Quacking aspen (Populus tremuloides Michx.) is an iconic species in western United States that offers multiple ecosystem services, including carbon sequestration. A shift in forest cover towards ...coniferous species due to natural succession, land management practices, or climate change may modify soil organic carbon (SOC) dynamics and CO2emissions. The objectives of this study were to: (i) assess the effects of overstory composition on SOC storage and stability across the aspen-conifer ecotone, (ii) use Fourier transform infrared spectroscopy attenuated total reflectance (FTIR-ATR) to assess whether SOC storage is associated with preferential adsorption of certain organic molecules to the mineral surfaces, and (iii) develop models using near-infrared reflectance spectroscopy (NIRS) to predict aspen- and conifer-derived SOC concentration. Mineral soils (0 - 15 cm) were sampled in pure and mixed aspen and conifer stands in Utah and subjected to physical fractionation to characterize SOC stability (i.e., SOC protected against microbial decomposition), long term laboratory incubations (i.e., SOC decomposability), and hot water extractions (i.e., SOC solubility). Vegetation cover had no effect on SOC storage (47.0 ± 16.5 Mg C ha−1), SOC decomposability (cumulative released CO2-C of 93.2 ± 65.4 g C g−1 C), SOC solubility (9.8 ± 7.2 mg C g−1 C). Mineral-associated SOC (MoM) content was higher under aspen (31.2 ± 15.1 Mg C ha−1) than under mixed (25.7 ± 8.8 Mg C ha−1) and conifer cover (22.8 ± 9.0 Mg C ha−1), indicating that aspen favors long-term SOC storage. FTIR-ATR spectral analysis indicated that higher MoM content under aspen is not due to higher concentration of recalcitrant compounds (e.g., aliphatic and aromatic C), but rather to stabilization of simple molecules (e.g., polysaccharides) of plant or microbial origin. NIRS models performed well during calibration-validation stage (ratio of standard deviation of reference values to standard error of prediction (RPD) ≥ 2). However, model performance decreased during independent validation (RPD = 1.2 - 1.6), probably due to the influence of soil texture, mineralogy, understory vegetation, and land history on SOC spectra. Further improvement of NIRS models could provide insight on SOC dynamics under potential conifer encroachment in semiarid montane forests.
Question: Forest species influence soil organic carbon (SOC) storage through litter input and microclimate, which in interaction with soil texture and mineralogy may lead to differences in SOC ...stabilization and chemistry. The decline of quacking aspen (Populus tremuloides) and expansion of conifers in the western United States due to natural succession, management practices and climate change could affect SOC dynamics. The objectives of this study were to: (i) assess the effects of overstory composition on SOC storage and stability across the aspen-conifer ecotone, and (ii) characterize the chemical composition of SOC with Fourier transform infrared spectroscopy.Methods: We sampled mineral soil (0-15 cm) across the natural gradient of aspen and mixed conifers stands (Abies lasiocarpa and Pseudotsuga menziesii) in semi-arid montane forests from Utah. SOC was divided into light fraction (LF), mineral-associated SOC in the silt and clay fraction (MoM), and a dense subfraction > 53 µm (SMoM) using wet sieving and electrostatic attraction. SOC decomposability and solubility was derived from long term laboratory incubations and hot water extractions. Mineral matrix samples were obtained by removal of organic matter with NaOCl (6%) at room temperature. We applied Fourier transform infrared spectroscopy to analyze the chemistry of organic matter (OM) (i.e., bulk soil spectra – mineral matrix spectra), LF, and MoM.Results: Vegetation cover did not affect SOC storage (47.0 ± 16.5 Mg C ha−1), SOC decomposability (cumulative CO2-C release of 93.2 ± 65.4 g C g−1 C), or SOC solubility (9.8 ± 7.2 mg C g−1 C), but MoM content increased with presence of aspen pure aspen (31.2 ± 15.1 Mg C ha-1) > mixed (25.7 ± 8.8 Mg C ha−1) > conifer (22.8 ± 9.0 Mg C ha−1). Silt+clay (%) had a positive effect on MoM content (r = 0.64, p < 0.0001), and was negatively correlated to decomposable SOC per gram of C (r = -0.48, p = 0.001) or soluble SOC (r = -0.59, p < 0.0001), indicating that organo-mineral complexes reduced biological availability of SOC. Differences in chemistry among vegetation classes were patent in the LF, with greater proportion of polysaccharides and C-O groups (e.g., esters, phenols, carboxylate) for aspen and mixed LF and greater proportion of aliphatic C for mixed and conifer LF. The same patterns remained in MoM, although the effect of vegetation was statistically significant only for aliphatic C. Conclusions: Our results suggest that aspen dominance favors SOC storage as MoM, although the influence of vegetation may be surpassed by texture in sites with relatively high content of silt and clay (i.e., > 70 %). Management efforts towards the conservation and regeneration of aspen may promote long-term C sequestration in sites with silt + clay content around 40 - 70 %. Greater storage of MoM under aspen may be caused by chemical protection of relatively simple molecules resulting from litter breakdown, fine root turnover, or rhizodeposition, rather than the preservation of recalcitrant compounds (i.e., aliphatic C).
Question: Forest species influence soil organic carbon (SOC) storage through litter input and microclimate, which in interaction with soil texture and mineralogy may lead to differences in SOC ...stabilization and chemistry. The decline of quacking aspen (Populus tremuloides) and expansion of conifers in the western United States due to natural succession, management practices and climate change could affect SOC dynamics. The objectives of this study were to: (i) assess the effects of overstory composition on SOC storage and stability across the aspen-conifer ecotone, and (ii) characterize the chemical composition of SOC with Fourier transform infrared spectroscopy.Methods: We sampled mineral soil (0-15 cm) across the natural gradient of aspen and mixed conifers stands (Abies lasiocarpa and Pseudotsuga menziesii) in semi-arid montane forests from Utah. SOC was divided into light fraction (LF), mineral-associated SOC in the silt and clay fraction (MoM), and a dense subfraction > 53 µm (SMoM) using wet sieving and electrostatic attraction. SOC decomposability and solubility was derived from long term laboratory incubations and hot water extractions. Mineral matrix samples were obtained by removal of organic matter with NaOCl (6%) at room temperature. We applied Fourier transform infrared spectroscopy to analyze the chemistry of organic matter (OM) (i.e., bulk soil spectra – mineral matrix spectra), LF, and MoM.Results: Vegetation cover did not affect SOC storage (47.0 ± 16.5 Mg C ha−1), SOC decomposability (cumulative CO2-C release of 93.2 ± 65.4 g C g−1 C), or SOC solubility (9.8 ± 7.2 mg C g−1 C), but MoM content increased with presence of aspen pure aspen (31.2 ± 15.1 Mg C ha-1) > mixed (25.7 ± 8.8 Mg C ha−1) > conifer (22.8 ± 9.0 Mg C ha−1). Silt+clay (%) had a positive effect on MoM content (r = 0.64, p < 0.0001), and was negatively correlated to decomposable SOC per gram of C (r = -0.48, p = 0.001) or soluble SOC (r = -0.59, p < 0.0001), indicating that organo-mineral complexes reduced biological availability of SOC. Differences in chemistry among vegetation classes were patent in the LF, with greater proportion of polysaccharides and C-O groups (e.g., esters, phenols, carboxylate) for aspen and mixed LF and greater proportion of aliphatic C for mixed and conifer LF. The same patterns remained in MoM, although the effect of vegetation was statistically significant only for aliphatic C. Conclusions: Our results suggest that aspen dominance favors SOC storage as MoM, although the influence of vegetation may be surpassed by texture in sites with relatively high content of silt and clay (i.e., > 70 %). Management efforts towards the conservation and regeneration of aspen may promote long-term C sequestration in sites with silt + clay content around 40 - 70 %. Greater storage of MoM under aspen may be caused by chemical protection of relatively simple molecules resulting from litter breakdown, fine root turnover, or rhizodeposition, rather than the preservation of recalcitrant compounds (i.e., aliphatic C).
Land use and agricultural practices, in interaction with soil texture, influence soil fauna and soil microbial community, water holding capacity, and C and nutrient cycling among other agroecosystem ...properties. Detailed knowledge on the spatial distribution of soil texture can improve land-use planning and crop management. Our objective was to predict soil texture in agricultural land for the Region Centre (France), combining regression models and area-to-point kriging. The French soil-test database (BDAT) is largely populated with topsoil analysis requested by farmers mainly interested in soil fertility. To protect the anonymity of the farms, their coordinates are unknown and texture is aggregated by municipality. The nature of the data requires novel disaggregation techniques (i.e., area-to-point kriging) to develop high-resolution maps on point support. We applied an additive log-ratio transformation (alr-transform) on texture data to remove the closure effect and achieve normality. Average values of environmental covariates by municipality were used to fit predictive models with multiple linear regression, Cubist, and boosted regression trees (BRT). Data from 104 plots from the systematic soil quality monitoring network (RMQS) were used for independent validation. Only BRT models provided better predictions (clay-alr R2 = 0.54, sand-alr R2 = 0.76) than reference BDAT texture values averaged by commune (clay-alr R2 = 0.33, sand-alr R2 = 0.64). In a second step, BRT predictions were used as auxiliary variables for area-to-point kriging following the summary statistics approach developed by Orton et al. (2012). To deal with the dependence between clay- and sand-alr transforms we applied a linear model of coregionalization. This approach allowed to include the relationships between soil forming factors and soil texture, and to account for the uncertainty on areal means in the area-to-point kriging step. We are currently testing whether incorporating remote sensing data (e.g., Landsat 8) in the regression models further improves soil texture predictions despite the loss of information when averaging by municipality. The combination of regression and area-to-point kriging is a promising method to produce high-resolution maps from soil-test data missing the exact coordinates.
Land use and agricultural practices, in interaction with soil texture, influence soil fauna and soil microbial community, water holding capacity, and C and nutrient cycling among other agroecosystem ...properties. Detailed knowledge on the spatial distribution of soil texture can improve land-use planning and crop management. Our objective was to predict soil texture in agricultural land for the Region Centre (France), combining regression models and area-to-point kriging. The French soil-test database (BDAT) is largely populated with topsoil analyses requested by farmers mainly interested in soil fertility. To protect the anonymity of the farms, their coordinates are unknown and all measurements are aggregated by municipality. The nature of the data requires novel disaggregation techniques (i.e., area-to-point kriging) to develop high-resolution maps on point support. We applied an additive log-ratio transformation (alr-transform) on texture data to account for the compositional constraint (i.e. that the sum of sand, silt and clay contents must be 100 %) and achieve normality. Average values of 25 environmental covariates by municipality were used to fit predictive models with multiple linear regression, Cubist, and boosted regression trees (BRT). Data from 104 plots from the systematic soil quality monitoring network (RMQS) were used for independent validation. Only BRT models provided better predictions (clay-alr R2 = 0.54, sand-alr R2 = 0.76) than reference BDAT texture values averaged by commune (clay-alr R2 = 0.33, sand-alr R2 = 0.64). In a second step, BRT predictions were used as auxiliary variables for area-to-point kriging following the summary statistics approach developed by Orton et al. (2012). To deal with the dependence between clay- and sand-alr transforms we fitted a linear model of coregionalization, and applied area-to-point cokriging to the BRT residuals. This approach allowed us to include the relationships between soil forming factors and soil texture (through the BRT model), and to account for the uncertainty in the areal means, correlations between sand, silt and clay contents, and spatial autocorrelation (in the area-to-point cokriging step). We are currently testing whether incorporating remote sensing data (e.g., Landsat 8) in the regression models further improves soil texture predictions despite the loss of information when averaging by municipality. The combination of regression and area-to-point kriging is a promising method to produce high-resolution maps as expected in the GlobalSoilMap project from soil-test data missing the exact coordinates.
