## 3 Results
### Prediction with Multiple Linear Regression  
An MLR model with stepwise selection was employed based on a combination of weather and host-plant phenology variables (Table 4). Table 4 shows the standardized beta coefficients that present the contributions of each variable to the model. The t and \(p\) values show the impact of the independent variables on the dependent variable. Only Tmin and NDVI showed a significant positive relationship with BPH trap catches. The large \(t\) value (t = 5.140) and corresponding lowest \(p\)-value (\(p\) < 0.01) support the result for NDVI at a one-month lag, which had the highest beta coefficient. A model based on weather variables alone (Model 1) could explain approximately 35% (adjusted R\({}^{2}\) = 0.356) of the variability in the BPH light trap catch. In contrast, the model involving incorporated weather and host-plant phenology variables could explain approximately 58% of the variability of the light trap catch (adjusted R\({}^{2}\) = 0.585) (Model 2) with an acceptable variance inflation factor (VIF <10) value, indicating that there was no multicollinearity among independent variables. Considering both the adjusted \(R^{2}\) and the standard error of estimate (SEE), the model with the combination of both significant weather and host-plant phenology was selected as the best model (Model 2). The SEE \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Variables** & **Beta** & **Std. Beta** & **t value** & **\(p\) Value** & **VIF** \\ \hline \multirow{2}{*}{(1) Weather} & (constant) & \(-\)15.879 & & \(-\)4.120 & 0.000 & \\  & Tmin at lag 0 & 0.909 & 0.608 & 5.192 & 0.000 & 1.000 \\ \hline \multirow{5}{*}{(2) Weather and Host Plant} & \multicolumn{3}{c}{N = 48 R\({}^{2}\) = 0.369 Adjusted R\({}^{2}\) = 0.356 SEE = 2.447} & & \\ \cline{1-1}  & (constant) & \(-\)11.470 & & \(-\)3.573 & 0.001 & \\ \cline{1-1}  & Tmin at lag 0 & 0.432 & 0.289 & 2.564 & 0.014 & 1.436 \\ \cline{1-1}  & NDVI at lag & 10.512 & 0.579 & 5.140 & 0.000 & 1.436 \\ \cline{1-1}  & 1 month & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical diagnostic of the stepwise multiple linear regression models. SEE: standard error of estimate. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Predictor Variables** & **Correlation Coefficient (R)** & **Sig.** \\ \hline
**Tmin at lag 0 month** & **0.608** & **0.000 ** \\ Tmin at lag 1 month & 0.147 & 0.318 \\
**Tmax at lag 0 month** & **0.546** & **0.000 ** \\
**Tmax at lag 1 month** & **0.457** & **0.001 ** \\ RH at lag 0 month & 0.227 & 0.121 \\ RH at lag 1 month & 0.230 & 0.115 \\ RH at lag 0 month & 0.284 & 0.050 \\ RH at lag 1 month & 0.059 & 0.691 \\ NDVI at lag 0 month & 0.183 & 0.213 \\
**NDVI at lag 1 month** & **0.738** & **0.000 ** \\ \hline \hline \end{tabular}  
* Significant at \(p\) < 0.01, Tmin: minimum temperature (°C), Tmax: maximum temperature (°C), RH: relative humidity (%), RF: rainfall (mm), NDVI: normalized differential vegetation index.  
\end{table} 
Table 3: Pearson’s correlation coefficient (R) between the natural log-transformed BPH light trap catch and its lag-distributed environmental factors from December to March in the dry rice crop season. represents the degree to which the predicted values differ from the observed values on the criterion measure. Lower values of the SEE indicate greater accuracy in prediction [66]. The residual of the stepwise regression model for the prediction of BPH was tested for normality and homoscedasticity. The normal probability plot (Figure 5a) indicates that residuals follow normality. However, the residual plot (Figure 5b) showed a signal of funneling-shape, suggesting that the residuals were not random.