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標題: 番茄晚疫病流行病學預測模式之建立
Establishment of the Predicted Models for Tomato Late Blight in Epidemiology
作者: 洪藜瑛
Hung, Li-Ying
關鍵字: Late blight;晚疫病;Epidemics;Logistic model;Locally weighted regression;流行病;邏輯式模式;局部權重回歸
出版社: 農藝學系所
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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。
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。

Late 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.
其他識別: U0005-2408200718191900
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