Mid‐infrared spectroscopy for prediction of soil health indicators in the United States

Abstract

Quantifying the holistic notion of soil health requires a large suite of measurements spanning the physical, chemical and biological properties of soil. The cost of measuring the full suite of soil health indicators via traditional methods is cost prohibitive for the spatial and temporal monitoring many land managers would like. Here we investigated the ability to make predictions of numerous soil health indicators from the large mid‐infrared (MIR) spectral library developed by the National Soil Survey Center. We developed nationally applicable models with test‐set validation coefficient of determination (R2) > 0.80 for water retention (WT), bulk density (BD), texture (as percentages of sand, silt and clay), cation exchange capacity (CEC), exchangeable bases, base saturation (BS), electrical conductivity (EC), pH, soil organic carbon (SOC) and total nitrogen (TN). Using the memory‐based learning (MBL) model, a locally weighted approach, we found that this database could also predict aggregate stability, plant available P and K with an R2 > 0.70 on independent validation samples. Models for nitrate and micronutrients were less successful but these properties were only sparsely represented in the database. Two test cases were used to demonstrate the utility of MIR spectroscopy in providing a wealth of data for a fraction of the time and cost involved with traditional analyses. While MIR spectroscopy‐based predictions may not always be appropriate for applications requiring very high levels of accuracy, results from this work and elsewhere suggest that this technology can replace, or at least supplement, traditional analyses for many purposes.

How microbes can, and cannot, be used to assess soil health

“…Hermans et al, 2017). This is analogous to the use of spectral libraries to calibrate infra-red measurements of soil health properties (Sanderman et al, 2020). These measurements use infrared reflectance of soils to correlate with known, measured soil properties, which requires large libraries of spectral signatures across many soil types.…”

Soil organic carbon fractions in the Great Plains of the United States: an application of mid-infrared spectroscopy

“…All samples had measured SOC data, but gaps needed to be filled for other properties. MIR-based predictions, as developed byDangal et al (2019) andSanderman et al (2020), were used to estimate clay content for 277 samples, pH on 170 samples and BD on 3782…”

Using Diffuse Reflectance Spectroscopy as a High Throughput Method for Quantifying Soil C and N and Their Distribution in Particulate and Mineral-Associated Organic Matter Fractions

“…Although previous studies have reported satisfactory accuracies for C prediction in the POM fraction using MIR spectroscopy (R 2 0.71-0.83), they were carried out in the 0.2-16.8gCkg −1 range of concentration (Janik et al, 2007;Baldock et al, 2018). However, recent studies using heterogeneous soil samples at a country level similarly reported that SOM C and MAOM C were better predicted than POM C (Sanderman et al, 2020). These results can be FIGURE 5 | Scatter plots for predictions of (A) total soil nitrogen (TN), (B) nitrogen content in mineral-associate organic matter (MAOM N) and (C) nitrogen content in particulate organic matter (POM N) resulting from global calibrations from a large-scale dataset using mid infrared spectroscopy and partial least square regression.…”

“…Infrared spectroscopy has been proposed as an alternative method for a rapid determination of soil quality (Seybold et al, 2019) as well as to predict the distribution of SOM fractions (Baldock et al, 2018). Mid-infrared (MIR) spectroscopy combined with chemometrics is a feasible quantitative analysis method in soils (Sanderman et al, 2020). Additionally, MIR provides detailed information about fundamental vibrations of minerals (i.e., quartz -Si-O frequencies) and organic matter (Soriano-Disla et al, 2014).…”