## 4 Analysis of Results
#### 4.2.2 Spatial Panel Measurement Model Inspection and Selection  
(1) Panel data stationarity test  
Before the quantitative analysis of the influence mechanism of agricultural technological innovation on agricultural green development, the sequence stability of each variable must be tested first, and the unit root is generally used to judge whether it is stable. In this study, the LLC test method was used for the homogeneous panel hypothesis, and the Fisher test method was used for the heterogeneous panel hypothesis. The test results are shown in Table 7. It can be seen that each variable rejects the null hypothesis of the existence of unit roots using the three statistical test methods (\(p\)-value \(<0.05\)), which indicates that the panel data used in this article have good stability. Figure 11: Moran scatter diagram of green agriculture development in 2006 and 2019. (2) Panel model inspection and selection  
Based on the stability test of panel data, the spatial Hausman test was used to determine whether random effects were present. The null hypothesis was rejected at a significance level of 1%. Therefore, the fixed-effect model should be adopted as the spatial panel econometric model. First, OLS regression analysis was carried out, and the LM test was performed on the residual term of OLS to determine whether LM-lag and LM-error test values rejected the null hypothesis. If none of them reject the original hypothesis, the OLS result is kept. If LM-lag rejects the null hypothesis, the spatial lag model is selected; if LM-error rejects the null hypothesis, the spatial error model is selected; if both LM-error and LM-lag reject the null hypothesis, the robust LM test is performed to further determine its significance. Secondly, the saliency of the robust LM-error and robust LM-lag was compared. If both are significant, the spatial Dubin model is selected. If the robust LM-lag is significant and the robust LM-error is not significant, it indicates that the spatial lag model and spatial error model may exist simultaneously, in which case, the spatial Durbin model is preferred. Similarly, if robust LM-lag is not significant and the robust LM-error is significant, the spatial Dubin model is also preferred. In this case, the spatial Durbin model needs to be further tested by performing the LR test and Wald test to check whether it will degenerate into a spatial lag model or a spatial error model. If the null hypothesis is rejected by LR and the Wald test at a significance level of 1%, the spatial Durbin model can be selected. Because different spatial panel econometric models are applicable to different spatial weight matrices, spatial panel econometric models applicable to the economic matrix should be considered when using spatial econometric analysis to analyze the spatial spillover effect of agricultural science and technology innovation on green agricultural development, as shown in Tables 8-10. As can be seen from Table 8, under the economic matrix, the LM-lag test, LM-error test and robust LM-lag test all pass the 1% significance level test, while the robust LM-error test fails the 10% significance level test. These results show that the spatial lag model and spatial error model may exist simultaneously, so the spatial Dubin model is preferred for studying the impact of agricultural science and technological innovation on agricultural green development.