Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/89546
標題: 臺灣水稻褐飛蝨族群變化與發生之預測模式
Population Fluctuation and Forecasting Model for Brown Planthopper on Rice Crops in Taiwan
作者: Sin-Hong Lai
賴信宏
關鍵字: brown planthopper;Nilaparvata lugens;population change rate;exponential growth observation error (EGOE);population fluctuations;forecasting;threshold autoregressive model;褐飛蝨;族群變化率;水稻生育期;指數成長觀測值誤差模式;族群發生動態;預測;門檻自我迴歸模式
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摘要: 
褐飛蝨(Nilaparvata lugens, Stal)為臺灣重要水稻害蟲之一,如防治不當經常導致二期作水稻品質及產量顯著降低。一般用於調查褐飛蝨族群之方式,是在水稻田附近設置誘蟲燈與高空捕蟲網等器具,以及在水稻移植後進行的田間目視或網掃等工作,前者為進行偵(監)測褐飛蝨遷移之變動,後者為監測褐飛蝨在水稻本田發生之進展。由於褐飛蝨之發生程度會隨著時間而變動,具有時間上之關聯性,因此使用統計上時間序列方法來探究褐飛蝨之族群變化率,應屬解決方法之一。
本研究使用1988至2012年在嘉義分所溪口農場所監測之4種調查方式資料,依照褐飛蝨嚴重影響水稻產量之階段,將二期作水稻生育期劃分成3期(約營養生長期、生殖生長期與成熟期),透過指數成長觀測值誤差模式(exponential growth observation error model, EGOE)來估計族群變化率與相關參數,結果得知在水稻生殖生長期族群變化率有顯著增加,建議在水稻移植40日之後應注意褐飛蝨族群之增長,但在水稻營養生長期與成熟期內族群豐度幾乎沒有顯著增加。
褐飛蝨族群發生動態深受多種因子影響,每年遭受不同時間與數量之海外遷入,以及氣候變遷造成族群數量發生變動,透過長時間的監測數據,可建立預測模式來量化褐飛蝨族群發生動態,找尋可能大發生之日期與程度,提供早期預警與適時施藥防治等資訊,達成水稻害蟲有效管理及安全生產之目標。因此,本研究也使用這筆資料中誘蟲燈捕捉褐飛蝨之每日數據,利用資料具有時間的自我相關特性來分析建模數據(1988-2003)在,發展3情況之門檻自我迴歸模式(3-regime threshold autoregressive model),並以近9年2004-2012的資料來驗證預測模式之準確性。模式之驗證分為長期與短期預測,長期預測為進行長時間(約110-160日)每日蟲數之預測,可評估二期作水稻生育期間每日蟲數。結果顯示,族群發生動態與實際趨勢大部分一致;短期預測之發展為評估第1至14日後預測値之比較,結果顯示從預測起始日第7日後之預測值具有相當高的穩健性,說明此模式可有效運用於7日後之族群發生動態趨勢。期望能以此研究的結果,提供未來運用於防治決策的參考。
總之,透過此研究結果,不僅可了解褐飛蝨在臺灣水稻生育期族群之變化率,且可預測褐飛蝨族群發生之趨勢,相信將有助於未來運用於植物保護防治策略之參考。

The brown planthopper (Nilaparvata lugens, Stal) is an important pest insect which affects rice production in Taiwan. Incorrect control strategies will reduce the quality and yield of rice crops. The most commonly used traps to monitor the migration of brown planthoppers in Taiwan are light traps and air borne net traps placed in the rice field. Other sampling methods include visual counting and sweep net counts in monitored fields after transplanting. The population abundance is an important issue because it affects the rice yielding loss. As usual, data on the brown planthoppers are monitored sequentially by time. Thus, using the statistical method of time series to estimate the population abundance is a feasible approach.
In this study, population abundance data based on four survey methods collected from monitored paddy fields and traps from 1988 to 2012 in Chiayi County were investigated. In our approach, the rice development was stratified into three phases and the change rate of the brown planthopper population was estimated according to three rice phases. Population abundance was estimated by using an exponential growth observation error model (EGOE). Relationships between population abundances and each of the survey methods were presented. The results showed that population change rates during reproductive phase were larger than those of the other phases. The 40th day after transplanting is a critical time point for the outbreak of the brown planthopper population. According to our data, during reproductive phase, the population change rate of the brown planthopper population increased drastically. Without prompt and appropriate action, this could lead to serious reduction in quality and yield for rice crops.
The brown planthoppers can generally immigrate into Taiwan every year from neighboring areas. Moreover, the immigration time and population abundance are often changeable. By a system of long-term monitoring of the insect, a time-series population fluctuation of brown planthoppers can be recorded. The forecasting system for the outbreak time of brown planthoppers can provide early warning and information on chemical application for safety rice production. In this study, population fluctuations based on daily data collected from light traps in Chiayi County from 1988 to 2012 were used. Owing to the autocorrelation of the data, the three-regime threshold autoregressive (TAR) statistical model of time series was utilized. Firstly, the data from 1988 to 2003 was used to establish the predicted model. Secondly, the data from 2004 to 2012 were employed to test the validity of the predicted model. A long-term forecast provided 110-160 days of prediction after the first prediction date and was used to estimate daily forecasting data in the second crop season. Results showed that most of the forecasting trends are near the trends of the observed data. For short-term forecasting, we used the results of one day forecasting to those of fourteen day forecasting to describe the precision of the forecasting model. The results indicated that the trend of seven day forecasting is recommended. That is, our forecasting model could effectively estimate population fluctuations seven days in advance.
In short, the results of this study are helpful in characterizing fluctuations in brown planthopper population and in providing trends for a forecasting model for these fluctuations, and their effects on rice production in Taiwan. Thus, I believe that our finding can be helpful in the field of plant protection.
URI: http://hdl.handle.net/11455/89546
其他識別: U0005-0402201519275100
Rights: 同意授權瀏覽/列印電子全文服務,2018-02-06起公開。
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