Influence of soil sample preparation on the quantification of NPK content via spectroscopy

Coutinho, Marcos Antonio Neris; Alari, Fernando de Oliveira; Ferreira, Márcia M. C.; Amaral, Lucas Rios do

doi:10.1016/j.geoderma.2018.12.021

Cited by 30 publications

(24 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These results differed from those presented by Xu et al [11], which is the only research that explored VNIR, XRF, and LIBS sensors for predicting soil fertility attributes. The authors obtained better prediction performances with the VNIR sensor, in comparison to the XRF and LIBS sensors, for OM, pH, ex-P, ex-K, and ex-N. Our results, however, are in line with those of other research carried out in tropical soils that also evaluated the standalone use of VNIR [2,[51][52][53][54] and XRF [19,[55][56][57][58] techniques. Studies using LIBS sensors for the assessment of fertility attributes are incipient in tropical soils.…”

Section: Individual Performance Of Vnir Xrf and Libs Sensors: Coverage Of Key Soil Fertility Attributessupporting

confidence: 88%

“…It is important to consider practical aspects of sample preparation necessary to ensure the application of each technique. The VNIR and XRF techniques are less demanding in terms of sample preparation, and can be applied on loose soil samples (i.e., dried and sieved with 2 mm of particle size) without major impacts on the sensors performance [30,51].…”

Section: Operational Aspects Related To Sample Preparationmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Sensor Approach for Tropical Soil Fertility Analysis: Comparison of Individual and Combined Performance of VNIR, XRF, and LIBS Spectroscopies

et al. 2021

View full text Add to dashboard Cite

Rapid, cost-effective, and environmentally friendly analysis of key soil fertility attributes requires an ideal combination of sensors. The individual and combined performance of visible and near infrared (VNIR) diffuse reflectance spectroscopy, X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) was assessed for predicting clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients in tropical soils. A set of 102 samples, collected from two agricultural fields, with broad ranges of fertility attributes were selected. Two contrasting data fusion approaches have been applied for modeling: (i) merging spectral data of different sensors followed by partial least squares regression (PLS), known as fusion before prediction; and (ii) applying the Granger and Ramanathan (GR) averaging approach, known as fusion after prediction. Results showed VNIR as individual technique to be the best for the prediction of clay and OM content (2.61 ≤ residual prediction deviation (RPD) ≤ 3.37), while the chemical attributes CEC, V, ex-P, ex-K, ex-Ca, and ex-Mg were better predicted (1.82 ≤ RPD ≤ 4.82) by elemental analysis techniques (i.e., XRF and LIBS). Only pH cannot be predicted regardless the technique. The attributes OM, V, and ex-P were best predicted using single-sensor approaches, while the attributes clay, CEC, pH, ex-K, ex-Ca, and ex-Mg were overall best predicted using multi-sensor approaches. Regarding the performance of the multi-sensor approaches, ex-K, ex-Ca, and ex-Mg, were best predicted (RPD of 4.98, 5.30, and 4.11 for ex-K, ex-Ca and ex-Mg, respectively) using two-sensor fusion approach (VNIR + XRF for ex-K and XRF + LIBS for ex-Ca and ex-Mg), while clay, CEC and pH were best predicted (RPD of 4.02, 2.63, and 1.32 for clay, CEC, and pH, respectively) with the three-sensor fusion approach (VNIR + XRF + LIBS). Therefore, the best combination of sensors for predicting key fertility attributes proved to be attribute-specific, which is a drawback of the data fusion approach. The present work is pioneering in highlighting benefits and limitations of the in tandem application of VNIR, XRF, and LIBS spectroscopies for fertility analysis in tropical soils.

show abstract

Section: Individual Performance Of Vnir Xrf and Libs Sensors: Coverage Of Key Soil Fertility Attributessupporting

confidence: 88%

Section: Operational Aspects Related To Sample Preparationmentioning

confidence: 99%

Multi-Sensor Approach for Tropical Soil Fertility Analysis: Comparison of Individual and Combined Performance of VNIR, XRF, and LIBS Spectroscopies

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In the present paper, CEC, V, ex-K, ex-Ca, and ex-Mg presented significant correlations with OM (0.44 ≤ r ≤ 0.62) and clay (0.64 ≤ r ≤ 0.82) ( Figure 4 ), which explain the successful predictions of such secondary attributes. In Brazilian tropical soils, different studies have reported satisfactory predictions for CEC, V, ex-K, ex-Ca, and ex-Mg using vis-NIR spectroscopy sensors, with R 2 values ranging from 0.46 to 0.92 for CEC [ 17 , 21 , 74 ], from 0.56 to 0.79 for V [ 74 ], from 0.61 to 0.94 for ex-K [ 73 , 78 ], from 0.42 to 0.74 for ex-Ca [ 7 , 17 , 74 ], and from 0.41 to 0.81 for ex-Mg [ 7 , 17 , 74 ].…”

Section: Discussionmentioning

confidence: 99%

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Tavares

Molin

Javadi

et al. 2020

Sensors

View full text Add to dashboard Cite

Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data. Six data fusion approaches were evaluated and compared against individual models using relative improvement (RI). The data fusion approaches included (i) two spectra fusion approaches, which simply combined the data of both sensors in a merged dataset, followed by support vector machine (SF-SVM) and PLS (SF-PLS) regression analysis; (ii) two model averaging approaches using the Granger and Ramanathan (GR) method; and (iii) two data fusion methods based on least squares (LS) modeling. For the GR and LS approaches, two different combinations of inputs were used for MLR. The GR2 and LS2 used the prediction of individual sensors, whereas the GR3 and LS3 used the individual sensors prediction plus the SF-PLS prediction. The individual vis-NIR models showed the best results for clay and OM prediction (RPD ≥ 2.61), while the individual XRF models exhibited the best predictive models for CEC, V, ex-K, ex-Ca, and ex-Mg (RPD ≥ 2.57). For eight out of nine soil attributes studied (clay, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg), the combined use of vis-NIR and XRF sensors using at least one of the six data fusion approaches improved the accuracy of the predictions (with RI ranging from 1 to 21%). In general, the LS3 model averaging approach stood out as the data fusion method with the greatest number of attributes with positive RI (six attributes; namely, clay, CEC, pH, ex-P, ex-K, and ex-Mg). Meanwhile, no single approach was capable of exploiting the synergism between sensors for all attributes of interest, suggesting that the selection of the best data fusion approach should be attribute-specific. The results presented in this work evidenced the complementarity of XRF and vis-NIR sensors to predict fertility attributes in tropical soils, and encourage further research to find a generalized method of data fusion of both sensors data.

show abstract

“…The main difficulties to obtain good prediction results of available P via spectroscopy are that this fraction is related to the soil solution and chemistry of the soil matrix (Janik et al, 1998) and does not present direct spectral response (Stenberg et al, 2010;Oliveira et al, 2015). Still, Coutinho et al (2019) argue that available level of P shows relationship with iron and aluminum oxyhydroxides in the soil, allowing its indirect prediction via spectroscopy. Furthermore, low contents found in highly weathered soils (Oxisoil) might compromise even more its detection.…”

Section: Quantification Of Soil P Availabilitymentioning

confidence: 99%

Estimation of soil phosphorus availability via visible and near-infrared spectroscopy

Souza

Franco

Amaral

2020

Sci. agric. (Piracicaba, Braz.)

View full text Add to dashboard Cite

Spectroscopic techniques have great potential to evaluate soil properties. However, there are still questions regarding the applicability of spectroscopy to analyze soil phosphorous (P) availability, especially in tropical soils with low nutrient contents. Therefore, this study evaluated the possibility to estimate P availability in soil and its pools (labile, moderately labile and non-labile) via Vis-NIR spectroscopy based on intra-field calibration. We used soils from two different locations, a plot experiment that received application of phosphate fertilizers (Field-A) and a cultivated field where a grid soil sampling was performed (Field-B). We used the technique of diffuse reflectance in the visible and near-infrared (Vis-NIR) to obtain the spectra of soil samples. Predictive modeling for P availability and labile, moderately labile and non-labile pools of P in soil were obtained via partial least squares (PLS) regression; classification modeling was performed via Soft Independent Modeling of Class Analogy (SIMCA) on three P availability levels in order to overcome the limitation on quantifying P via Vis-NIR spectroscopy. We found that isolating P contents as the only variable (Field-A), Vis-NIR spectroscopy does not allow estimating P pools in the soil. In addition, quantification of P available in the soil via predictive modeling has limitations in tropical soils. On the other hand, estimating P content in soil through classes of availability is a feasible and promising alternative.

show abstract

Influence of soil sample preparation on the quantification of NPK content via spectroscopy

Cited by 30 publications

References 36 publications

Multi-Sensor Approach for Tropical Soil Fertility Analysis: Comparison of Individual and Combined Performance of VNIR, XRF, and LIBS Spectroscopies

Multi-Sensor Approach for Tropical Soil Fertility Analysis: Comparison of Individual and Combined Performance of VNIR, XRF, and LIBS Spectroscopies

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Estimation of soil phosphorus availability via visible and near-infrared spectroscopy

Contact Info

Product

Resources

About