## 3 Results
### Empirical Analysis of Factors Affecting Corn Production Cost  
From the above analysis, the CPC is observed to have significant geographic spatial relevance. Therefore, this paper employed a spatial measurement model to conduct an empirical analysis of sensitive factors affecting the CPC in China for the period between 2008 and 2018. More specifically, this study used LMlag (Robust LMlag) and LMerr (Robust LMerr) to determine which of the two models is more appropriate. Table 4 illustrates that the test values of LMlag, Robust LMlag, LMerr and Robust LMerr are all significant at the 1% level. However, the statistic value of LMlag is greater than that of LMerr, and the statistic value of Robust LMlag is greater than Robust LMerr. These observations suggest that SLPDM is better than SEPDM. Furthermore, the spatial Hausman test is significant at the 1% level, indicating that the spatial panel fixed effects model is more suitable in comparison to the spatial random effects model. Since both the LogL value of the time fixed effects model and the R\({}^{2}\) value are observed to be the largest, the model fitting effect is taken to be the best one. Therefore, this paper employs the time fixed effects model of the spatial lag panel model to explain the sensitive factors affecting the CPC. In order to observe the impact of various influencing factors on the CPC, the model parameters (model 1 to model 5) are estimated by gradually adding independent variables on the basis of controlling the dependent variables. The test results are provided in Table 5. \begin{table}
\begin{tabular}{c c c c} \hline
**Inspection Method** & **Observations** & **Statistic** & \(\boldsymbol{p}\) **Value** \\ \hline _LMlag_ & 220 & 83.108 & 0.000 \\ _Robust LMlag_ & 220 & 14.672 & 0.000 \\ _LMerr_ & 220 & 82.636 & 0.000 \\ _Robust LMerr_ & 220 & 14.199 & 0.000 \\ _Hausman test_ & 220 & 38.50 & 0.000 \\ \hline \end{tabular}
\end{table} 
Table 4: Model statistical test results. Model 1 considers the scale of land management to be the basic variable for estimating parameters. The research results suggest that the impact of the scale of land management on the CPC is negative and significant at the 1% level. Furthermore, Model 2 includes the labor structure variable. Its parameter estimate is positive, indicating that growth of the proportion of the non-agricultural population will promote the CPC. Nevertheless, the parameter estimate of the scale of land management is still negative in Model 2. Then, Model 3 considers the variable of the degree of mechanization. The estimated value of its parameter is negative, indicating that the improvement of the comprehensive mechanization level of corn cultivation and harvest has a role in reducing corn production costs. Moreover, Model 4 introduces the per capita annual net income of farmers in the socio-economic condition. Here, the estimated value is significantly negative at the 1% level. Lastly, Model 5 takes into consideration the non-agricultural output value ratio index, and its parameter estimate is significantly negative at the level of 5%, indicating that the increase in the non-agricultural output value ratio can be used to reduce the CPC. Based on the estimation results of Model 1 to Model 5, the signs of the variables introduced in the model appear not to have changed significantly. The only visible change is observed in the size of the parameter estimate, although the magnitude of the change is not large. All of the above results indicate that, considering the existence of spatial effects, the above sensitive factors have a real impact on the CPC. The parameter estimation results of Model 5 demonstrate the following:  
(1) The marginal effect of the scale of land management on the CPC is \(-0.0938\), while the coefficient of elasticity is measured at \(-0.1114\). The above results show that the scale of land management has a significantly negative impact on the CPC.