# Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale
## 4 Discussion  
In this paper, we take the black soil area in Northeast China as a case study for land productivity evaluation methodology. In this study, we have thoroughly investigated the performance of different soil productivity evaluation models, including the MCI model, the MPI model, and a variety of classical machine learning models and their optimization models. The MCI model, after data pre-processing such as 3\(\sigma\) principle, box-and-line diagram and Z-score, can provide a preliminary soil productivity estimation, applicable to the situation of limited data, but cannot deal with the non-linear relationship, and is not sensitive enough to high-dimensional data and complex features. The performance of the MPI model has been improved in relation to the traditional PI model, which can more comprehensively take into account the nature of the soil and the environmental factors, and has a better performance in yield prediction relative to the MCI model (Figure 3), and a more comprehensive performance on complex relationship between soil and crops. The MPI model is more suitable for multifactorial soil productivity evaluation. However, the MPI model is still unable to deal with non-linear relationships and high-dimensional data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Evaluation Index** & **MCI** & **MPI** & **SVR** & **SA-SVR** & **GPR** & **RFR** & **MLF-RFR** \\ \hline MAE & & & 32.84 & 24.46 & 24.28 & 33.79 & 31.78 \\ MAPE & & & 0.04 & 0.03 & 0.03 & 0.04 & 0.04 \\ MSE & 3589.19 & 1146.29 & 1743.24 & 967.84 & 932.19 & 1391.06 & 1234.58 \\ RMSE & 59.91 & 33.86 & 41.75 & 31.11 & 30.53 & 37.30 & 35.14 \\ R\({}^{2}\) & 0.23 & 0.38 & 0.47 & 0.70 & 0.71 & 0.57 & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Table of evaluation indicators for linear regression analysis. The data in Table 5 indicate that the machine learning models have advantages over the MCI model, as well as the MPI model, in terms of feature selection and model optimization. Based on the results of model performance analysis, SA optimization was chosen to effectively solve the problems of SVM model regarding computational resources and tuning time, and the SVM model searches for the optimal hyperparameters by combining the SA algorithm [38]; the MLP-RFR model effectively mitigates the overfitting problem of complex data patterns and has better robustness to outliers by combining the advantages of MLP and RF. These models can better capture the complex relationship between nonlinear soils and crops. Previous studies on land productivity evaluation often used machine learning algorithms to build predictive models, mainly by combining a large number of remote sensing data and public historical datasets [32; 39; 40; 41; 42], which has the advantage that a large amount of data can be applied to train a predictive model with a high degree of accuracy, but these predictive models generally have regional limitations [11]. The land productivity evaluation method studied in this paper still performs well when applied to a small dataset containing only soil physicochemical properties, with significant improvements in R\({}^{2}\), MSE, and other indicators compared to the MCI model as well as the MPI model; as an example, in Table 5 of this study, the SVR model, SA-SVR model, GPR model, RFR model, and the MLP-RFR model studied have improved R\({}^{2}\) compared to the MPI model with an improved R\({}^{2}\), indicating that soil variables are closely related to yield and can effectively predict yield. Despite the investment of significant manpower in collecting samples from the field and determining their specific soil physicochemical properties in the laboratory for this dataset, our research on land productivity evaluation, using black soil in Northeast China as a case study and based on the field scale, has demonstrated the effectiveness of this research method through its prediction accuracy when applied to small datasets.