Interpretation of Brown Planthoper (Nilaparvata lugens Stal.) Attacks Using Exponential Smoothing and Spatial Autocorelation

1st Annual Applied Science and Engineering Conference

工业技术;自然科学

Simanjuntak, B.H.^1 ; Prasetyo, S.Y.J.^1 ; Widyawati, N.^1 ; Agus, Y.H.^1 ; Dewi, C.^1

Study Centre SIMITRO, Informatic Engineering Faculty and Agriculture and Business Faculty, Satya Wacana Christian University, Salatiga, Indonesia^1

关键词: Attack patterns;    Brown planthopper;    Exponential smoothing;    Exponential smoothing method;    Predictive information;    Rainfall data;    Spatial autocorrelations;    Wet and dry seasons;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/180/1/012246/pdf
DOI  :  10.1088/1757-899X/180/1/012246

来源: IOP

【 摘 要 】

Different ways of controlling and preventing for brown planthopper attacks (Nilaparvata lugen Stal.) called BPH in Boyolali regency has been done, but until this time the loss due to this pest occur periodically. This study aims to explore the interpretation of the results for prediction BPH attacks in Boyolali district, which is done using a combination of Exponential Smoothing methods and Spatial Autocorrelation. Training data for predicting the BPH attacks, has been taken from the events in 2001 until 2007, to predict the BPH attacks in 2008. Prediction for the spatial distribution of BPH attack using Local Moran's and Local Geary, whereas for visualization is using choropleth Map and Local Moran's Map. Rainfall data from 2001 until 2010 is used to determine the periods of wet and dry seasons in Boyolali. To visualizing the results of the analysis, was used a predictive maps for attacks of monthly periods and with the sub districts area as the smallest unit area. The results showed that the combination of methods can be used to predict the BPH attacks and can provide predictive information expansive dynamics and time attacks, include the deployment of attack pattern based on the sub district area, the initial attacks and the time peak of attacks. This prediction scan be used as a consideration to formulating early warning information and the priority how to overcome them by region.