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.

Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar

“…These results confirm that the CNN-based model can outperform PLS and other machine learning approaches for estimating soil properties, as suggested by previous studies [31,32]. Another update from our previous report using PLS regression analysis [42] was that the 1D-CNN model enabled the prediction of soil P as a single model, even based on the dataset collected from different land-use systems in Madagascar. These improvements are considered to be advances in the holistic understanding of soil P dynamics and their rational management in agriculture and natural ecosystems in Madagascar.…”

“…Table 2 summarizes the minimum, maximum, median, mean, and standard deviation (SD) values of Pox (mg P kg -1 ) for the training (n = 238) and test (n = 80) datasets, including data collected from different land-use systems (natural and cultivated). Soils in cultivated systems showed higher Pox values and wider ranges compared with soils in natural systems, probably due to the effect of fertilization on cultivated land [42]. The data distributions of soil Pox for all systems in the training and test datasets are shown in Figure 3.…”

“…Our previous studies in a range of rice cultivation soils in Madagascar identified the important wavelength for estimation and developed a reliable prediction model for oxalate-extractable P in soils, which is a suitable indicator of P availability for rice in the region [41], using PLS analysis with waveband selection [23,42]. Furthermore, different P cycling between natural and cultivated systems resulted in different soil P forms, which consequently decreased the accuracy of a common model in both land-use systems (cultivated vs. natural) [42]. The estimation accuracy should be improved by applying a comprehensive model to soil diagnosis in farm fields and other ecosystems for data compatibility and usability.…”

“…Therefore, the purpose of the present study was to develop a model based on the 1D-CNN architecture for estimating the soil available P content (Pox) in Madagascar. We used the dataset from our previous reports [23,42] because in Madagascar, only a small number of qualified datasets have been recorded in the soil spectral libraries: continent scale (n = 82) [13] and global scale (n = 18) [15]. Furthermore, because most soil spectral libraries use total P (TP) as reference data, the transfer learning approach cannot be applied to localize the model to Madagascar.…”

Empower Innovations in Routine Soil Testing

“…Zhang and Zhang (2015) [98] utilized NIRS to estimate different P fractions in soil (i.e., Fe-P, Mg-P, Ca-P, and Al-P) and found correlation coefficients (R 2 ) ranging between 0.85 and0.90. Rakotonindrina et al (2020) [99] was able to estimate P-ox with R 2 values ranging between 0.70 and 0.90. Niederberger et al (2015) [100] divided soil P into labile, moderate labile, and stabile P and found R 2 values ranging from 0.08 to 0.85.…”