Wavelet analysis of soil variation at nanometre‐ to micrometre‐scales: an example of organic carbon content in a micro‐aggregate

This paper demonstrates the potential of wavelet analysis to investigate fine‐scale spatial variation in soil without statistical assumptions that are generally implausible. We analysed the optical densities of different forms of carbon which were measured at intervals of 50 nm along a 16‐µm transect on a soil micro‐aggregate using near‐edge X‐ray fine‐structure spectroscopy (NEXAFS). We found different patterns of scale‐dependent variation between the carbon forms, which could be represented by pair‐wise wavelet correlations at the different scales, and by principal components analysis of all the correlations at each scale. These results represent only one small soil micro‐aggregate and are not presented as general findings about soil carbon, but they do indicate that fine‐scale variation of soil carbon can be complex in ways that the wavelet analysis can accommodate but alternative spatial statistics such as variograms cannot. Among the patterns of variation that the analysis could identify were scale‐dependent correlations of the different forms of carbon. In some cases, positive correlations were found at coarser scales and negative at the finest scales, suggesting a multi‐scale pattern in which contrasting forms of carbon are deposited in common clumps but at finer scales either one or the other form dominates. Aromatic and carboxylic carbon varied jointly in this way. Other forms of carbon, such as carboxylic and aliphatic carbon, were strongly correlated at the finest scales but not the coarser scales. We found evidence for changes in the variance and correlation of forms of carbon along the transect, indicating that the spatial distribution of carbon at these fine scales may be very complex in ways that are inconsistent with the assumptions of geostatistics. This quantitative analysis of the spatial patterns of different soil components at micro‐scales offers a basis for formulating and testing specific hypotheses on replicated samples.     

Scale‐ and location‐dependent correlation of nitrous oxide emissions with soil properties: an analysis using wavelets

This paper shows how the wavelet transform can be used to analyse the complex spatial covariation of the rate of nitrous oxide (N₂O) emissions from the soil with soil properties that are expected to control the evolution of N₂O. We use data on N₂O emission rates from soil cores collected at 4‐m intervals on a 1024‐m transect across arable land at Silsoe in England. Various soil properties, particularly those expected to influence N₂O production in the soil, were also determined on these cores. We used the adapted maximal overlap discrete wavelet transform (AMODWT) coefficients for the N₂O emissions and soil variables to compute their wavelet covariances and correlations. These showed that, over the transect as a whole, some soil properties were significantly correlated with N₂O emissions at fine spatial scales (soil carbon content), others at intermediate scales (soil water content) and others at coarse spatial scales (soil pH). Ammonium did not appear to be correlated with N₂O emissions at any scale, suggesting that nitrification was not a significant source of N₂O from these soils in the conditions that pertained at sampling. We used a procedure to detect changes in the wavelet correlations at several spatial scales. This showed that certain soil properties were correlated with N₂O emissions only under certain conditions of topography or parent material. This is not unexpected given that N₂O is generated by biological processes in the soil, so the rate of emission may be subject to one limiting factor in one environment and a different factor elsewhere. Such changes in the relationship between variables from one part of the landscape to another is not consistent with the geostatistical assumption that our data are realizations of coregionalized random variables.     

Analysing spatially intermittent variation of nitrous oxide emissions from soil with wavelets and the implications for sampling

Emissions of gases from the soil are known to vary spatially in a complex way. In this paper we show how such data can be analysed with the wavelet transform. We analysed data on rates of N₂O emission from soil cores collected at 4‐m intervals on a 1024‐m transect across arable land at Silsoe in England. We used a thresholding procedure to represent intermittent variation in N₂O emission from the soil as a sparse wavelet process, i.e. one in which most of the wavelet coefficients are not significantly different from zero. This analysis made clear that the rate of N₂O emission varied more intermittently on this transect than did soil pH, for which many more of the wavelet coefficients had to be retained. This account of intermittent variation motivated us to consider a class of random functions, which we call wavelet random functions, for the simulation of spatially intermittent variation. A wavelet random function (WRF) is an inverse wavelet transform of a set of random wavelet coefficients with specified variance at each scale. We generated intermittent variation at a particular scale in the WRF by specifying a binormal process for the wavelet coefficients at this scale. We showed by simulation that adaptive sampling schemes are more efficient than ordinary stratified random sampling to estimate the mean of a spatial variable that is intermittent at a particular scale. This is because the sampling can be concentrated in the more variable regions. When we simulated values that emulate the intermittency of our data on N₂O we found that the gains in efficiency from simple adaptive sampling schemes were small. This was because the emission of N₂O is intermittent over several disparate scales. More sophisticated adaptive sampling is needed for these conditions, and it should embody knowledge of the relevant soil processes.     

Scale- and location-dependent correlations of soil strength and the yield of wheat

The relationship between soil strength and crop yield may be summarized by a linear correlation coefficient (usually negative). It is likely, however, that this over-simplifies a complex situation in which the relationship between these variables depends on spatial scale and location. We used the wavelet transform to assess this scale- and location-dependence. We established a transect on an arable field in Eastern England, and studied the correlations of soil strength (top- and subsoil) with crop yield. The transect comprised 267 contiguous 0.72 m × 0.72 m plots. Measurements were taken during two consecutive growing seasons of winter wheat (harvest dates of August 2004 and 2005). Soil strength was measured with a penetrometer in the spring of each growing season. As expected, the overall correlation of soil strength with yield was negative but weak. Wavelet analysis revealed that, at fine spatial scales, topsoil and subsoil strength were correlated more or less equally with yield; however, at coarse spatial scales, topsoil strength had a stronger correlation with yield than did subsoil strength. The correlation of topsoil strength with yield at fine spatial scales (corresponding to about 1 m on the ground) was negative. A likely source of this fine-scale variation was the soil compaction associated with tractor wheelings. The correlation of topsoil strength with yield at the coarsest spatial scale (corresponding to about 50 m on the ground) was positive. This correlation was temporally stable, and might have reflected how soil strength can act as a proxy for other soil attributes. In the 2005 growing season, we found evidence that, at intermediate spatial scales, the correlation of soil strength with yield changed depending on the position on the transect. This was probably due to an interaction between the compaction associated with tractor wheelings and the local soil conditions. There was no evidence of such location-dependence in the correlation of soil strength with yield in the 2004 growing season. In summary, the effect of soil strength on crop yield was not expressed in a constant negative correlation across all spatial scales and locations: the negative correlation occurred mainly at fine spatial scales, and the correlation changed according to the position in the landscape and the prevailing local soil conditions.     

Exploring the variation in soil saturated hydraulic conductivity under a tropical rainforest using the wavelet transform

Saturated hydraulic conductivity (K_s) of the soil is a key variable in the water cycle. For the humid tropics, information about spatial scales of K_s and their relation to soil types deduced from soil map units is of interest, as soil maps are often the only available data source for modelling. We examined the influence of soil map units on the mean and variation in K_s along a transect in a tropical rainforest using undisturbed soil cores at 0–6 and 6–12 cm depth. The K_s means were estimated with a linear mixed model fitted by residual maximum likelihood (REML), and the spatial variation in K_s was investigated with the maximum overlap discrete wavelet packet transform (MODWPT). The mean values of K_s did not differ between soil map units. The best wavelet packet basis for K_s at 0–6 cm showed stationarity at high frequencies, suggesting uniform small‐scale influences such as bioturbation. There were substantial contributions to wavelet packet variance over the range of spatial frequencies and a pronounced low frequency peak corresponding approximately to the scale of soil map units. However, in the relevant frequency intervals no significant changes in wavelet packet variance were detected. We conclude that near‐surface K_s is not dominated by static, soil‐inherent properties for the examined range of soils. Several indicators from the wavelet packet analysis hint at the more dominant dynamic influence of biotic processes, which should be kept in mind when modelling soil hydraulic properties on the basis of soil maps.     

Inference about soil variability from the structure of the best wavelet packet basis

The plausibility of the assumption that soil variation can be treated as a realization of a random spatial process that is stationary in the variance can break down in various ways. It is possible to test the assumption using methods based on the wavelet transform. To date these approaches have been applied using the discrete wavelet transform. A drawback of this approach is that it uses a partition of the spatial frequencies represented in the data into intervals (scales) that are arbitrarily defined in advance and are not necessarily suitable for the representation of the variation of the data in question. A solution to this problem is to identify the best basis for the data from a wavelet packet library. An interesting question is whether the structure of this best basis is in itself informative about the plausibility of the stationarity assumption. In this paper, I show that this is indeed the case. The best basis for a stationary random variable from some packet library is the basis on the maximum dilation of the mother wavelet, which gives the finest resolution in the frequency domain. I propose the ratio of the entropy cost functional for this basis to that of the empirical best basis as a measure of evidence against the null hypothesis of stationarity in the variance. Critical values of this statistic may be obtained by Monte Carlo methods. I demonstrate the method using data on the clay content of soil on a transect in central England. The null hypothesis of stationarity in the variance may be rejected. Tests for the uniformity of variance can then be applied to wavelet packets in the best basis. The dominant local feature that is reflected in this behaviour is the unique pattern of variation in alluvium around a drainage channel that crosses the transect. This variation contrasts with that seen at most positions on the transect, variation that arises from a more or less regular pattern of boundaries between contrasting Jurassic strata.     

Vertical distribution of soil properties under short‐rotation forestry in Northern Germany

Short‐rotation forestry (SRF) on arable soils has high potentials for biomass production and leads to long‐term no‐tillage management. In the present study, the vertical distributions of soil chemical and microbial properties after 15 y of SRF with willows and poplar (Salix and Populus spp.) in 3‐ and 6‐year rotations on an arable soil were measured and compared to a pertinent tilled arable site. Two transects at different positions in the relief (upper and lower slope; transect 1 and 2) were investigated. Short‐rotation forestry caused significant changes in the vertical distribution of all investigated soil properties (organic and microbial C, total and microbial N, soil enzyme activities), however, the dimension and location (horizons) of significant effects varied. The rotation periods affected the vertical distribution of the soil properties within the SRF significantly. In transect 1, SRF had higher organic‐C concentrations in the subsoil (Bv horizon), whereas in transect 2, the organic‐C concentrations were increased predominantly in the topsoil (Ah horizon). Sufficient plant supply of P and K in combination with decreased concentrations of these elements in the subsoil under SRF pointed to an effective nutrient mobilization and transfer from the deeper soil horizons even in the long term. In transect 1, the microbial‐C concentrations were higher in the B and C horizons and in transect 2 in the A horizons under SRF than under arable use. The activities of β‐glucosidases and acid phosphatases in the soil were predominantly lower under SRF than under arable use in the topsoil and subsoil. We conclude, that long‐term SRF on arable sites can contribute to increased C sequestration and changes in the vertical distribution of soil microbial biomass and soil enzyme activities in the topsoil and also in the subsoil.     

A multiscale study of silty soil structure

Dependency of soil properties on scale is a crucial issue in soil physics. In this paper, fractal approaches are used in two case studies in France and Australia, respectively, to study how measured physical soil properties change with the sample spacing and the scale of observation. At a scale of 10–1000 m (10⁴ to 10⁶ mm), fractals were applied to sample data from a linear transect, while at the 10^?6 to 10² mm scale, fractals were applied in two dimensions to analyse both soil micro‐ and macrostructure, based on thin section samples. Porosity was characterized by short‐range spatial variations using sample spacings of 0.5 and 5 m (from the transect data), and a sample spacing of 1 cm (from the thin section analysis). The size of the representative elementary volume (REV) or representative elementary area (REA), required to represent statistically the elementary soil structure, was identified in three ways: (i) by the correlation length of a representative interconnected pore network, (ii) by the upper limit of the non‐linear increase with observation scale of mean porosity (upper limit of the solid mass fractal domain), and (iii) by the non‐linear decrease with observation scale of the coefficient of variation, CV, of mean porosity. Two embedded REAs were identified: the first (0.1–0.4 mm) related to the soil microstructure whereas a second (11–44 mm) related to the soil macrostructure. The solid mass fractal dimensions of the two embedded structural domains showed that hierarchical heterogeneity of soil structure was more pronounced for microstructures than for macrostructures. The mean area ratio of microstructural matrix/total surface and the CV of mean microporosity both scale similarly at observation scales smaller than the REA size. Their scaling exponents were both related to the fractal dimension of microstructural matrix. This preliminary study shows that the theory of fractals applied to soil structures at a specific scale range cannot be directly applied to predict soil physical properties at another scale range. This is because there are different interdependent structuring processes operating at different scales resulting in fractal dimensions being consistent only over particular domain limits.     

Scaling analysis of soil water storage with missing measurements using the second‐generation continuous wavelet transform

Information on the spatial variability of soil water storage (SWS) at different scales is important for understanding various hydrological, ecological and biogeochemical processes in the landscape. However, various obstructions such as roads or water bodies may result in missing measurements and create an irregular spatial series. The wavelet transform can quantify spatial variability at different scales and locations but is restricted to regular measurements. The objective of this study was to analyse the spatial variability of SWS with missing measurements using the second‐generation continuous wavelet transform (SGCWT). Soil water content (converted to SWS by multiplying with depth) was measured with a neutron probe and time‐domain reflectrometry along a transect of 128 points. Because there were missing measurements, I used SGCWT to partition the total variation into different scales and locations. Whilst there were some small‐scale variations (< 20 m) along the transect, the medium scale variations (20–70 m with an average of about 30–45 m) were mainly concentrated within the depressions along the transect. The strongest variations were observed at around 90–110 m scale, representing the variations resulting from alternating knolls and depressions. Similar spatial patterns at different scales were observed during different seasons, indicating temporal stability in the spatial pattern of SWS. Among the controlling factors, the wavelet spectra of relative elevation (RE) and organic carbon (OC) were very similar to that of SWS. The wavelet covariance was also large between SWS and RE and OC at all seasons. As the OC reflects the long‐term history of water availability and might be controlled by topographic setting or elevation, it can be concluded that elevation is an important controlling factor of SWS irrespective of seasons in this type of landscape. The SGCWT provides a new way of analysing the spatial variability of regularly measured soil properties or those with missing measurements.     

Spatial variation of ammonia volatilization from soil and its scale‐dependent correlation with soil properties

Quantitative predictions of ammonia volatilization from soil are useful to environmental managers and policy makers and empirical models have been used with some success. Spatial analysis of the soil properties and their relationship to the ammonia volatilization process is important as predictions will be required at disparate scales from the field to the catchment and beyond. These relationships are known to change across scales and this may affect the performance of an empirical model. This study is concerned with the variation of ammonia volatilization and some controlling soil properties: bulk density, volumetric water content, pH, CEC, soil pH buffer power, and urease activity, over distances of 2, 50, 500, and >2000 m. We sampled a 16 km × 16 km region in eastern England and analyzed the results by a nested analysis of (co)variance, from which variance components and correlations for each scale were obtained. The overall correlations between ammonia volatilization and the soil properties were generally weak: –0.09 for bulk density, 0.04 for volumetric water content, –0.22 for CEC, –0.08 for urease activity, –0.22 for pH and 0.18 for the soil pH buffer power. Variation in ammonia volatilization was scale‐dependent, with substantial variance components at the 2‐ and 500‐m scales. The results from the analysis of covariance show that the relationships between ammonia volatilization and soil properties are complex. At the >2000 m scale, ammonia volatilization was strongly correlated with pH (–0.82) and CEC (–0.55), which is probably the result of differences in parent material. We also observed weaker correlations at the 500‐m scale with bulk density (–0.61), volumetric water content (0.48), urease activity (–0.42), pH (–0.55) and soil pH buffer power (0.38). Nested analysis showed that overall correlations may mask relationships at scales of interest and the effect of soil variables on these soil processes is scale‐dependent.     

Spatial covariation of Azotobacter abundance and soil properties: A case study using the wavelet transform

The soil microflora is very heterogeneous in its spatial distribution. The origins of this heterogeneity and its significance for soil function are not well understood. A problem for understanding spatial variation better is the assumption of statistical stationarity that is made in most of the statistical methods used to assess it. These assumptions are made explicit in geostatistical methods that have been increasingly used by soil biologists in recent years. Geostatistical methods are powerful, particularly for local prediction, but they require the assumption that the variability of a property of interest is spatially uniform, which is not always plausible given what is known about the complexity of the soil microflora and the soil environment. We have used the wavelet transform, a relatively new innovation in mathematical analysis, to investigate the spatial variation of abundance of Azotobacter in the soil of a typical agricultural landscape. The wavelet transform entails no assumptions of stationarity and is well suited to the analysis of variables that show intermittent or transient features at different spatial scales.In this study, we computed cross-variograms of Azotobacter abundance with the pH, water content and loss on ignition of the soil. These revealed scale-dependent covariation in all cases. The wavelet transform also showed that the correlation of Azotobacter abundance with all three soil properties depended on spatial scale, the correlation generally increased with spatial scale and was only significantly different from zero at some scales. However, the wavelet analysis also allowed us to show how the correlation changed across the landscape. For example, at one scale Azotobacter abundance was strongly correlated with pH in part of the transect, and not with soil water content, but this was reversed elsewhere on the transect.The results show how scale-dependent variation of potentially limiting environmental factors can induce a complex spatial pattern of abundance in a soil organism. The geostatistical methods that we used here make assumptions that are not consistent with the spatial changes in the covariation of these properties that our wavelet analysis has shown. This suggests that the wavelet transform is a powerful tool for future investigation of the spatial structure and function of soil biota.     

A practical two‐step approach for mixed model‐based kriging,with an application to the prediction of soil organic carbon concentration

Soil scientists often use prediction models to obtain values at unsampled locations. The spatial variation in the soil is best captured by using the empirical best linear unbiased predictor (EBLUP) based on a restricted maximum likelihood (REML) approach that efficiently exploits available data on both mean trends and correlation structures. We proposed a practical two‐step implementation of the REML approach for model‐based kriging, exemplified by predicting soil organic carbon (SOC) concentrations in mineral soils in Estonia from the large‐scale digital soil map information and a previously established prediction model. The prediction model was a linear mixed model with soil type, physical clay content (particle size < 0.01 mm) and A‐horizon thickness as fixed effects and site, transect, plot, year, year‐transect random intercepts and site‐specific random slopes for clay content. We used only the site‐specific intercept EBLUPs for estimating spatial correlation parameters as they described most of the variation in the random effects (86.8%). Fitting an exponential correlation model to these EBLUPs resulted in an estimated range of 10.5 km and the estimated proportion of the variance from the nugget effect was 0.23. The results of a simulation study showed a downwards bias that decreased with sample size. The results were validated through an external dataset, resulting in root mean square errors (RMSE) of 1.06 and 1.07% for the two‐step approach for kriging and the model with only fixed effects (no kriging), respectively. These results indicate that using the two‐step approach for kriging may improve prediction.     

Spatial analysis of categorical soil variables with the wavelet transform

This paper describes a wavelet transform for the analysis of categorical (multistate) soil variables, i.e. ones (such as profile classes) that have two or more discrete states. The states are transformed to a continuous variable by a mapping which is optimized by scale and location to highlight local variation. The method is illustrated with data from a transect across a gilgai landscape in Australia. A categorical variable on relief, with three states, was recorded from the sample sites, from which soil cores had also been collected and analysed. The wavelet analysis showed a transient feature of the variation at scales up to 32 m. There was an interval where the characteristic alternation of depressions with the level plain was interrupted. The variation at scale 64 m appeared to be non-stationary. The relief was more variable on one side of a change point than it was on the other. This complex variation of relief was matched by that of the electrical conductivity of the soil, most strongly at the 64-m scale. The periodicity of conductivity, and the strength of its correlation with relief, were also different either side of the change point identified in the analysis of relief alone. Conductivity also showed similar transient features to relief. Evidently the wavelet transform can be used to elucidate the variation of categorical soil variables. The information from such an analysis is likely to be useful for planning surveys of the soil to measure continuous variables by sampling and laboratory analysis.     

Distribution and fractionation of heavy metals in pairs of arable and afforested soils in Denmark

Afforestation of sandy arable soils in northern Europe is likely to lead to an increase in the soil's acidity and changes in the behaviour of the organic matter, and this might affect the ability of the soil to retain heavy metals. It is important to assess the impact of such a change in the land use on the solubility of the heavy metals and to assess the risk of leaching to surface‐ and groundwater and the possible entrapment of heavy metals in the tree canopy. The impact of afforestation was assessed by excavating soil profiles in adjacent 34‐year‐old Norway spruce stands and arable plots at four different sites. We found that after 34 years the pH had decreased and cations were depleted in the topsoil under forest. The aqua regia‐extractable heavy metals were determined, and the heavy metal binding within the soil was assessed using a modified version of the BCR (Community Bureau of Reference) sequential extraction procedure. Higher contents of heavy metal were found in the arable plots in the loamy sand soils. Cadmium was found only in the most mobile fractions. The content of Pb in the subsoil was strongly correlated with the clay content, but not in the topsoil, which suggested that Pb had been added to the topsoil. We found strong correlations between the clay content and the Cu, Ni and Zn in the residual fraction, leading us to conclude that much of the Cu, Ni and Zn is of geological origin. No significant differences in the heavy metal fractionation between forest and arable soil were found, presumably because 34 years of different land use is not long enough to produce such differences.     

Dispersion and migration of fine particles in two palygorskite‐containing soils of the Jordan Valley

Migration of different mineral particles within columns of soil‐sand mixtures containing 10 or 20 mass % of soil was investigated by establishing differences in the mineral suite between the ”︁bulk clay” and the ”︁mobile fine material” fractions. The ”︁bulk clay” fractions of all soils contained smectite, palygorskite, kaolinite, quartz, feldspar, and calcite. The soils were saturated with sodium by leaching with NaCl solution, and then leached with distilled water. Clay dispersion and particle migration occurred in the columns. Values of SAR (sodium adsorption ratio) of the effluent decreased with time due to carbonate dissolution. At a certain SAR value, the clays apparently formed aggregates, and as a consequence particle migration stopped in the column. In addition to clay‐sized particles (< 2 μm), very‐fine‐silt‐sized particles (2— 5 μm) were able to migrate in the soil‐sand mixtures, too, and to some extent fine‐silt‐sized particles (5—10 μm) as well. Average size of mobile particles decreases with increase of soil content in the soil‐sand mixtures. The mineralogical composition of the ”︁mobile fine material” changed during the experiment. At the beginning of the experiment, the ”︁mobile fine material” was enriched in the non‐phyllosilicates (especially in calcite, and in some cases in quartz, feldspar and dolomite) and contained low concentrations of phyllosilicates (smectite, palygorskite and kaolinite). At the end of the experiment, the proportion of non‐phyllosilicates decreased, and as a consequence, the proportion of phyllosilicates increased. Among the non‐phyllosilicates, calcite was the most mobile mineral. Among the phyllosilicates, palygorskite was preferentially mobilized in topsoil horizons. In subsoil horizons, on the other hand, kaolinite was preferentially mobilized. This difference was explained by the different nature of carbonates in the topsoil and subsoil horizons. Palygorskite is preferentially occluded within the soil carbonates of lacustrine origin over smectite and kaolinite. These carbonates are present mainly in the subsoil horizons. As a consequence, the presence of these carbonates in the subsoil horizons decreases the migration of mainly palygorskite.     

Improved analysis and modelling of soil diffuse reflectance spectra using wavelets

Diffuse reflectance spectroscopy using visible (vis), near‐infrared (NIR) and mid‐infrared (mid‐IR) energy can be a powerful tool to assess and monitor soil quality and function. Mathematical pre‐processing techniques and multivariate calibrations are commonly used to develop spectroscopic models to predict soil properties. These models contain many predictor variables that are collinear and redundant by nature. Partial least squares regression (PLSR) is often used for their analysis. Wavelets can be used to smooth signals and to reduce large data sets to parsimonious representations for more efficient data storage, computation and transmission. Our aim was to investigate their potential for the analyses of soil diffuse reflectance spectra. Specifically we wished to: (i) show how wavelets can be used to represent the multi‐scale nature of soil diffuse reflectance spectra, (ii) produce parsimonious representations of the spectra using selected wavelet coefficients and (iii) improve the regression analysis for prediction of soil organic carbon (SOC) and clay content. We decomposed soil vis‐NIR and mid‐IR spectra using the discrete wavelet transform (DWT) using a Daubechies’s wavelet with two vanishing moments. A multiresolution analysis (MRA) revealed their multi‐scale nature. The MRA identified local features in the spectra that contain information on soil composition. We illustrated a technique for the selection of wavelet coefficients, which were used to produce parsimonious multivariate calibrations for SOC and clay content. Both vis‐NIR and mid‐IR data were reduced to less than 7% of their original size. The selected coefficients were also back‐transformed. Multivariate calibrations were performed by PLSR, multiple linear regression (MLR) and MLR with quadratic polynomials (MLR‐QP) using the spectra, all wavelet coefficients, the selected coefficients and their back transformations. Calibrations by MLR‐QP using the selected wavelet coefficients produced the best predictions of SOC and clay content. MLR‐QP accounted for any nonlinearity in the data. Transforming soil spectra into the wavelet domain and producing a smaller representation of the data improved the efficiency of the calibrations. The models were computed with reduced, parsimonious data sets using simpler regressions.     

Magnetic depletion and enhancement in the evolution of paddy and non‐paddy soil chronosequences

The objective of this study was to understand the rates and controlling factors of magnetic depletion and enhancement during anthropogenic soil evolution. To this end, the study compared the dynamic changes in magnetic properties as well as iron oxide species of paddy and non‐paddy soil chronosequences with the same parent materials. A two‐way analysis of variance (anova ) showed that paddy management resulted in significant (P < 0.01) decreases in magnetic susceptibility (χ) and other magnetic properties. Paddy management‐induced χ losses increased gradually from 24 to 55% as the cultivation history increased from 50 to 700 years. The rates of χ decrease were most rapid within the first 50 years of paddy cultivation, after which the rate slowed. The rapid decline in χ is probably caused by accelerated depletion of fine‐grained maghemite and ultrafine magnetite by iron‐reducing bacteria during soil waterlogging and consequent reducing conditions. By contrast, a significant decrease in hard isothermal remanent magnetization (HIRM) occurred only after 700 years of paddy cultivation, which matches the time taken to leach CaCO₃ from the profile. In contrast, although magnetic enhancement was observed in the non‐paddy surface horizon, there was no increasing trend at the millennium time‐scale, probably because of the large CaCO₃ content of the soil. We show that magnetic properties of paddy and non‐paddy soil derived from calcareous sediments are mainly controlled by the changing soil moisture regime and soil carbonate status along different paths of soil development. Our study suggests that differences in soil moisture regime caused by land use are substantially more important than the period of cultivation.     

Vertical gradients of potential enzyme activities in soil profiles of European beech,Norway spruce and Scots pine dominated forest sites

Management of forest sites has the potential to modulate soil organic matter decomposition by changing the catalytic properties of soil microorganisms within a soil profile. In this study we examined the impact of forest management intensity and soil physico-chemical properties on the variation of enzyme activities (β-glucosidase, β-xylosidase, α-glucosidase, phenol oxidase, N-acetyl-glucosaminidase, l-leucine aminopeptidase, phosphatase) in the topsoil and two subsoil horizons in three German regions (Schorfheide-Chorin, Hainich-Dün, Schwäbische Alb). The sandy soils in the Schorfheide-Chorin (SCH) showed lower ratios of the activity of carbon (C) acquiring enzymes (β-glucosidase) relative to nitrogen (N) acquiring enzymes (N-acetyl-glucosaminidase + l-leucine aminopeptidase), and activity of C acquiring enzymes relative to phosphorous (P) acquiring enzymes (phosphatase) than the finer textured soils in the Hainich-Dün (HAI) and Schwäbische Alb (ALB), indicating a shift in investment to N and P acquisition in the SCH. All enzyme activities, except phenol oxidase activity, decreased in deeper soil horizons as concentrations of organic C and total N did, while the decrease was much stronger from the topsoil to the first subsoil horizon than from the first subsoil to the second subsoil horizon. In contrast, phenol oxidase activity showed no significant decrease towards deeper soil horizons. Additionally, enzyme activities responsible for the degradation of more recalcitrant C relative to labile C compounds increased in the two subsoil horizons. Subsoil horizons in all regions also indicate a shift to higher N acquisition, while the strength of the shift depended on the soil type. Further, our results clearly showed that soil properties explained most of the total variance of enzyme activities in all soil horizons followed by study region, while forest management intensity had no significant impact on enzyme activities. Among all included soil properties, the clay content was the variable that explained the highest proportion of variance in enzyme activities with higher enzyme activities in clay rich soils. Our results highlight the need for large scale studies including different regions and their environmental conditions in order to derive general conclusions on which factors (anthropogenic or environmental) are most influential on enzyme activities in the whole soil profile in the long term at the regional scale.     

Detecting zones of abrupt change in soil data, with an application to an agricultural field

We propose a new method for estimating and testing the zones where a variable has discontinuities or sharp changes in the mean. Such zones are called Zones of Abrupt Change (ZACs). Our method is based on the statistical properties of the estimated gradient of the variable. The local gradient is first interpolated by kriging. Then we test whether the estimated local gradient exceeds some critical threshold computed under the null hypothesis of a constant mean. The locations where the local test is rejected define the potential ZACs, which are then tested globally. Using this method, we analysed soil data from an agricultural field. The analysis of the main soil components of the ploughed layer (clay, silt and sand particles and calcium carbonate content) reveals the structural variations in the field, linked to boundaries between soil types. Its application to non‐permanent variables (soil water and mineral nitrogen content of the soil profile to 120 cm taken at several dates) shows that water content has the same ZACs for all dates, whereas mineral nitrogen has none.