氣候變化對中國水稻生產的影響研究進展

凌霄霞 張作林 翟景秋 葉樹春 黃見良,*

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氣候變化對中國水稻生產的影響研究進展

凌霄霞1張作林1翟景秋2葉樹春3黃見良1,*

1農業部長江中游作物生理生態與耕作重點實驗室/ 華中農業大學植物科學技術學院, 湖北武漢 430070;2中國人民解放軍31010部隊, 北京 100081;3廣東省云浮市氣象局, 廣東云浮 527300

水稻生產系統是響應氣候變化最敏感的農業生態系統之一, 本文綜述了當前和未來氣候變化對我國水稻生產的影響。氣候變化背景下, 我國水稻生長季的熱量資源增多, 輻射資源減少, 降水不均一性加大。高溫熱害、干旱、暴雨和洪澇災害發生更頻繁, 這可能降低水、熱資源的有效性。氣候變化使我國單季稻和雙季稻潛在種植邊界顯著北移, 導致單季稻、早稻和晚稻的主要生育期縮短。基于統計模型和水稻生長模型的研究結果表明, 如果不考慮品種改良和栽培技術的進步, 氣候變化使單季稻、早稻和晚稻產量下降, 但不同稻作區和方法間存在差異。我國水稻生產重心北移、實測生育期延長和產量增加的變化趨勢, 反映了水稻生產系統通過種植分布調整、品種改良和技術改進來適應氣候變化的能力。未來氣候變化將進一步導致水稻生育期縮短和產量下降, 對我國水稻生產和糧食安全帶來嚴峻挑戰。仍需加強氣候變化影響機制的研究及其在影響評估中的應用, 減小影響評估的不確定性并增加其系統性, 為制定有效的應對策略提供可靠的理論支持。

氣候變暖; 種植北界; 稻作制度; 生育期; 產量

政府間氣候變化專門委員會第五次評估報告(IPCC_AR5)指出, 1880—2012年全球地表平均溫度升高約0.85℃, 過去3個10年歷史時期全球地表溫度已連續上升。氣候系統的變化已對全球糧食生產造成了普遍影響, 未來氣候變化嚴重影響作物產量的風險也可能增長[1]。

水稻是中國最主要的口糧作物, 我國65%以上的人口以稻米為主食[2]。據統計, 2012—2016年我國水稻年均播種面積為3.023×107hm2, 占糧食作物年均播種面積(11.245×107hm2)的26.9%; 水稻年均總產為2.059×108t, 占糧食年均總產(6.072×108t)的33.9%[3]。雖然過去30年來我國各縣市水稻產量翻倍增長, 但近期有一半以上的縣市出現了水稻增產停滯現象[4], 這可能與溫度和太陽輻射等氣候變化有關[5]。因此, 科學評估氣候變化對水稻生產的影響并制定有效的應對策略比以往更顯重要, 為突破水稻產量瓶頸提供氣候影響的理論支持。

統計模型和作物生長模型是評估氣候變化對農業生產影響中最常見且有效的方法[6-9]。因此, 本文主要對2000年以來基于這兩類模型的水稻生產影響評估研究進行綜述, 以期為氣候變化對農業影響評估等工作提供參考。

1 氣候變化對水稻生長環境的影響

1.1 氣候資源變化特征

1980—2008年全球水稻生長季氣溫明顯升高, 65%國家的增幅已超過年際變化的標準差, 中國部分稻區的增幅甚至大于年際變化標準差的2倍[10]。1961—2010年, 我國水稻生長季的最低氣溫和平均氣溫分別升高0.61℃和0.47℃, 氣溫日較差則降低0.38℃[11]。氣溫變化特征在水稻種植區、稻作類型和生育階段間存在明顯差異。總體而言, 北方稻區的升溫幅度大于南方[12]; 早稻生長季平均氣溫和最高氣溫的增溫速率大于晚稻[13]。長江中下游地區早稻和晚稻生殖生長期的增溫趨勢顯著高于營養生長期, 單季稻則相反[14]。除氣溫的升高, 稻田水溫也呈增加趨勢, 但升高幅度較氣溫小[15]。與1960s相比, 2000s中國稻作區≥10℃總有效積溫平均增加9.4%, 東北和西南稻區的增加幅度大于中部和南部[16]。

從輻射資源來看, 主要稻作區2000s的日照時數比1960s減少11.9%[16], 太陽總輻射量降低9.4%[11], 這一現象在長江中下游單季稻生長季尤為明顯[14]。從降水情況來看, 降水總量的長期變化趨勢并不明顯, 但平均降水強度增加約3.2%[11,16]。Ye等[17]研究表明, 氣候變化降低了南方單、雙季稻生產可利用的水熱資源有效性, 這意味著當前氣候變化對我國水稻生產的不利影響可能被低估。熱量資源增加、輻射資源減少而降水量的時空不均一性加大, 這一系列變化對優化我國稻作制度的空間分布、提高水稻生產資源利用效率提出了新的挑戰。

1.2 農業氣象災害變化特征

我國水稻生產所遭受的農業氣象災害種類多、地域性強、時期明顯, 其中高溫熱害和低溫冷害是最主要的氣象災害[18]。東北單季稻和南方晚稻抽穗開花期發生低溫冷害的風險最大, 而高溫熱害則在長江流域單季稻孕穗期至灌漿期、南方早稻抽穗開花期風險最大[19]。1960—2009年間, 我國長江流域單季稻和南方早稻抽穗揚花期的高溫脅迫積溫每年增加0.12℃; 東北、長江流域、云貴高原單季稻和南方晚稻抽穗揚花期的低溫脅迫積溫每年減少0.21℃[19]。

據農業氣象災害觀測數據顯示, 與前10年相比, 2000—2009年南方早稻孕穗期至成熟期發生高溫脅迫的頻次增加6~15次, 東南晚稻移栽期和孕穗期的高溫脅迫增加14次和24次; 湖南和廣西早稻移栽期發生低溫冷害的頻次增加59次, 單季稻和晚稻孕穗期至成熟期的低溫冷害增加15~42次; 冷害的發生還表現出延遲型冷害減少而障礙型冷害增多的特征[20]。在干旱和洪澇災害變化方面, 早稻、晚稻和單季稻抽穗期之后發生干旱的頻次增加最多, 而單季稻和晚稻孕穗期遭受洪澇災害的頻次增加更顯著[20]。為應對水稻生產當前所面臨的災變環境, 需加強防災減災技術的創新和應用。

2 氣候變化對水稻生產的影響

評估正在發生的氣候變化對水稻生產的影響, 有利于客觀評價氣候變化背景下水稻生產所面臨的挑戰, 為提出應對氣候變化對策提供理論參考[10,21]。

2.1 對水稻種植制度的影響

氣候變暖導致熱量資源增多, 有利于擴大農作物潛在種植面積, 增加糧食生產總能力。1980—2010年間, 氣候變化使我國水稻適宜種植面積的比例增加約4個百分點, 東北地區增加幅度最大[22]。黑龍江省水稻潛在種植區隨2000℃ d等值線北移約4個緯度[23], 實際集中種植區北移約1個緯度[24]。雨養條件下, 中國單季稻可種植北界到達黑龍江漠河縣北部, 灌溉條件下, 單季稻可種植北界則可達我國最北端[25]。南方雙季稻潛在種植邊界北移34~60 km, 部分稻-麥兩熟區可滿足早、晚雙季稻的光熱需求[26-27]。氣候變暖對我國北方稻區種植邊界的影響較南方稻區明顯。在氣候適宜性方面, 雙季稻低適宜種植面積有所減少, 中、高適宜種植面積有所增加[28]。

近60年來, 我國水稻實際種植重心和產量重心分別向東北遷移約2個和3個緯度, 水稻種植面積的擴張和位置遷移與氣溫變化趨勢高度一致[22,29-30]。這說明氣候變化是驅動我國水稻種植區域調整的重要因素, 同時也體現了我國水稻生產快速適應氣候變化的能力[31]。

2.2 對水稻生育進程的影響

生育期觀測數據的趨勢分析表明, 近30年來我國水稻播種和移栽期提前[14,32-35], 單季稻成熟期推后, 早、晚稻成熟期提前[14,33-35]。此外, 我國單季稻營養生長期、生殖生長期和全生育期延長[14,33-34,36], 晚稻主要生育階段呈縮短趨勢[14,32-33,37], 早稻生育期的變化并沒有一致結論。水稻生育期的變化主要受氣候、品種和栽培管理等因素影響。不考慮品種熟期變化和管理措施調整的情況下, 氣候變暖可導致作物物候期提前和生育期縮短[38]。我國水稻營養生長期、生殖生長期和全生育期因氣候變暖而分別縮短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), 營養生長期的縮短比生殖生長期明顯[32,36-37]。除溫度外, 光周期、CO2濃度和非生物逆境等因素也可調節水稻的生長發育速度[33,39-40], 但在評估氣候變化對水稻生育期的影響時, 很少考慮這些因素的作用。

在適應氣候變化過程中, 農民為充分利用熱量資源或為避免單季稻在高溫時間段抽穗揚花, 往往提早播種或改種生育期較長的品種[36,41], 這補償了氣候變化的不利影響, 使觀測到的單季稻生育期延長。對晚稻而言, 為躲避成熟期低溫而種植短生育期品種則可能加速生育期的縮短[32]。另有研究表明, 水稻成熟期受其分布地區、種植模式和移栽時間的影響比受溫度的影響更大[34], 非氣候因素對水稻生育期的影響可能大于氣候因素[14,33,42]。

2.3 對水稻產量的影響

氣候變化對水稻產量的影響是最受關注的內容, 前人主要研究了氣候變化的影響趨勢和程度、氣候與非氣候因素的貢獻、關鍵氣候因素及影響機制等。

2.3.1 氣候變化對水稻產量的影響 水稻生產是個復雜的自然-社會系統, 產量的長期變化同時摻雜了氣候變化和人為因素信號。總體而言, 1980—2010年我國單季稻、早稻和晚稻的實測單產每10年增加0.69 (0.37~1.07) t hm–2(表1)。單就氣候因素的影響而言, 近幾十年的氣候變化對我國水稻產量造成了不利影響?；谒旧L模型的評估表明(表1), 1980—2010年氣候平均態的變化使我國水稻單產減少0.25 (0.01~0.56) t hm–210 yr–1, 1961—2010年間則造成水稻單產減少約12.0% (11.5%~12.4%)。在氣候變化過程中, 改種生育期長或者灌漿期長的品種可提高水稻產量[36-37], 種植抗逆性強的品種或提高栽培管理水平則降低了水稻產量的年際波動性[43]。品種改良和合理施肥等措施對水稻產量的正效應甚至超過了氣候變化的負效應[44-46]。可見, 氣候變化雖然嚴重制約了水稻產量的增長, 但我國水稻生產系統已通過適宜的方式來積極應對這種不利影響, 使水稻產量穩步提高。然而, 未來氣候變化仍將嚴重制約技術進步對糧食生產的貢獻[39], 增加農業技術創新的難度。

氣候變化因素對我國水稻生產的影響又與地區和稻作類型有關?；诮y計模型與生長模型的結果表明(表1), 在氣候長期變化影響下, 華北、華東、華中(長江中下游單季稻)和西南(四川盆地單季稻)地區水稻、南方雙季稻減產顯著, 長江中下游晚稻、東北和云貴高原單季稻產量有所增加。極端天氣是造成產量損失的另一重要原因, 其對水稻產量的影響可能大于氣候要素的長期變化和年際波動[47-48]。我國近30年的極端溫度脅迫導致全國灌溉稻產量損失約6.1%, 四川盆地單季稻、長江中下游單季稻、南方早稻因此造成的產量損失顯著上升[49]。此外, 氣候資源的合理配置有利于提高水稻產量和光、溫資源利用效率[50], 資源配置不合理的年份則可造成嚴重的產量損失[51]。另有研究表明, 氣溶膠濃度影響入射的太陽總輻射以及散射輻射所占的比例, 重度大氣污染對水稻產量將造成不利影響[52-53]。與不利的大氣環境相反, 大氣CO2濃度升高有利于水稻增產[54], 且晚稻產量對CO2濃度升高的響應大于早稻和單季稻[44-45]。CO2濃度升高的增產效應在很大程度上減少了氣候變化造成的產量損失, 近30年來甚至基本補償了氣候變化造成的減產(表1)。

表1 當前氣候變化對中國水稻產量的影響

(續表1)

稻作類型Rice system研究區域Region研究時段Period變化趨勢Change trend評估方法Method參考文獻Reference 統計模型aStatistical modela(t hm–210 yr–1)作物模型bCrop modelb(t hm–210 yr–1) 單季稻Single rice東北Northeast China1980–20080.59% yr–1—Statistical model[94] 單季稻Single rice云貴高原Yunnan-Guizhou Plateau1980–20080.34% yr–1—Statistical model[94] 單季稻Single rice四川盆地Sichuan Basin1980–2008–0.29% yr–1—Statistical model[94]

a統計模型列是基于統計模型對歷史水稻產量實測數據的分析結果, 斜體數值為實測產量隨時間的變化趨勢, 其他數值為實測產量對氣候變化的響應;b作物模型列是基于水稻生長模型, 將品種和管理參數設為定值得到的模擬產量的變化趨勢或變化百分率, ( )中的值為考慮CO2濃度升高的模擬結果, 其他數值是將CO2濃度設為定值的模擬結果。

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 影響水稻產量的關鍵氣候因素 影響水稻產量的關鍵氣候因素, 是制定氣候變化應對策略的重要依據。然而, 因研究區域氣候的復雜性、氣候要素的自相關性以及稻作類型等原因, 使該問題尚未得出統一結論。研究表明, 熱帶地區水稻產量下降的主要原因是最低氣溫的升高[55], 而中國部分稻作區的水稻產量卻與溫度呈正相關[56-57]。在溫度較低的華北地區, 氣溫日較差減小則是水稻產量下降的首要原因[11]。另有研究認為, 我國水稻產量對太陽輻射的長期變化趨勢比溫度更敏感[54,56,58], 而作物產量的年際波動則更多地由降水量和太陽輻射變異以及溫度脅迫解釋[12,18,59-60]。此外, 溫度、輻射和降水量等氣候要素存在自相關性, 忽略該問題得出的結論可能是錯誤的[12,56,61], 影響產量變化的關鍵因素或許不應歸結為單個氣候要素[59,62]。

2.3.3 氣候變化影響水稻產量的機制 目前, 水稻響應氣候變化的機制研究主要集中在高溫、干旱等非生物逆境方面[63]。氣候變暖一方面縮短水稻生育期, 另一方面造成光合作用減少和呼吸作用增加[64]。水稻孕穗期高溫主要影響花器官發育, 如影響穎花分化和退化、縮短穎花長度、抑制花藥充實[65-66]; 抽穗揚花期高溫主要傷害正在開放的穎花, 影響花粉活力、數量以及穎花授粉受精過程, 增加空、秕粒率[67-70]; 灌漿結實期高溫使灌漿過程提早結束, 造成粒長和粒寬減小、粒重下降[67,71]。白天高溫造成水稻產量降低最突出的原因是結實率下降, 夜間高溫對結實率、每穗穎花數、粒重和生物量的影響相當[64]。水稻遭受低溫脅迫時, 因生殖生長期絨氈層變厚和營養失衡而使花粉失去育性, 還可能導致籽粒敗育[72]。弱光逆境則降低了植株凈光合速率, 使干物質生產和積累速度減慢, 干物質分配到穗部的比例下降[73]。干旱脅迫下, 葉片氣孔導度的下降使植株蒸騰速率減慢, 胞間CO2通量的減小則限制了光合作用。蒸騰速率的下降又減少了植株對營養物質的吸收、升高了冠層溫度, 進一步導致呼吸消耗增多以及存儲器官建成的時間縮短[63]。大氣CO2濃度升高時, 葉片氣孔導度和密度均呈下降趨勢, 造成蒸騰作用降低。但此時冠層光合作用的增加將促進有機物累積[74], 且地下部干物質的增加幅度比地上部更顯著[75]。當多種非生物逆境同時發生時, 對植物的影響往往不是單因子影響效應的簡單疊加, 需要在復雜環境條件下研究其影響機制[74-77]。

3 未來氣候變化對水稻生產的影響

3.1 對水稻生產的有利影響

與2000s相比, 預計2030s、2050s、2070s我國水稻生長季日均溫分別增加0.8~2.7℃、1.7~3.4℃、2.3~4.1℃[78]。我國一年兩熟帶和一年三熟帶的潛在邊界將持續北移[79-80], 21世紀末三熟制占種植制度總面積的潛在比例最大可達到75.0%[81]。未來單季稻和雙季稻潛在種植邊界也將繼續北移。與1961—1990年相比, 2080s我國單季稻和雙季稻可擴種面積約為5.0×105hm2和6.2×106hm2 [82]。熱量資源增多使作物潛在生長季延長, 大大增加了水稻生長季節彈性[15,79], 有利于水稻生產靈活地制定應對氣候變化策略。

3.2 對水稻生產的不利影響

IPCC第五次評估報告指出, 氣候變化和極端氣候事件對作物產量的不利影響比較普遍[1]。若未來氣溫升高1~3℃, 我國水稻生育期縮短的概率為100%[83]。當溫度升高1.5℃和2.0℃時, 我國雙季稻的生育期將縮短4%~8%和6%~10%, 單季稻的生育期約縮短2%[84]。一項集合網格作物模型、單點作物模型、統計模型和觀測試驗的研究表明, 氣溫每升高1℃可能導致全球水稻產量平均下降3.2%[85]。到21世紀末, 溫度持續上升可能使全球水稻產量減少3.3%~10.8% (表2)。未來氣候變化造成我國水稻產量變化幅度為?40.2%~6.3%, 平均減產10.7%, 且空間差異明顯(表2)。若考慮CO2濃度升高對產量的影響, 其對氣候變化造成的減產有一定補償作用(表2)。但這種補償作用在某些情景和地區仍無法抵消增溫幅度過高的負效應, 也不能降低水稻產量的年際變率[82-83,86]。此外, 降水和溫度變率增大可能導致低產年出現頻次增多, 減產幅度增加[82,87]。水稻產量減少和不穩定性增加最明顯的區域是四川盆地、長江流域和黃淮海平原, 這些地區或將成為水稻響應未來氣候變化的高敏感區[82]。研究還表明, 若能采用合理的應對策略, 可以有效減緩氣候變化對水稻產量的不利影響(表2)。未來可以從培育強抗逆性品種和高效利用CO2濃度品種、優化栽培管理和抗逆栽培技術、調整播期和種植面積等方面, 加強水稻生產應對氣候變化措施的研究。

值得注意的是, 越來越多的影響評估關注了極端天氣事件的變化及其對水稻生產的可能影響[84,88-90]。2000s到2050s, 全球水稻生殖生長期遭受極端高溫脅迫的面積將由8%增加到27%[91]。我國水稻生產遭受高溫脅迫的概率、強度和面積也將增加, 這可能抵消熱量資源增多及低溫危害減少帶來的正效應[92-94]。若溫度升高1.5℃和2.0℃, 熱脅迫可能導致我國水稻產量分別下降2%和5%[84]。四川盆地和長江中下游流域或將成為高溫熱害高發區, 東北、云貴高原和華東稻區經歷嚴重低溫危害的風險比其他地區大[89,94]。未來降水變率增加則可能導致季節性干旱和暴雨發生頻次增多[95], 在江蘇等東部地區, 極端降水事件對水稻產量的影響可能比極端溫度事件更顯著[88]。此外, 氣溫升高導致參考作物蒸散量普遍增加, 我國西南地區將經歷濕潤指數明顯減小的干旱化過程[79]。

4 問題與展望

氣候變化已導致我國水稻生長季氣候條件的改變, 對水稻種植面積、氣候適宜性、生長發育、產量等造成一定影響。現有的評估工作是在當前科學認知和技術水平上的有益嘗試, 未來還有許多亟待解決的問題需要進一步深入探索。

4.1 加強氣候變化影響機制的研究及其在影響評估中的應用

水稻響應氣候變化的機制是氣候變化影響評估的重要理論基礎。前人主要研究了高、低溫脅迫和干旱脅迫等極端天氣事件的影響, 對增溫、CO2濃度升高等氣候平均態變化的影響研究較少; 對水稻光合作用、白天蒸騰等生理過程的研究較多, 對夜間蒸騰等其他生理生化過程的研究還比較薄弱; 對單因子脅迫的影響機制研究較多, 對多因子脅迫、非生物逆境與高CO2濃度互作等復雜環境的影響機制研究較少[63,76-77]。更值得注意的是, 基于作物響應氣候變化機制來改進生理生態模型的研究遠遠滯后于機制研究本身, 需要設計專門的田間試驗并將試驗結果與模型改進緊密聯系起來[63]。重點關注葉片光合模型參數在環境變化中的適應性[96]、植株氮素動態的響應等[97]受環境變化影響較大的生理生態過程, 使水稻響應氣候變化的機制研究在區域尺度的影響評估中發揮更充分的作用。

4.2 減小氣候變化影響評估的不確定性

當前氣候變化農業影響評估的結果還存在較大的不確定性, 阻礙了應對氣候變化策略的科學制定[59,98]。目前處理不確定性的方法主要有敏感性分析、模型對比、集合模擬和概率風險評估等[83,98], 這些方法對減少影響評估的不確定性以及客觀認識氣候變化的影響仍顯不足。未來迫切需要發展適應非生物逆境的作物生長模型、減小排放情景的不確定性以及改進影響評估方法來獲得更可靠的預估結果。

4.3 改進氣候變化影響評估的方法和技術

應用統計模型進行評估時需注意非氣候因素的影響及其與氣候因素的互作、氣候要素的自相關性以及選擇合適的時空研究尺度等[62]?；谧魑锷L模型的評估則需注意模型參數不穩定性、與氣候模式的空間匹配性、建模機制不完善等問題。此外, 多方法融合也是改進氣候變化影響評估方法的重要發展方向[99], 如統計模型、作物生長模型與觀測試驗的集合評估[85], 作物生長模型與社會-經濟模型的組合應用[100], 作物生長模型與衛星遙感、無人機監測及作物表型觀測相結合等, 有助于提高評估結果的可信度和系統性。

4.4 注重氣候變化對水稻生產影響評估的系統性

將農業生產系統作為有機整體來全面評估氣候變化的影響和適應是有待發展的重要方向[101-102]。如加強評估氣候變化對稻米品質、病蟲害發生、生產環境代價的影響, 加強評估適應措施、社會-經濟因素對減緩氣候變化影響的作用[100], 加強評估多氣候要素、CO2濃度升高、大氣污染、氣候波動和極端天氣事件對水稻生產的綜合影響。

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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;3Meteo-rological 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

黃見良, E-mail: jhuang@mail.hzau.edu.cn, Tel: 027-87284131

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

2019-01-07.

本研究由國家重點研發計劃項目(2016YFD0300210, 2017YFD0300101)資助。

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