Prediction of soil oxalate phosphorus using visible and near-infrared spectroscopy in natural and cultivated system soils of madagascar

Abstract

Phosphorus is among the main limiting nutrients for plant growth and productivity in both agricultural and natural ecosystems in the tropics, which are characterized by weathered soil. Soil bioavailable P measurement is necessary to predict the potential growth of plant biomass in these ecosystems. Visible and near-infrared reflectance spectroscopy (Vis-NIRS) is widely used to predict soil chemical and biological parameters as an alternative to time-consuming conventional laboratory analyses. However, quantitative spectroscopic prediction of soil P remains a challenge owing to the difficulty of direct detection of orthophosphate. This study tested the performance of Vis-NIRS with partial least square regression to predict oxalate-extractable P (Pox) content, representing available P for plants in natural (forest and non-forest including fallows and degraded land) and cultivated (upland and flooded rice fields) soils in Madagascar. Model predictive accuracy was assessed based on the coefficient of determination (R2), the root mean squared error of cross-validation (RMSECV), and the residual predictive deviation (RPD). The results demonstrated successful Pox prediction accuracy in natural (n = 74, R² = 0.90, RMSECV = 2.39, and RPD = 3.22), and cultivated systems (n = 142, R² = 0.90, RMSECV = 48.57, and RPD = 3.15) and moderate usefulness at the regional scale incorporating both system types (R² = 0.70, RMSECV = 71.87 and RPD = 1.81). These results were also confirmed with modified bootstrap procedures (N = 10,000 times) using selected wavebands on iterative stepwise elimination–partial least square (ISE–PLS) models. The wavebands relevant to soil organic matter content and Fe content were identified as important components for the prediction of soil Pox. This predictive accuracy for the cultivated system was related to the variability of some samples with high Pox values. However, the use of “pseudo-independent” validation can overestimate the prediction accuracy when applied at site scale suggesting the use of larger and dispersed geographical cover sample sets to build a robust model. Our study offers new opportunities for P quantification in a wide range of ecosystems in the tropics.