Estimation of soil phosphorus availability via visible and near-infrared spectroscopy
Souza, Micael Felipe de
Sci. agric. (Piracicaba, Braz.)
DATA DE PUBLICAÇÃO
ABSTRACT: 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.
- Near-infrared spectroscopy parameters in patients undergoing continuous venovenous hemodiafiltration
- Human cortical perfusion and the arterial pulse: a near-infrared spectroscopy study
- Chemometric evaluation of pharmaceutical properties of antipyrine granules by near-infrared spectroscopy
- High-pressure near-infrared Raman spectroscopy of bacteriorhodopsin light to dark adaptation.
- Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm