Search Results (2)

The estimation of soil organic matter (SOM) content is essential for understanding the chemical, physical, and biological functions of soil. It is also an important attribute reflecting the quality of black soil. In this study, machine learning algorithms of support vector machine (SVM), neural network (NN), decision tree (DT), random forest (RF), extreme gradient boosting machine (GBM), and generalized linear model (GLM) were used to study the accurate prediction model of SOM in Tieling County, Tieling City, Liaoning Province, China. The models were trained by using 1554 surface soil samples and 19 auxiliary variables. Recursive feature elimination was used as a feature selection method to identify effective variables. The results showed that Normalized Difference Vegetation Index (NDVI) and elevation were key auxiliary variables. Based on 10-fold cross-validation, the RF model had the highest prediction accuracy. In terms of accuracy, the coefficient of determination of RF was 0.77, and the root mean square error was 2.85. The average soil organic matter content was 20.15 g/kg. The spatial distribution of SOM shows that higher content is concentrated in the east and west, while lower content is found in the middle. The SOM content of cultivated land was lower than that of forest land. Full article

Soil quality is related to food security and human survival and development. Due to the acceleration of urbanization and the increase in abandoned land, the quality of topsoil has deteriorated, thus resulting in land degradation in recent years. In this study, a minimum data set (MDS) was constructed through principal component analysis (PCA) to determine the indicator data set for evaluating topsoil quality in Tieling County, northeast China. In addition, the soil quality index (SQI) was calculated to analyze the spatial distribution characteristics of the topsoil quality and the influencing factors. The results showed that the MDS included total potassium (TK), clay, zinc (Zn), soil organic matter (SOM), soil water content (SWC), cation exchange capacity (CEC), pH, and copper (Cu), which could replace all other indicators for assessing the topsoil quality in the research region. The overall soil quality of Tieling County showed a trend of being low in the east and high in the west, and it gradually increased from the hilly area to the plain area. The topsoil quality of Tieling County is divided into one to five levels, with grade-I being the best and grade-V being the worst. The proportion of Grade-II and grade-III is the largest, which is 28.5% and 26.3%, respectively, and grade-V is the smallest, which is 9.6%. The evaluation results are consistent with field research, which can provide a reference for other topsoil quality evaluations, and it also provides a basis for the formulation of soil quality improvement measures. Full article