Variability Of Soil Organic Matter Research Articles

The Spatial Variability of Organic Matter and Decomposition Processes at the Marsh Scale

AbstractAccretion rate in salt marshes is governed by inorganic soil deposition and soil organic matter (SOM) accumulation. Existing (limited) observations and modeling results suggest that SOM amounts, biomass production, and decomposition processes should vary widely and systematically at the marsh scale. However, we lack observations aimed at understanding how SOM production is modulated spatially within a marsh, and at elucidating the relative importance of the controlling processes. The little existing data suggest that competing effects between biomass production and decomposition processes determine an approximately spatially constant contribution of SOM to total accretion. Here we investigate this idea using concurrent observations of SOM and decomposition rates from marshes in North Carolina. Our results indicate that systematic spatial variations in SOM are small, possibly as a result of an at least partial compensation of opposing trends in biomass production and decomposed organic matter. Our analyses show that deeper soil layers are, on average, characterized by lower decomposition rates and higher stabilization factors than shallower layers, likely because of differences in the persistence of water‐logged conditions. Overall, decomposition processes are sufficiently rapid that the labile material in the fresh biomass is completely decomposed before it can be sufficiently buried and stabilized. Our findings point to the importance of the fraction of initially refractory material and of stabilization processes in determining the final distribution of SOM within the soil column.

Microbial biodiversity in rhizospheric soil of Torreya grandis 'Merrillii' relative to cultivation history

To examine the effects of different cultivation history (5 a,10 a, and 15 a) on soil microbial communities, we used Illumina sequencing to investigate the diversity and structure of soil bacterial and fungal communities from Torreya grandis 'Merrillii' fields. The results showed that bacterial Shannon index, the richness estimators Chao1 and ACE were lower in soil in 15 year-old stand than those in other cultivation histories, while Simpson index showed no significant variation. Results from bacterial community NMDS showed that cultivation history played a vital role in driving the changes of soil bacteria communitiy structure. The bacterial communities in 5 and 10 year-old stand had the similar composition. The variations of bacterial richness and diversity as well as community structure (comprised basically of Proteobacteria, Actinobacteria, Acidobacteria, and Chloroflexi) were significantly correlated with soil organic matters, soil C/N, and total nitrogen. The fungi richness estimators of Chao1 and ACE were significantly decreased with increasing cultivation history. Shannon and Simpson indices were significantly higher in soil with 10 year-old stand than soils with other cultivation history. Fungal NMDS could be clustered in the same era. Fungal communities were comprosed of Ascomycota, Basidiomycota and Zygomycota. Changes in fungal richness/diversity and community structure were mainly controlled by the variation of soil organic matter. In conclusion, the predominant factors affecting soil microbial communities were the cultivation history, soil C/N, total nitrogen and organic matter, respectively.

Identifying localized and scale-specific multivariate controls of soil organic matter variations using multiple wavelet coherence

Environmental factors have shown localized and scale-dependent controls over soil organic matter (SOM) distribution in the landscape. Previous studies have explored the relationships between SOM and individual controlling factors; however, few studies have indicated the combined control from multiple environmental factors. In this study, we compared the localized and scale-dependent univariate and multivariate controls of SOM along two long transects (northeast, NE transect and north, N transect) from China. Bivariate wavelet coherence (BWC) between SOM and individual factors and multiple wavelet coherence (MWC) between SOM and factor combinations were calculated. Average wavelet coherence (AWC) and percent area of significant coherence (PASC) were used to assess the relative dominance of individual and a combination of factors to explain SOM variations at different scales and locations. The results showed that (in BWC analysis) mean annual temperature (MAT) with the largest AWC (0.39) and PASC (16.23%) was the dominant factor in explaining SOM variations along the NE transect. The topographic wetness index (TWI) was the dominant factor (AWC = 0.39 and PASC = 20.80%) along the N transect. MWC identified the combination of Slope, net primary production (NPP) and mean annual precipitation (MAP) as the most important combination in explaining SOM variations along the NE transect with a significant increase in AWC and PASC at different scales and locations (e.g. AWC = 0.91 and PASC = 58.03% at all scales). The combination of TWI, NPP and normalized difference vegetation index (NDVI) was the most influential along the N transect (AWC = 0.83 and PASC = 32.68% at all scales). The results indicated that the combined controls of environmental factors on SOM variations at different scales and locations in a large area can be identified by MWC. This is promising for a better understanding of the multivariate controls in SOM variations at larger spatial scales and may improve the capability of digital soil mapping.

Mid-long term effects of no tillage and Ca-amendment on degraded acid soils under contrasting weather conditions

Inadequate soil management such as excessive ploughing is one of the main problems affecting the sustainability of agroecosystems and may exacerbate problems in acid soils. The application of Ca-amendments to ameliorate acid soils and no tillage practices to recover degraded soils are common management strategies. However, few studies have focused on their interactive effects under conditions of variable rainfall. In this study, we assessed the changes in soil properties over a period of seven years in response to no tillage (NT) and tillage, with or without the application of a Ca-amendment (sugar beet foam and red gypsum). Our split-plot experiment, which started in 2005 was carried out in a degraded Palexerult in Southwestern Spain with an annual rainfed forage crop. The Ca-amendment increased soil pH, Ca content and ameliorated Al-toxicity down to a depth of 50 cm, even under NT, due to the relatively high solubility of the amendment. However, after seven years of experiment, soil organic matter variables were enhanced only to a soil depth of 0–5 cm: NT and the Ca-amendment increased total organic carbon by 31% and 25%, respectively, whereas particulate organic carbon was increased to a larger extent (by a factor of 2.5 and 2, respectively). The positive effect of NT on organic matter variables increased with time, whereas the beneficial effect of the Ca-amendment was stronger in the early years of the experiment. Adverse weather conditions, with either excessive precipitation in autumn-winter or scarce precipitation in spring, favoured biomass production under NT. Given that NT mitigated the adverse effects of both water excess and deficit on biomass, it constitutes a valuable tool to combine with Ca-amendment to maintain crop productivity and recover degraded acid soils. This is especially the case under the changing weather conditions of a Mediterranean climate or other climates with increasing occurrences of periods of drought and/or water excess.

Effects of land subsidence resulted from coal mining on soil nutrient distributions in a loess area of China

Underground coal-mining results in severe land subsidence then changes soil nutrient distributions. Soil organic matter (SOM) and total nitrogen (TN) are critical indicators of soil quality and play a key role for plant growth. However, fewer scholars pay attention to the soil nutrient distributions in mining subsidence areas. In this study, a total of 64 soil sampling points, taken from 4 different plots (one unmined plot, two subsided plots and one reclaimed plot), were collected from No.3 Anjialing underground coal mine in the loess area of China. SOM and TN at the depths of 0–20, 20–40, 40–60 and 60–80 cm in these sampling plots were measured. The classical statistics and geo-statistics were used to analyze the vertical and horizontal spatial variability of SOM and TN. The mechanisms of the effects of coal mining subsidence on soil nutrient distribution were revealed based on the distribution of the surface cracks of subsided plots. All of SOM and TN at the depth of 0–20 cm in subsided plots were less than those in unmined plot. Below the depth of 20 cm, TN in subsided plots were higher than those in unmined plot and SOM were less than those in unmined plot. SOM in reclaimed plot were higher than those in subsided plots; however, TN did not exhibit a clear regularity. Land subsidence increased horizontal spatial heterogeneity of SOM and TN distributions. The crack width, crack depth and the distance between sampling point and edge of crack were the main influence factors resulting in horizontal spatial heterogeneity of SOM and TN. This study can provide a theoretical and technical reference for land rehabilitation in subsided lands in loess area.

Distribution of radiocesium in different density fractions of temperate forest soils in Fukushima

Abstract In forested areas of Fukushima, Japan, radiocesium emitted by the Fukushima Daiichi nuclear power plant accident has accumulated on the forest floor and in surface mineral soils. To estimate and model the mobility of radiocesium in forest soils, the concept of physical variations in soil organic matter is useful. We applied density fractionation to soil samples collected in two different forest stands in Fukushima; a deciduous broadleaf forest (DBF) and an evergreen needleleaf forest (ENF). To determine the relative contributions of the two density fractions to retention of radiocesium in surface mineral soils in a temperate forest region we investigated the concentrations and stocks of radiocesium in the low- and high-density fractions (LF and HF, respectively) of the soils, as well as in the litter, and temporal changes therein between 2012 and 2014. The 137Cs stock in the litter decreased from 2012 to 2014, whereas in the soil, as the sum of the LF and HF, it increased. On average, more than 40% of the 137Cs stock in surface mineral soil (depth of 0–5 cm) was retained in LF particles, which were composed mainly of particulate organic matter. Although the LF accounted for only 10.3% of the dry mass of the soil on average, it was also characterized by an average concentration of 137Cs that was 7.8 times higher than that of the HF, which was composed of organo-mineral particles. The mean increase in the 137Cs stock in the LF from 2012 to 2014 was equivalent to 70.1% and 52.5% of those in the soils in the DBF and ENF, respectively. The relationship between loss on ignition and 137Cs content indicated that the spatial heterogeneity of radiocesium in bulk soil was substantially affected by the heterogeneity in particulate organic matter. The coarse-size (>250 μm) of the LF suggested that it was less physically protected against microbial attack. Accordingly, the LF was considered to act as a large, but temporal reservoir of radiocesium that was originally retained in the litter layer. In contrast, the accumulation of 137Cs in the HF was low, especially in 2013–2014. The HF (or organo-mineral particles) might have a more important role than the LF in 137Cs fixation over a long period, and should be continuously studied as part of the current radiocesium monitoring network.

The implications of spatially variable pre-emergence herbicide efficacy for weed management.

BACKGROUNDThe efficacy of pre‐emergence herbicides within fields is spatially variable as a consequence of soil heterogeneity. We quantified the effect of soil organic matter on the efficacy of two pre‐emergence herbicides, flufenacet and pendimethalin, against Alopecurus myosuroides and investigated the implications of variation in organic matter for weed management using a crop–weed competition model.RESULTSSoil organic matter played a critical role in determining the level of control achieved. The high organic matter soil had more surviving weeds with higher biomass than the low organic matter soil. In the absence of competition, surviving plants recovered to produce the same amount of seed as if no herbicide had been applied. The competition model predicted that weeds surviving pre‐emergence herbicides could compensate for sublethal effects even when competing with the crop. The ED50 (median effective dose) was higher for weed seed production than seedling mortality or biomass. This difference was greatest on high organic matter soil.CONCLUSIONThese results show that the application rate of herbicides should be adjusted to account for within‐field variation in soil organic matter. The results from the modelling emphasised the importance of crop competition in limiting the capacity of weeds surviving pre‐emergence herbicides to compensate and replenish the seedbank. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Identifying scale-specific controls of soil organic matter distribution in mountain areas using anisotropy analysis and discrete wavelet transform

Soil organic matter (SOM) is an important index to evaluate soil fertility. Knowing the spatial distribution of SOM and its controlling factors at different scales is basic to sustainable farmland management. The variability was explored mostly in plain farmlands or at small scales in previous studies. In the present study, combined with anisotropy analysis (AA) and discrete wavelet transform (DWT), we examined the spatial variability of SOM and its controlling factors at various scales in a mountainous area. Transect with dominant directions (major axis and minor axis) of SOM variability was extracted using AA and then the scale-specific variability was examined using DWT. Dominant factors of SOM variability at different scales were identified using correlation coefficients between SOM at different scales and various soil environmental factors. The results showed that the major axis along which SOM varied the most was 24° south by west, consistent with the strike of Wuling Mountains. The minor axis was perpendicular to the major axis direction. DWT separated the SOM variations into nine scale components (eight details, D1 through D8, and one approximation, A8) along the major axis and into eight scale components (seven details, D1 through D7, and one approximation, A7) along minor axis. The largest-scale component (A8 in major axis and A7 in minor axis) explained the most variance of SOM along both axes, accounting for half of the total variance. Compared with the original SOM before separation of scale components (undecomposed SOM), the scale components showed significant correlation with environmental factors. Both elevation and mean annual precipitation had positive correlation with SOM at large scales. However, there was a negative correlation between SOM and mean annual temperature. This indicates that the topography and local climate may have a stronger influence in controlling SOM spatial distribution in mountain regions. The relationship provides important information on environmental covariate selection in mapping soil resource. The combination of AA and DWT shows promise quantifying SOM spatial distribution and its control factors at different scales in mountainous areas.

Small-scale spatial variation of soil organic matter pools generated by cork oak trees in Mediterranean agro-silvo-pastoral systems

The aim of this study was to assess the role of cork oak (Quercus suber L.) trees on the small-scale variation of soil organic matter (SOM) pools in an agro-silvo-pastoral system under Mediterranean semi-arid conditions in northeastern Sardinia, Italy. Six cork oak trees were selected in a wooded grassland (30% tree ground cover). For each tree, and along two opposite transects (NE and SW), floor litter and soil (20-cm depth) were sampled at five points starting next to the trees' trunk and ending beyond the tree crown projection. Soil organic matter quality was characterized by measuring the content of water extractable organic matter (WEOM), free particulate SOM (POMf), occluded aggregates particulate SOM (POMo), and mineral-associated SOM (MOM). Carbon (C) input from floor litter was larger in the sampling points under the tree crown projection than in those beyond the tree crown. The C content of SOM pools differed among sampling positions regardless of the transect orientation, decreasing from the trunk to the positions beyond the tree crown projection, from 24.1 to 15.7gCkg−1 for MOM, and from 9.9 to 5.7gCkg−1 for the sum of WEOM, POMf and POMo. The C in MOM next to the tree trunk was above the saturation level sensu Hassink and Whitmore (1997), but below saturation beyond the tree crown projection. The nitrogen (N) distribution showed a similar trend. These results indicate that in these agro-silvo-pastoral systems, oak trees generate hot spots of soil C storage, by controlling the rate of C inputs via litterfall. Hence, conservation strategies designed to maintain cork oak trees in grasslands will also contribute to maintain a high stock of C stored and a resilient, multipurpose system.

Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study was conducted in a regional scale study area in Shaanxi Province, China. A total of 472 topsoil (0–20 cm) samples were collected. A total of 14 auxiliary variables (predictors) were obtained from remote sensing data and environmental factors. The proposed method was compared with the ability of traditional geostatistical methods such as simple kriging (SK) and ordinary kriging (OK), in addition to hybrid geostatistical methods such as regression-ordinary kriging (ROK) and artificial neural network-ordinary kriging (ANNOK). The results showed that the extreme learning machines (ELM) model used principal components (PCs) as input variables, and performed better than both multiple linear regression (MLR) and artificial neural network (ANN) models. Compared with geostatistical and hybrid geostatistical prediction methods of SOM spatial distribution, the ELMOK model had the highest coefficient of determination (R2 = 0.671) and ratio of performance to deviation (RPD = 2.05), as well as the lowest root mean square error (RMSE = 1.402 g kg−1). In conclusion, the application of remote sensing imagery and environmental factors has a deeper driven significance of a non-linear and multi-dimensional hierarchy relationship for explaining the spatial variability of SOM, tracing local carbon sink and high quality SOM maps. More importantly, it is possibly concluded that the sustainable monitoring of SOM is a significant process through the pixel-based revisit sampling, an analysis of the mapping results of SOM, and methodological integration, which is the primary step in spatial variations and time series. The proposed ELMOK methodology is a promising and effective approach which can play a vital role in predicting the spatial variability of SOM at a regional scale.

Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA

ABSTRACTThe main purpose of this study, is to evaluate an advanced feature selection technique, artificial bee colony (ABC) algorithm; to reduce the number of auxiliary variables derived from a digital elevation model (DEM) and remotely sensed data (e.g. Landsat images). A combination of depth functions (e.g. power, logarithmic and spline) and data miner methods (artificial neural network: ANN and support vector regression: SVR) were applied for three-dimensional mapping of soil organic matter (SOM) in Big Sioux River watershed, South Dakota, USA. Unsurprisingly, the ABC feature selection algorithm indicated that remote sensing data (e.g. NDVI) are powerful predictors at soil surface, however, with the increasing soil depth, the terrain parameters (e.g. wetness index) became more relevant. Our findings from this study demonstrated that both the spatial models generally performed well. The mean R2 values calculated by 10-fold cross validation suggested that SVR and ANN models could explain approximately 50 and 57% of total SOM variability, respectively. However, predictive power of both models increased when ABC feature selection algorithm applied, particularly when it combined with the ANN model. Results showed that DSM approaches are very important and powerful tool to explain the 3D spatial distribution of SOM across the study watershed.

Spatial Variability of Soil Organic Matter and Cation Exchange Capacity in an Oxisol under Different Land Uses

ABSTRACTSoil properties may exhibit large spatial variability. Frequently this variability is auto-correlated at a certain scale. In addition to soil-forming factors, soil management, land cover, and agricultural system may affect the spatial variability of agricultural soils. Soil organic matter (OM) is an important soil property contributing toward soil fertility and a key attribute in assessing soil quality. Increasing soil OM increases cation exchange capacity (CEC) and enhances soil fertility. We analyzed the impact of land use on the spatial variability of OM and CEC in a tropical soil, an Oxisol, within São Paulo state, Brazil. Land uses were prairie, maize, and mango. Soil samples were taken at 0–10 and 10–20 cm depths at 84 points within 1-ha plots, i.e., 100 m × 100 m. Statistical variability was higher for soil OM than for CEC. The mango plot contained the highest soil OM, whereas prairie the lowest. Also, soil OM and CEC were significantly related at all land use treatments and depths studied. All soil OM data sets and most of the CEC data sets (with two exceptions) exhibited spatial dependence. When spatial variability was present, the semivariograms showed a nugget effect plus a spherical or an exponential structure. Patterns of soil OM and CEC spatial variability (i.e., model type, ranges of spatial dependence, and nugget effects) were different between land uses and soil depths. In general, CEC exhibited a lower spatial autocorrelation and a weaker spatial structure than soil OM. Moreover, soil OM displayed a higher autocorrelation and was more strongly structured at the 0–10 cm depth than at the 10–20 cm depth. Interpolation by kriging or inverse distance weighting (IDW) allowed to illustrate how the spatial variability of soil OM and CEC differed due to land cover and sampling depth. Modeling and mapping the spatial distribution of soil OM and CEC provided a framework for spatially implicit comparisons of these soil properties, which may be useful for practical applications.

Small-Scale Spatial Variability of Plant Nutrients and Soil Organic Matter: An Arable Cropping Case Study

ABSTRACTSoil properties can vary spatially due to differences in topography, parent material, and land management practices. For site-specific management within the field, information on spatial variation of soil properties is essential. This case study was conducted to quantify the spatial variability of available plant nutrients and organic matter under arable cropping in New Zealand. Within a 10.4-ha paddock that had a long-term history of arable cropping, 91 samples (0–7.5 cm) were collected in a grid pattern for determination of mineral nitrogen (Min N), anaerobically mineralizable N (AMN), Olsen P, total carbon (TC), and total N (TN). The data were evaluated using geostatistical and classical statistical methods. Although the paddock had a flat topography and had been managed uniformly for many years, nutrient concentrations exhibited substantial variability. All measured variables except mineral N showed moderate positional dependence. Autocorrelation distances were 400 m for Olsen P, 293 m for AMN, and 347 m for TC. Soil C showed a strong, positive correlation with the amount of clay plus fine silt (<5 µm fraction) and a negative correlation with sand content. These results suggest that textural variation was a major factor influencing within-field variability in soil organic matter. Using the spatial data, zones with different plant nutrient requirements were identified (four for N; two for P). An application strategy that accounts for fertility differences between zones may improve fertilizer use efficiency and contribute to better environmental outcomes; P inputs could be reduced by 50% by avoiding application to the high Olsen P zone.

Increasing Soil Organic Matter Enhances Inherent Soil Productivity while Offsetting Fertilization Effect under a Rice Cropping System

Understanding the role of soil organic matter (SOM) in soil quality and subsequent crop yield and input requirements is useful for agricultural sustainability. SOM is widely considered to affect a wide range of soil properties, however, great uncertainty still remains in identifying the relationships between SOM and crop yield due to the difficulty in separating the effect of SOM from other yield-limiting factors. Based on 543 on-farm experiments, where paired treatments with and without NPK fertilizer were conducted during 2005–2009, we quantified the inherent soil productivity, fertilization effect, and their contribution to rice yield and further evaluated their relationships with SOM contents under a rice cropping system in the Sichuan Basin of China. The inherent soil productivity assessed by rice grain yield under no fertilization (Y-CK) was 5.8 t/ha, on average, and contributed 70% to the 8.3 t/ha of rice yield under NPK fertilization (Y-NPK) while the other 30% was from the fertilization effect (FE). No significant correlation between SOM content and Y-NPK was observed, however, SOM content positively related to Y-CK and its contribution to Y-NPK but negatively to FE and its contribution to Y-NPK, indicating an increased soil contribution but a decreased fertilizer contribution to rice yield with increasing SOM. There were significantly positive relationships between SOM and soil available N, P, and K, indicating the potential contribution of SOM to inherent soil productivity by supplying nutrients from mineralization. As a result, approaches for SOM accumulation are practical to improve the inherent soil productivity and thereafter maintain a high crop productivity with less dependence on chemical fertilizers, while fertilization recommendations need to be adjusted with the temporal and spatial SOM variation.

Assessment of spatial uncertainty for delineating optimal soil sampling sites in rubber tree management using sequential indicator simulation

Where and how to sample soils in highly variable tree plantations are important to soil testing and nutrient recommendation for rubber trees (Hevea brasiliensis). The objectives of the study were to determine optimal sampling sites representing means of key soil nutrients at micro scale and to delineate probability maps for optimal soil sampling sites for nutrient management planning for rubber trees. The study was conducted in a rubber tree plantation in the tropical island of Hainan, China in 2011. A total of 168 soil samples (0–0.2m in the soil depth) were collected in a 1m×0.5m grid in equivalent rectangles. The air-dried soil samples were then analyzed for total nitrogen (TN) and soil organic matter (SOM) variables. Using the sequential indicator simulation (SIS) we discovered that sampling sites were with high probability for both soil TN and SOM within 10% relative standard deviation above and below the means. The high probability regions of uncertainties were near the rubber rhizome neck areas and in the shrub and ruderal zone in the high locations, where were no-tillage zones and at the specific non-cultivated land between the adjacent rubber planting strips with natural vegetation growth. The spatial variability in TN and SOM variables could be attributed to the combined effects of topographic micro-features and tree management practices. It was concluded that SIS method could be useful for effective determination of optimal sites for soil sampling for spatial uncertainty and nutrient management in rubber tree plantations.

Spatial variability of soil organic matter using remote sensing data

Estimation of soil organic matter (SOM) at unsampled locations is crucial in agronomical and environmental studies. In this study, the ability of geostatistical methods such as ordinary kriging (OK), simple kriging (SK) and cokriging (CK) and hybrid geostatistical methods such as regression-simple kriging (RSK)/-ordinary kriging (ROK) and artificial neural network-simple kriging (ANNSK)/-ordinary kriging (ANNOK) was evaluated to predict SOM content. To this end, a set of 100 soil samples were collected from 0 to 15cm depth of agricultural soils in Selin plain, northwest of Iran. The organic carbon was measured using Walkley–Black method. An auxiliary variable was provided by remote sensing data (Landsat 7 ETM+). Three performance criteria including mean error (ME), root mean square error (RMSE) and coefficient of determination (R2) were used to evaluate the performance of the derived models. The results showed that the ANN model that used principal components (PCs) as input variables, performed better than the multiple linear regression (MLR) model. The hybrid geostatistical methods, which include ANNOK, ANNSK, ROK and RSK provided more reliable predictions than the geostatistical methods, which include SK, OK and CK. In general, the best prediction method for the estimation of SOM spatial distribution was the ANNOK model, which had the smallest RMSE (0.271%) and the highest R2 (0.633). It was concluded that information from Landsat ETM+ imagery is potential auxiliary variables for improving spatial prediction, monitoring SOM and development of high quality SOM maps, which is the primary step in site-specific soil management.

Land-use and land-cover effects on soil microbial community abundance and composition in headwater riparian wetlands

Headwater riparian wetlands are relatively small in size but functionally significant as expected hot spots of microbial activity in the landscape. Despite their roles as biogeochemical drivers, little is known about how microbial communities in headwater riparian wetlands are affected by surrounding land-uses and land-covers (LULCs). The primary objective of this study was to determine if and how wetland soil microbial abundance and community composition varied as a function of landscape metrics as mediated through on-site edaphic properties. Forty-two soil samples, collected from eight headwater riparian wetlands in the Ridge and Valley Region of central Pennsylvania, were used for phospholipid fatty acid (PLFA) profiling of soil microbial communities. These samples were used to create microbial habitat models describing plot-level relationships between edaphic properties and microbial measures (i.e., microbial biomarker abundances, ratios and composition). Soil organic matter (SOM) content was a strong predictor of microbial biomarker abundances and fungi/bacteria ratios, while soil pH was a strong predictor of microbial composition (i.e., relative abundance of individual fatty acids) and potential microbial stress indices (i.e., cy19:0a/18:1ω7c and cy17:0/16:1ω7c ratios). Soil texture, soil moisture, and litter total nitrogen had smaller, but significant effects in these empirical microbial habitat models. Microbial habitat models were subsequently used to estimate microbial measures for a larger regional headwater riparian wetland dataset (n = 87), where edaphic property information was compiled. Site-average microbial measures were correlated with wetland elevation, and with landscape composition metrics in a landscape assessment area (i.e., 125,664 m2). Wetland elevation explained high among-site variability in microbial abundance measures, as mediated through SOM content, in headwater riparian wetlands in forested landscapes. However, wetland elevation was confounded by landscape composition, for headwater riparian wetland in mixed forested-agricultural landscapes. Hydrology, geomorphology, and changes in vegetation could be used to explain SOM variation across wetland elevation and variability in surrounding landscape composition. Microbial composition measures, including the cy19:0/18:1ω7c ratio and principal component (PC) axes derived from 23 individual PLFA biomarkers were negatively related to the Landscape Development Intensity (LDI) index, and the percent area of row crops, pastures and grasslands, and positively related to the percent area of forest. The cy17:0/16:1ω7c ratio was positively related to the LDI index, and the percent area of pasture and grasslands, and negatively related to the percent area of forest and wetland elevation. Differences along PC axes were most clearly related to the relative abundances of fungal communities; headwater riparian wetland in forested landscapes had significantly higher relative abundances of saprophytic fungi biomarkers, while headwater riparian wetland in mixed landscapes had significantly higher relative abundances of the arbuscular mycorrhizal fungi biomarker. This study highlights the utility of simple landscape metrics, which describe wetland position and landscape composition, for predicting differences in edaphic variability and associated microbial community composition and biomass among headwater riparian wetlands at a regional scale.

Revealing the scale-specific controls of soil organic matter at large scale in Northeast and North China Plain

Soil organic matter (SOM) plays an important role in terrestrial ecosystem functioning and is closely related to soil fertility and soil health. Better understanding on the spatial distribution of SOM is vital to agriculture and C-cycle management. With the advancement of digital soil mapping framework and data mining technology, selection of environmental covariates become critical to identify the controls of SOM spatial distribution at different scales and were rarely discussed in previous studies. The objectives of this study were to separate the scale-specific variations in SOM and their dominant controls at those scales along two transects from Northeast and North China Plain. Spatial distribution of SOM was separated into seven scale components (six details, D1 through D6 and one approximation, A6) along each transect using the discrete wavelet transform. The largest variations in SOM were separated in A6 (>1280km) along the northeast transect (91.2% of the total) and D5 (320–640km) along the north transect (40.6% of the total). Unlike the northeast transect, considerable amount of variations was also separated in other scale components of the north transect. There were no significant correlations between the scale-components of SOM and terrain factors along both the transects. While a relatively stronger correlation was observed between SOM and climatic and vegetation factors along the northeast transect, no significant correlation was observed along the north transect at large scales. This may be due to the long-term cultivation in the Northern China Plain. Principal components 1 and 3 identified from the proximally sensed visible and near infrared (vis–NIR) spectra had strong correlation with SOM along the north transect while the principle component 2 was highly correlated with SOM along the northeast transect. The scale-specific controlling factors at different locations may help in selecting environmental factors in digital soil mapping at different scales for improving mapping accuracy.

Updating digital soil maps with new data: a case study of soil organic matter in Jiangsu, China

SummaryThe rapid developments in the acquisition of data on soil should enable pedologists to update existing digital soil maps readily. The methods by which that is done must take into account temporal change in soil properties and local differences in spatial variation. The common mapping techniques will have to be modified to make full use of digital data. We show what can be achieved with a case study on updating maps of soil organic matter (SOM) in Jiangsu Province, China, with three sets of soil data collected in the 1980s, 2000 and 2006. Our results showed that temporal changes in SOM between the three sampling periods occurred in only very small parts of the regions. Models of spatial variation of SOM based on the data collected in the 1980s and 2006 for the whole region differed somewhat, whereas models based on the data collected in the 1980s, 2000 and 2006 for the Taihu region (south Jiangsu) were significantly different. As updating with Bayesian maximum entropy continued, the accuracy of prediction increased and that of the prediction variance decreased. Finally, our study leads us to suggest improved technologies for updating digital soil maps with new data.

Characterization of field scale soil variability using remotely and proximally sensed data and response surface method

Soil salinization of the reclaimed tidelands is problematic. Therefore, there is a need to characterize the spatial variability of soil salinity associated with soil moisture and other soil properties across the reclaimed tidelands. One approach is the use of easily-acquired ancillary data as surrogates for the arduous conventional soil sampling. In a reclaimed coastal tideland in the south of Hangzhou Gulf, backscattering coefficient (σ0) from remotely sensed ALOS/PALSAR radar imagery (HH polarization mode) and apparent soil electrical conductivity (ECa) from a proximally sensed EM38 were used to indicate the spatial distribution of soil moisture and salinity, respectively. After that, response surface methodology (RSM) was employed to determine an optimal set of 12 soil samples using spatially referenced σ0 and ECa data. Spatial distributions of three soil chemical properties [i.e. soil organic matter (SOM), available nitrogen (AN), and available potassium (AK)] were predicted using inverse distance weighted method based on the 12 samples and were then compared with the predictions generated using 42 samples obtained from a conventional grid sampling scheme. It was concluded that combination of radar imagery and EM induction data can delineate the spatial variability of two key soil properties (i.e. moisture and salinity) across the study area. Besides, RSM-based sampling using radar imagery and EM induction data was highly effective in characterizing the spatial variability of SOM, AN and AK, compared with the conventional grid sampling. This new approach may be used to assist site specific management in precision agriculture.