3.1. Soil Properties and Organic Fractions
Concentrations of TSOC, TSN, and RCAH were not statistically different within a given depth in the two southern tall grass prairie watersheds (STGP1 and STGP2) (
Table 1). The minimally disturbed, winter wheat, summer forage watershed (MDWWSF) soils contained significantly less TSOC and RCAH within the 0 to 5 cm depth relative to both the STGP1 and 2 watersheds (
Table 1). The STGP1 and 2 watersheds contained more than 40% greater TSOC and ~50 greater RCAH relative to the MDWWSF watershed at the 0 to 5 cm depth. There were nearly no statistical differences in measured concentrations among the three watersheds below 15 cm (
Table 1). Our concentrations of whole total soil carbon followed a similar pattern as soil organic matter (SOM) concentrations measured via loss on ignition [
28] from a set of soil samples taken in 2004 on Watersheds STGP1 and 2. These earlier SOM measurements and the current experiment verify that the majority of variability in SOM and soil C occurs within the top 10 cm of soil and drops off dramatically beyond 15 cm.
Carbon in RCAH constituted between 52% and 64% of TSOC and varied due to the watershed management. The RCAH fraction constituted 52% of the TSOC in the MDWWSF watershed and ~60% in watersheds STGP1 and STGP2. Other researchers have also reported similar increases in resistant C in soils under grassland management relative to row crop management [
4,
5]. The resistant C fraction is assumed to contain a minimal amount of biologically active C, although it may still contain residual lignin [
4]. Resistant C improves soil water holding capacity and contributes to aggregate formation [
4,
5].
Although the three watersheds were individually mapped as silt loams, there were significant differences in the percentage of sand and clay fractions varying by watershed and soil depth. The agronomically managed watershed (MDWWSF) contained a greater percent of sand relative to STGP1 and STGP2 (
Table 1). In contrast, the clay content was significantly higher in the lower depths of the STGP1 and STGP2 watersheds relative to the MDWWSF (
Table 1). Particulate organic matter N did not vary due to the watershed management and constituted between 1.26% and 1.65% of TSN (
Table 2). In contrast, POMC was significantly greater in the STGP1 and STGP2 watersheds comprising between 17% and 18% of TSOC relative to the MDWWSF (15%).
Particulate organic matter N, POMC, TSN, TSOC, and RCAH were all positively correlated with one another (
p < 0.01) (
Table 3). Specifically, correlation coefficients descriptive of POMC and N fractions and TSOC and TSN were
r = 0.98 and 0.99, respectively. As mentioned previously, POM C and N make up a measurable and biologically significant percentage of TSOC and TSN. Therefore, POM C and N are significantly positively correlated with TSOC and TSN (
Table 3). The correlation coefficients ranged from
r = 0.73 to 0.77. The resistant fraction, RCAH, was significantly (
p < 0.01) positively correlated with TSOC (
r = 0.88) and POMC (
r = 0.76). Percent silt was positively (
p < 0.01) correlated with RCAH (
r = 0.28), TSOC (
r = 0.27), TSN (
r = 0.25), and POMC (
p < 0.05,
r = 0.21). In contrast, the percentage of clay content had a significant (
p < 0.01) but negative effect on all C and N fractions with correlation coefficients ranging from
r = −0.33 to −0.43. These results corroborate the findings of other researchers who have used naturally occurring, stable carbon isotopes and “prebomb” carbon to date C associated with silt and clay fractions and found that C associated with silt is older than C typically associated with the clay fraction [
4,
5]. The mechanisms that drive such reactions are still unclear and merit further investigation. Clays may be physically and or chemically protecting more biologically available C fractions containing plant material that would not interact with the silt fraction and, as a result, would undergo further decomposition. Future applications of in situ field analyses of intact soil pedons using a spectroradiometer equipped with an illuminating contact probe may provide further insight into how mineral and organic soil fractions interact.
3.2. Calibration and Validation of Reflectance-Based Algorithms
Prior to spectral analysis, each dataset (i.e., whole ground soil, whole unground soil, ground POM, and acid hydrolysis) was separated into a calibration and validation dataset using a random function (see
Section 2.3 and
Section 2.4 and
Table 4 and
Table 5 for the exact number of samples used in the calibration and validation models for a given measurement). The calibration datasets typically contained 70% of the original data and the remaining 30% of the data was used for validation. As was previously mentioned, less than 7% of all spectral scans were considered outliers and removed. Coefficients of determination (
R2)—or the degree of the relationship among two variables—represent the predictive capability of a model. Weak quantitative predictions can be inferred from
R2s ranging from 0.66 to 0.81, while
R2s between 0.82 and 0.90 represent good predictions and
R2s > 0.91 are considered excellent, i.e., highly quantitative predictions [
29]. The coefficients of determination of the calibration models for unground and ground whole soil N and C were highly correlated,
R2 = 0.99 (
Table 4), indicating that the additional preparatory work of grinding did not improve the calibrations. Coefficients of determination for ground POM N and C were
R2 = 0.94 and 0.97, respectively, whereas
R2 for RACH was the lowest but still an excellent fit (
R2 = 0.92). Researchers in Canada developed models for TSN, TSOC, and POM C and POM N using eight soils under varying land uses, obtaining similar coefficients of determination [
20]. The ratios of the standard deviation to the standard error of cross-validation (RSC) were all above three with the exception of the ground POM N (2.84).
All seven calibration models were highly successful in predicting the validation measurements. The coefficients of determination were all at or above 0.90 as were the slopes of the measured versus predicted regression lines (
Table 5). The goodness of fit was also demonstrated by the similarities between the standard error of the prediction (SEP) and the SEP corrected for bias (SEP(C)). The bias measurements themselves were all close to zero with the exception of ground POMN (
Table 5). Finally, the ratios of the standard deviation to the standard error of prediction (RPD) were all >2 (six of the seven models were >3.3) the threshold considered for a model to be quantitatively predictive [
27]. Our RPD values are comparable to those of Zhang et al.’s [
20] as previously described above.
3.3. Comparison of Spectral Data in Soil C and N Fractions
The wavelengths that had the greatest influence on spectral scans of ground and unground soil samples were identical. Therefore, ground and whole soil scans could not be distinguished from one another. The correlogram of the correlation and reflectance at a given wavelength (
Figure 2) contains unground whole soil. Obtaining comparable results from unground soil reduces the amount of time required to process samples and suggests that accurate in situ field measurements are likely to be possible at this LTAR location and others. The reflectance spectra of whole soil samples were equally useful in predicting the C and N constituents. Similarly, the POM spectral scans were equally useful in the prediction of the C and N constituents in this fraction.
Figure 2 is a correlogram of the correlation (
r,
y-axis) and reflectance at a given wavelength (
x-axis) for whole soil (C or N), POM (N or C), or RACH. The correlogram contains wavelengths that are correlated with mineral and organic components [
17,
30]. Prior to the adaption of routine spectral analyses of soil, numerous studies had previously applied a number of laborious physical, chemical, and biological measures to verify the resistance, age, and size of organic fractions associated with soil separates (sand, silt, and clay) [
5,
31,
32]. Our data verify that spectroradiometry can be correlated to a range of soil C and N fractions, reducing the number of chemometric measurements required. Although a significant portion of resistant C, estimated via acid hydrolysis (RACH), was determined to be made of humic substance, Plante et al. [
5] concluded that the fraction also contained plant-derived compounds. Correlating measured carbon fractions representing the continuum of C in the soil, such as POMC, whole soil C, and RACH, with spectroradiometer readings enables researchers to rapidly derive information about organo-mineral associations in soil [
33,
34]. Particularly, the visible (VIS) and NIR ranges that provide quantitative information with respect to mineral and organic constituents often without prior preparation of soil samples via grinding for example [
30,
33].
Whole soil, POM, and RCAH contained iron bearing minerals (
Table 6 and
Figure 2), all three dominant soil series in the watershed treatments contained iron oxides and active to super-active clay minerals [
24]. Whole soil spectral scans (solid, black lines) were positively correlated with ferrihydrite, (Fe–O–OH) (
x = 975 nm,
r = 0.20) and more negatively correlated with gibbsite, γ-Al(OH)
3 (
x = 2261 nm,
r = −0.77). The POM fraction (solid, gray line) containing the sand size separates was positively correlated with three wavelengths that potentially correspond to Goethite, FeO(OH), (
x = 661 nm,
r = 0.50;
x = 916 nm,
r = 0.76;
x = 960 nm,
r = 0.70) and was negatively correlated with ferrihydrite, (Fe–O–OH) (x = 987 nm,
r = −0.69). The RACH fraction (hatched, black line) was positively correlated with nontronite, an iron enriched smectite (
x = 2408 nm,
r = 0.74). These minerals have been previously shown to chemically and physically protect plant-derived and humic C compounds [
4,
5,
33,
34].
Scans from each measured variable—whole soil, POM, and RCAH—contained wavelengths that potentially represented varying organic C compounds that included plant-derived fractions, such as carbohydrates and cellulose, and constituents of humified fractions (
Table 6 and
Figure 2). Previous research reported spectral overlap in the “greatest influence infrared range” among NIR C measurements associated with the sand, silt, and clay fractions [
19]. The POM fraction was positively correlated with organic matter, x = 1350 nm (
r = 0.58). Whole soil scans contained four wavelengths associated with organic C. Two were positively correlated
x = 1288 nm (
r = 0.65) with lignin, starch and protein and
x = 1748 nm (
r = 0.61) correlated with methyls (C–H). The third was negatively correlated
x = 1443 nm (
r = −0.69) with carboxylic acid. In contrast, RCAH was positively correlated with carboxylic acid
x = 1450 nm (
r = 0.77) as well as with aliphatic constituents,
x = 1758 nm (
r = 0.84). A similar positive correlation of carboxyl constituents in the resistant C fraction and negative correlation in whole soil C was reported by Rossel and Hicks [
30].
In a number of wavelengths associated with N compounds that included amines (general formula CO–NH), amides, RC(O)NH
2, and proteins, RCH(NH
2)COOH was present in scans associated with whole soil, POM, and RACH samples. Amines were positively correlated with the RACH fraction (
x = 990 nm,
r = 0.79). Amides were positively correlated with whole soil (
x = 1001 nm,
r = 0.18). The same wavelength corresponding to protein was found in whole soil
x = 2192 nm (
r = 0.72) and POM (
x = 2190 nm,
r = 0.72) but was not present in RACH samples. There was also an overlap of organic N and C compounds in whole soil scans at
x = 1288 nm (
r = 0.65) representative of lignin, starch, and protein compounds and POM at
x = 1042 nm (
r = 0.80 nm) associated with CONH. Previous research reported spectral overlap among NIR N and C:N measurements associated with the sand, silt, and clay fractions [
19].