## 4 Discussion  
Forecasting pest population abundance can help in developing pest management strategies that reduce the quantity of insecticide application, and is a useful component for successful area-wide implementation of IPM [31]. For a pest forecasting model, weather variables such as temperature, humidity, rainfall, sunshine duration and others are often used as abiotic predictors for model development [67, 68, 69, 54]. biotic factors such as host plant phenology can also affect pest population dynamics [31, 70]. We found that the minimum temperature affects BPH development and population dynamics. This is similar to [PERSON] et al. [27] who found that increasing temperatures positively influence BPH populations at low temperatures but have a negative influence at high temperatures. Satellite-based NDVI time series were applied to represent the host plant phenology factor to study the association between rice growth stages and pest population. We found that during the months of January and March in the dry crop season, NDVI at a lag time of one month with regard to rice paddy fields has a significant positive correlation with BPH population fluctuations. This relationship may be due to the association between peak BPH population in each generation and different rice growth stages [10, 71]. In general, the peak catches of adult BPH were observed when rice was mature and post-harvest, as a result of peak nymph populations occurring in the rice paddy fields in the preceding month [71]. In terms of the utilization of phenological information with regard to rice paddy fields based on the satellite time series, these results are in agreement with a previous study in which rice phenology was assessed by remote sensing for the first time to observe BPH migration in the Vietnamese Mekong Delta; the peak BPH occurrence periodically changed according to the harvesting stage in the rice cultivated area [14]. One advantage of this remotely sensed host-plant indicator is the reduction in cost, time and labor required to obtain rice development status from field observations, since satellite earth observation data can take advantage of both spatial and temporal crop information. The stepwise multiple regression model developed in this study suggests that the combination of the satellite-based rice phenology variable and ground-based weather variables could slightly increase  
Figure 9: Actual versus predicted natural log transformed ln (BPH) developed by (**a**) the MLR model, (**b**) the ANN model, and (**c**) the RF model. the accuracy of the model (adjusted R\({}^{2}\) = 0.585) when compared with weather factors alone (adjusted R\({}^{2}\) = 0.356), to a statistically significant degree (R\({}^{2}\)change = 0.229, \(p<0.01\)). ANN is an appropriate choice for pest occurrence forecasting [46]. As has been observed in previous studies [45], our findings indicated that the ANN model yielded more accurate pest prediction than the MLR model. This may be because the weights assigned to each neural network connection allow a more precise pattern in the input data to be identified [44]. The RF model also performed better than the MLR model, but not better than the ANN model for our dataset. This difference does not mean that RF could not predict the insect catches. To make the ANN and RF models more robust, all related factors such as hyperparameters should be adjusted appropriately and the amount of collected data (in terms of spatial area and length of collection period) should be greater. Since a rice paddy field is an open environment, factors driving BPH population growth are variable. Apart from meteorological and host plant factors, natural enemies, rice resistance and even farmer practices may affect BPH population dynamics. The differences between the observed and predicted BPH abundance, especially at the SPB light trap station, could be due to changes in the seasonal and surrounding environment [26].