郭建茂,吳 越,楊沈斌,江曉東,謝曉燕,王錦杰,申雙和
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典型高溫年不同播期一季稻產(chǎn)量差異及其原因分析*
郭建茂1,2,吳 越2,楊沈斌1,2,江曉東2,謝曉燕2,王錦杰2,申雙和1
(南京信息工程大學(xué)氣象災(zāi)害預(yù)報(bào)預(yù)警與評估協(xié)同創(chuàng)新中心/江蘇省農(nóng)業(yè)氣象重點(diǎn)實(shí)驗(yàn)室,南京 210044;2. 南京信息工程大學(xué)應(yīng)用氣象學(xué)院,南京 210044)
為研究自然高溫對水稻產(chǎn)量的影響,以南粳45為試材,于2013年在南京信息工程大學(xué)農(nóng)業(yè)氣象試驗(yàn)站進(jìn)行3個(gè)播期的分期播種試驗(yàn),分別為4月30日(第1播期,No.1)、5月15日(第2播期,No.2)和5月31日(第3播期,No.3),并分析水稻產(chǎn)量及其性狀、產(chǎn)量貢獻(xiàn)因子、灌漿期莖和葉向穗的干物質(zhì)轉(zhuǎn)運(yùn)及收獲指數(shù)(Harvest index, HI)對高溫的響應(yīng)特征。結(jié)果表明:(1)在試驗(yàn)播期范圍內(nèi),隨著播期的延后水稻表現(xiàn)為增產(chǎn)的趨勢,其中No.1與其它兩個(gè)播期間產(chǎn)量差異達(dá)到顯著性水平(P<0.05),相比No.2和No.3,No.1產(chǎn)量分別降低3495.08kg·hm-2和6319.58kg·hm-2;就產(chǎn)量性狀來看,No.1的結(jié)實(shí)率與其它兩個(gè)播期達(dá)到顯著性差異(P<0.05),而3個(gè)播期間千粒重和穗粒數(shù)的差異均達(dá)到顯著性水平(P<0.05),總體上來看,高溫主要表現(xiàn)為降低結(jié)實(shí)率和穗粒數(shù);(2)抽穗末穗干重P0、灌漿期同化的干物質(zhì)量ΔW、灌漿期莖和葉向穗轉(zhuǎn)移的干物質(zhì)量ΔT這3個(gè)產(chǎn)量貢獻(xiàn)因子的貢獻(xiàn)量均隨著播期的推遲逐漸增大;從貢獻(xiàn)率來看,對No.1和No.3產(chǎn)量貢獻(xiàn)率最大的是ΔW,而No.2是ΔT;(3)3個(gè)播期中莖的干物質(zhì)輸出率(Dry matter export rate, DMER)和轉(zhuǎn)化率(Dry matter transformation rate, DMTR)均超過葉的兩倍(除No.1的DMER),葉的DMER和DMTR均表現(xiàn)為No.1最大,No.3最小,分別相差4.37和7.35個(gè)百分點(diǎn),但No.1莖的DMER和DMTR均最?。唬?)3個(gè)播期HI大小趨勢與產(chǎn)量一致,表現(xiàn)為No.1(28.84%)<No.2(39.60%)<No.3(46.92%)。由此可見,在2013年將播期調(diào)整至5月中下旬有助于緩解高溫對水稻造成的危害,從而保證產(chǎn)量。
水稻;分期播種;高溫脅迫;產(chǎn)量貢獻(xiàn)因子;干物質(zhì)轉(zhuǎn)運(yùn)
水稻是中國主要的糧食作物之一,其播種面積占糧食作物總面積的1/4,產(chǎn)量接近糧食總產(chǎn)的1/2[1],而中國水稻由于種植面積廣、生長季長,水稻生長發(fā)育過程中常受到各種氣象災(zāi)害的不利影響[2-4],其中,高溫?zé)岷λ镜挠绊懺诮瓴粩嗉觿 ?880-2012年,全球地表持續(xù)升溫,平均溫度已升高0.85℃(0.65~1.06℃),而地表均溫的上升,意味著多數(shù)地區(qū)在平日與季節(jié)性的時(shí)間尺度下會出現(xiàn)更多高溫日數(shù)[5];此外,根據(jù)IPCC第五次報(bào)告對未來氣溫的預(yù)估,全球平均氣溫在2016-2035年期間相較于1986-2005年可能還將上升0.3~0.7℃[5],縱觀上述,都預(yù)示著水稻遭受高溫的危害將越來越嚴(yán)重。
在水稻生長過程中,不同時(shí)段的高溫會對水稻造成不同的影響。駱宗強(qiáng)等[6]研究表明,孕穗期高溫會降低水稻的穗粒數(shù)、結(jié)實(shí)率和千粒重,從而造成水稻減產(chǎn),這與高溫導(dǎo)致穎花數(shù)減少,光合產(chǎn)物向穗轉(zhuǎn)移受阻有關(guān);鄭建初等[7-8]研究則指出,抽穗期高溫會降低水稻的結(jié)實(shí)率,導(dǎo)致水稻干物質(zhì)積累量、SPAD值和凈光合速率顯著下降;而灌漿期高溫則會加速籽粒灌漿,縮短灌漿時(shí)間,導(dǎo)致谷粒灌漿不充分,出現(xiàn)提前成熟或早衰等現(xiàn)象[9]。由此可見,研究高溫對水稻生長發(fā)育及產(chǎn)量的影響至關(guān)重要。當(dāng)前分期播種的方法已廣泛運(yùn)用于水稻[10-11]、小麥[12]、玉米[13-14]等重要糧食作物,用以研究作物的生長特性及確定最適播期。分期播種的方法可以通過改變作物不同生育期內(nèi)的氣象條件[13],實(shí)現(xiàn)同年內(nèi)同一品種作物不同生長條件下的對比,是縮短研究周期,充分利用自然資源(如高溫),降低土壤、農(nóng)業(yè)措施等影響的有效方法??紤]到當(dāng)前針對水稻高溫?zé)岷Φ难芯恐饕峭ㄟ^人工氣候箱、盆栽等人工恒溫控制試驗(yàn)獲得[6,15-18],忽視了田間自然狀態(tài)下各氣象因子間的交互影響及時(shí)刻變動的特點(diǎn),并且自然高溫危害同時(shí)還受到灌溉、風(fēng)速、大氣濕度、輻射增溫、冠層結(jié)構(gòu)等因素的影響[19-20],由此可見,人工控制試驗(yàn)很難真實(shí)反映自然狀況下水稻高溫?zé)岷Φ臓顩r。因此,本研究利用2013年自然高溫開展田間試驗(yàn),采用分期播種的方法改變高溫與水稻生育階段的配置情況,并通過對不同播期引起水稻產(chǎn)量差異的因素進(jìn)行比較,對高溫危害造成水稻的損失及其原因進(jìn)行探討。
1.1 試驗(yàn)設(shè)計(jì)
試驗(yàn)于2013年在南京信息工程大學(xué)農(nóng)業(yè)氣象試驗(yàn)站(32°12′24″N,118°42′17″E)進(jìn)行,供試品種為南粳45,屬遲熟中粳稻品種,適宜在江蘇省蘇中及寧鎮(zhèn)揚(yáng)地區(qū)種植,一般5月上中旬播種,6月上中旬移栽。試驗(yàn)采用隨機(jī)區(qū)組設(shè)計(jì),在2013年設(shè)置3個(gè)播期,分別為4月30日(第1播期,No.1)、5月15日(第2播期,No.2)和5月31日(第3播期,No.3),移栽密度為46穴×m-2,單株栽插。每個(gè)播期設(shè)置4個(gè)重復(fù),每個(gè)重復(fù)小區(qū)面積4m×4m,區(qū)組設(shè)計(jì)完全隨機(jī)。水肥等田間管理按照常規(guī)高產(chǎn)田的要求進(jìn)行。
試驗(yàn)過程中觀測和記錄3個(gè)播期不同生育階段對應(yīng)的日期,包括播種(Sowing, S)、移栽(Transplanting, T)、幼穗分化(Panicle initiation, PI)、抽穗期(中期)(Heading, H),成熟期(Maturity, M),并根據(jù)抽穗期往前、往后各推三天作為抽穗始期(Start of heading, SH)和抽穗末期(End of heading, EH)。試驗(yàn)過程中,從移栽至成熟,按每7d(根據(jù)實(shí)際情況觀測間隔略有調(diào)整)在隨機(jī)取樣的基礎(chǔ)上,選取具有代表性的2~5穴水稻植株進(jìn)行地上部分生物量的測定,包括莖(包括莖和葉鞘)、綠葉、枯葉和穗等,并按照種植密度轉(zhuǎn)化為單位面積的質(zhì)量數(shù)。收獲時(shí),選取20穴具有代表性的水稻植株計(jì)算總有效穗數(shù),并從中選取部分穗進(jìn)行產(chǎn)量性狀的測定,包括穗粒數(shù)、千粒重和結(jié)實(shí)率(實(shí)粒數(shù)/總粒數(shù)×100%)等,然后再將小樣本穗的產(chǎn)量性狀結(jié)合整個(gè)20穴的大樣本,反推求得單位面積產(chǎn)量。
試驗(yàn)期間的氣象數(shù)據(jù)來自南京信息工程大學(xué)自動氣象觀測站W(wǎng)atchdog每10min一次的觀測數(shù)據(jù),包括氣溫、太陽輻射、降水等。
1.2 數(shù)據(jù)處理
1.2.1 產(chǎn)量指數(shù)
水稻產(chǎn)量可以通過產(chǎn)量性狀,包括有效穗、穗粒數(shù)、結(jié)實(shí)率和千粒重計(jì)算得到;另一方面,水稻產(chǎn)量的形成是一個(gè)源、庫、流三者相互作用的過程[21],根據(jù)產(chǎn)量形成的來源可以通過產(chǎn)量貢獻(xiàn)因子對產(chǎn)量進(jìn)行描述。由于水稻成熟時(shí)稻谷干重占穗干重的比值是一個(gè)相對穩(wěn)定的數(shù)值0.87[22],因此,收獲時(shí)的穗干重可以反映籽粒干重(即產(chǎn)量)的高低。本文在已有研究[23-25]及Katsura等[26]關(guān)于水稻產(chǎn)量貢獻(xiàn)因子描述的基礎(chǔ)上,將收獲時(shí)水稻穗干重分為3個(gè)部分,并以此反應(yīng)水稻產(chǎn)量形成的干物質(zhì)來源,即
式中,P(Dry weight of panicle at maturity)為成熟期穗干重(kg·hm-2);P0(Dry weight of panicle at end of heading)表示抽穗末穗干重(kg·hm-2);ΔT(Dry matter transferred from stem and leaf to panicle during grain filling stage)表示灌漿期莖和葉向穗轉(zhuǎn)移的干物質(zhì)量(kg·hm-2),為灌漿期莖、葉最大干重與最小干重的差值之和;ΔW(Newly assimulated dry matter during grain filling stage)表示灌漿期同化的干物質(zhì)量(kg·hm-2),由P-ΔT-P0得到。
根據(jù)文獻(xiàn)[27],本文將水稻莖、葉灌漿期(抽穗末-成熟)對穗干物質(zhì)的貢獻(xiàn)通過干物質(zhì)輸出率(Dry matter export rate,DMER)和轉(zhuǎn)化率(Dry matter transformation rate,DMTR)來描述,DMER和DMTR分別是抽穗末和成熟期莖(或葉)干重的差值與抽穗末莖(或葉)干重和成熟期穗干重的比值。試驗(yàn)觀測發(fā)現(xiàn),水稻抽穗末不一定是水稻莖和葉干重累積達(dá)到最大的時(shí)刻,并且莖和葉達(dá)到干重最大出現(xiàn)的時(shí)間也往往不一致,鑒于此,本文對水稻莖和葉灌漿期的DMER和DMTR的計(jì)算公式進(jìn)行適當(dāng)?shù)恼{(diào)整,修改后公式為
式中,DMER為干物質(zhì)輸出率(%),DMTR為干物質(zhì)轉(zhuǎn)化率(%);DMmax(Maximum dry weight at grain filling stage)和DMmin(Minimum dry weight at grain filling stage)分別表示灌漿期水稻莖(或葉)能達(dá)到的最大和最小干重(kg·hm-2);P為成熟期穗干重(kg·hm-2)。收獲指數(shù)(Harvest index, HI)反映了作物光合產(chǎn)物轉(zhuǎn)化為經(jīng)濟(jì)產(chǎn)品的能力,是作物品種高產(chǎn)基因型的一個(gè)重要指標(biāo)[28],收獲指數(shù)又叫經(jīng)濟(jì)系數(shù),是籽粒干重與地上部分總干重的比值。
1.2.2 熱害分析方法
眾多研究表明,水稻孕穗期、抽穗開花期和灌漿期的致害溫度為35℃[29-32],呂厚荃[33]通過對不同高溫?zé)岷?biāo)準(zhǔn)的分析比較也發(fā)現(xiàn),相比日均溫≥30℃持續(xù)3d以上、日均溫≥30℃且平均相對濕度≤70%持續(xù)3d以上、日最高氣溫≥35℃且平均相對濕度≤70%持續(xù)3d以上,日最高氣溫≥35℃持續(xù)3d以上是一季稻對高溫災(zāi)害反應(yīng)最為敏感的指標(biāo),因此,本研究以日最高氣溫不低于35℃作為水稻高溫的致害指標(biāo),并根據(jù)高溫持續(xù)天數(shù)劃分水稻的熱害等級,即3~4d為一次輕度高溫?zé)岷Γ?~7d為一次中度高溫?zé)岷Γ弧?d為一次重度高溫?zé)岷34]。一般來說,水稻植株在抽穗前后較短時(shí)期內(nèi)遭遇35℃以上的高溫也會對植株造成高溫危害[35],因此,本文以抽穗始期前3d至抽穗末期后3d作為水稻高溫?zé)岷Φ拿舾须A段。在上述基礎(chǔ)上,對3個(gè)播期水稻抽穗開花期(抽穗始-抽穗末)、灌漿期、高溫敏感期的高溫天數(shù)及高溫?zé)岷Φ燃夁M(jìn)行統(tǒng)計(jì),同時(shí)對全生育期高溫天數(shù)進(jìn)行統(tǒng)計(jì)。
利用自動氣象觀測站每10min一次的溫度數(shù)據(jù),對試驗(yàn)期間高溫日(即滿足日最高氣溫≥35℃)日最高氣溫、氣溫≥35℃最早出現(xiàn)時(shí)間、氣溫≥35℃累積出現(xiàn)時(shí)長進(jìn)行統(tǒng)計(jì)分析。
1.2.3 數(shù)據(jù)統(tǒng)計(jì)分析方法
對各播期水稻產(chǎn)量及其性狀進(jìn)行單因素方差分析,對通過方差齊次性檢驗(yàn)的因子采用SNK法進(jìn)行多重比較,方差不齊次的采用Dunnett’s T3進(jìn)行多重比較。統(tǒng)計(jì)分析通過SPSS Statistics 20完成;其余數(shù)據(jù)處理通過Microsoft Excel 97-2003和Matlab R2014a完成。
2.1 不同播期產(chǎn)量及其性狀的差異分析
2.1.1 產(chǎn)量性狀
由表1可見,3個(gè)播期水稻產(chǎn)量存在一定差異,其中,No.1產(chǎn)量最低,不足No.3的50%,其次是No.2,約為No.3的3/4,No.3的產(chǎn)量最高。方差分析顯示,No.1與其兩個(gè)播期間的產(chǎn)量差異達(dá)到顯著性水平(P<0.05),No.2與No.3產(chǎn)量差異不顯著。分析4個(gè)產(chǎn)量性狀可見,3個(gè)播期有效穗數(shù)基本一致,播期間并無顯著差異;No.2與No.3結(jié)實(shí)率的差異不顯著,但No.1顯著小于其它兩個(gè)播期;穗粒數(shù)和千粒重在3個(gè)播期間的差異均達(dá)到顯著性水平(P<0.05),其中No.3的穗粒數(shù)最大,No.1最小,而千粒重表現(xiàn)為No.2最大,No.1最小。綜合來看,No.1由于結(jié)實(shí)率、千粒重和穗粒數(shù)均小于其它兩個(gè)播期,從而導(dǎo)致最終產(chǎn)量顯著小于其它兩個(gè)播期,而No.2和No.3產(chǎn)量統(tǒng)計(jì)差異不顯著,這與千粒重和穗粒數(shù)這兩個(gè)因子在播期間的變化規(guī)律相反有關(guān)。
表1 不同播期水稻產(chǎn)量及其性狀的比較(平均值±均方差)
注:同列小寫字母表示處理間在0.05水平上的差異顯著性。
Note: Lowercase indicates the difference significance among sowing dates at 0.05 level. SD is sowing date; SSR is seed setting rate; SW is 1000-grain weight; EP is number of effective panicle; GP is grain numbers per panicle; Y is yield.
2.1.2 產(chǎn)量貢獻(xiàn)因子
根據(jù)生育期觀測的水稻各器官干重的結(jié)果,利用式(1)將各播期收獲期穗干重按照“源”進(jìn)行分解,并以此來反映產(chǎn)量來源的貢獻(xiàn)因子(P0為抽穗末穗干重,ΔW為灌漿期同化的干物質(zhì)量,ΔT為灌漿期莖和葉向穗轉(zhuǎn)移的干物質(zhì)量)。分別計(jì)算3個(gè)因子對產(chǎn)量的貢獻(xiàn)量和貢獻(xiàn)率,結(jié)果見圖1。由圖1a可見,3個(gè)產(chǎn)量貢獻(xiàn)因子的貢獻(xiàn)量均表現(xiàn)為No.3>No.2>No.1,其中,No.1和No.3中ΔW貢獻(xiàn)量的差異達(dá)到3445.68kg·hm-2,遠(yuǎn)大于No.1和No.2之間的差異1530.85kg·hm-2和No.2與No.3播期間的1914.83kg·hm-2;3個(gè)播期間ΔT貢獻(xiàn)量的差異相對P0較大,其中No.1最小,與No.2與No.3分別相差1848.18和1873.38kg·hm-2;P0是3個(gè)因子中播期間差異最小的,No.3分別比No.1和No.2大492.25和360.21kg·hm-2,No.1與No.2僅相差132.04kg·hm-2。綜合來看,造成No.1和No.3及No.2和No.3播期產(chǎn)量差異最主要的因子是ΔW,分別占No.1和No.3及No.2和No.3產(chǎn)量差異的59.29%和83.24%,而造成No.1和No.2播期產(chǎn)量差異最主要的因子是ΔT,占產(chǎn)量差異的52.64%。從圖1b中3個(gè)因子的貢獻(xiàn)率來看,3個(gè)播期對產(chǎn)量貢獻(xiàn)率最小的均是P0,分別為20.87%、14.75%和14.93%,No.1和No.3對產(chǎn)量貢獻(xiàn)率最大的是ΔW,分別占40.52%和49.52%,而對No.2中貢獻(xiàn)率最大的為ΔT,占43.62%。由此可見,在3個(gè)產(chǎn)量貢獻(xiàn)因子中,ΔW是決定產(chǎn)量最主要的因子,其次是ΔT,P0相對最小。
注:P0表示抽穗末期的干重;ΔW表示灌漿期同化的干物質(zhì)量;ΔT表示灌漿期莖和葉向穗轉(zhuǎn)移的干物質(zhì)量。三項(xiàng)之和表示成熟期穗的干重
Note: P0indicates the dry weight of panicle at end of heading stage; ΔW indicates the newly assimulated dry matter during the grain filling stage; ΔT indicates the dry matter transferred from stem and leaf to panicle during grain filling stage. The sum of these three factors is equal to the dry weight of panicle at maturity stage
2.1.3 灌漿期莖和葉向穗的干物質(zhì)轉(zhuǎn)運(yùn)
產(chǎn)量貢獻(xiàn)因子中莖和葉向穗的干物質(zhì)轉(zhuǎn)移部分(ΔT)是由莖轉(zhuǎn)移量和葉轉(zhuǎn)移量(均來自莖和葉自身的干重而非光合產(chǎn)物)共同完成的。3個(gè)播期葉干物質(zhì)對穗的輸出量分別為707.03、651.68、467.59kg·hm-2,而莖的輸出量分別為1727.72、3631.26、3840.55kg·hm-2。結(jié)合表2可知,3個(gè)播期葉的干物質(zhì)輸出率(DMER)和轉(zhuǎn)化率(DMTR)均小于莖,并且隨著播期的推遲,葉的DMER和DMTR均逐漸減小,而莖的DMER逐漸增大,水稻莖、葉的DMER比值和DMTR比值也呈現(xiàn)增加的趨勢,與產(chǎn)量變化一致,由此可見,在水稻產(chǎn)量形成過程中,莖干物質(zhì)對穗的輸出貢獻(xiàn)大于葉干物質(zhì),且莖葉的DMER和DMTR比值越大,產(chǎn)量相對越高,說明高溫很可能通過抑制莖干物質(zhì)的輸出優(yōu)勢導(dǎo)致水稻減產(chǎn)。
2.1.4 收獲指數(shù)
高溫除影響莖和葉干物質(zhì)的輸出和轉(zhuǎn)化外,還間接改變了地上部分干物質(zhì)分配到籽粒的情況(收獲指數(shù),HI)。由表2可見,隨著播期的延后,收獲指數(shù)逐漸增大。其中No.1的HI最小,No.3最大,No.3和No.2的HI分別比No.1提高18.08%和10.76%,說明相比No.3和No.2,No.1地上部分的干物質(zhì)更多地轉(zhuǎn)化為無效的秸稈。
表2 不同播期水稻灌漿期莖、葉干物質(zhì)輸出率(DMER)和轉(zhuǎn)化率(DMTR)及收獲指數(shù)(HI)
Note:DMER is the dry matter export rate; DMTR is the dry matter transf ormation rate; HI is the harvest index.
2.2不同播期水稻生育期內(nèi)高溫情況分析
2.2.1 水稻關(guān)鍵生育期高溫情況比較
根據(jù)熱害指標(biāo)及熱害等級的劃分,統(tǒng)計(jì)水稻不同生育階段的高溫情況,結(jié)果見表3。從水稻的整個(gè)生育期來看,3個(gè)播期均出現(xiàn)35d高溫,但各生育階段高溫情況表現(xiàn)不同。抽穗開花期3個(gè)播期高溫發(fā)生天數(shù)分別為7d、2d和1d,按照熱害標(biāo)準(zhǔn)僅No.1達(dá)到中度高溫?zé)岷Α乃靖邷孛舾须A段(抽穗前后各3d)來看,No.1出現(xiàn)1次重度高溫?zé)岷?,No.2出現(xiàn)1次中度高溫?zé)岷Γ鳱o.3僅發(fā)生1d高溫,未達(dá)到高溫?zé)岷Φ燃?。由此可見,兩種高溫統(tǒng)計(jì)時(shí)段下,No.1發(fā)生高溫天數(shù)均最多,隨著播期延后,高溫天數(shù)呈現(xiàn)減少的趨勢,但界定高溫危害時(shí)段的不同會造成3個(gè)播期遭遇熱害等級判定的差異。從抽穗末至成熟期(灌漿期)來看,3個(gè)播期間高溫天數(shù)的差異不大,且均未達(dá)到高溫?zé)岷Φ燃墶?/p>
表3 不同播期水稻遭遇高溫天數(shù)及熱害情況統(tǒng)計(jì)
Note: Day is high temperature days (Tmax≥35℃); HDG is heat damage grade (including the frequency and severity); SH-EH is the phase from start of heading to end of heading; EH-M is the phase from end of heading to maturity(or grain filling stage); HTSP is high temperature sensitive phase of rice (3 days before start of heading to 3 days after end of heading); WDP is the whole development phase of rice.
2.2.2 高溫發(fā)生特點(diǎn)分析
水稻高溫危害程度與高溫強(qiáng)度、高溫出現(xiàn)時(shí)間和持續(xù)時(shí)長有關(guān)。由圖2a可見,2013年試驗(yàn)期間高溫日(日最高氣溫≥35℃)日最高氣溫與氣溫≥35℃最早出現(xiàn)時(shí)間之間具有很好的線性負(fù)相關(guān)關(guān)系,即高溫日日最高氣溫越高,當(dāng)日氣溫≥35℃出現(xiàn)時(shí)間相對越早;由圖2b可見,高溫日日最高氣溫與當(dāng)日氣溫≥35℃累積時(shí)長之間也符合很好的線性相關(guān)關(guān)系,表現(xiàn)為日最高氣溫越高,當(dāng)日氣溫≥35℃累積出現(xiàn)時(shí)間越長??梢姡?013年高溫日具有日最高氣溫越高,氣溫≥35℃出現(xiàn)時(shí)間相對越早,且氣溫≥35℃累積時(shí)間越長的特點(diǎn)。一般來說,水稻在8:00-10:00開花,10:00-12:00為開花盛期,15:00之后開花數(shù)量很少[36],因此,高溫日這些特點(diǎn)的同時(shí)出現(xiàn)會加劇高溫對水稻穎花開放的不利影響,加重危害。以No.2和No.3為例,No.2抽穗開花期內(nèi)兩個(gè)高溫日(8月17日和8月18日)日最高氣溫分別為38.0℃和36.9℃,氣溫≥35℃最早出現(xiàn)時(shí)間為11:00和10:30,持續(xù)時(shí)長為7.7h和5.8h,而No.3抽穗開花期內(nèi)高溫日(8月28日)日最高氣溫為35.6℃,≥35℃最早出現(xiàn)時(shí)間為14:00,持續(xù)時(shí)長僅為1.2h,從3個(gè)高溫日高溫發(fā)生特點(diǎn)的比較可以推斷,高溫發(fā)生特點(diǎn)的不同也可能是造成No.2和No.3產(chǎn)量差異的一個(gè)原因。
3.1 結(jié)論
在本試驗(yàn)設(shè)定的3個(gè)播期下,隨著播期的推遲,水稻產(chǎn)量呈現(xiàn)增加的趨勢,其中No.1與其它兩個(gè)播期產(chǎn)量差異達(dá)到顯著性水平,結(jié)合3個(gè)播期遭遇的高溫情況,抽穗開花期高溫是造成產(chǎn)量差異最主要的原因,而通過將播期延后至5月中下旬可以使高溫敏感期避開高溫,起到緩解甚至避免高溫危害的作用。此外,抽穗開花期高溫強(qiáng)度、高溫出現(xiàn)時(shí)間的早晚和持續(xù)時(shí)間的長短也會造成播期間產(chǎn)量的差異,對于水稻抽穗開花期來說,僅以天為尺度衡量高溫的危害很可能會造成低估。
在產(chǎn)量性狀方面,播期主要影響千粒重和穗粒數(shù),3個(gè)播期間差異均達(dá)到顯著性水平,而對于結(jié)實(shí)率,No.1與其它兩個(gè)播期間的差異顯著。根據(jù)楊宏遠(yuǎn)等[37]指出水稻理論上可以達(dá)到的最大產(chǎn)量(籽?!皫臁比荩┲饕伤肓?shù)和有效穗數(shù)決定。3個(gè)播期有效穗差異不顯著,但穗粒數(shù)隨著播期的推遲逐漸增加,由此可見,高溫影響穗粒數(shù)導(dǎo)致籽粒“庫”容減小可能是造成減產(chǎn)的原因之一。
從產(chǎn)量貢獻(xiàn)因子來看,高溫敏感階段出現(xiàn)高溫,尤其是抽穗開花期的高溫會降低3個(gè)產(chǎn)量因子的貢獻(xiàn)量,從而降低產(chǎn)量;3個(gè)因子中,灌漿期同化的干物質(zhì)量(ΔW)是決定產(chǎn)量最主要的因子,也是No.1和No.3對產(chǎn)量貢獻(xiàn)率最大的因子,但對No.2貢獻(xiàn)率最大的是灌漿期莖和葉向穗轉(zhuǎn)移的干物質(zhì)量(ΔT);在灌漿期莖、葉干物質(zhì)轉(zhuǎn)化為籽粒的方面,葉的干物質(zhì)輸出率(DMER)和轉(zhuǎn)化率(DMTR)均表現(xiàn)為隨著播期的推遲逐漸減小,而莖的DMER表現(xiàn)出相反的趨勢,但3個(gè)播期在干物質(zhì)的輸出和轉(zhuǎn)化方面均表現(xiàn)為莖的優(yōu)勢大于葉。
綜上可見,水稻受高溫危害的程度不僅與高溫強(qiáng)度、持續(xù)時(shí)間、出現(xiàn)特點(diǎn)等有關(guān),也取決于高溫是否出現(xiàn)在水稻的高溫敏感階段,而播期的調(diào)整通過改變高溫與水稻生育階段的配置起到緩解高溫危害的作用,這也是造成三個(gè)播期最終產(chǎn)量和收獲指數(shù)(HI)差異的原因。
3.2 討論
當(dāng)前,全球增溫已毋庸置疑,隨著高溫?zé)岷λ居绊懙募觿?,相關(guān)的研究也不斷見諸報(bào)道。但縱觀當(dāng)前普遍采用的人工控溫試驗(yàn),雖然對水稻關(guān)鍵生育期內(nèi)白天高溫進(jìn)行了很好的模擬和研究[6,8,18],但在對試驗(yàn)的設(shè)置上往往忽視了氣象因子間的連續(xù)性及交互性。據(jù)Coast等[38]指出,在實(shí)際全球增溫情況下,水稻生育期內(nèi)相對晝溫的升高,夜間升溫更明顯。而夜溫升高到一定數(shù)值時(shí)同樣會對水稻開花特性、花粉萌發(fā)、結(jié)實(shí)率、產(chǎn)量相關(guān)參數(shù)等造成影響[38-40]。此外,全球增溫造成的相對濕度的變化也會影響高溫對水稻的危害程度[41-42]。由此可見,全球增溫趨勢下高溫對水稻造成的影響是多種因素綜合作用的結(jié)果,采用人工控制試驗(yàn)雖然在一定程度上可以反應(yīng)高溫對水稻的影響,但以此衡量全球增溫趨勢下水稻的發(fā)展情況則與實(shí)際情況有較大的不同,所得結(jié)論在指導(dǎo)自然條件下實(shí)際大田生產(chǎn)上效果往往不好。本試驗(yàn)在2013年自然高溫條件下,采用分期播種開展水稻不同生育階段高溫的試驗(yàn)研究,得出自然狀況下,不同生育階段高溫對水稻產(chǎn)量的影響,同時(shí)驗(yàn)證了播期調(diào)整在緩解水稻高溫?zé)岷?,保證水稻產(chǎn)量穩(wěn)定方面的可行性。當(dāng)然,本文仍存在試驗(yàn)品種單一、試驗(yàn)時(shí)間不長等不足,還不能得到更多的普遍結(jié)論。因此,今后研究擬依據(jù)對高溫的準(zhǔn)確預(yù)報(bào),開展自然條件下水稻高溫?zé)岷Φ南嚓P(guān)試驗(yàn),擴(kuò)展試驗(yàn)范圍,包括水稻品種、播期及處理等,并進(jìn)一步加強(qiáng)對水稻產(chǎn)量來源、結(jié)構(gòu)、性狀等的分析驗(yàn)證。
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Yield Differences and its Causes for One Season Rice Under Different Sowing Dates in Typical High Temperature Year
GUO Jian-mao1,2, WU Yue2, YANG Shen-bin1,2, JIANG Xiao-dong2, XIE Xiao-yan2, WANG Jin-jie2, SHEN Shuang-he1
(Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster/Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science & Technology/Nanjing 210044, China; 2. College of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044 )
In order to reveal the impacts of high temperature on rice yield, an interval sowing experiment was conducted with Nanjing 45 as experiment material, at agro-meteorological experimental station in Nanjing University of Information Science & Technology in 2013. Three sowing dates were April 30 (the first sowing date, denoted as No.1), May 15 (the second sowing date, denoted as No.2) and May 31 (the third sowing date, denoted as No. 3) respectively. During the experiment, the characteristics of rice yield and yield components, yield contribution factors, dry matter transportation from stem and leaf to panicle during grain filling stage, and harvest index (HI) to high temperature were analyzed. The results showed that: (1) the yield increased with sowing date postponing. Yield differences between No.1 and the other two reached 0.05 significant level and yield of No.1 was less than No.2 and No.3 by 3495.08 kg·ha-1and 6319.58 kg·ha-1respectively. As for yield components, seed setting rate difference between No.1 and the other two reached 0.05 significant level, and 1000-grain weight and grain number per panicle differences among the three reached 0.05 significant level. In general, the main performance of high temperature was to decrease seed setting rate and grain number per panicle. (2) The contribution amount of three contribution factors (dry weight of panicle at end of heading, P0; newly assimilated dry matter during grain filling stage, ΔW; dry matter transferred from stem and leaf to panicle during grain filling stage, ΔT) all increased with the sowing date postponing. The contribution rate of ΔW was the largest both in No.1 and No.3, while in No.2 contribution rate of ΔT was the largest. (3) The dry matter export rate (DMER) and transformation rate (DMTR) of stem were both twice more than that of leaf (except for the DMER in No.1). Among three sowing dates, the DMER and DMTR of leaf were the largest in No.1, but the smallest in No.3. The differences were 4.37% and 7.35% respectively. However, the DMER and DMTR of stem were both the smallest in No.1. (4) HI showed the same tendency as yield. With the sowing date postponing, the HI of No.3 showed the biggest (46.92%), then No.2 (39.60%), and No.1 showed the smallest (28.84%). So, choosing mid to late of May as the sowing date could help to alleviate the harm caused by high temperature and to ensure the yield of rice in 2013.
Rice; Interval sowing; High temperature stress; Yield contribution factors; Dry matter transportation
10.3969/j.issn.1000-6362.2017.02.007
2016-07-25
科技部行業(yè)專項(xiàng)(GYHY201506018);江蘇省重點(diǎn)研發(fā)計(jì)劃(現(xiàn)代農(nóng)業(yè))項(xiàng)目(BE2015365);中國氣象局預(yù)報(bào)預(yù)測核心業(yè)務(wù)發(fā)展專項(xiàng)(CMAHX20160311);江蘇省農(nóng)業(yè)氣象重點(diǎn)實(shí)驗(yàn)室基金(KYQ201304);重慶市氣象局開放基金課題(Kfjj-201201);重慶市博士后基金資助(RC2012002)
郭建茂(1968-),博士,副教授,主要從事農(nóng)業(yè)遙感和作物生長模擬研究。E-mail:guojianmao2004@163.com