4.1. The Calculation Results of AGTFPBased on the super-efficiency SBM model with undesirable outputs, we calculated the AGTFP in 2009–2020 using the MaxDEA pro 6.18 software. The calculation results are shown in Figure 3 and Figure 4 below.

Through calculation, we found that the average value of the AGTFP in China from 2009 to 2020 was 0.8909 (AGTFP < 1), which means that the AGTFP in China has yet to reach an effective state, and there is still room for further improvement. Therefore, in future agricultural development, it is necessary to further improve the level of cleaner production and reduce the environmental pollution caused by agricultural production to enhance the AGTFP in China.

Figure 3 describes the regional distribution characteristics of the AGTFP in China. Among the 30 provinces, only 7 regions have AGTFP values greater than 1, reaching the effective frontier, namely, Beijing (1.3067), Shanghai (1.1407), Guangdong (1.1338), Jiangxi (1.0800), Tianjin (1.0551), Chongqing (1.0406), and Jiangsu (1.0331), respectively, and others are less than 1. It is illustrated that most provinces with a high AGTFP are located in the eastern region, while those provinces in the central and western regions have relatively low AGTFP. To some extent, agricultural production in most regions has the problems of high pollution, high emissions, and low efficiency. The level of green agricultural production needs to be further improved.Figure 4 describes the temporal distribution characteristics of the AGTFP in China. From 2009 to 2020, the AGTFP showed an apparent fluctuating upward trend. The AGTFP value in 2009 was 0.8258, which increased to 0.9678 in 2020, with a growth rate of 17.18%. This shows that the AGTFP has been continuously improved, and more attention has been paid to environmental protection in agricultural production. From the perspective of the regional level, the average values of the AGTFP in the eastern, central, and western regions were 0.9977, 0.9231, and 0.8068, respectively. The AGTFP in the eastern and central regions was higher than the national average, while in the western regions it was lower than the national average. Moreover, the AGTFP in three regions also presented a fluctuating upward trend in temporal characteristics. 4.3. Results of Benchmark RegressionBefore the analysis, we needed to use the LR test and Wald test to verify whether SAR, SEM, or SDM models should be used [34,38]. Table 7 shows the results of the LR and Wald tests under spatial matrices W1, W2, and W3. According to the results, we can find that the p-values of the LR and Wald tests were all 0.000, indicating that the SDM model could not degenerate into the SAR and SEM models. Therefore, the SDM model is suitable for further analysis.To test the impact of agricultural credit input on the AGTFP, we give the benchmark regression results of the panel data model and spatial econometric model in Table 8.Table 9 shows the results of direct, indirect, and total effects calculated according to Formula (11). It can be found that the results are close to Column (5) in Table 8, which indicates that the regression results are authentic and effective.In Table 8, Column (1) and Column (2) are the estimated results of the panel data fixed-effect model and random-effect model, respectively. The Hausman test proves that the fixed-effect model is superior to the random-effect model. Therefore, we use the estimation results in Column (1) to explain the impact of agricultural credit input on the AGTFP. In addition, the estimation method of the fixed-effect model is the least square method.

From Column (1), the coefficient of lnFin is 0.0528, which is significant and positive at the level of 5%. It indicates that a 1% increase in agricultural credit input can increase the AGTFP by 0.0528. Namely, the agricultural credit input can significantly promote the AGTFP. For control variables, the coefficients of Open and lnRGDP are 0.0499 and 0.1103, respectively, which are significant and positive at the level of 1%. This means that the degree of opening up and economic development level can improve the AGTFP. The coefficients of Urban and lnDis are −0.5316 and −0.0096, which are significant and negative at the level of 10%. The result indicates that the level of urbanization and natural disasters in a region is not conducive to the improvement of the AGTFP. The regression coefficients of the industry structure and agricultural fiscal expenditure are positive but insignificant.

Furthermore, we use the spatial econometric model to analyze the spatial spillover effect of agricultural credit input on the AGTFP. Columns (3) to (5) in Table 8 are the regression results of SAR, SEM, and SDM, respectively. The spatial econometric model assumes a significant spatial correlation between the variables. Therefore, the spatial econometric model does not conform to the classical assumptions of the OLS model, and the results obtained by using the MLE method may be more accurate.

Based on Columns (3) to (5), it is suggested that the regression coefficients of lnFin on the AGTFP are significant and positive under the spatial econometric model. The results are the same as the result of the fixed-effect model. These results once again prove that agricultural credit input has a significant positive impact on the AGTFP.

In Column (5), the coefficient of the spatial lag term (ρ) is 0.4217, which is significant and positive under the significance level of 1%. This result shows that the AGTFP has a significant positive spatial spillover effect. Namely, the improvement of the AGTFP in the local region can drive the development of the AGTFP in the adjacent regions. The regression coefficients of lnFin and W × lnFin are 0.0461 and −0.1688, respectively, which are all significant. This means that the agricultural credit input positively impacts the AGTFP in the local region, but it hinders the improvement of the AGTFP in the surrounding regions.

For control variables in Column (5), the level of urbanization and natural disasters have a negative impact on the AGTFP in the local region. The industry structure in the local region may have a negative impact on the AGTFP in the adjacent regions. The degree of opening up in the local region positively impacts the AGTFP in the adjacent regions.

The direct effect of this paper is to explore the overall impact of agricultural credit input on the AGTFP in the local region. According to Du et al. [29], this overall impact usually includes two aspects. On the one hand, it includes the direct impact on the AGTFP when the agricultural credit input may be changed. On the other hand, the agricultural credit input may affect the AGTFP in the local region through affecting the AGTFP in the surrounding regions, thus generating a “feedback effect”. Therefore, the direct effect is the sum of spatial Durbin model estimation results and the “feedback effect”. Column (1) in Table 9 is the result of the direct effect. The results show that agricultural credit input has a significant and positive impact on the AGTFP in the local region. This is consistent with the conclusion estimated by the spatial Durbin model. The “feedback effect” of agricultural credit input on the AGTFP is 0.0392, which means agricultural credit input can promote the AGTFP in the local region by affecting the AGTFP of other regions.The indirect effect of this paper indicates the impact of agricultural credit input on the AGTFP in surrounding regions. In other words, the indirect effect represents the spatial spillover effect of agricultural credit input on the AGTFP. Column (2) of Table 9 shows the results of the indirect effect. When the agricultural credit input in the local region increases by 1%, the AGTFP of the surrounding provinces will be decreased by 5.89%. This conclusion verifies again that the agricultural credit input positively impacts the AGTFP in the local region, but it hinders the improvement of the AGTFP in the surrounding regions.

The total effect is equal to the direct effect plus the indirect effect. In Column (3), the total effect is 0.0263. In addition, the estimation results of the direct and indirect effects of each control variable are similar to the benchmark regression results.