Digital mapping of topsoil pH by random forest with residual kriging (RFRK) in a hilly region
Lei Wang
A , Wei Wu B and Hong-Bin Liu A C
+ Author Affiliations
- Author Affiliations
A College of Resources and Environment, Southwest University, Beibei, Chongqing 400715, China.
B College of Computer and Information Science, Southwest University, Beibei, Chongqing 400715, China.
C Corresponding author. Email: lhbin@swu.edu.cn
Soil Research 57(4) 387-396 https://doi.org/10.1071/SR18319
Submitted: 22 October 2018 Accepted: 1 March 2019 Published: 9 April 2019
Abstract
Soil pH is a vital attribute of soil fertility. The accurate and efficient prediction of soil pH can provide the necessary basic information for agricultural development. In the present study, random forest with residual kriging (RFRK) was used to predict soil pH based on stratum, climate, vegetation and topography in a hilly region. The performance of RFRK was compared with those of the classification and regression tree (CART) and the random forest (RF). Comparative results showed that RFRK provided the best performance. The corresponding values of Lin’s concordance correlation coefficient, coefficient of determination, mean absolute error and root mean square error were as follows: 0.70, 0.51, 0.44 and 0.61 for CART; 0.80, 0.70, 0.34 and 0.48 for RF; and 0.88, 0.80, 0.25 and 0.39 for RFRK. Stratum and average annual temperature were the most important factors affecting the soil pH in the study area. Results indicate that RFRK is a feasible and reliable tool for predicting soil pH in hilly regions.
Additional keywords: classification and regression tree; digital soil mapping; soil pH.
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