Spatial Prediction of Soil Particle-Size Fractions Using Digital Soil Mapping in the North Eastern Region of India

Numerous applications in agriculture, climate, ecology, hydrology, and the environment are severely constrained by the lack of detailed information on soil texture. The purpose of this study was to predict soil particle-size fractions (PSF) in the Ri-Bhoi district of Meghalaya state, India, using a random forest model (RF). For the modeling of soil particle-size fractions, we employed 95 soil profiles (456 depth-wise layers) gathered from a recent national land resource inventory as well as currently accessible environmental variables. Sand, silt, and clay content were predicted using the Random Forest model at varied depths of 0-5, 5-15, 30-60, 60-100, and 100-200 cm. Our results showed the R-2 for sand was found to be 0.30 (0-5 cm), 0.28 (5-15 cm), and 0.21 (15-30 cm). For the sand, silt, and clay fractions, respectively, the concordance correlation coefficient (CCC) was found to be greater in the 0-30 cm, 0-60 cm, and 0-15 cm depths. When there is a reasonably close monitoring of the coverage probability with a confidence level along the 1:1 line, prediction interval coverage probability (PICP) gives a decent indicator of what to anticipate. The most crucial variables for the prediction of sand and silt were channel network base level (CNBL) and LS-Factor, whereas Min Temperature of Coldest Month (& DEG;C) (BIO6) was discovered for clay prediction. For all three soil texture fractions, the range between the 5% lower and 95% higher prediction bounds was large, indicating that the existing spatial predictions may be improved. The maps of soil texture were significantly more precise, and they accurately depicted the spatial variations of particle-size fractions. Additionally, there is still a need to investigate novel methodologies for extensive digital soil mapping, which will be very advantageous for many international initiatives.

Automated summary

This study used a random forest model to predict soil particle-size fractions in the Ri-Bhoi district of Meghalaya state, India. The results show that the prediction accuracy of sand, silt, and clay varies at different depth ranges. Channel network base level and LS-Factor were found to be the most crucial variables for sand and silt prediction, while Min Temperature of Coldest Month (BIO6) was discovered for clay prediction. Additional research is needed to explore novel methodologies for extensive digital soil mapping.