1981—2010年長(zhǎng)江中下游地區(qū)單季稻生殖生長(zhǎng)期對(duì)氣候變化和技術(shù)進(jìn)步的響應(yīng)

劉二華 周廣勝,,* 武炳義 宋艷玲 何奇瑾 呂曉敏 周夢(mèng)子

1981—2010年長(zhǎng)江中下游地區(qū)單季稻生殖生長(zhǎng)期對(duì)氣候變化和技術(shù)進(jìn)步的響應(yīng)

劉二華1,2,3周廣勝1,2,3,4,*武炳義2宋艷玲1,3何奇瑾4呂曉敏1,3周夢(mèng)子1,3

1中國(guó)氣象科學(xué)研究院, 北京 100081;2復(fù)旦大學(xué)大氣科學(xué)研究院, 上海 200439;3中國(guó)氣象科學(xué)研究院與鄭州大學(xué)生態(tài)氣象聯(lián)合實(shí)驗(yàn)室, 河南鄭州 450001;4中國(guó)農(nóng)業(yè)大學(xué)資源與環(huán)境學(xué)院, 北京 100193

作物生殖生長(zhǎng)期長(zhǎng)度與作物產(chǎn)量和品質(zhì)密切相關(guān)。為深入探究作物生殖生長(zhǎng)期長(zhǎng)度(reproductive growth period lengths, RGLs)對(duì)氣候變化和技術(shù)進(jìn)步的響應(yīng), 基于1981—2010年長(zhǎng)江中下游地區(qū)單季稻生殖生長(zhǎng)期和氣象數(shù)據(jù), 量化不同RGLs (孕穗期—抽穗期(booting to heading, BDHD)、抽穗期—乳熟期(heading to milking, HDMS)、乳熟期—成熟期(milking to maturity, MSMD)和孕穗期—成熟期(booting to maturity, BDMD))對(duì)平均溫度(mean temperature, TEM)、累積降水量(cumulative precipitation, PRE)和累積日照時(shí)數(shù)(cumulative sunshine duration, SSD)的敏感性, 并分離氣候變化和技術(shù)進(jìn)步對(duì)不同RGLs的影響。結(jié)果表明, 1981—2010年長(zhǎng)江中下游地區(qū)單季稻BDMD呈延長(zhǎng)趨勢(shì)(0.24 d a–1), 其中, HDMS延長(zhǎng)趨勢(shì)最明顯(0.16 d a–1)。氣候因子中高溫和寡照不利于單季稻不同RGLs延長(zhǎng), 其中, TEM對(duì)BDHD、HDMS和MSMD變化趨勢(shì)的平均相對(duì)貢獻(xiàn)分別為–50.0%、–50.7%和–21.9%, SSD對(duì)BDHD、HDMS和MSMD變化趨勢(shì)的平均相對(duì)貢獻(xiàn)分別為–47.2%、–48.7%和–67.6%。技術(shù)進(jìn)步彌補(bǔ)了氣候變化對(duì)不同RGLs變化趨勢(shì)的不利影響。研究表明, 技術(shù)進(jìn)步可能是當(dāng)前單季稻穩(wěn)產(chǎn)高產(chǎn)和趨利避害的主要手段, 未來(lái)可以采用較長(zhǎng)生殖生長(zhǎng)期和耐熱性品種來(lái)適應(yīng)持續(xù)的氣候變化。

單季稻; 氣候變化; 生殖生長(zhǎng)期; 技術(shù)進(jìn)步

作物生育期是重要的農(nóng)業(yè)信息, 對(duì)合理安排作物布局、調(diào)控作物生長(zhǎng)發(fā)育和預(yù)測(cè)產(chǎn)量具有重要意義[1-2]。同時(shí), 作物生育期在陸面動(dòng)態(tài)過(guò)程、生物物理過(guò)程中扮演重要角色[3]。作物關(guān)鍵生育期的相對(duì)長(zhǎng)短是決定作物產(chǎn)量的關(guān)鍵因素[4]。氣候變暖普遍加快作物生長(zhǎng)發(fā)育, 縮短作物生長(zhǎng)期, 縮短作物生物量的積累時(shí)間, 威脅作物的生產(chǎn)力和糧食安全[5]。因此, 隨氣候變暖的加劇[6], 作物生育期對(duì)氣候變化的響應(yīng)已然成為研究的熱點(diǎn)[7]。

作物生育期變化反映了作物生長(zhǎng)過(guò)程對(duì)自然因素和人為因素的綜合響應(yīng)[8-9], 自然因素主要為氣候因素(包括溫度、水分和光照等), 人為因素表現(xiàn)為技術(shù)進(jìn)步(包括品種、種植方式、栽培管理和生產(chǎn)投入等)。作物生育期的變化表現(xiàn)為提前或推遲, 生長(zhǎng)期縮短或延長(zhǎng)[10]。研究表明中國(guó)單季稻生育期呈延長(zhǎng)趨勢(shì)[11-13], 尤其以生殖生長(zhǎng)期(reproductive growth periods lengths, RGLs)顯著延長(zhǎng)為主[14]。同時(shí), 中國(guó)玉米、小麥、大豆以及棉花等作物RGLs普遍延長(zhǎng)[5,15-19]。已有大量研究通過(guò)統(tǒng)計(jì)模型和作物模擬模型等多種方法廣泛評(píng)價(jià)了作物生育期對(duì)氣候變化和技術(shù)進(jìn)步的響應(yīng)[20-22]。例如, Zhang等[14]的研究表明水稻RGLs由溫度和品種特性共同決定, 溫度升高加快水稻抽穗期, 品種改變延緩水稻抽穗期。而Hu等[23]認(rèn)為中國(guó)水稻生育期延長(zhǎng)主要由播種期和品種改變調(diào)節(jié), Bai等[24]進(jìn)一步研究表明播種期顯著影響水稻營(yíng)養(yǎng)生長(zhǎng)期, 而對(duì)RGLs影響不明顯, 品種改變使得水稻RGLs每10年延長(zhǎng)0.2～2.8 d。然而, 氣候因素(如降水量和日照時(shí)數(shù))對(duì)作物生育期的影響也至關(guān)重要, 但以往研究通常忽略這些氣候因素[19,25-26]。因此, 厘清氣候變化和技術(shù)進(jìn)步對(duì)作物生育期的影響程度對(duì)于準(zhǔn)確實(shí)施適應(yīng)氣候變化的管理措施具有重要參考價(jià)值[5,25,27-28]。由于技術(shù)的不斷進(jìn)步, 作物生育期對(duì)氣候變化的響應(yīng)難以評(píng)估[8]。當(dāng)前已有一些研究開(kāi)展了關(guān)于量化氣候變化和作物管理對(duì)作物生育期的影響研究[23,26], 但是, 鮮少有關(guān)于氣候變化和技術(shù)進(jìn)步對(duì)作物不同RGLs變化趨勢(shì)的綜合定量評(píng)價(jià)研究。

水稻(L.)是世界上一半以上人口的主食[29], 長(zhǎng)江中下游地區(qū)是中國(guó)最古老最重要的水稻主產(chǎn)地之一[30], 水稻種植面積和總產(chǎn)量均占全國(guó)45%以上[31]。長(zhǎng)江中下游地區(qū)水稻種植制度為單季稻和早、晚雙季稻[32]。1980—2008年呈現(xiàn)出單季稻種植面積增加和雙季稻種植面積減少的態(tài)勢(shì)[33-35], 這表明單季稻在長(zhǎng)江中下游地區(qū)水稻生產(chǎn)中占有越來(lái)越重要的地位。本研究假設(shè)單季稻不同RGLs是氣候變化和技術(shù)進(jìn)步綜合作用的結(jié)果?；谝陨霞僭O(shè), 本研究的目標(biāo)是(1) 闡明長(zhǎng)江中下游地區(qū)單季稻不同RGLs時(shí)空變化特征; (2) 單季稻不同RGLs對(duì)平均溫度(mean temperature, TEM)、累積降水量(cumulative precipitation, PRE)和累積日照時(shí)數(shù)(cumulative sunshine duration, SSD)的敏感性; (3) 分離和量化氣候變化和技術(shù)進(jìn)步對(duì)單季稻RGLs變化趨勢(shì)影響的貢獻(xiàn)。

1 材料與方法

1.1 研究區(qū)域與資料來(lái)源

研究區(qū)域位于中國(guó)長(zhǎng)江中下游地區(qū)(26°～35°N, 103°～122°E), 該地區(qū)屬于雨熱同期的亞熱帶季風(fēng)氣候區(qū)。本研究涉及的地區(qū)主要包括江蘇、河南南部、湖北、重慶和四川東部(圖1)。本研究所用資料主要包括氣象資料和單季稻生育期資料, 資料均來(lái)源于中國(guó)氣象局。氣象資料主要包括1981—2010年10個(gè)氣象站點(diǎn)觀測(cè)的逐日平均溫度、降水量和日照時(shí)數(shù)。根據(jù)長(zhǎng)期觀測(cè)資料缺失情況, 選取的10個(gè)農(nóng)業(yè)氣象站位于與上述氣象站相同的地點(diǎn)。單季稻生育期主要包括分蘗期、孕穗期、抽穗期、乳熟期和成熟期, 本研究將生殖生長(zhǎng)階段進(jìn)一步劃分為孕穗期—抽穗期(booting to heading, BDHD)、抽穗期—乳熟期(heading to milking, HDMS)、乳熟期—成熟期(milking to maturity, MSMD)和孕穗期—成熟期(booting to maturity, BDMD), 同時(shí)探討了分蘗期—孕穗期(tillering to booting, TDBD)的變化特征。

圖1 10個(gè)氣象站點(diǎn)地理位置

JXH、JZJ、HGS、HXY、HFX、HZX、SDZ、SGP、CFD和CYY分別表示興化站、鎮(zhèn)江站、固始站、信陽(yáng)站、房縣站鐘祥站、大竹站、高坪站、豐都站和酉陽(yáng)站。矢量地圖數(shù)據(jù)來(lái)源: 全國(guó)地理信息資源目錄服務(wù)系統(tǒng)(https://www.webmap.cn/)。

JXH, JZJ, HGS, HXY, HFX, HZX, SDZ, SGP, CFD, and CYY represent Xinghua station, Zhenjiang station, Gushi station, Xinyang station, Fangxian station, Zhongxiang station, Dazhu station, Gaoping station, Fengdu station, and Youyang station, respectively. The above vector map is from the National Catalogue Service for Geographic Information (https://www.webmap.cn/).

1.2 分析方法

利用線性回歸方法分析單季稻關(guān)鍵生殖生長(zhǎng)期和不同RGLs (Booting、Heading、Milking、Maturity、TDBD、BDHD、HDMS、MSMD和BDMD)以及相應(yīng)時(shí)期氣候因子(TEM、PER和SSD)的變化趨勢(shì), 采用雙尾t檢驗(yàn)分析確定統(tǒng)計(jì)顯著性。

Y=T×X+B(1)

式(1)中,Y是第年各變量的值; T為線性回歸斜率;X是年份(= 1, 2, 3, …, 30); B是截距。

面板回歸模型法[36]已被證明適合氣候變化影響的研究[37]。本研究利用面板回歸模型法得到單季稻RGLs對(duì)氣候因子的響應(yīng)及其敏感性。為降低氣候因子間的多重共線性, 本研究根據(jù)相關(guān)性分析并結(jié)合前人研究[25,38], 選取TEM、PRE和SSD三個(gè)氣候因子作為預(yù)測(cè)變量。

首先, 構(gòu)建了5個(gè)單季稻RGLs (TDBD、BDHD、HDMS、MSMD和BDMD)多元面板回歸模型。模型形式如下:

RGLt,s=β0,s+β1×TEMt,s+β2×PREt,s+β3×SSDt,s+(2)

式(2)中, RGLt,s表示第s站點(diǎn)和第t年的生殖生長(zhǎng)期長(zhǎng)度(d), TEMt,s、PREt,s和SSDt,s分別表示第s站點(diǎn)和第t年的生殖生長(zhǎng)階段的TEM (℃)、PRE (mm)和SSD (h)。β0表示站點(diǎn)的固定效應(yīng)。β1、β2和β3分別表示RGLs對(duì)TEM、PRE和SSD的敏感性(d ℃–1、d mm–1和d h–1)。ε是一個(gè)隨機(jī)誤差項(xiàng), 表示氣候變化以外的因素對(duì)作物生育期的影響。

其次, 將各個(gè)站點(diǎn)單季稻分蘗期、孕穗期、抽穗期、乳熟期和成熟期的日期確定為1981—2010年實(shí)際觀測(cè)日期平均值, 統(tǒng)計(jì)1981—2010年各站點(diǎn)氣候因子在不同RGLs的變化趨勢(shì), 據(jù)此分離氣候變化趨勢(shì)對(duì)單季稻RGLs變化趨勢(shì)的影響[39]。因此, 僅受氣候變化影響的單季稻RGLs變化趨勢(shì)的估算方法如下:

TRGL_cli=β1×TTEM+β2×TPRE+β3×TSSD(3)

式(3)中, TRGL_cli表示氣候變化引起的生殖生長(zhǎng)期變化趨勢(shì)。TTEM、TPRE和TSSD分別表示特定生殖生長(zhǎng)階段的TEM、PRE和SSD的變化趨勢(shì)(℃ a–1、mm a–1和h a–1)。TTEM、TPRE和TSSD均由公式(1)計(jì)算得到。

第三, 從氣候變化和技術(shù)進(jìn)步的綜合影響中消除氣候變化的影響, 得到技術(shù)進(jìn)步對(duì)單季稻RGLs變化趨勢(shì)的影響:

TRGL_tec=TRGL_cli_tec–TRGL_cli(4)

式(4)中, TRGL_tec表示技術(shù)進(jìn)步對(duì)單季稻RGLs變化趨勢(shì)的影響(d a–1), TRGL_cli_tec表示氣候變化和技術(shù)進(jìn)步對(duì)單季稻RGLs變化趨勢(shì)的綜合影響(d a–1), 即實(shí)際觀測(cè)得到的不同RGLs變化趨勢(shì)。TRGL_cli_tec可由公式(1)計(jì)算得到。

最后, 分離出氣候變化和技術(shù)進(jìn)步導(dǎo)致單季稻RGLs變化趨勢(shì)的貢獻(xiàn), 計(jì)算式如下:

式中, CRGL_cli和CRGL_tec分別表示氣候變化和技術(shù)進(jìn)步對(duì)RGLs變化趨勢(shì)的貢獻(xiàn)(%), RCRGL_TEM表示TEM對(duì)單季稻RGLs變化趨勢(shì)的平均相對(duì)貢獻(xiàn)(%), 同樣,利用公式(7)計(jì)算PRE和SSD對(duì)單季稻RGLs變化趨勢(shì)的平均相對(duì)貢獻(xiàn)(RCRGL_PRE和RCRGL_SSD)。參考Li等[10]研究, 計(jì)算氣候變化和技術(shù)進(jìn)步對(duì)單季稻RGLs變化趨勢(shì)的平均貢獻(xiàn), 以及各個(gè)氣候因子對(duì)單季稻RGLs變化趨勢(shì)的平均相對(duì)貢獻(xiàn)。

2 結(jié)果與分析

2.1 單季稻生殖生長(zhǎng)期時(shí)空變化

1981—2010年長(zhǎng)江中下游地區(qū)單季稻分蘗期、孕穗期、抽穗期、乳熟期和成熟期以及不同RGLs的時(shí)空變化趨勢(shì)如圖2所示。研究發(fā)現(xiàn), 分蘗期以0.37 d a–1的趨勢(shì)提前, 其中10個(gè)站點(diǎn)中9個(gè)呈提前趨勢(shì), 7個(gè)顯著提前。不同于其他站點(diǎn), 興化站分蘗期呈顯著推遲趨勢(shì)(0.39 d a–1)。孕穗期和抽穗期時(shí)空變異較大, 平均變化趨勢(shì)分別為0.08 d a–1和0.11 d a–1。乳熟期和成熟期主要呈不同程度的推遲趨勢(shì), 分別為0.27 d a–1和0.32 d a–1。以上結(jié)果表明生殖生長(zhǎng)期前生育期呈提前趨勢(shì), 隨生育進(jìn)程推進(jìn), 各個(gè)生育期逐漸推遲。

圖2-f～j分別表示長(zhǎng)江中下游地區(qū)單季稻TDBD、BDHD、HDMS、MSMD和BDMD時(shí)空變化趨勢(shì)。分蘗期顯著提前導(dǎo)致TDBD以0.45 d a–1的速率延長(zhǎng), 其中10個(gè)站點(diǎn)中9個(gè)呈延長(zhǎng)趨勢(shì), 7個(gè)顯著延長(zhǎng), 而鐘祥站TDBD以0.34 d a–1速率縮短。單季稻BDHD變化不明顯(0.03 d a–1)。單季稻HDMS以0.16 d a–1速率延長(zhǎng), MSMD變化不明顯(0.05 d a–1), BDMD以0.24 d a–1速率延長(zhǎng)。綜合而言, 長(zhǎng)江中下游地區(qū)單季稻RGLs呈延長(zhǎng)趨勢(shì), 其中以HDMS延長(zhǎng)為主。

2.2 單季稻生殖生長(zhǎng)期氣候因子變化趨勢(shì)

1981—2010年長(zhǎng)江中下游地區(qū)單季稻不同RGLs的TEM、PRE和SSD特征及變化趨勢(shì)如下圖3。研究發(fā)現(xiàn), 長(zhǎng)江中下游地區(qū)單季稻分蘗期到成熟期的TEM先升高后降低(圖3-a), BDHD的TEM達(dá)到最高(27.2℃), MSMD的TEM最低(24.5℃)。生殖生長(zhǎng)期前(TDBD)的平均PRE (120.5 mm)和平均SSD (245.5 h)最高, BDHD的平均PRE (26.6 mm)和平均SSD (60.1 h)最低(圖3-b, c)。就單季稻整個(gè)生殖生長(zhǎng)階段(BDMD)而言, TEM增加趨勢(shì)不明顯, 而MSMD的TEM有略微下降趨勢(shì)(–0.017℃ a–1), 其他RGLs的TEM均以增加為主。單季稻MSMD的TEM降低可能是乳熟期和成熟期推遲導(dǎo)致TEM季節(jié)性降低引起的。單季稻BDHD的TEM增加趨勢(shì)最明顯(0.027℃ a–1), 其次為HDMS的TEM (0.013℃ a–1) (圖3-d)。單季稻BDMD的平均PRE呈增加趨勢(shì)(1.27 mm a–1), 其中, HDMS的平均PRE增加趨勢(shì)最明顯(0.71 mm a–1), BDHD和MSMD的平均PRE變化趨勢(shì)不明顯(圖3-e)。單季稻BDMD的平均SSD以減小為主(-0.17 h a–1), 其中, BDHD和MSMD的平均SSD呈減小趨勢(shì)(–0.16 h a–1和–0.45 h a–1), 而HDMS的平均SSD呈增加趨勢(shì)(0.44 h a–1) (圖3-f)。由此可見(jiàn), 單季稻BDHD的氣候變暖速率最強(qiáng), 平均PER變化不明顯,平均SSD呈減小趨勢(shì), HDMS的氣候變暖速率次強(qiáng), 平均PRE和平均SSD均主要呈增加趨勢(shì), 其他生殖生長(zhǎng)階段氣候因子變化趨勢(shì)各異。

圖2 1981–2010年長(zhǎng)江中下游地區(qū)單季稻生殖生長(zhǎng)期和生殖生長(zhǎng)階段長(zhǎng)度變化趨勢(shì)

a、b、c、d、e、f、g、h、i和j分別表示分蘗期、孕穗期、抽穗期、乳熟期、成熟期、分蘗期–孕穗期、孕穗期–抽穗期、抽穗期–乳熟期、乳熟期–成熟期和孕穗期–成熟期。各指標(biāo)變化趨勢(shì)由1981–2010年觀測(cè)的各生殖生長(zhǎng)期時(shí)間序列依據(jù)公式(1)計(jì)算得到。*表示在0.05概率水平差異顯著, no表示統(tǒng)計(jì)不顯著。矢量地圖數(shù)據(jù)來(lái)源: 全國(guó)地理信息資源目錄服務(wù)系統(tǒng)(https://www.webmap.cn/)。

a, b, c, d, e, f, g, h, i, and j represent tillering stage, booting stage, heading stage, milking stage, maturity stage, tillering-booting, booting-heading, heading-milking, milking-maturity, and booting-maturity, respectively. Trends of indexes were calculated according to formula (1) for each reproductive growth period observed from 1981 to 2010. *:< 0.05; no: no significant difference. The above vector map is from the National Catalogue Service for Geographic Information (https://www.webmap.cn/).

2.3 單季稻生殖生長(zhǎng)期對(duì)氣候因子的敏感性分析

單季稻不同RGLs對(duì)氣候因子(TEM、PRE和SSD)的敏感性如表1所示。面板模型回歸系數(shù)β1、β2和β3分別表示不同RGLs對(duì)TEM、PRE和SSD的敏感性, 決定系數(shù)(2)表示模型回歸系數(shù)對(duì)因變量的解釋率。結(jié)果表明, TEM與RGLs均呈負(fù)相關(guān)關(guān)系, TEM每升高1℃, BDHD、HDMS和MSMD的平均長(zhǎng)度分別縮短0.77、1.00和1.17 d, 且RGLs對(duì)TEM的敏感性隨生育進(jìn)程增加。PRE和SSD與不同RGLs均呈正相關(guān)關(guān)系, PRE每增加100 mm, BDHD、HDMS和MSMD的平均長(zhǎng)度分別延長(zhǎng)1.24、0.86和0.71 d, 且RGLs對(duì)PRE的敏感性隨生育進(jìn)程減小; SSD每增加100 h, BDHD、HDMS和MSMD的平均長(zhǎng)度分別延長(zhǎng)7.46、8.06和6.88 d?？傮w而言, TEM增加不利于延長(zhǎng)RGLs, PRE和SSD增加有利于延長(zhǎng)RGLs。標(biāo)準(zhǔn)化處理不同RGLs對(duì)氣候因子的敏感性表明BDHD、HDMS和MSMD均對(duì)SSD的敏感性最強(qiáng), 對(duì)TEM的敏感性次之, 對(duì)PRE的敏感性最小(表2)。

圖3 不同生殖生長(zhǎng)期氣候因子變化特征

a、b和c分別表示不同生殖生長(zhǎng)期的平均溫度、累積降水量和累計(jì)日照時(shí)數(shù); d、e和f分別表示平均溫度、累積降水量和累積日照時(shí)數(shù)的變化趨勢(shì)。TEM、PRE和SSD分別表示平均溫度、累積降水量和累積日照時(shí)數(shù)。TDBD、BDHD、HDMS、MSMD和BDMD分別表示分蘗期–孕穗期、孕穗期–抽穗期、抽穗期–乳熟期、乳熟期–成熟期和孕穗期–成熟期。各指標(biāo)變化趨勢(shì)由1981–2010年觀測(cè)的氣象數(shù)據(jù)時(shí)間序列依據(jù)公式(1)計(jì)算得到。

a, b, and c represent mean temperature, cumulative precipitation, and cumulative sunshine duration of different reproductive growth periods lengths, respectively. d, e, and f represent trends of mean temperature, cumulative precipitation, and cumulative sunshine duration, respectively. TEM, PRE, and SSD represent mean temperature, cumulative precipitation, and cumulative sunshine duration, respectively. TDBD, BDHD, HDMS, MSMD, and BDMD represent tillering to booting, booting to heading, heading to milking, milking to maturity, and booting to maturity, respectively. The change trends of indexes were calculated by the time sequence of meteorological data observed from 1981 to 2010 based on formula (1).

表1 面板模型中不同生殖生長(zhǎng)期對(duì)平均溫度、累計(jì)降水量和累計(jì)日照時(shí)數(shù)的相關(guān)系數(shù)

*:< 0.05;**:< 0.01.

表2 標(biāo)準(zhǔn)化面板模型中不同生殖生長(zhǎng)期對(duì)平均溫度、累計(jì)降水量和累計(jì)日照時(shí)數(shù)的相關(guān)系數(shù)

*表示在0.05概率水平差異顯著,**表示在0.01概率水平差異顯著。*:< 0.05;**:< 0.01.

2.4 氣候變化和技術(shù)進(jìn)步對(duì)生殖生長(zhǎng)期變化趨勢(shì)的影響以及貢獻(xiàn)

氣候變化和技術(shù)進(jìn)步對(duì)單季稻不同RGLs變化趨勢(shì)的單獨(dú)影響如圖4。研究發(fā)現(xiàn), 氣候變化不利于RGLs延長(zhǎng), 對(duì)不同RGLs均有縮短效應(yīng)。技術(shù)進(jìn)步有利于延長(zhǎng)不同RGLs, 對(duì)HDMS延長(zhǎng)效應(yīng)最大, 延長(zhǎng)趨勢(shì)為0.22 d a–1, 對(duì)全生殖生長(zhǎng)期的延長(zhǎng)趨勢(shì)為0.33 d a–1。氣候變化和技術(shù)進(jìn)步對(duì)RGLs變化趨勢(shì)的綜合作用與技術(shù)進(jìn)步對(duì)RGLs變化趨勢(shì)的單獨(dú)作用相似。

分離后的氣候變化和技術(shù)進(jìn)步對(duì)單季稻RGLs變化趨勢(shì)的平均貢獻(xiàn)如圖5。研究發(fā)現(xiàn), 氣候變化和技術(shù)進(jìn)步對(duì)單季稻BDMD變化趨勢(shì)的平均貢獻(xiàn)分別為–20.0%和80.0% (圖5-a)。然而, 氣候變化和技術(shù)進(jìn)步的平均貢獻(xiàn)在單季稻BDHD、HDMS和MSMD存在差異。對(duì)于BDHD, 氣候變化的平均貢獻(xiàn)(–65.1%)大于技術(shù)進(jìn)步的平均貢獻(xiàn)(34.9%), 對(duì)于HDMS, 氣候變化的平均貢獻(xiàn)(–24.7%)小于技術(shù)進(jìn)步的平均貢獻(xiàn)(75.3%), 而對(duì)于MSMD, 氣候變化的平均貢獻(xiàn)(–48.1%)略微小于技術(shù)進(jìn)步的平均貢獻(xiàn)(51.9%)。由此可見(jiàn), 技術(shù)進(jìn)步對(duì)RGLs變化趨勢(shì)的延長(zhǎng)起主導(dǎo)作用, 技術(shù)進(jìn)步對(duì)RGLs變化趨勢(shì)的貢獻(xiàn)彌補(bǔ)了氣候變化的不利影響。

TEM、PRE和SSD對(duì)單季稻RGLs變化趨勢(shì)的平均相對(duì)貢獻(xiàn)如圖5-b。PRE對(duì)不同RGLs變化趨勢(shì)的平均相對(duì)貢獻(xiàn)絕對(duì)值最小。TEM和SSD是影響RGLs變化趨勢(shì)的主要?dú)夂蛞蜃? 兩者對(duì)不同RGLs變化趨勢(shì)的平均相對(duì)貢獻(xiàn)差異大。對(duì)于TDBD, TEM的平均相對(duì)貢獻(xiàn)最大(–69.9%), SSD的平均相對(duì)貢獻(xiàn)次之(–27.7%), 表明該階段單季稻生長(zhǎng)發(fā)育主要受TEM的影響。對(duì)于BDHD, TEM和SSD的平均相對(duì)貢獻(xiàn)相當(dāng)(–50.0%和–47.2%), HDMS變化趨勢(shì)受氣候因子的影響與BDHD類似, TEM和SSD的平均相對(duì)貢獻(xiàn)相當(dāng)(–50.7%和–48.8%), 表明這2個(gè)階段單季稻生長(zhǎng)發(fā)育受制于TEM和SSD。對(duì)于MSMD, SSD的平均相對(duì)貢獻(xiàn)最大(–67.6%), TEM的平均相對(duì)貢獻(xiàn)次之(–21.9%), 表明該階段單季稻生長(zhǎng)發(fā)育主要受SSD的影響。因此, TEM和SSD均為影響長(zhǎng)江流域地區(qū)單季稻RGLs變化趨勢(shì)的主要?dú)夂蛞蜃印?/p>

圖4 氣候變化和技術(shù)進(jìn)步對(duì)單季稻不同生殖生長(zhǎng)期變化趨勢(shì)的影響

T_cli、T_tec、T_cli_tec分別表示氣候因素、技術(shù)進(jìn)步、氣候因素和技術(shù)進(jìn)步綜合作用對(duì)不同生殖生長(zhǎng)期長(zhǎng)度的影響趨勢(shì)。生殖生長(zhǎng)期長(zhǎng)度縮寫同圖3。

T_cli, T_tec, and T_cli_tecindicate the effect trends of climatic factors, technological progress, and the combined of climatic factors and technological progress on different reproductive growth periods lengths, respectively. Abbreviations of different reproductive growth periods lengths are the same as those given in Fig. 3.

圖5 氣候變化和技術(shù)進(jìn)步對(duì)單季稻生殖生長(zhǎng)期長(zhǎng)度的平均貢獻(xiàn)

a表示氣候變化和技術(shù)進(jìn)步對(duì)不同生殖生長(zhǎng)期的平均貢獻(xiàn); b表示氣候因子對(duì)不同生殖生長(zhǎng)期的平均相對(duì)貢獻(xiàn)。CRGL_cli和CRGL_tec分別表示氣候因素和技術(shù)進(jìn)步對(duì)不同生殖生長(zhǎng)期長(zhǎng)度的平均貢獻(xiàn); RCRGL_TEM、RCRGL_PRE和RCRGL_SSD分別表示平均溫度、累積降水量和累積日照時(shí)數(shù)對(duì)不同生殖生長(zhǎng)期長(zhǎng)度的平均相對(duì)貢獻(xiàn)。生殖生長(zhǎng)期長(zhǎng)度縮寫同圖3。

a: the mean contributions of climate change and technological progress to different reproductive growth periods lengths; b: the mean relative contributions of climatic factors to different reproductive growth periods lengths. CRGL_cliand CRGL_tecindicate mean contribution of climatic factors and technological progress to different reproductive growth periods lengths, respectively; RCRGL_TEM, RCRGL_PRE, and RCRGL_SSDindicate mean relative contribution of mean temperature, cumulative precipitation, and cumulative sunshine duration to different reproductive growth periods lengths, respectively. Abbreviations of different reproductive growth periods lengths are the same as those given in Fig. 3.

3 討論

3.1 單季稻生殖生長(zhǎng)期對(duì)氣候變化和技術(shù)進(jìn)步的響應(yīng)

中國(guó)不同地區(qū)水稻生育期和生長(zhǎng)階段長(zhǎng)度的變化趨勢(shì)各不相同[38]。研究發(fā)現(xiàn), 長(zhǎng)江中下游單季稻乳熟期和成熟期顯著推遲, 生殖生長(zhǎng)期尤其是抽穗期到乳熟期顯著延長(zhǎng)(圖1), 該研究結(jié)果與Tao等[32]的結(jié)果一致。抽穗期到乳熟期延長(zhǎng)為灌漿期提供了充足的時(shí)間, 從而有利于生物量累積和產(chǎn)量提高[40]。本研究結(jié)果表明, 過(guò)去30年長(zhǎng)江中下游地區(qū)農(nóng)民采用了長(zhǎng)生殖生長(zhǎng)期單季稻品種, 這也表明延長(zhǎng)單季稻生殖生長(zhǎng)期是適應(yīng)氣候變化的有效措施[38]。

氣候變化對(duì)作物生育期的影響效應(yīng)因研究對(duì)象和生育期時(shí)段不同而存在差異[12]。本研究表明單季稻RGLs隨平均溫度的升高而縮短, 隨累積降水量和累積日照時(shí)數(shù)的增加而延長(zhǎng), 該結(jié)果與以往一些研究結(jié)果一致[11,25], 而與Liu等[38]的研究不同。其他作物生育期對(duì)溫度、累積降水量和累積日照時(shí)數(shù)的響應(yīng)與本研究結(jié)論一致[7,18,41]。本研究揭示了孕穗期至抽穗期、抽穗期至乳熟期和乳熟期至成熟期對(duì)累積日照時(shí)數(shù)的敏感性最強(qiáng), 對(duì)平均溫度的敏感性次之, 對(duì)累積降水量的敏感性最低。平均溫度和累積日照時(shí)數(shù)對(duì)不同生殖生長(zhǎng)期變化趨勢(shì)的平均相對(duì)貢獻(xiàn)占比均較大, 且兩者的長(zhǎng)期變化趨勢(shì)均不利于生殖生長(zhǎng)期延長(zhǎng), 這進(jìn)一步表明高溫和寡照是長(zhǎng)江中下游地區(qū)單季稻生殖生長(zhǎng)期的主要限制因子。本研究發(fā)現(xiàn)氣候變化對(duì)單季稻生殖生長(zhǎng)期的貢獻(xiàn)小于技術(shù)進(jìn)步, 并且技術(shù)進(jìn)步在延長(zhǎng)生殖生長(zhǎng)期變化趨勢(shì)上占主導(dǎo)作用(圖5), 該結(jié)果與Li等[10]、Wang等[12]、Liu和Dai[27]利用統(tǒng)計(jì)方法和作物模型法的研究結(jié)果一致。因此, 采用長(zhǎng)生殖生長(zhǎng)期品種, 可緩解氣候變化帶來(lái)的生育期縮短的負(fù)面影響[42]。

3.2 單季稻生殖生長(zhǎng)期適應(yīng)氣候變化的措施

實(shí)施作物管理有利于協(xié)調(diào)水稻生育進(jìn)程, 充分利用水稻生長(zhǎng)季的有效光溫資源, 獲得最大的光合生產(chǎn)量, 提高結(jié)實(shí)率和千粒重, 實(shí)現(xiàn)高產(chǎn)、優(yōu)質(zhì)和高效栽培[23]。1981—2010年中國(guó)單季稻主產(chǎn)區(qū)高溫持續(xù)時(shí)間和高溫強(qiáng)度顯著上升[43]。湖北中東部、安徽中部、江蘇西部、浙江北部和湖南東北部均易發(fā)生熱脅迫, 在未來(lái)的2021—2050年和2071—2100年2個(gè)時(shí)段, 整個(gè)區(qū)域的熱脅迫顯著增加[44]。長(zhǎng)江中下游地區(qū)熱脅迫主要發(fā)生在7月至8月, 該時(shí)間段正值單季稻生殖生長(zhǎng)階段, 尤其是單季稻開(kāi)花期和灌漿期與最高氣溫時(shí)期重疊[45-46], 張倩等[47]研究也表明長(zhǎng)江中下游地區(qū)熱脅迫主要影響單季稻孕穗期、開(kāi)花期和灌漿期。由本研究各生殖生長(zhǎng)階段氣候因子變化特征可知, 長(zhǎng)江中下游地區(qū)單季稻產(chǎn)量形成關(guān)鍵期處于熱脅迫容易發(fā)生的時(shí)段。且研究表明單季稻乳熟期前后為溫度敏感期[48], 本研究結(jié)果也證實(shí)了這一點(diǎn)(表2)。而這些關(guān)鍵階段的熱脅迫是導(dǎo)致水稻減產(chǎn)的主要原因[29]。因此單季稻生殖生長(zhǎng)期極易遭受熱脅迫的影響, 而避免高溫是預(yù)防熱脅迫的主要策略[47], 培育耐熱品種是保障糧食安全的最有效措施之一[49]。優(yōu)化播種期是緩解氣候變暖不利影響的另一有效措施, Bai等[24]研究表明調(diào)整播種期主要改變了單季稻的開(kāi)花期和成熟期, 而對(duì)生殖生長(zhǎng)期長(zhǎng)度沒(méi)有影響, 因此調(diào)整播種期獲得不同的光溫配置是提高水稻資源利用率和適應(yīng)氣候變化的能力的重要途徑[50]。長(zhǎng)江中下游地區(qū)適當(dāng)早播可提高單季稻品種的光溫資源利用[51], 合理的播種期科學(xué)配套適宜單季稻品種類型對(duì)充分利用當(dāng)?shù)毓鉁刭Y源、發(fā)揮品種潛力、確保高產(chǎn)穩(wěn)產(chǎn)具有十分重要的意義。因此, 單季稻提前播種以避開(kāi)花期高溫、轉(zhuǎn)向現(xiàn)有耐高溫品種以及培育長(zhǎng)生殖生長(zhǎng)期優(yōu)良品種等3項(xiàng)適應(yīng)性措施可在不同程度上有效抵消氣候變化的負(fù)面影響[52]。另外, 合理灌溉、施肥和噴灑外源化學(xué)物質(zhì)可以緩解外界環(huán)境的不利影響。例如, 灌溉是降低水稻花期冠層溫度的一種有效的實(shí)時(shí)栽培措施[46], 合理灌溉可以緩解因氣候變暖導(dǎo)致的開(kāi)花期提前[53];灌漿期熱脅迫影響單季稻品質(zhì), 主要通過(guò)降低葉片抗氧化酶活性, 從而降低光合作用, 導(dǎo)致光合同化物降低等[54], 灌漿期前噴灑外源亞精胺可顯著提高超氧化物歧化酶和過(guò)氧化物酶的活性, 降低丙二醛的積累, 提高熱脅迫下水稻葉片的可溶性糖含量, 維持葉片的滲透平衡, 提高光合和蒸騰速率[55]; 施氮可以增強(qiáng)單季稻生殖生長(zhǎng)期對(duì)熱脅迫的耐受性, 進(jìn)而增加葉片壽命和生長(zhǎng)期長(zhǎng)度, 然而, 需肥量應(yīng)根據(jù)當(dāng)?shù)厮靖魃A段實(shí)際日照狀況計(jì)算得到, 日照不足時(shí)應(yīng)少施肥。例如, 長(zhǎng)江中下游地區(qū)分蘗期到孕穗期和抽穗期到乳熟期日照時(shí)數(shù)呈增加趨勢(shì)(圖3), 這2個(gè)關(guān)鍵生長(zhǎng)階段可以適當(dāng)施肥。

3.3 問(wèn)題和挑戰(zhàn)

研究氣候變化和技術(shù)進(jìn)步對(duì)作物生育期長(zhǎng)度的影響十分關(guān)鍵。氣候變化和技術(shù)進(jìn)步對(duì)作物生育期的定量影響最大的問(wèn)題仍然是有效資料的匱乏, 這在理解生育期響應(yīng)氣候變化機(jī)制方面仍存在很大的不確定性。現(xiàn)有長(zhǎng)期作物生育期觀測(cè)資料是不同站點(diǎn)不同技術(shù)措施下得到的, 每個(gè)站點(diǎn)品種類型、施肥量、灌溉量以及農(nóng)藥使用等相關(guān)資料很難準(zhǔn)確獲得, 因此技術(shù)進(jìn)步的貢獻(xiàn)僅是一個(gè)綜合結(jié)果, 不同管理措施難以定量化, 這可能限制了作物適應(yīng)氣候變化的精細(xì)化措施的實(shí)施。以往研究通常用品種替換代替技術(shù)進(jìn)步的影響, 一定程度上可能高估了品種改變對(duì)作物生育期的影響。而基于田間試驗(yàn)的有關(guān)品種變化對(duì)作物生育期影響的資料是有限的[5]。因此, 今后應(yīng)利用田間試驗(yàn)、原位觀測(cè)和模型模擬等多源數(shù)據(jù)開(kāi)展研究[9,18,25]。

4 結(jié)論

本研究詳細(xì)地分析了1981—2010年長(zhǎng)江中下游地區(qū)單季稻不同生殖生長(zhǎng)期長(zhǎng)度時(shí)空變化特征, 以及單季稻不同生殖生長(zhǎng)期長(zhǎng)度對(duì)氣候變化和技術(shù)進(jìn)步的響應(yīng)。近30年來(lái)長(zhǎng)江中下游地區(qū)單季稻乳熟期和成熟期推遲, 全生殖生長(zhǎng)期延長(zhǎng), 其中以抽穗期到乳熟期延長(zhǎng)為主。平均溫度升高縮短了單季稻生殖生長(zhǎng)期長(zhǎng)度, 累積降水量和累積日照時(shí)數(shù)增加延長(zhǎng)了單季稻生殖生長(zhǎng)期長(zhǎng)度, 且生殖生長(zhǎng)期長(zhǎng)度對(duì)累積日照時(shí)數(shù)的敏感性最強(qiáng)、對(duì)平均溫度的敏感性次之, 對(duì)降水量的敏感性最小。長(zhǎng)江中下游地區(qū)高溫和寡照不利于單季稻生殖生長(zhǎng)期的延長(zhǎng), 而技術(shù)進(jìn)步彌補(bǔ)了氣候變化對(duì)生殖生長(zhǎng)期的縮短效應(yīng)。因此, 氣候變暖背景下, 長(zhǎng)江中下游地區(qū)采取強(qiáng)耐熱型和長(zhǎng)生殖生長(zhǎng)期單季稻品種仍然是適應(yīng)氣候變化的關(guān)鍵措施。

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Response of reproductive growth period length to climate warming and technological progress in the middle and lower reaches of the Yangtze River during 1981–2010 in single-cropping rice

LIU Er-Hua1,2,3, ZHOU Guang-Sheng1,2,3,4,*, WU Bing-Yi2, SONG Yan-Ling1,3, HE Qi-Jin4, LYU Xiao-Min1,3, and ZHOU Meng-Zi1,3

1State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China;2Institute of Atmospheric Sciences, Fudan University, Shanghai 200439, China;3Joint Eco-Meteorological Laboratory of Chinese Academy of Meteorological Sciences and Zhengzhou University, Zhengzhou 450001, Henan, China;4College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China

Crop growth period length is closely linked to climate change and technological progress. Even though the extensive researches conducting on crop growth period length variation and its response to climate change, particularly temperature change, the response of reproductive growth periods lengths (RGLs) to climate change and technological progress remains unclear. Based on the reproductive growth periods and meteorological data of single-cropping rice in the middle and lower reaches of the Yangtze River (MLYR) during 1981?2010, the trends of the RGLs (including booting to heading (BDHD), heading to milking (HDMS), milking to maturity (MSMD), and booting to maturity (BDMD)) and climatic variations were analyzed. In addition, to explore the confounding effects of climate change and technological progress on the RGLs, the sensitivities of the RGLs to mean temperature (TEM), cumulative precipitation (PRE), and cumulative sunshine duration (SSD) were measured. The results showed that the BDMD had an extension trend (0.24 d a–1), among which the extension trend in the HDMS (0.16 d a–1) was the most obvious in RGLs, while the extension trends of BDHD (0.03 d a–1) and MSMD (0.05 d a–1) were not significant. High temperature and low sunshine duration were unfavorable to the extension of the RGLs. The mean relative contributions of TEM to the BDHD, HDMS, and MSMD were –50.0%, –50.7%, and –21.9%, which were –47.2%, –48.7%, and –67.6% in terms of SSD, respectively. Technological progress compensated for the adverse impacts of climate change on the trends of different RGLs. These results suggested that cultivar selection and agronomic management were the effective adaptation strategies benefiting for the stable and high yield of single-cropping rice. Single-cropping rice cultivar with longer RGLs and heat-tolerant may be suitable to cope with the continuous climate change in the future.

single-cropping rice; climate change; reproductive growth period lengths; technological progress

10.3724/SP.J.1006.2023.22026

本研究由國(guó)家重點(diǎn)研究計(jì)劃項(xiàng)目(2018YFA0606103), 國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目(42130514, 4213000565)和中國(guó)氣象科學(xué)研究院基本科研業(yè)務(wù)費(fèi)專項(xiàng)(2020Z004)資助。

This study is supported by the National Key Research and Development Program of China (2018YFA0606103), the National Natural Science Foundation of China (42130514, 4213000565), and the Basic Research Fund of Chinese Academy of Meteorological Sciences (2020Z004).

周廣勝, E-mail: zhougs@cma.gov.cn

E-mail: leh4179@163.com

2022-04-30;

2022-10-10;

2022-10-31.

URL: https://kns.cnki.net/kcms/detail/11.1809.S.20221028.1445.002.html

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).