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      基于COSIM模型的新疆棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)方法

      2017-10-13 06:27:17王雪姣潘學(xué)標(biāo)胡莉婷郭燕云李新建
      關(guān)鍵詞:氣象棉花作物

      王雪姣,潘學(xué)標(biāo),王 森,胡莉婷,郭燕云,李新建

      ?

      基于COSIM模型的新疆棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)方法

      王雪姣1,2,潘學(xué)標(biāo)2※,王 森1,胡莉婷2,郭燕云1,李新建1

      (1. 新疆農(nóng)業(yè)氣象臺(tái),烏魯木齊 830002;2. 中國農(nóng)業(yè)大學(xué)資源與環(huán)境學(xué)院,北京 100193)

      該文在對(duì)棉花生長(zhǎng)模擬模型COSIM進(jìn)行模型調(diào)試、驗(yàn)證實(shí)現(xiàn)本地化應(yīng)用的基礎(chǔ)上,探討運(yùn)用作物模型進(jìn)行棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)的方法,重點(diǎn)解決未知?dú)庀髷?shù)據(jù)替代問題。作物模型應(yīng)用于產(chǎn)量預(yù)報(bào)時(shí),未來天氣的不確定性是影響預(yù)報(bào)準(zhǔn)確率的關(guān)鍵因子,該影響隨著當(dāng)年實(shí)際天氣數(shù)據(jù)增多而減小。該文以近50 a的氣象數(shù)據(jù),依次替代預(yù)報(bào)日至收獲期的氣象數(shù)據(jù)(即預(yù)報(bào)日之前使用預(yù)報(bào)年當(dāng)年氣象數(shù)據(jù),預(yù)報(bào)日之后使用替代年氣象數(shù)據(jù)),模擬棉花生長(zhǎng)發(fā)育和產(chǎn)量形成過程,以近50、40、30、20、10、5 a歷史氣候數(shù)據(jù)依次替代預(yù)報(bào)日之后的逐日數(shù)據(jù)獲得的模擬產(chǎn)量平均值作為預(yù)報(bào)產(chǎn)量,根據(jù)對(duì)預(yù)報(bào)準(zhǔn)確率進(jìn)行比較,最終確定以近10 a實(shí)測(cè)數(shù)據(jù)替代獲得的模擬產(chǎn)量平均值作為最終預(yù)報(bào)產(chǎn)量。經(jīng)驗(yàn)證該預(yù)報(bào)方法對(duì)不同播種時(shí)間棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)的準(zhǔn)確率在81.3%~99.6%,預(yù)測(cè)精度較好。作為案例分析,該文僅進(jìn)行每月1次預(yù)測(cè)分析,實(shí)際應(yīng)用中可進(jìn)行逐日替代動(dòng)態(tài)預(yù)報(bào),經(jīng)過進(jìn)一步改進(jìn),提高預(yù)報(bào)精度,未來可望達(dá)到業(yè)務(wù)應(yīng)用水平。

      棉花;模型;氣象;動(dòng)態(tài)預(yù)報(bào);產(chǎn)量;新疆

      0 引 言

      作物產(chǎn)量預(yù)報(bào)是農(nóng)業(yè)氣象業(yè)務(wù)的重要組成部分,及時(shí)、準(zhǔn)確地預(yù)測(cè)作物長(zhǎng)勢(shì)和產(chǎn)量對(duì)于國家宏觀調(diào)控、進(jìn)出口政策、農(nóng)業(yè)生產(chǎn)管理等都有指導(dǎo)作用。針對(duì)不同的作物,學(xué)者們運(yùn)用統(tǒng)計(jì)模型[1-5]、作物模型[6-9]、遙感與作物模型耦合[10-14]等方法開展了大量產(chǎn)量預(yù)報(bào)研究。

      棉花是中國主要的經(jīng)濟(jì)作物,新疆是中國最大的優(yōu)質(zhì)棉主產(chǎn)區(qū),其棉花產(chǎn)量占全國棉花總產(chǎn)的50%以上[15],因此新疆棉區(qū)棉花產(chǎn)量預(yù)報(bào)的準(zhǔn)確性就顯得尤為重要。目前,常用的作物產(chǎn)量預(yù)報(bào)方法中,大田調(diào)查統(tǒng)計(jì)的方法只能根據(jù)當(dāng)前作物生長(zhǎng)發(fā)育狀況進(jìn)行估產(chǎn),預(yù)測(cè)精度受后期天氣因素和樣本代表性影響較大,且需要消耗大量人力物力,不適合大面積應(yīng)用。農(nóng)業(yè)氣象統(tǒng)計(jì)模型預(yù)報(bào)方法,只考慮了氣象因子對(duì)作物產(chǎn)量的影響,模型難以外推應(yīng)用,且短期內(nèi)篩選預(yù)報(bào)因子較為不易,不適于動(dòng)態(tài)預(yù)報(bào)。遙感技術(shù)在大范圍的作物估產(chǎn)中有較大優(yōu)勢(shì),但是遙感數(shù)據(jù)的質(zhì)量受天氣條件影響較大,且只能通過外部表現(xiàn)來反應(yīng)作物的生長(zhǎng)狀態(tài),難以了解作物內(nèi)在生長(zhǎng)發(fā)育和產(chǎn)量形成過程。此外,運(yùn)用遙感進(jìn)行產(chǎn)量動(dòng)態(tài)預(yù)測(cè)時(shí),預(yù)測(cè)步長(zhǎng)受衛(wèi)星運(yùn)行周期限制。相比之下,作物模型不僅揭示了作物生長(zhǎng)發(fā)育的內(nèi)在機(jī)理,且綜合考慮了氣候、土壤、管理措施和品種對(duì)作物生長(zhǎng)發(fā)育的影響,能夠在任意時(shí)間模擬作物產(chǎn)量形成過程[16],近年來被廣泛應(yīng)用于作物產(chǎn)量動(dòng)態(tài)預(yù)報(bào)[6-9]。作物產(chǎn)量預(yù)報(bào)過程中,未來天氣的不確定性是影響預(yù)報(bào)準(zhǔn)確率的關(guān)鍵因子,該影響隨著作物生長(zhǎng)發(fā)育進(jìn)程的繼續(xù)和當(dāng)年實(shí)際天氣增多而減小。目前常用于未知天氣數(shù)據(jù)替代的有天氣發(fā)生器和相似年型。其中,天氣發(fā)生器[17]能夠通過已知天氣數(shù)據(jù)模擬未來的氣象特征,可以生成任意長(zhǎng)度逐日的天氣數(shù)據(jù)系列。各國學(xué)者做了大量研究,針對(duì)不同的應(yīng)用目標(biāo),開發(fā)了不同類型的天氣發(fā)生器[18-19]。但由于天氣發(fā)生器參數(shù)調(diào)控方法的不足,目前應(yīng)用其模擬數(shù)據(jù)進(jìn)行產(chǎn)量預(yù)報(bào)仍存在一些不確定性。相似年型是替代未知?dú)庀髷?shù)據(jù)的主要來源,目前已有大量研究根據(jù)相似年型進(jìn)行產(chǎn)量預(yù)報(bào)[20-23],以綜合聚類法克服根據(jù)單一相似年型預(yù)測(cè)產(chǎn)量的不足之處。但棉花具有無限生長(zhǎng)性,如遇非致命災(zāi)害具有可恢復(fù)性,即使產(chǎn)量預(yù)報(bào)前的氣象條件與歷史某年相似,預(yù)報(bào)日至收獲時(shí)的氣象條件也不一定相似,從而影響根據(jù)相似年型預(yù)報(bào)出的產(chǎn)量準(zhǔn)確性,且近年來極端天氣事件頻發(fā),大大增強(qiáng)了預(yù)報(bào)至收獲期氣象條件的未知性。因此,本文以新疆棉區(qū)代表性站點(diǎn)烏蘇縣為例,探索運(yùn)用COSIM棉花生長(zhǎng)模擬模型[20]進(jìn)行棉花產(chǎn)量動(dòng)態(tài)預(yù)測(cè)的方法,重點(diǎn)探討產(chǎn)量預(yù)報(bào)過程中未知?dú)庀髷?shù)據(jù)的替代問題,以期為農(nóng)業(yè)氣象業(yè)務(wù)服務(wù)提供新方法。

      1 材料與方法

      1.1 試驗(yàn)地概況與試驗(yàn)設(shè)計(jì)

      2011年田間試驗(yàn)在新疆烏蘇(44°43′N,84°67′E)進(jìn)行,屬于溫帶大陸性干旱氣候,無霜期195 d、≥10 ℃積溫4 002.3 ℃、日照時(shí)數(shù)1 936.9 h、棉花生長(zhǎng)季(4—10月)降水量139.8 mm。2011年4—10月≥10 ℃積溫、日照時(shí)數(shù)和降水量分別為4 235.6 ℃、1 965.3 h和146.8 mm。試驗(yàn)區(qū)土壤為黏壤土,土壤容重為1.41 g/cm3、田間持水率為40%(體積含水率)、土壤有機(jī)質(zhì)為15 g/kg、全氮質(zhì)量分?jǐn)?shù)為0.91%、堿解氮為54 mg/kg、速效磷為5 mg/kg、速效鉀為280 mg/kg、pH值為7.8,土壤屬于輕度鹽堿土。

      試驗(yàn)設(shè)置5個(gè)播期處理,分別為4月10日、4月20日、4月30日、5月10日、5月20日。小區(qū)10 m′2 m,南北行向,采用完全隨機(jī)區(qū)組設(shè)計(jì),設(shè)置3個(gè)重復(fù)。供試品種為冀棉958(L.),采用播種覆膜滴灌帶鋪設(shè)一次完成的種植模式,1膜種植4行棉花,滴灌帶鋪設(shè)在寬行的2行棉花中間,行距依次為10、60和10 cm,播種密度為22.5 萬株/hm2。播種后灌出苗水40 mm,此后,6月上旬開始灌水,8月下旬結(jié)束灌水,灌水間隔約為10 d,遇降雨天氣灌水日期順延,5個(gè)播期處理累計(jì)灌水量分別為366、360、348、320和295 mm。肥料施用量參考當(dāng)?shù)爻R?guī)用量,尿素為675 kg/hm2、磷酸二銨為255 kg/hm2、硫酸鉀為75 kg/hm2,除出苗水外,每次灌水均隨水滴肥,單次施肥量為總施肥量的1/9。5個(gè)播期處理分別于9月10日、9月15日、9月21日、9月29日、10月5日開始分3次收獲棉花(由于棉花自下而上、自內(nèi)而外的生長(zhǎng)習(xí)性,最先結(jié)鈴的下部棉鈴先吐絮成熟,最晚結(jié)鈴的頂部棉鈴最后吐絮成熟,整株棉鈴全部成熟歷時(shí)1個(gè)月以上。因此,試驗(yàn)過程中,隨著棉鈴的成熟,每個(gè)播期分3次收獲。)。

      1.2 測(cè)定項(xiàng)目與方法

      生育期:以全小區(qū)50%棉株達(dá)到發(fā)育要求為標(biāo)準(zhǔn),記錄棉花播種、出苗、現(xiàn)蕾、開花、吐絮出現(xiàn)的時(shí)間。

      葉面積和干物質(zhì):每30 d在各小區(qū)隨機(jī)取樣5株,用長(zhǎng)寬比法測(cè)定全株葉面積;將棉株各器官分離稱鮮質(zhì)量,而后分別裝于紙袋,在105 ℃下殺青30 min后,在80 ℃下烘干至質(zhì)量恒定,分別測(cè)定各器官干物質(zhì)量。

      產(chǎn)量及其構(gòu)成要素:收獲期在各小區(qū)隨機(jī)選取10株,測(cè)定單株鈴數(shù)、鈴質(zhì)量和衣分;選取各小區(qū)中間5 m的區(qū)域測(cè)定籽棉產(chǎn)量。

      1.3 作物模型

      COSIM模型[24]借鑒COTGROW棉花模型[25-26]的建模理論,主要包括發(fā)育期模擬、干物質(zhì)分配、水分平衡模擬、光合生產(chǎn)、產(chǎn)量形成等模塊,其中發(fā)育期模擬以溫度為變量(>12 ℃有效積溫)計(jì)算發(fā)育速率,根 據(jù)群體對(duì)太陽輻射的吸收量和輻射能轉(zhuǎn)化率計(jì)算干物質(zhì)生產(chǎn)量,通過各器官質(zhì)量占總干物質(zhì)質(zhì)量的比例得到各器官質(zhì)量。該模型能夠反應(yīng)環(huán)境因子(天氣和土壤)、管理措施和品種特性的互作效應(yīng)對(duì)棉生長(zhǎng)發(fā)育、產(chǎn)量形成的影響,模擬輸出棉花各發(fā)育期出現(xiàn)時(shí)間和單位面積籽棉產(chǎn)量、皮棉產(chǎn)量等。目前,該模型在棉花冷害指標(biāo)分析、預(yù)測(cè)和診斷[27-28],以及氣候變化對(duì)棉花生產(chǎn)的影響方面[29-30]已得到廣泛應(yīng)用?;诙嗄暄芯砍晒⒌男陆迏^(qū)土壤和棉花品種信息數(shù)據(jù)集[31],可為應(yīng)用該模型進(jìn)行農(nóng)業(yè)氣象業(yè)務(wù)服務(wù)提供數(shù)據(jù)支持。此外,COSIM模型以日為步長(zhǎng)動(dòng)態(tài)模擬棉花生長(zhǎng)過程,可實(shí)現(xiàn)棉花產(chǎn)量逐日動(dòng)態(tài)預(yù)報(bào)。

      本研究中試驗(yàn)地氣象資料(日最高溫、日最低溫、日照時(shí)數(shù)、降水量)由國家氣象信息中心提供,模型模擬所需的土壤、管理措施和棉花品種信息由田間試驗(yàn)獲得。采用均方根誤差(root mean square error,RMSE)對(duì)實(shí)測(cè)值和模擬值的吻合程度進(jìn)行統(tǒng)計(jì)分析。

      式中RMSE為均方根誤差;X為觀測(cè)值;Y為模擬值;為樣本數(shù)量。

      1.4 基本原理和方法

      未來天氣的不確定性是影響預(yù)報(bào)準(zhǔn)確率的關(guān)鍵因子,目前還難于準(zhǔn)確預(yù)報(bào)未來月尺度的天氣,而獲取各地實(shí)時(shí)的實(shí)測(cè)氣象數(shù)據(jù)對(duì)氣象部門已不是難事。本研究用近50 a的氣象數(shù)據(jù)依次替代預(yù)報(bào)日至收獲期的氣象數(shù)據(jù)(即播種至預(yù)報(bào)日使用當(dāng)年實(shí)測(cè)氣象數(shù)據(jù),預(yù)報(bào)日至收獲日用歷史各年氣象數(shù)據(jù)替代)生成50個(gè)天氣文件,運(yùn)用模型依次讀取天氣文件模擬棉花產(chǎn)量,以距預(yù)報(bào)年最近的50、30、20、10、5 a逐年氣象數(shù)據(jù)替代所得到的模擬產(chǎn)量的平均值作為預(yù)報(bào)產(chǎn)量。最終根據(jù)預(yù)報(bào)準(zhǔn)確率選定預(yù)報(bào)方法。

      2 結(jié)果與分析

      2.1 模型適應(yīng)性分析

      在COSIM模型輸入2011年天氣數(shù)據(jù),模擬棉花生長(zhǎng)發(fā)育及產(chǎn)量,利用田間分期播種試驗(yàn)的生育期和產(chǎn)量實(shí)測(cè)資料對(duì)模型模擬結(jié)果進(jìn)行有效性驗(yàn)證。由圖1可知,模擬得到的棉花生育期出現(xiàn)日期和皮棉產(chǎn)量與實(shí)測(cè)值擬合較好。出苗期、現(xiàn)蕾期、開花期和吐絮期日序的觀測(cè)值和模擬值的RMSE分別為2.2、2.9、2.3和5.9 d,其中吐絮期模擬偏差較大,主要原因是,田間試驗(yàn)后期對(duì)5月10日和5月20日2個(gè)播期較晚的處理采用了少量乙烯利催熟,導(dǎo)致吐絮期觀測(cè)值略大于模擬值。皮棉產(chǎn)量觀測(cè)值與模擬值的RMSE為165.9 kg/hm2,模擬準(zhǔn)確率為90%(RMSE為觀測(cè)值和模擬值的絕對(duì)偏差,RMSE/觀測(cè)值可知模擬誤差為10%,即模擬準(zhǔn)確率為90%)。因此,COSIM模型對(duì)烏蘇地區(qū)棉花生產(chǎn)狀況符合實(shí)際情況,在該地區(qū)具有較好的適用性。

      圖1 烏蘇地區(qū)棉花生育期和皮棉產(chǎn)量實(shí)測(cè)值與模擬值的比較

      2.2 預(yù)報(bào)方法篩選

      COSIM模型可逐日動(dòng)態(tài)模擬棉花生長(zhǎng)發(fā)育和產(chǎn)量形成過程,即可逐日動(dòng)態(tài)預(yù)報(bào)棉花產(chǎn)量。本文以2011年烏蘇棉花田間分期播種試驗(yàn)資料為基礎(chǔ),利用播期為4月20日的棉花產(chǎn)量資料篩選預(yù)報(bào)方法,利用播期為4月10日、4月30日、5月10日和5月20日的棉花產(chǎn)量資料對(duì)預(yù)報(bào)方法進(jìn)行驗(yàn)證。為篩選預(yù)報(bào)方法,對(duì)4—10月棉花生長(zhǎng)進(jìn)行逐日預(yù)報(bào)。

      在任意預(yù)報(bào)時(shí)間,以1961—2010年每年的天氣數(shù)據(jù),依次替代預(yù)報(bào)日之后的天氣數(shù)據(jù),得到50個(gè)天氣文件(預(yù)報(bào)日之前為2011年的天氣數(shù)據(jù),預(yù)報(bào)日之后為替代年的天氣數(shù)據(jù)),以此為驅(qū)動(dòng)運(yùn)行模型,得到50個(gè)模擬產(chǎn)量,分別以1961—2010年(50 a)、1981—2010年(30 a)、1991—2010年(20 a)、2001—2010年(10 a)和2006—2010年(5 a)模擬產(chǎn)量的平均值作為預(yù)報(bào)產(chǎn)量。皮棉產(chǎn)量實(shí)測(cè)值與模擬值比較如圖2所示。

      注:播種時(shí)間為2011年4月20日。

      由圖2可知,5種預(yù)報(bào)方法在個(gè)別預(yù)報(bào)時(shí)間均有較高的準(zhǔn)確率,但在任意預(yù)報(bào)時(shí)間的預(yù)報(bào)準(zhǔn)確率差異較大。從7次預(yù)報(bào)結(jié)果來看,50、30、20、10、5 a模擬結(jié)果平均值與實(shí)測(cè)值的標(biāo)準(zhǔn)偏差分別為171、123、82、86、106 kg/hm2,其中20和10 a模擬結(jié)果的標(biāo)準(zhǔn)偏差最小。此外,運(yùn)用作物模型進(jìn)行產(chǎn)量預(yù)報(bào)的過程中,隨預(yù)報(bào)時(shí)間的推進(jìn),當(dāng)年實(shí)測(cè)氣象數(shù)據(jù)越來越多,預(yù)報(bào)產(chǎn)量逐漸接近當(dāng)年模擬值,因此預(yù)報(bào)值與當(dāng)年模擬值的偏差也是評(píng)判預(yù)報(bào)是否準(zhǔn)確的重要指標(biāo)。由圖2可知,50、30、20、10、5 a模擬結(jié)果平均值與當(dāng)年模擬值的標(biāo)準(zhǔn)偏差分別為293、213、147、106、125 kg/hm2。綜合考慮幾種產(chǎn)量預(yù)報(bào)方法的預(yù)報(bào)準(zhǔn)確性和穩(wěn)定性(即在任意預(yù)報(bào)時(shí)間準(zhǔn)確率均較高),同時(shí)考慮到氣候變暖的單傾向性導(dǎo)致溫度差異隨時(shí)間距離增加而增大和年數(shù)增加的計(jì)算量問題,最終選擇以近10 a(2001—2010年)模擬產(chǎn)量的平均值作為皮棉產(chǎn)量的預(yù)報(bào)值,動(dòng)態(tài)預(yù)報(bào)結(jié)果見表1,其中預(yù)報(bào)準(zhǔn)確率為標(biāo)準(zhǔn)偏差與實(shí)測(cè)或者模擬產(chǎn)量的比值,%。由表可知,播種前(4月1日)預(yù)報(bào)偏差最大,準(zhǔn)確率低于90%;其余6次的預(yù)報(bào)準(zhǔn)確率均在93%以上。由此可見,根據(jù)該預(yù)測(cè)方法在任意預(yù)報(bào)時(shí)間均能獲得較高的預(yù)報(bào)準(zhǔn)確率。

      表1 2011年烏蘇棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)結(jié)果

      2.3 預(yù)報(bào)方法檢驗(yàn)

      根據(jù)已確定的預(yù)報(bào)方法,對(duì)播種時(shí)間為4月10日、4月30日、5月10日和5月20日的處理進(jìn)行產(chǎn)量動(dòng)態(tài)預(yù)測(cè),驗(yàn)證該方法的準(zhǔn)確性和穩(wěn)定性,不同播種時(shí)間的產(chǎn)量動(dòng)態(tài)預(yù)報(bào)結(jié)果見表3。與實(shí)測(cè)產(chǎn)量相比(表2),該預(yù)報(bào)方法對(duì)播種時(shí)間較早的棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)準(zhǔn)確率較高,4月10日播種的棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)準(zhǔn)確率均在95%以上;對(duì)播種時(shí)間較晚的棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)準(zhǔn)確率較低,逐月動(dòng)態(tài)預(yù)報(bào)準(zhǔn)確率均在80%以上,其中4月30日、5月10日播種的棉花在5月1日和7月1日的預(yù)報(bào)準(zhǔn)確率均高于90%。與模擬產(chǎn)量相比,預(yù)報(bào)產(chǎn)量準(zhǔn)確率均在85%以上,其中模擬產(chǎn)量為所有未知?dú)庀髷?shù)據(jù)均被實(shí)際氣象替代后最終的動(dòng)態(tài)預(yù)報(bào)產(chǎn)量,任意預(yù)報(bào)時(shí)間下的預(yù)報(bào)產(chǎn)量均收斂于此,因此可通過提高模型模擬精度提高對(duì)實(shí)際產(chǎn)量的預(yù)報(bào)準(zhǔn)確率。

      表2 2011年烏蘇不同播種時(shí)間棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)準(zhǔn)確率

      3 結(jié)論與討論

      本文探討了利用作物生長(zhǎng)模型進(jìn)行產(chǎn)量動(dòng)態(tài)預(yù)測(cè)的方法,重點(diǎn)解決產(chǎn)量預(yù)報(bào)過程中未知?dú)庀髷?shù)據(jù)的替代問題。目前,新疆棉花產(chǎn)量預(yù)報(bào)以基于相似年型的預(yù)報(bào)方法為主,分別在棉花生長(zhǎng)中期和收獲前進(jìn)行2次預(yù)報(bào),相比于傳統(tǒng)的產(chǎn)量預(yù)報(bào)方法,本研究可實(shí)現(xiàn)以日為步長(zhǎng)的動(dòng)態(tài)預(yù)報(bào),且克服了棉花生產(chǎn)前期實(shí)際天氣數(shù)據(jù)少產(chǎn)量預(yù)報(bào)準(zhǔn)確率偏低的弱點(diǎn)。目前,應(yīng)用作物模型進(jìn)行產(chǎn)量預(yù)報(bào),受模型模擬精度和未知天氣數(shù)據(jù)不確定性的限制,產(chǎn)量預(yù)報(bào)精度在90%上下。本研究在預(yù)報(bào)方法確定及驗(yàn)證過程中共進(jìn)行27次產(chǎn)量預(yù)測(cè),其中40%預(yù)報(bào)準(zhǔn)確率在80%~90%,52%預(yù)報(bào)準(zhǔn)確率在95%以上。

      氣候變化過程中新疆暖濕化趨勢(shì)顯著。COSIM模型以積溫為驅(qū)動(dòng)模擬棉花生長(zhǎng)過程,而氣候變暖的單傾向性導(dǎo)致溫度差異隨時(shí)間距離增加而增大,導(dǎo)致以近50、30、20、10 a氣象數(shù)據(jù)替代未知天氣數(shù)據(jù)進(jìn)行產(chǎn)量模擬時(shí),模擬精度隨年代的縮短而升高。而以近5 a氣象數(shù)據(jù)為替代時(shí),由于時(shí)間序列較短,所包含的氣候年型較少,從而影響模擬精度。

      近年來高溫、局地強(qiáng)對(duì)流等極端天氣事件頻繁發(fā)生,其對(duì)棉花生長(zhǎng)發(fā)育和產(chǎn)量形成影響的模擬需要更深入的研究,這也是作物模型應(yīng)用于農(nóng)業(yè)氣象業(yè)務(wù)服務(wù)過程中需要改進(jìn)之處。運(yùn)用作物模型進(jìn)行產(chǎn)量預(yù)報(bào)是個(gè)復(fù)雜的科學(xué)問題,本研究初步探討了未知?dú)庀髷?shù)據(jù)的替代問題,今后還將深入考慮極端天氣及減災(zāi)措施對(duì)產(chǎn)量的影響,進(jìn)一步完善和改進(jìn)預(yù)報(bào)方法。

      此外,單站棉花產(chǎn)量只可反映當(dāng)?shù)孛藁ㄉa(chǎn)水平,而區(qū)域總產(chǎn)對(duì)于棉花價(jià)格走勢(shì)、政府宏觀調(diào)控以及相關(guān)農(nóng)業(yè)政策的制定而言更為重要。區(qū)域棉花生產(chǎn)中棉花播種期不是具體的某一天,而是一段適宜的播種區(qū)間,預(yù)測(cè)區(qū)域總產(chǎn)時(shí)需考慮播種時(shí)間對(duì)產(chǎn)量影響。本研究根據(jù)田間分期播種試驗(yàn)資料,預(yù)測(cè)不同播種時(shí)間下的棉花產(chǎn)量取得較高的預(yù)報(bào)準(zhǔn)確率,為區(qū)域預(yù)報(bào)奠定基礎(chǔ),在今后的研究中筆者將在單點(diǎn)產(chǎn)量預(yù)報(bào)的基礎(chǔ)上探討區(qū)域產(chǎn)量預(yù)報(bào)方法。

      本研究通過預(yù)報(bào)方法篩選,最終確定以近10 a的氣象資料依次替代預(yù)報(bào)日至收獲期的未知?dú)庀髷?shù)據(jù)模擬得到的10個(gè)模擬產(chǎn)量的平均值作為預(yù)報(bào)產(chǎn)量,經(jīng)驗(yàn)證該預(yù)報(bào)方法對(duì)不同播種時(shí)間棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)的準(zhǔn)確率在81.3%~99.6%,預(yù)測(cè)精度較好。作為案例分析,本文僅進(jìn)行每月1次預(yù)測(cè)分析,實(shí)際操作中可進(jìn)行逐日替代動(dòng)態(tài)預(yù)報(bào),經(jīng)過進(jìn)一步改進(jìn),提高預(yù)報(bào)精度,未來可望達(dá)到業(yè)務(wù)應(yīng)用水平。

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      Dynamic prediction method for cotton yield based on COSIM model in Xinjiang

      Wang Xuejiao1,2, Pan Xuebiao2※, Wang Sen1, Hu Liting2, Guo Yanyun1, Li Xinjian1

      (1.830002,; 2.100193,)

      Xinjiang is the largest cotton producing area in China accounting for more than 50% of the total cotton production in China. So the accuracy of the prediction of cotton production in Xinjiang is particularly important. Based on calibration and validation of cotton growth model COSIM, in this paper, we used a dynamic prediction model for cotton yield forecast and focused on solving the problem of the unknown climatic data substitution during the prediction period. In the process of prediction, the model read the climatic data day by day. For predicting the growth, development and yield of cotton by the dynamic prediction model, in this study, we substituted the measured climatic data in the recent 50, 30, 20, 10, and 5 years for the unknown climatic data from forecasting day to harvest day, respectively. Meanwhile, the climatic data measured in the year was input into the model before forecasting day. In this way, the cotton yield and development could be predicted day by day. To test the reliability of the method, an experiment with 5 different sowing date (April 10th, April 20th, April 30th, May 10th, May 20th) was designed in 2011 at Wusu, Xinjiang (44°43′ N,84°67′ E). Each treatment was replicated 3 times. The cotton was harvested on September 10th, September 15th, September 21th, September 29thand October 5th, respectively. During the experiment, the growing stage of the cotton was recorded. The leaf area and biomass were determined. These parameter values were input into the COSIM model for cotton lint yield prediction. The model reliability was evaluated by comparing the simulated and measured values of lint yield and growing stages. For the simulation, the climatic data measured in 2011 was used. The results showed that the root mean square error (RMSE) of the cotton growing from emergence to flowering stage was 2.2-5.9 d. The determination coefficient was 0.99. For the lint yields simulations, the RMSE was 165.9 kg/hm2. It indicated that the model was reliable in simulating cotton development and lint yield. Based on experimental results of treatment 1 (sowing date was April 20th), we selected the best substitution one for the unknown climatic data from the 5 schemes (climatic data of the recent 50, 30, 20, 10, and 5 years) and then validated by the results from the other treatments. The results showed that the for the randomly selected 7 predicting time (April 1st, May 1st, June 1st, July 1st, August 1st, September 1st, October 1st), the standard deviation of the measured and predicted lint yield of the 5 schemes from 50 to 5 years’ climatic data was 171, 123, 82, 86 and 106 kg/hm2, respectively. The predicting accuracy was above 87% compared with the measured values and above 83% compared with the simulated values for the lint yields. Among them, the accuracy in the predicting time after the sowing date was above 93%. Based on the predicting accuracy and the standard deviation, the best scheme was the 10 years’ climatic data substation scheme. The validation of the best scheme using the results from the other treatments showed that predicting accuracy could reach 81.3%-99.6%, indicating the reliability of the best scheme for cotton lint yield prediction. Compared with a single station forecasting, the regional forecasting of cotton yield is more important to national macro-control. In a large region, cotton is not sowing on the same day but during a time period. Therefore, in predicting the regional cotton yield, the effect of sowing time should be taken into consideration. As a case, this study only does the forecast once a month. In practice, the daily dynamic forecast would be realized.

      cotton; models; meteorology; dynamic prediction; yield; Xinjiang

      10.11975/j.issn.1002-6819.2017.08.022

      S165+.27

      A

      1002-6819(2017)-08-0160-06

      2016-08-11

      2017-03-10

      公益性行業(yè)(氣象)科研專項(xiàng)(GYHY201206022、GYHY(QX)201506001);.新疆氣象科研課題 (MS201707);中國沙漠氣象科學(xué)研究基金(Sqj2016013)

      王雪姣,工程師,博士,主要從事作物模型和農(nóng)業(yè)氣象災(zāi)害研究。北京 中國農(nóng)業(yè)大學(xué)資源與環(huán)境學(xué)院,100193。Email:wxjby@126.com

      潘學(xué)標(biāo),壯族,博士,教授,主要從事生物氣候模型與信息系統(tǒng)、氣候變化影響評(píng)價(jià)與農(nóng)牧業(yè)適應(yīng)技術(shù)等方面的研究。北京 中國農(nóng)業(yè)大學(xué)資源與環(huán)境學(xué)院,100193。Email:panxb@cau.edu.cn

      王雪姣,潘學(xué)標(biāo),王 森,胡莉婷,郭燕云,李新建.基于COSIM模型的新疆棉花產(chǎn)量動(dòng)態(tài)預(yù)報(bào)方法[J]. 農(nóng)業(yè)工程學(xué)報(bào),2017,33(8):160-165. doi:10.11975/j.issn.1002-6819.2017.08.022 http://www.tcsae.org

      Wang Xuejiao, Pan Xuebiao, Wang Sen, Hu Liting, GuoYanyun, Li Xinjian. Dynamic prediction method for cotton yield based on COSIM model in Xinjiang[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(8): 160-165. (in Chinese with English abstract) doi:10.11975/j.issn.1002-6819.2017.08.022 http://www.tcsae.org

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