Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/36856
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dc.contributor安寶貞zh_TW
dc.contributorPao-Jen Annen_US
dc.contributor王添成zh_TW
dc.contributor鍾文全zh_TW
dc.contributorTien-Cheng Wangen_US
dc.contributorWen-Chien Chungen_US
dc.contributor.advisor蔣國司zh_TW
dc.contributor.advisorKuo-Szu Chiangen_US
dc.contributor.author洪藜瑛zh_TW
dc.contributor.authorHung, Li-Yingen_US
dc.contributor.other中興大學zh_TW
dc.date2008zh_TW
dc.date.accessioned2014-06-06T07:58:04Z-
dc.date.available2014-06-06T07:58:04Z-
dc.identifierU0005-2408200718191900zh_TW
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dc.identifier.urihttp://hdl.handle.net/11455/36856-
dc.description.abstract由病原真菌 Phytophthora infestans (Mont.) de Bary 所引起的晚疫病 (late blight)是世界各國極關切之重大作物病害,在1998年以前台灣極少發生晚疫病。近年來由於致病力強且具有抗藥性的新菌系出現,以致晚疫病已成為冬、春兩季,番茄及馬鈴薯最主要的病害之一,如果疏忽疫情或防治不當,均會遭受嚴重損失,甚至全無收成。 在國外文獻中,氣象因子常在晚疫病預測模式中扮演重要角色,由此模式可預測病勢嚴重度、發病時間與噴藥最佳時機等。但國外所發展之模式,大部份皆是在溫帶氣候及大面積種植情況下所發展而出,在台灣仍是憑藉著經驗模式來推斷發病的日期,故有必要對於台灣的氣候條件,來研提晚疫病之預測模式。所以本研究收集彙整最近六年(2002~2007)農試所栽培的7組番茄病勢進展資料與亞蔬中心所收集8組番茄病勢進展資料,並加上在台中改良場埔里分場所栽種一季番茄資料,總共為16組皆未噴灑殺菌劑之病害調查資料,根據這些資料之發病日期與病勢進展曲線,再結合田區之氣象資料,從而建立台灣本土之預測模式,稱之為“台灣晚疫病預測模式”。 台灣晚疫病預測模式可分為兩部分,第一部分是預測晚疫病何時發病,第二部分是依照田間實際氣象狀況給予噴灑殺菌劑適當時機的建議。第一部分是利用由溫、溼度所對照岀的嚴重度單位,作為晚疫病發生預測模式之依據,整體而言,皆比國外之預測模式 (Hyre、Wallin和Blitecast)表現較佳,皆較能精確預測出發病日期,其讓栽培者能準確施用第一次藥劑,保護田間與環境之負荷減至最低,並讓晚疫病之發生能夠防患於未然。 第二部分是為了能有效抑制病害的蔓延,需先找出是在何種氣象條件下會影響病害的擴展與蔓延,本研究使用邏輯式 (Logistic)模式之生長曲線去描述病勢進展的情形,並採用病勢進展曲線的斜率表示病害發展蔓延速度,再結合前七天內的溫、濕度及雨量,利用局部權重回歸 (Locally weighted regression, Loess),篩選並建立其病害蔓延的預測模式,以方便未來田間根據其氣象資料就能精確預測病害蔓延的速度,給予殺菌劑噴灑最佳時機的建議,如此能協助栽種者依照氣象狀況把握防治適機,以期減少無謂的損失。zh_TW
dc.description.abstractLate blight of potato and tomato caused by Phytophthora infestans (Mont.) de Bary is one of the most concerned plant diseases worldwide. Before 1998, late blight was seldom found in the field in Taiwan. Recently, there was a new strain with higher virulence and resistance to metalaxyl, has become dominant and replaced the old one. It almost destroyed all the potato and tomato fields. Hence the disease has become a major concern of tomato and potato production during the winter to spring season. If no suitable disease management strategy is performed, it will cause a huge loss owing to the cultivar ruined out. According to the past literatures, the weather was often the most essential key factor for the disease occurrence and development. Based on the models, we are able to forecast the severity of disease, predict the time point of the outbreak and administer to the recommendation of fungicide application in the suitable time. However, these foreign models were almost set up in the temperate zone under large scale cropping system and so far we mostly relied on the past experience to forecast the outbreak and the spread of late blight in Taiwan. Therefore, we collected seven epidemics dataset from Taiwan Agricultural Research Institute for past six years (2002~2007) and eight epidemics dataset from the Asian Vegetable Research and Development Center (AVRDC) in this research. Furthermore, an epidemic executed at Pu-Li Branch of Taichung District Agricultural Improvement Station in 2006 was also included. The total data included 16 epidemics without any fungicide spray on the tomato cultivars investigated. Combining the data of the disease assessment of late blight and the weather record of each field, we established the forecasting model, called“forecasting model of late blight in Taiwan”. Forecasting model of late blight in Taiwan can be divided into two parts. The first part of the forecasting model predicts the first occurrence of disease symptom of late blight. The second part of the forecasting model recommends fungicide applications based on the weather. For the first part of the model, we used severity units, which both temperature and relative humidity make up, to represent the degree of weather favored for late blight. It performed better than the previously foreign forecast model e.g., Hyre, Wallin, Blitecat models and so on. Hence, the new model can help the grower to catch the first disease symptom to spray the protectant fungicide in advance. The second part is to search the relationship of weather and the infection rate among the historical dataset to predict severity of late blight in the field. It enables us to schedule the fungicide applications for improving the control measures. In the thesis, we used Logistic model to quantify the disease progress curve and compute the slope per time observation, because the slope represented the infected velocity. Moreover, we combined relative humidity, temperature and precipitation, which were collected in the past seven days with the corresponding slope value, to model the relationship by Locally Weighted Regression (LOESS). Therefore, the model will provide the suggestion for the subsequent spray-timing to make effective disease control and reduce the economic losses.en_US
dc.description.tableofcontents一、 研究動機及目的..............................................................................1 二、 晚疫病的病徵與生態......................................................................6 (一) 晚疫病菌的生活史.................................................................6 (二) 晚疫病發病情況.....................................................................7 (三) 晚疫病發病特徵.................................................................8 (四) 晚疫病防治方法.....................................................................9 三、 前人研究........................................................................................11 (一) 晚疫病的預測模式...............................................................11 1. Dutch rules.........................................................................11 2. Irish rules...........................................................................12 3. Hyre...................................................................................13 4. Wallin.................................................................................14 5. Blitecast.............................................................................15 6. Phytoprog..........................................................................16 7. NegFry...............................................................................17 8. Forsund..............................................................................17 9. Winstel...............................................................................18 (二) 晚疫病的預測軟體...............................................................19 四、 材料與方法...................................................................................20 (一) 試驗資料介紹.......................................................................20 1. 田間資料...........................................................................20 (1) 資料來源.......................................................................20 (2) 資料合併.......................................................................22 (3) 資料篩選.......................................................................24 2. 環境氣象資料...................................................................26 (二) 分析方法...............................................................................27 1. 晚疫病發生時機之預測....................................................27 2. 病勢進展模式之建立........................................................30 五、 結果與討論......................................................................................35 (一) 分析結果...............................................................................35 1. 晚疫病發生時機之預測....................................................35 2. 病勢進展模式之建立........................................................40 (1) 使用前三天的天氣資料.........................................40 ○1 兩變數的模式建立..............................................40 ○2 三變數的模式建立..............................................43 (2) 使用前七天的天氣資料.........................................47 ○1 兩變數的模式建立..............................................47 ○2 三變數的模式建立..............................................50 (二) 討論..........................................................................................53 1. 晚疫病發生時機之預測....................................................53 2. 病勢進展模式之建立........................................................58 六、 參考文獻..........................................................................................63 七、 附錄..................................................................................................70 表目次 表1、番茄晚疫病之田間調查資料..........................................................70 表2、兩品種感病程度的K-S檢驗..........................................................71 表3、番茄晚疫病病害級數分類表..........................................................71 表4、16組Epidemics實際發病日期與五種預測模式之預測日期.......72 表5、Wallin85及台灣晚疫病預測模式所使用的嚴重度單位對照表...73 表6、潛伏期為三天的模式效果檢定......................................................73 表7、潛伏期為七天的模式效果檢定......................................................74 表8、四種模式效果比較..........................................................................74 表9、原始斜率分為四份的切點..............................................................74 圖目次 圖1 a-g、農試所七組資料的病勢進展曲線圖,每一組分別繪出兩品種的病勢進展曲線.....................................................................75 圖2、氣象資料(sev3、rain3)與斜率(slope)的散佈圖..............................76 圖3、預測晚疫病發生之模式的準確度:每種預測模式建立在16組番茄Epidemics之結果................................................................77 圖4、預測晚疫病發生之模式的總差異天數:每種預測模式建立在較具可信度的16組番茄Epidemics之結果...................................78 圖5、預測晚疫病發生之模式的差異天數:每種預測模式建立在較具可信度的16組番茄Epidemics之結果.......................................79 圖6、預測晚疫病發生之模式的總差異天數:每種預測模式建立在較具可信度的6組番茄Epidemics之結果.....................................80 圖7、預測晚疫病發生之模式的差異天數:每種預測模式建立在較具可信度的6組番茄Epidemics之結果.........................................81 圖8、氣象資料(sev3、rain3)與斜率(slope)的3D散佈圖........................82 圖9a、rain3條件固定下,sev3與slope的散佈圖....................................83 圖9b、sev3條件固定下,rina3與slope的散佈圖....................................83 圖10、單變數與模式一(model.2x.3days: slope~sev3*rain3, span=1)所得殘差散佈圖..............................................................................84 圖11a、rain3條件固定下,sev3與模式一的殘差散佈圖.......................85 圖11b、sev3條件固定下,rain3與模式一的殘差散佈圖.......................85 圖12a-d、使用模式一其所得的殘差分佈情形及常態機率圖...............86 圖13a、rain3條件固定下,sev3與經由模式一所得的模式預測圖.......87 圖13b、sev3條件固定下,rain3與經由模式一所得的模式預測圖.......87 圖14、氣象資料(mt3、rain3、rh3)與斜率(slope)的散佈圖......................88 圖15a、rh3與mt3條件固定下,rain3與slope的散佈圖.........................89 圖15b、rain3與rh3條件固定下,mt3與slope的散佈圖........................89 圖15c、rain3與mt3條件固定下,rh3與slope的散佈圖.........................89 圖16、各變數與模式二(model.3x.day:slope ~ rh3 * mt3 * rain3, span =0.75)所得殘差散佈圖............................................................90 圖17a-f、a-c三圖是各變數與模式三的殘差散佈圖,d-f三圖是各變數與模式四的殘差散佈圖.......................................................90 圖18a、rh3與mt3條件固定下,rain3與模式四所得殘差散佈圖..........91 圖18b、rain3與rh3條件固定下,mt3與模式四所得殘差散佈圖..........91 圖18c、rain3與rh3條件固定下,mt3與模式四所得殘差散佈圖..........91 圖19a-e、使用模式四其所得的殘差分佈情形及常態機率圖...............92 圖20a、rh3與mt3條件固定下,rain3由模式四所得的模式預測圖......93 圖20b、rh3與rain3條件固定下,mt3由模式四所得的模式預測圖......93 圖20c、rain3與mt3條件固定下,rh3由模式四所得的模式預測圖......93 圖21、氣象資料(sev7、rain7)與斜率(slope)的散佈圖............................94 圖22、氣象資料(sev7、rain7)與斜率(slope)的3D散佈圖......................94 圖23、單變數與模式五(model.2x:slope~sev7*rain7, span=1)所得殘差散佈圖..........................................................................................95 圖24a、rain7條件固定下,sev7與模式五的殘差散佈圖.......................96 圖24b、sev7條件固定下,rain7與模式五的殘差散佈圖.......................96 圖25a-d、使用模式五其所得的殘差分佈情形及常態機率圖...............97 圖26a、 rain7條件固定下,sev7與經由模式五所得的模式預測圖.....98 圖26b、sev7條件固定下,rain7與經由模式五所得的模式預測圖.......98 圖27、氣象資料(mt7、rain7、rh7)與斜率(slope)的散佈圖......................99 圖28a、rh7與mt7條件固定下,rain7與slope的散佈圖.......................100 圖28b、rain7與rh7條件固定下,mt7與slope的散佈圖......................100 圖28c、rain7與mt7條件固定下,rh7與slope的散佈圖.......................100 圖29、單變數與模式六(model.3x:slope ~ rh7 * mt7 * rain7, span=0.75)所得殘差散佈圖........................................................................101 圖30a-f、a-c三圖是各變數與模式七的殘差散佈圖,d-f三圖是各變數與模式八的殘差散佈圖.......................................................101 圖31a、rh7與mt7條件固定下,rain7與模式八的殘差散佈圖............102 圖31b、rain7與rh7條件固定下,mt7與模式八的殘差散佈圖............102 圖31c、rain7與mt7條件固定下,rh7與模式八的殘差散佈圖............102 圖32a-e、使用模式八其所得的殘差分佈情形及常態機率圖.............103 圖33a、rh7與mt7條件固定下,rain7由模式八所得的模式預測圖....104 圖33b、rain7與rh7條件固定下,mt7由模式八所得的模式預測圖....104 圖33c、rain7與mt7條件固定下,rh7由模式八所得的模式預測圖....104zh_TW
dc.language.isoen_USzh_TW
dc.publisher農藝學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2408200718191900en_US
dc.subjectLate blighten_US
dc.subject晚疫病zh_TW
dc.subjectEpidemicsen_US
dc.subjectLogistic modelen_US
dc.subjectLocally weighted regressionen_US
dc.subject流行病zh_TW
dc.subject邏輯式模式zh_TW
dc.subject局部權重回歸zh_TW
dc.title番茄晚疫病流行病學預測模式之建立zh_TW
dc.titleEstablishment of the Predicted Models for Tomato Late Blight in Epidemiologyen_US
dc.typeThesis and Dissertationzh_TW
item.languageiso639-1en_US-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.fulltextno fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:農藝學系
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