FRAMEWORKPREDIKSI KONEKTIVITAS SPASIAL MENGGUNAKAN METODE EXPONENTIAL SMOOTHING â SPATIAL AUTOCORRELATION UNTUK PENENTUAN WILAYAH ENDEMIK WERENG BATANG COKELAT (Nilaparvata lugens Stal.) DI PROVINSI JAWA TENGAH - , sri yulianto joko prasetyo ([Yogyakarta] : Universitas Gadjah Mada, 2014)
In Indonesia, Brown Planthopper (BPH) is one kind of rice plant diseases which have caused the crop failure since 1961. Central Java is one of provinces suffering high attack of BPH with the extent up to 50.390 Ha in 2011, especially in Klaten, Sukoharjo, Wonogiri, Sragen, Karanganyar and Boyolali regencies. The Agriculture Ministry of RI through Center for Pest Forecasting (BBPOPT) have had the prediction and mapping procedures of pest endemic areas.However, those methods have not be able yet to provide empirical information of the exploration and attack distribution of pest based on spatial features, cause factors of pest attack phenomena and occurrence dynamics of attack causes. The research aimed to set up a theoretical framework, method and technology of population dynamics prediction and mapping, migration pattern and population distribution of BPH as the tools for determining BPH endemic areas by using Exponential Smoothing - Spatial Autocorrelation method. The whose result of the research was completed through three steps: (1) the comparison of prediction and mapping of pest endemic areas based on BBPOPT of The Agriculture Ministry of RI by using Exponential Smoothing - Spatial Autocorrelation method, (2) the prediction of BPH spatial distribution by using Exponential Smoothing - Spatial Autocorrelation method, and (3) the representation of geographical information of BPH endemic areas. The applied prediction method in this research is Exponential Smoothing and Spatial Autocorrelation method used is Moranâs I and G Statistic. The result of pest endemic areas mapping according to BBPOPT of The Agriculture Ministry of RI showed that based on the historical data in 2001 â 2012 in 120 subdistricts, it was identified that 9,5% of subdistrict areas was endemic, 28,3% was identified as potential areas, 37,5% was identified as seporadis, and 24,7% was secure. The result of BPH endemic areas analysis by using Spatial Autocorrelation method indicated the dynamics of BPH endemic areas every year in the average of 59% identified as hotspot area is high endemic subdistricts surrounded by the high endemic subdistricts as well. Every year, the average of 35% of subdistricts included into coldspot is the high endemic subdistricts surrounded by low endemic subdistricts and 4% of low endemic subdistricts surrounded by low endemic subdistricts as well. The result of BPH spatial distribution prediction by using Exponential Smoothing - Spatial Autocorrelation method depicted that attack potential and endemic areas of BPH could be predicted according to spatial association degree among the subdistrict areas and its surroundings. This approach enabled to carry out the prediction of distribution pattern and migration wave of BPH in 1 â 2 following period in Boyolali, Klaten, Karanganyar and Sragen regencies, whereas the other regencies were independent. The migration wave occurred due to some factors, including topography, biotic and anthropogenic interaction at the point, site, local and landscape scale. The result of geographical information representation of BPH endemic areas research showed that the conceptual framework of Exponential Smoothing - Spatial Autocorrelation could be developed into geographical information system which could generate information of spatial association degree among the studied areas based on the KLTS historical data and the other environment elements, such as climate, variety and predator in the following period. The result recommended that spatial connectivity prediction can be used to determine pest endemic area by using GISA, LISA and Getis Ord Statistic technique. The representation of geographical information recommended for the purpose of BPH surveillance in local, landscape and regional scale comprises choropleth, scatterplot, map (LISA and Getis Ord Statistic) and correlogram.