Prediction of the soil properties of Malagasy rice soils based on the soil color and magnetic susceptibility

ABSTRACT Accurate assessments of soil properties are required to improve fertilizer management practices for crop production. Conventional chemical analysis in the laboratory is costly and time-consuming. Soil color is related to different soil compositions, while soil magnetic susceptibility (MS) has been found to reflect the abundance of magnetic minerals relevant to soil properties. Improving proximal sensing techniques for the analysis of soil color and MS provides opportunities for affordable and rapid assessments of soil properties. The aim of this study was to evaluate the potential use of soil color parameters and MS values to predict soil properties using stepwise multiple linear regression (SMLR), random forest (RF), and nonlinear regression approaches in lowland and upland fields in the central highlands of Madagascar. The target properties included the contents of soil organic carbon (SOC), total nitrogen (TN), oxalate-extractable phosphorus and iron (Feox), and the soil texture. The model prediction accuracy was assessed using the coefficient of determination (R2), root-mean-square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). The use of soil color parameters yielded an acceptable prediction accuracy of the Feox content (loge Feox) for all rice fields (R2 = 0.54, RMSE = 0.55, RPIQ = 1.70) using the RF algorithm, while the SMLR approach gave the most accurate prediction for upland fields with acceptable reliabilities for SOC, Feox, and clay and sand content prediction, with R2 ranging from 0.43 to 0.67 and RPIQ from 1.63 to 1.77. In lowland fields, TN content was predicted with acceptable accuracy (R2 = 0.34, RMSE = 0.49, RPIQ = 1.71) using SMLR with the color parameter. The combination of the soil color parameters with the MS value as predictor variables increased SOC prediction for lowland fields using the RF approach (R2 = 0.57, RMSE = 6.37, RPIQ = 1.96). Use of the soil color and MS parameters was revealed to be a promising way to simplify the assessment of soil properties in upland and lowland ecosystems by using RF and SMLR approaches. A combined use of the soil color and MS parameters improved the prediction accuracy for the SOC content.