COMPARISON OF PREDICTIONS OUTPUT OF BROWN PLANTHOPPER (Nilaparvata lugens Stal.) ABUNDANCE MODEL USING THREE TYPES OF TEMPERATURE DATA (CASE STUDY: JATISARI, KARAWANG, WEST JAVA)

• Main Author: PRATIDINA PRAYITNO (NIM : 12812030), INTAN
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/28079
• Institution: Institut Teknologi Bandung

Analysis of Brown Planthopper attack rate in Karawang shows that the temperature greatly affects the extent of attack in this area. However, this analysis only used climate data from Darmaga Station whose spatial resolution is very small compared to Brown Planthopper’s habitat or body size. Whereas, the temperature data used in the pest prediction model should be based on the pest conditions because many studies suggest that climate data in large spatial resolution is wrong in representing the temperature of the habitat of living things, especially small organisms such as insects. Therefore, it is necessary to reduce the spatial scale mismatch between the climate data used in pest model and the pest’s body size. <br /> <br /> This study compared the prediction outputs of Brown Planthopper abundance model with three temperature data inputs having different spatial resolution, i.e. global temperature data from ECMWF database, local temperature data recorded by the Stasiun Meteorologi Pertanian Khusus/ Special Agriculture Meteorology Station (abbreviated as SMPK), and micro temperature data taken primarily using a special temperature logger placed on the Balai Besar Peramalan Organisme Pengganggu Tanaman/ Center for Forecasting Plant Disturbing Organisms’s rice field from November 9 to December 19, 2016. The prediction model of Brown Planthopper abundance consists of developmental rate curves made based on nonlinear thermodynamic models by Schoolfield. <br /> <br /> Linear regression results show that the best prediction model is a model with ECMWF temperature data input, while models with SMPK and micro temperature data do not show significant correlation. This is due to fluctuations in the observation data of Brown Planthopper which is not compatible with temperature data and there are other factors that are influential but not included in the model, such as migration, survival rate, and pest's fecundity.