Abstract
As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability.
Highlights
Phosphorus (P) deficiency is a major constraint for rice production in the tropics [1] because strongly weathered soils, which cover vast regions of the tropics, contain low concentrations of readily exchangeable inorganic phosphate [2,3]
A timely and accurate assessment of the soil P content is crucial for resource-limited farmers in Madagascar to improve rice production by site-specific fertilizer management
We investigated the performance of genetic algorithm (GA)-partial least squares (PLS) regression analysis in laboratory visible and near-infrared (Vis-NIR) reflectance spectroscopy for estimating soil oxalate-extractable P contents as a diagnostic indicator of soil P status in rice fields of Madagascar
Summary
Phosphorus (P) deficiency is a major constraint for rice production in the tropics [1] because strongly weathered soils, which cover vast regions of the tropics, contain low concentrations of readily exchangeable inorganic phosphate [2,3]. We assumed that oxalate-extractable P reflects bioavailable P for rice production in the region and applied this assumption in the current study. Our previous study revealed that these soil P contents and forms largely varied among neighboring fields. These observations indicate that P nutrient management for rice production can be further improved by understanding field-to-field variations in bioavailable P (i.e., oxalate-extractable P) in the tropics. To overcome the issues with the standard procedure, laboratory visible and near-infrared (Vis-NIR) spectroscopy has been widely adopted for soil studies as a non-destructive, rapid and reproducible analytical method and has been used for the simultaneous prediction of a variety of primary and secondary soil attributes [11]. Soil P was extracted using the acid ammonium oxalate method as described by Schwertmann [38], and the concentration of P in the oxalate extraction was analyzed using the malachite green colorimetric method [39]
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