臺灣水稻褐飛蝨族群發生動態之預測模式

Forecasting Model for Brown Planthopper Population Fluctuation and Its Effects on Rice Production in Taiwan

賴信宏(Sin-Hong Lai) ； 黃守宏(Shou-Horng Huang) ； 鄭清煥(Ching-Huan Cheng) ； 蔣國司(Kuo-Szu Chiang)

《作物、環境與生物資訊》 11卷2期 (2014/06) Pp. 65-79

https://doi.org/10.30061/CEB.201406_11(2).0002

摘要

褐飛蝨（Nilaparvata lugens, Stål）為臺灣最重要水稻害蟲，如防治不當常導致二期作水稻產量及品質顯著下降。為達到水稻害蟲經濟安全有效的管理，建立早期預警，作為適時防治之依據，是為解決此一問題之重要課題。本研究使用1988至2012年在嘉義分所溪口農場所設置誘蟲燈每日捕捉褐飛蝨之數據，利用1988-2003年之數據分析，發現資料在時間序列上存在高度的自我相關。首先嘗試運用三情況門檻自我迴歸分析建立模式，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120-160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日後預測值之比較，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。

關鍵字

褐飛蝨 ； 族群發生動態 ； 預測 ； 門檻自我迴歸模式

並列摘要

The brown planthopper (Nilaparvata lugens, Stål) is an important pest insect which affects rice production in Taiwan. Incorrect control strategies will reduce the quality and yield of rice production. The brown planthoppers can migrate to 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 planthopper can be recorded. The forecasting system for the outbreak time of brown planthoppers will provide early warning and information on chemical application for safe rice production. In this study, population fluctuations based on daily data collected from paddy fields and traps were monitored in Chiayi County from 1988 to 2012. Owing to the autocorrelation of the data, we analyzed them by using the three-regime threshold autoregressive (TAR) statistical model of time series. Firstly, the data from 1988 to 2003 was used to establish the prediction model. Secondly, the data from 2004 to 2012 were employed to test the validity of the predicted model. A long-term forecast provided 120-160 days of prediction after the first prediction date 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, the forecasting model could effectively estimate population fluctuations seven days in advance. Accordingly, the results of this study are applicable to be included in the plant protection measures.

並列關鍵字

Brown planthopper ； ＂Nilaparvata lugens＂ ； Population fluctuations ； Forecasting ； Threshold autoregressive model