Abstract
Visible near-infrared reflectance spectroscopy (VNIRS) and laser-induced breakdown spectroscopy (LIBS) are potential methods for the rapid and less expensive assessment of soil quality indicators (SQIs). The specific objective of this study was to compare VNIRS and LIBS for assessing SQIs. Data was collected from over 140 soil samples taken from multiple agricultural management systems in New Mexico, belonging to arid and semiarid agroecosystems. Sampled sites included New Mexico State University Agricultural Science Center research fields and several commercial farm fields in New Mexico. Partial least squares regression (PLSR) was used to establish predictive relationships between spectral data and SQIs. Fifteen soil measurements were modeled including the soil organic matter (SOM), permanganate oxidizable carbon (POXC), total microbial biomass (TMB), total bacteria biomass (TBB), total fungi biomass (TFB), mean weight diameter of dry aggregates (MWD), aggregates 2–4 mm (AGG > 2 mm), aggregates < 0.25 mm (AGG < 0.25 mm), wet aggregate stability (WAS), electrical conductivity (EC), calcium (Ca), magnesium (Mg), sodium (Na), and iron (Fe). Overall, calibrations based on measurements irrespective of locations performed better for LIBS and combined VNIRS-LIBS. Measurements separated according to locations highly improved the quality of prediction for VNIRS as compared to combined locations. For example, the prediction R2 values for regression of VNIRS were 0.19 for SOM, 0.30 for POXC, 0.24 for MWD, 0.15 for AGG > 2 mm, and 0.13 for EC in combined datasets irrespective of location. When separated according to locations, for one of the locations, the predictive R2 values for VNIRS were 0.48 for SOM, 0.70 for POXC, 0.67 for MWD, 0.60 for AGG > 2 mm, and 0.51 for EC. The prediction values varied with the sampling time for both LIBS and VNIRS. For example, the prediction values of some SQIs using VNIRS were higher in samples collected in winter for measurements, including SOM (0.90), MWD (0.96), WAS (0.66), and EC (0.94). Using the VNIRS, the corresponding predictive values for the same SQIs were lower for samples collected in the fall (SOM (0.61), MWD (0.45), WAS (0.46), and EC (0.65)). While this study illustrates the prospects of VNIRS and LIBS for estimating SQIs, a more comprehensive evaluation, using a larger regional dataset, is required to understand how the site and soil factors affect VNIRS and LIBS, in order to enhance the utility of these methods for soil quality assessment in arid and semiarid agroecosystems.
Highlights
Transformations of the data could reduce the impact of skewness, no transformations of the data were undertaken in this study because the downside of the transformations would be a loss of interpretability of the individual values, and the soil quality indicators (SQIs) would not be in the original units
This study demonstrated the feasibility of Visible near-infrared reflectance spectroscopy (VNIRS) and laser-induced breakdown spectroscopy (LIBS) spectroscopies as innovative methods for soil quality monitoring, which is fundamental for sustainable agriculture development in arid and semi-arid agroecosystems
Relationships between multiple soil quality indicator measurements and spectral data generated by visible near-infrared (VNIRS) and laser induced breakdown (LIBS) spectroscopies were studied in selected agricultural soils of the arid Southwestern United States
Summary
Conventional assessments of soil quality quality, and Conventional assessments of soil quality rely rely on laboratory measurements known as soil quality indicators (SQIs). On laboratory and and fieldfield measurements known as soil quality indicators [3,4].[3,4]. The the necessary laboratory and field soil analyses for determining. SQIs are usually expensive, destructive, necessary laboratory and field soil analyses for determining SQIs are usually expensive, destructive, time-consuming, often complicated, and can time-consuming, often complicated, and can require require extensive extensive chemical chemical use use [5]. SQIs would be useful to reduce costs and improve efficiency. Methods to estimate SQIs would be useful to reduce costs and improve efficiency.
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