的影响和适应是有待发展的重要方向[101-102]。如加强评估气候变化对稻米品质、病虫害发生、生产环境代价的影响, 加强评估适应措施、社会-经济因素对减缓气候变化影响的作用[100], 加强评估多气候要素、CO2浓度升高、大气污染、气候波动和极端天气事件对水稻生产的综合影响。

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A review for impacts of climate change on rice production in China

LING Xiao-Xia1, ZHANG Zuo-Lin1, ZHAI Jing-Qiu2, YE Shu-Chun3, and HUANG Jian-Liang1,*

1Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, Ministry of Agriculture / College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China;231010 of PLA Troops, Beijing 100081, China;3Meteo­rological Bureau of Yunfu City, Yunfu 527300, Guangdong, China

Rice production system is one of the most sensitive agricultural ecosystems in response to climate change. Here, we reviewed the effects of current and future climate change on rice production in China. Over the past few decades, the thermal resources during rice growing seasons showed an increasing trend, while solar radiation resources showed a decreasing trend and the precipitation’s heterogeneity increased. The frequencies of high temperature stress, heavy precipitation, drought and flood increased, which may lower down the effectiveness of hydrothermal resources. Climate change has led to a significant northward shift of potential planting boundaries for single and double rice production systems, resulted in a negative impact on the length of growth period for single rice, early rice and late rice. The researches based on statistical models and process-based crop models showed that climate change hampered rice production of China. Most reports indicated a reducing trend of yield caused by climate change for single rice, early rice and late rice, but there were still some differences in results from different methods and rice cropping regions. The trends of prolonging growth period and increasing yield are a reflection of the capability of rice production system in China to adapt to climate change, through regulating planting regionalization and improving variety and culture technics. The impact assessment with different climate scenarios showed that the projected growth period of rice would shorten and projected yield would decrease in future. That means climate change will seriously challenge the rice production and food security in China. For further study, deeper understanding of abiotic stress physiology and its incorporation into ecophysiological models, reducing the uncertainty and extending the systematicness of impact assessment are the important research areas that require much attention.

global warming; northern boundary; rice planting system; growth stage; grain yield

2018-08-19;

2018-12-25;

10.3724/SP.J.1006.2019.82044

黄见良, E-mail: jhuang@mail.hzau.edu.cn, Tel: 027-87284131

E-mail: lingxiaoxia@mail.hzau.edu.cn, Tel: 027-87282213

2019-01-07.

本研究由国家重点研发计划项目(2016YFD0300210, 2017YFD0300101)资助。

This study was supported by the National Key Research and Development Program of China (2016YFD0300210, 2017YFD0300101).

URL:http://kns.cnki.net/kcms/detail/11.1809.S.20190103.1739.013.html