Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library

We need to determine the best use of soil vis–NIR spectral libraries that are being developed at regional, national and global scales to predict soil properties from new spectral readings. To reduce the complexity of a calibration dataset derived from the Chinese vis–NIR soil spectral library (CSSL), we tested a local regression method that combined geographical sub‐setting with a local partial least squares regression (local‐PLSR) that uses a limited number of similar vis–NIR spectra (k‐nearest neighbours). The central idea of the local regression, and of other local statistical approaches, is to derive a local prediction model by identifying samples in the calibration dataset that are similar, in spectral variable space, to the samples used for prediction. Here, to derive our local regressions we used Euclidean distance in spectral space between the calibration dataset and prediction samples, and we also used soil geographical zoning to account for similarities in soil‐forming conditions. We tested this approach with the CSSL, which comprised 2732 soil samples collected from 20 provinces in the People's Republic of China to predict soil organic matter (SOM). Results showed that the prediction accuracy of our spatially constrained local‐PLSR method (R² = 0.74, RPIQ = 2.6) was better than that from local‐PLSR (R² = 0.69, RPIQ = 2.3) and PLSR alone (R² = 0.50, RPIQ = 1.5). The coupling of a local‐PLSR regression with soil geographical zoning can improve the accuracy of local SOM predictions using large, complex soil spectral libraries. The approach might be embedded into vis–NIR sensors for laboratory analysis or field estimation.     

Regional and local scale variations in soil organic carbon stocks in West Greenland

The soil organic carbon (SOC) pool of the Northern Hemisphere contains about half of the global SOC stored in soils. As the Arctic is exceptionally sensitive to global warming, temperature rise and prolonged summer lead to deeper thawing of permafrost‐affected soils and might contribute to increasing greenhouse gas emissions progressively. To assess the overall feedback of soil organic carbon stocks (SOCS) to global warming in permafrost‐affected regions the spatial variation in SOCS at different environmental scales is of great interest. However, sparse and unequally distributed soil data sets at various scales in such regions result in highly uncertain estimations of SOCS of the Northern Hemisphere and here particularly in Greenland. The objectives of this study are to compare and evaluate three controlling factors for SOCS distribution (vegetation, landscape, aspect) at two different scales (local, regional). The regional scale reflects the different environmental conditions between the two study areas at the coast and the ice margin. On the local scale, characteristics of each controlling factor in form of defined units (vegetation units, landscape units, aspect units) are used to describe the variation in the SOCS over short distances within each study area, where the variation in SOCS is high. On a regional scale, we investigate the variation in SOCS by comparing the same units between the study areas. The results show for both study areas that SOCS are with 8 kg m^?2 in the uppermost 25 cm and 16 kg m^?2 in the first 100 cm of the soil, i.e., 3 to 6 kg m^?2 (37.5%) higher than existing large scale estimations of SOCS in West Greenland. Our approach allows to rank the scale‐dependent importance of the controlling factors within and between the study areas. However, vegetation and aspect better explain variations in SOCS than landscape units. Therefore, we recommend vegetation and aspect for determining the variation in SOCS in West Greenland on both scales.     

The behaviour of soil process models of ammonia volatilization at contrasting spatial scales

Process models are commonly used in soil science to obtain predictions at a spatial scale that is different from the scale at which the model was developed, or the scale at which information on model inputs is available. When this happens, the model and its inputs require aggregation or disaggregation to the application scale, and this is a complex problem. Furthermore, the validity of the aggregated model predictions depends on whether the model describes the key processes that determine the process outcome at the target scale. Different models may therefore be required at different spatial scales. In this paper we develop a diagnostic framework which allows us to judge whether a model is appropriate for use at one or more spatial scales both with respect to the prediction of variations at those scale and in the requirement for disaggregation of the inputs. We show that spatially nested analysis of the covariance of predictions with measured process outcomes is an efficient way to do this. This is applied to models of the processes that lead to ammonia volatilization from soil after the application of urea. We identify the component correlations at different scales of a nested scheme as the diagnostic with which to evaluate model behaviour. These correlations show how well the model emulates components of spatial variation of the target process at the scales of the sampling scheme. Aggregate correlations were identified as the most pertinent to evaluate models for prediction at particular scales since they measure how well aggregated predictions at some scale correlate with aggregated values of the measured outcome. There are two circumstances under which models are used to make predictions. In the first case only the model is used to predict, and the most useful diagnostic is the concordance aggregate correlation. In the second case model predictions are assimilated with observations which should correct bias in the prediction, and errors in the variance; the aggregate correlations would be the most suitable diagnostic.     

秸秆带状覆盖下培土对马铃薯产量与水分利用效率的影响

气候变化效应评估、土壤固碳潜力和肥力管理等,迫切需要详尽的土壤有机质（soil organic matter, SOM）空间分布信息。该文以江苏省第二次土壤普查的1 519个典型土壤剖面的表层（0～20 cm）SOM含量为例,选择1 217个样本为建模集,302个为验证集,选取年均温度、年均降雨、物理性黏粒和土壤pH值等因子进行SOM的地理加权回归（geographically weighted regression, GWR）建模。从建模集中分别随机抽取100%（1 217个）、80%（973个）、60%（730个）、40%（486个）,20%（243个）的样点,对比不同样点数量下GWR和传统全局回归模型的精度差异,并选择最优模型进行SOM空间预测制图。结果表明：1）江苏省SOM含量在不同空间尺度上存在极显著的空间自相关性。不同样点数量的建模集的全局自相关性和局部空间自相关聚类图结果相似。全局Moran''s I值介于0.25～0.61（P<0.001）。SOM含量空间分布以空间聚集特征为主,"高-高"聚集区主要分布在苏中和苏南地区,"低-低"聚集区主要分布在苏北地区。2）GWR建模结果均优于传统的传统全局回归建模,其残差在不同的空间尺度上均不存在空间自相关性。不同建模集的GWR的R2adj较全局建模均提高0.15～0.20,其AIC和RSS均比全局模型有大幅降低,为56.08～360.19和17.40～76.67。不同建模样本数量的GWR模型对SOM的解释能力差异较小。3）建模样点数量（除建模样本n=243）对GWR预测制图结果的精度影响不大,RMSE介于5.56～5.75 g/kg之间,MAE介于3.87～4.05 g/kg之间,R2介于0.52～0.48之间,均优于全部建模样点的普通克里格插值验证结果。该研究可为样点数较少的省级尺度地区SOM空间建模与制图提供借鉴。     

Modelling soil organic carbon concentration of mineral soils in arable land using legacy soil data

Soil organic carbon (SOC) concentration is an essential factor in biomass production and soil functioning. SOC concentration values are often obtained by prediction but the prediction accuracy depends much on the method used. Currently, there is a lack of evidence in the soil science literature as to the advantages and shortcomings of the different commonly used prediction methods. Therefore, we compared and evaluated the merits of the median approach, analysis of covariance, mixed models and random forests in the context of prediction of SOC concentrations of mineral soils under arable management in the A‐horizon. Three soil properties were used in all of the developed models: soil type, physical clay content (particle size <0.01 mm) and A‐horizon thickness. We found that the mixed model predicted SOC concentrations with the smallest mean squared error (0.05%²), suggesting that a mixed‐model approach is appropriate if the study design has a hierarchical structure as in our scenario. We used the Estonian National Soil Monitoring data on arable lands to predict SOC concentrations of mineral soils. Subsequently, the model with the best prediction accuracy was applied to the Estonian digital soil map for the case study area of Tartu County where the SOC predictions ranged from 0.6 to 4.8%. Our study indicates that predictions using legacy soil maps can be used in national inventories and for up‐scaling estimates of carbon concentrations from county to country scales.     

基于环境变量的渭干河-库车河绿洲土壤盐分空间分布

土壤属性的数字制图对精准农业生产和环境保护治理至关重要。为了在大尺度上尽可能精确的监测土壤盐分空间变异性,该文使用普通克里格(ordinary kriging,OK)、地理加权回归(geographically weighted regression,GWR)和随机森林(random forest,RF)方法,结合地形、土壤理化性质和遥感影像数据等16个环境辅助变量,绘制渭干河-库车河绿洲表层土壤盐分分布图。基于决定系数(R^2)、均方根误差(RMSE)和平均绝对误差(MAE)验证模型精度。结果表明:不同方法预测的盐分分布趋势没有显著差异,大体上从研究区的西北向东南部方向增加;结合辅助变量的不同预测方法中,RF方法预测精度最高,R^2为0.74,RMSE和MAE分别为9.07和7.90 mS/cm,说明该模型可以有效地对区域尺度的土壤盐分进行定量估算;RF方法对电导率(electric conductivity,EC)低于2 mS/cm时预测精度最高,RMSE为3.96 mS/cm,很好的削弱了植被覆盖对电导率EC的影响。     

Spatial aggregation of soil property predictions in support of local land management

Over the past decades, numerous practical applications of Digital Soil Mapping have emerged to respond to the need of land managers. One important contribution to this effort is the release of regional‐scale soil maps from the GlobalSoilMap (GSM) project. While the GSM project aims at producing soil property predictions on a fine 90 × 90 m grid at the global scale, land managers often require aggregated information over larger areas of interest (e.g. farms, watersheds, municipalities). This study evaluated a geostatistical procedure aiming at aggregating GSM grids to a land management scale, thereby providing land suitability maps with associated uncertainty for the French region ‘Languedoc‐Roussillon’ (27 236 km²). Specifically, maps were derived from three GSM prediction grids (pH, organic carbon and clay content) by calculating the proportion of ‘suitable’ agricultural land within a municipality, where suitability was defined as having soil property values below or above a predefined threshold (pH < 5.5, OC < 10 g/kg, clay > 375 g/kg). Calculation of these nonlinear spatial aggregates and the associated uncertainty involved a three‐step approach: (i) sampling from the conditional probability distributions of the soil properties at all grid cells by means of sequential Gaussian simulation applied to a regression kriging model, (ii) transformation of soil properties to suitability indicators for all grid cell samples generated in the first step and (iii) spatial aggregation of the suitability indicators from grid cells to municipalities. The maps produced show large differences between municipality areas for all three land suitability indicators. The uncertainties associated with the aggregated suitability indicators were moderate. This approach demonstrated that fine‐scale GSM products may also fulfil user demands at coarser land management scales, without jeopardizing uncertainty quantification requirements.     

Comparing local vs. global visible and near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) calibrations for the prediction of soil clay, organic C and inorganic C

Local, field-scale, VisNIR-DRS soil calibrations generally yield the most accurate predictions but require a substantial number of local calibration samples at every application site. Global to regional calibrations are more economically efficient, but don't provide sufficient accuracy for many applications. In this study, we quantified the value of augmenting a large global spectral library with relatively few local calibration samples for VisNIR-DRS predictions of soil clay content (clay), organic carbon content (SOC), and inorganic carbon content (IC). VisNIR models were constructed with boosted regression trees employing global, local + global, and local spectral data, using local samples from two low-relief, sedimentary bedrock controlled, semiarid grassland sites, and one granitic, montane, subalpine forest site, in Montana, USA. The local + global calibration yielded the most accurate SOC predictions for all three sites [Standard Error of Prediction (SEP) = 3.8, 6.7, and 26.2 g kg^− 1]. This was similarly true for clay (SEP = 95.3 and 102.5 g kg^− 1) and IC (SEP = 5.5 and 6.0 g kg^− 1) predictions at the two semiarid grassland sites. A purely local calibration produced the best validation results for soil clay content at the subalpine forest site (SEP = 49.2 g kg^− 1), which also had the largest number of local calibration samples (N = 210). Using only samples from calcareous soils in the global spectral library combined with local samples produced the best SOC and IC results at the more arid of the two semiarid sites. Global samples alone never achieved more accurate predictions than the best local + global calibrations. For the temperate soils used in this study, the augmentation of a large global spectral library with relatively few local samples generally improved the prediction of soil clay, SOC, and IC relative to global or local samples alone.     

Digital soil mapping of available water content using proximal and remotely sensed data

Two‐thirds of all irrigated agriculture in Australia is undertaken within the Murray–Darling Basin. However, climate change predictions for this region suggest rainfall will decrease. To maintain profitability, more will need to be done by irrigators with less water. In this regard, irrigators need to be aware of the spatial distribution of the available water content (AWC) in the root‐zone (i.e. 0.0–0.90 m). To reduce the cost, digital soil mapping (DSM) techniques are being used to map soil properties related to AWC (e.g. soil texture). The purpose of this study was to create a DSM of the AWC at the district scale. This is achieved by determining AWC by the difference between laboratory measured permanent wilting point (PWP) and field capacity (FC) and using pressure plate apparatus. The PWP and FC data are coupled to remote (i.e. gamma‐ray spectrometry) and proximal (i.e. EM38 and EM34) sensed data and two trend surface parameters. Using a hierarchical spatial regression (HSR), we predict PWP and FC across the areas of Warren and Trangie in the lower Macquarie valley, Australia. The reliability of the DSM of PWP and FC were compared using prediction precision (RMSE – root mean square error) and bias (ME – mean error). The best results were achieved using EM38‐v, EM34‐20, eU and eTh. The DSM map of AWC is consistent with known Pedoderms and provides a basis for agricultural water management.     

Evaluating soil maps for prediction of soil water properties

Conventional soil survey stratifies a region into mapping classes and characterizes each by a representative soil profile within it. The efficacy of the procedure for predicting particle-size fractions, bulk density, water retention, and available water capacity (AWC) of the soil at previously unvisited sites on the Plain of Languedoc in southern France is evaluated for three scales of survey (1/10 000, 1/25 000 and 1/100 000) and is compared to that of prediction from stratified random and simple random samples. Data from 85 soil profiles on a random transect were used for evaluation. Classification partitioned the variation of the measured properties, except for AWC, well at the 1/10 000 and 1/25 000 scales, whereas classification at the 1/100 000 scale was less effective. At the 1/10 000 and 1/25 000 scales both classification and stratified random sampling were better for prediction than simple random sampling for the same total sample. On average the representative profiles proved substantially better predictors than the stratified random samples, but in most situations where soil stratification performed well efficiencies of the two predictors were similar. In essence, the more successful the classification was the more difficult it was to improve prediction by selecting representatives instead of sampling randomly within classes. These results confirmed statistically that the soil surveyor can exercise intuition and judgement to classify and select representatives.     

Airborne radiometric survey data and a DTM as covariates for regional scale mapping of soil organic carbon across Northern Ireland

Soil scientists require cost-effective methods to make accurate regional predictions of soil organic carbon (SOC) content. We assess the suitability of airborne radiometric data and digital elevation data as covariates to improve the precision of predictions of SOC from an intensive survey in Northern Ireland. Radiometric data (K band) and, to a lesser extent, altitude are shown to increase the precision of SOC predictions when they are included in linear mixed models of SOC variation. However the statistical distribution of SOC in Northern Ireland is bimodal and therefore unsuitable for geostatistical analysis unless the two peaks can be accounted for by the fixed effects in the linear mixed models. The upper peak in the distribution is due to areas of peat soils. This problem may be partly countered if soil maps are used to classify areas of Northern Ireland according to their expected SOC content and then different models are fitted to each of these classes. Here we divide the soil in Northern Ireland into three classes, namely mineral, organo-mineral and peat. This leads to a further increase in the precision of SOC predictions and the median square error is 2.2 %². However a substantial number of our observations appear to be mis-classified and therefore the mean squared error in the predictions is larger (30.6 %²) since it is dominated by large errors due to mis-classification. Further improvement in SOC prediction may therefore be possible if better delineation between areas of large SOC (peat) and small SOC (non-peat) could be achieved.     

Australia-wide predictions of soil properties using decision trees

This paper describes the construction of Australia-wide soil property predictions from a compiled national soils point database. Those properties considered include pH, organic carbon, total phosphorus, total nitrogen, thickness, texture, and clay content. Many of these soil properties are used directly in environmental process modelling including global climate change models. Models are constructed at the 250-m resolution using decision trees. These relate the soil property to the environment through a suite of environmental predictors at the locations where measurements are observed. These models are then used to extend predictions to the continental extent by applying the rules derived to the exhaustively available environmental predictors. The methodology and performance is described in detail for pH and summarized for other properties. Environmental variables are found to be important predictors, even at the 250-m resolution at which they are available here as they can describe the broad changes in soil property.     

气候和土壤质地对中国东北地区旱地土壤有机碳影响的尺度效应

Understanding how spatial scale inffuences commonly-observed effiects of climate and soil texture on soil organic carbon (SOC) storage is important for accurately estimating the SOC pool at different scales. The relationships among climate factors, soil texture and SOC density at the regional, provincial, city, and county scales were evaluated at both the soil surface (0-20 cm) and throughout the soil profile (0-100 cm) in the Northeast China uplands. We examined 1022 profiles obtained from the Second National Soil Survey of China. The results indicated that the relationships between climate factors and SOC density generally weakened with decreasing spatial scale. The provincial scale was optimal to assess the relationship between climate factors and SOC density because regional differences among provinces were covered up at the regional scale. However, the relationship between soil texture and SOC density had no obvious trend with increasing scale and changed with temperature. There were great differences in the impacts of climate factors and soil texture on SOC density at different scales. Climate factors had a larger effect on SOC density than soil texture at the regional scale. Similar trends were seen in Heilongjiang and eastern Inner Mongolia at the provincial scale. But, soil texture had a greater effect on SOC density compared with climate factors in Jilin and Liaoning. At the city and county scales, the inffuence of soil texture on SOC density was more important than climate factors.     

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.     

Using scattered hyperspectral imagery data to map the soil properties of a region

Airborne hyperspectral imagery has been recently proved to be a successful technique for predicting soil properties of the bare soil surfaces that are usually scattered in the landscape. This new soil covariate could much improve the digital soil mapping (DSM) of soil properties over larger areas. To illustrate this, we experimented with digital soil mapping in a 24.6‐km² area located in the vineyard plain of Languedoc. As input data, we used 200 points with clay content measurements and 192 bare soil fields representing 3.5% of the total area in which the clay contents of the soil surface were successfully mapped at 5‐m resolution by hyperspectral remote sensing. The clay contents were estimated from CR₂₂₀₆, a spectrometric indicator that quantifies specific absorption features of clay at 2206 nm. We demonstrated by cross‐validation that the co‐kriging procedure based on our co‐regionalization model provided accurate error estimates at the clay measurement sites. Then, we applied a block co‐kriging model to map the mean clay content at increasing resolutions (50 , 100, 250 and 500 m). The results showed the following: (i) using hyperspectral data significantly increased the accuracy of the mean clay content estimations; (ii) a block co‐kriging procedure with reliable estimates of error variance can be used to estimate mean clay contents over larger areas and at coarser resolutions with acceptable and predictable errors and (iii) various maps can be produced that represent different compromises between prediction accuracy and spatial resolution.     

Emerging frontiers in soil science

With most of the basic discoveries and applications of science made by mankind applicable to agriculture a challenge is presented to soil scientists to improve their pedagogy and research. Present-day soil scientists, tenaciously linked with those in the disciplines of agronomy and crop sciences, have become intellectually isolated from geologists, hydrologists, engineers and ecologists who deal with soils for purposes other than crop production. Localized achievements of greater crop production come at the expense of our global environment. Ample opportunity based primarily upon regionalized variable analyses exists to improve the management potential of our natural resources on global, regional and local scales of space and time. Six such opportunities or frontiers are elucidated.     

Remote sensing of soil degradation: Progress and perspective

Soils constitute one of the most critical natural resources and maintaining their health is vital for agricultural development and ecological sustainability, providing many essential ecosystem services. Driven by climatic variations and anthropogenic activities, soil degradation has become a global issue that seriously threatens the ecological environment and food security. Remote sensing (RS) technologies have been widely used to investigate soil degradation as it is highly efficient, time-saving, and broad-scope. This review encompasses recent advances and the state-of-the-art of ground, proximal, and novel RS techniques in soil degradation-related studies. We reviewed the RS-related indicators that could be used for monitoring soil degradation-related properties. The direct indicators (mineral composition, organic matter, surface roughness, and moisture content of soil) and indirect proxies (vegetation condition and land use/land cover change) for evaluating soil degradation were comprehensively summarized. The results suggest that these above indicators are effective for monitoring soil degradation, however, no indicators system has been established for soil degradation monitoring to date. We also discussed the RS's mechanisms, data, and methods for identifying specific soil degradation-related phenomena (e.g., soil erosion, salinization, desertification, and contamination). We investigated the potential relations between soil degradation and Sustainable Development Goals (SDGs) and also discussed the challenges and prospective use of RS for assessing soil degradation. To further advance and optimize technology, analysis and retrieval methods, we identify critical future research needs and directions: (1) multi-scale analysis of soil degradation; (2) availability of RS data; (3) soil degradation process modelling and prediction; (4) shared soil degradation dataset; (5) decision support systems; and (6) rehabilitation of degraded soil resource and the contribution of RS technology. Because it is difficult to monitor or measure all soil properties in the large scale, remotely sensed characterization of soil properties related to soil degradation is particularly important. Although it is not a silver bullet, RS provides unique benefits for soil degradation-related studies from regional to global scales.     

Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale

Legacy data in the form of soil maps, which often have typical property measurements associated with each polygon, can be an important source of information for digital soil mapping (DSM). Methods of disaggregating such information and using it for quantitative estimation of soil properties by methods such as regression kriging (RK) are needed. Several disaggregation processes have been investigated; preferred methods include those which include consideration of scorpan factors and those which are mass preserving (pycnophylactic) making transitions between different scales of investigation more theoretically sound. Area to point kriging (AtoP kriging) is pycnophylactic and here we investigate its merits for disaggregating legacy data from soil polygon maps. Area to point regression kriging (AtoP RK) which incorporates ancillary data into the disaggregation process was also applied. The AtoP kriging and AtoP RK approaches do not involve collection of new soil measurements and are compared with disaggregation by simple rasterization. Of the disaggregation methods investigated, AtoP RK gave the most accurate predictions of soil organic carbon (SOC) concentrations (smaller mean absolute errors (MAEs) of cross-validation) for disaggregation of soil polygon data across the whole of Northern Ireland.Legacy soil polygon data disaggregated by AtoP kriging and simple rasterization were used in a RK framework for estimating soil organic carbon (SOC) concentrations across the whole of Northern Ireland, using soil sample data from the Tellus survey of Northern Ireland and with other covariates (altitude and airborne radiometric potassium). This allowed direct comparison with previous analysis of the Tellus survey data. Incorporating the legacy data, whether from simple rasterization of the polygons or AtoP kriging, substantially reduced the MAEs of RK compared with previous analyses of the Tellus data. However, using legacy data disaggregated by AtoP kriging in RK resulted in a greater reduction in MAEs. A jack-knife procedure was also performed to determine a suitable number of additional soil samples that would need to be collected for RK of SOC for the whole of Northern Ireland depending on the availability of ancillary data. We recommend i) if only legacy soil polygon map data are available, they should be disaggregated using AtoP kriging, ii) if ancillary data are also available legacy data should be disaggregated using AtoP RK and iii) if new soil measurements are available in addition to ancillary and legacy soil map data, the legacy soil map data should be first disaggregated using AtoP kriging and these data used along with ancillary data as the fixed effects for RK of the new soil measurements.     

Plant biomass, soil water content and soil N:P ratio regulating soil microbial functional diversity in a temperate steppe: A regional scale study

Soil microorganisms are influenced by various abiotic and biotic factors at the field plot scale. Little is known, however, about the factors that determine soil microbial community functional diversity at a larger spatial scale. Here we conducted a regional scale study to assess the driving forces governing soil microbial community functional diversity in a temperate steppe of Hulunbeir, Inner Mongolia, northern China. Redundancy analysis and regression analysis were used to examine the relationships between soil microbial community properties and environmental variables. The results showed that the functional diversity of soil microbial communities was correlated with aboveground plant biomass, root biomass, soil water content and soil N: P ratio, suggesting that plant biomass, soil water availability and soil N availability were major determinants of soil microbial community functional diversity. Since plant biomass can indicate resource availability, which is mainly constrained by soil water availability and N availability in temperate steppes, we consider that soil microbial community functional diversity was mainly controlled by resource availability in temperate steppes at a regional scale.