Precise prediction of soil organic matter in soils planted with a variety of crops through hybrid methods

• Published in: Computers and electronics in agriculture Vol. 200; p. 107246
• Main Authors: Lu, Mu-yuan, Liu, Yuan, Liu, Gui-jian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2022
• Subjects: Bayesian Maximum Entropy; Disturbance factors; Machine learning models; Soil organic matter; Soil organic matter; Machine learning models; Disturbance factors; Bayesian Maximum Entropy
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2022.107246

[Display omitted] •The influence of disturbance factors was quantified by transforming them into soft data.•The BME-RF model was proposed in the SOM prediction research.•The BME-RF model performed better than traditional ML model in SOM prediction.•The spatial distribution of SOM was determined by crop type. Determining the spatial distribution of soil organic matter (SOM) of cultivated lands efficiently and quickly using available data is crucial for the development of smart agriculture with low consumption and high efficiency. In this study, a total of 205 black soil samples from Hulunbuir, China, planted with a variety of crops were investigated for multiple soil nutrients. The SOM content in the studied black soil was rich, ranging from 32.1 to 62.8 g/kg. In addition, machine learning models based on spectral data and environmental variables were established to predict the overall spatial distribution of SOM. The prediction effect of the Random Forest (RF) model was the best (R2 = 0.84, CCC = 0.872, RMSE = 3.31, RPD = 2.19), and the environmental variables (mainly clay index) containing more information in recursive feature elimination had the greatest influence on the prediction accuracy of SOM. Further, to reduce the dependence on predictive indicators, this study quantitatively analyzed the disturbance factors (crop types) of cultivated land using Bayesian Maximum Entropy (BME), and the BME-RF model was established to combine crop types with prediction indicators to further optimize SOM prediction accuracy. The prediction accuracy of the model was improved to varying degrees in multiple groups of experiments by adding several groups of soft data to compare and analyze the effect of the BME-RF model, and it was the highest (R2 = 0.89, RI = 15.5%) when 1200 soft data were used. Crop types have certain influence on the pattern of SOM content. The SOM content was relatively low in rapeseed and barley planting areas, but was relatively high in potato and beet growing areas. In this paper, BME-RF method is innovated to combine historical data with as little experimental data as possible, which greatly improved the accuracy of digital map of soil nutrients. This study can provide a scheme for the development of smart agriculture and promote the sustainability of cultivated land environment.