Soil microbial communities directly affect soil functionality through their roles in the cycling of soil nutrients and carbon storage. Microbial communities vary substantially in space and time, ...between soil types and under different land management. The mechanisms that control the spatial distributions of soil microbes are largely unknown as we have not been able to adequately upscale a detailed analysis of the microbiome in a few grams of soil to that of a catchment, region or continent. Here we reveal that soil microbes along a 1000 km transect have unique spatial structures that are governed mainly by soil properties. The soil microbial community assessed using Phospholipid Fatty Acids showed a strong gradient along the latitude gradient across New South Wales, Australia. We found that soil properties contributed the most to the microbial distribution, while other environmental factors (e.g., temperature, elevation) showed lesser impact. Agricultural activities reduced the variation of the microbial communities, however, its influence was local and much less than the overall influence of soil properties. The ability to predict the soil and environmental factors that control microbial distribution will allow us to predict how future soil and environmental change will affect the spatial distribution of microbes.
Pedology and digital soil mapping (DSM) Ma, Yuxin; Minasny, Budiman; Malone, Brendan P. ...
European journal of soil science,
March 2019, 2019-03-00, 20190301, Letnik:
70, Številka:
2
Journal Article
Recenzirano
Pedology focuses on understanding soil genesis in the field and includes soil classification and mapping. Digital soil mapping (DSM) has evolved from traditional soil classification and mapping to ...the creation and population of spatial soil information systems by using field and laboratory observations coupled with environmental covariates. Pedological knowledge of soil distribution and processes can be useful for digital soil mapping. Conversely, digital soil mapping can bring new insights to pedogenesis, detailed information on vertical and lateral soil variation, and can generate research questions that were not considered in traditional pedology. This review highlights the relevance and synergy of pedology in soil spatial prediction through the expansion of pedological knowledge. We also discuss how DSM can support further advances in pedology through improved representation of spatial soil information. Some major findings of this review are as follows: (a) soil classes can be mapped accurately using DSM, (b) the occurrence and thickness of soil horizons, whole soil profiles and soil parent material can be predicted successfully with DSM techniques, (c) DSM can provide valuable information on pedogenic processes (e.g. addition, removal, transformation and translocation), (d) pedological knowledge can be incorporated into DSM, but DSM can also lead to the discovery of knowledge, and (e) there is the potential to use process‐based soil–landscape evolution modelling in DSM. Based on these findings, the combination of data‐driven and knowledge‐based methods promotes even greater interactions between pedology and DSM.
Highlights
Demonstrates relevance and synergy of pedology in soil spatial prediction, and links pedology and DSM.
Indicates the successful application of DSM in mapping soil classes, profiles, pedological features and processes.
Shows how DSM can help in forming new hypotheses and gaining new insights about soil and soil processes.
Combination of data‐driven and knowledge‐based methods recommended to promote greater interactions between DSM and pedology.
The dimensions of soil security McBratney, Alex; Field, Damien J.; Koch, Andrea
Geoderma,
January 2014, 2014, 2014-01-00, 20140101, Letnik:
213
Journal Article
Recenzirano
Odprti dostop
Soil security, an overarching concept of soil motivated by sustainable development, is concerned with the maintenance and improvement of the global soil resource to produce food, fibre and fresh ...water, contribute to energy and climate sustainability, and to maintain the biodiversity and the overall protection of the ecosystem. Security is used here for soil in the same sense that it is used widely for food and water. It is argued that soil has an integral part to play in the global environmental sustainability challenges of food security, water security, energy sustainability, climate stability, biodiversity, and ecosystem service delivery. Indeed, soil has the same existential status as these issues and should be recognized and highlighted similarly. The concept of soil security is multi-dimensional. It acknowledges the five dimensions of (1) capability, (2) condition, (3) capital, (4) connectivity and (5) codification, of soil entities which encompass the social, economic and biophysical sciences and recognize policy and legal frameworks. The soil security concept is compared with the cognate, but more limited, notions of soil quality, health and protection.
•Society's sustainability is facing six existential global environmental challenges.•These challenges may be addressed by the new concept of soil security.•Soil security has relationships with soil quality, soil health and soil protection.•Soil security is a much wider concept framed using five dimensions.•The dimensions are soil capability, condition, capital, connectivity & codification.
This paper is an extensive review of the research that has been undertaken on near-infrared (NIR) and mid-infrared (MIR) spectroscopy applied to soil particularly for determining carbon (C) content. ...The objective is to determine which acquisition method (NIR, MIR, in the field or in the laboratory) might be recommended for the purpose of C stock measurement with a particular interest in carbon credit trading. For this purpose, an optimal method has to satisfy the dual constraints of low-cost and high throughput analysis. The various methods proposed in the literature are compared. In order to make comparisons as reliable as possible, special attention has been paid to the conditions of data acquisition (sample preparation), and to calibration and validation procedures. In particular, whether the validation has been carried out on fully independent samples or on samples similar to the ones of the calibration set greatly influences the results. Also, for C stock measurement, it is absolutely necessary to measure the bias of the prediction in order to be conclusive about the feasibility of the method. However, only few researchers provide this parameter and we recommend including it as a matter of course in future reports. Finally, although MIR on dried and ground samples is the most accurate method, on-the-go and in-field sensors provide predictions accurate enough to show promise in being a valuable component of technologies that would be used for C-credit purposes. But in order to meet the cost/accuracy trade-off, the main issue using such field sensors is to be able to simultaneously measure the bulk density or, better, to directly measure the volumetric concentration of C in soil. This circumvents the costs of field extraction and laboratory analysis. This is the next great challenge to be met by soil scientists.
► Near-infrared (NIR) and mid-infrared (MIR) spectrometry were reviewed for C stock measurement. ► We present a comprehensive table comparing NIR and MIR technical and economic performances. ► Performances of NIR calibration models depend on the origin of calibration and validation samples. ► Mass concentration of C could be measured on fresh soil samples by NIR but accuracy is still an issue. ► In-field C stock estimation for Kyoto Protocol requires that bulk density also is measurable by NIR.
How fast does soil grow? Stockmann, Uta; Minasny, Budiman; McBratney, Alex B.
Geoderma,
03/2014, Letnik:
216
Journal Article
Recenzirano
Quantifying the rate of soil formation has become important in response to the consideration of soil as a renewable resource. The availability of new sophisticated laboratory techniques has opened up ...the possibility of addressing the demand of quantifying processes of soil landscape evolution in the critical zone. Here, we investigated the rate of soil formation of world soils based on published results of TCN-derived (terrestrial cosmogenic nuclides, 10Be) soil production rates (SPR). The compilation of published TCN-derived SPR for different climatic zones and lithologic conditions showed exponentially decreasing SPR with increasing soil thickness for the majority of the discussed studies. This implies that the presence of a soil mantle protects the bedrock from further weathering. We found that rates of soil production in Australia appear to be similar in range when compared with other parts of the world. We concluded that we can formulate an average quantitative estimate of ‘global’ soil production based on TCN: soil production rate (mm kyr−1)=114±11 exp (−2.05 soil thickness in mm). Such a rate is useful for global modelling of soil formation to better understand the role of soils in landscape evolution.
•Laboratory techniques have become available to quantify processes of soil formation.•We compiled TCN-derived soil production rates of world soils.•Soil production rates are reduced with an increasing layer of soil.•This implies that the soil mantle protects the bedrock from intense weathering.•The range of rates of soil production appears to be similar for world soils studied.
This paper presents the conditioned Latin hypercube as a sampling strategy of an area with prior information represented as exhaustive ancillary data. Latin hypercube sampling (LHS) is a stratified ...random procedure that provides an efficient way of sampling variables from their multivariate distributions. It provides a full coverage of the range of each variable by maximally stratifying the marginal distribution. For conditioned Latin hypercube sampling (cLHS) the problem is: given
N sites with ancillary variables (
X), select
x a sub-sample of size
n
(
n
⪡
N
)
in order that
x forms a Latin hypercube, or the multivariate distribution of
X is maximally stratified. This paper presents the cLHS method with a search algorithm based on heuristic rules combined with an annealing schedule. The method is illustrated with a simple 3-D example and an application in digital soil mapping of part of the Hunter Valley of New South Wales, Australia. Comparison is made with other methods: random sampling, and equal spatial strata. The results show that the cLHS is the most effective way to replicate the distribution of the variables.
•Spiked-regional model is no better than local models.•Localized models can be derived using MBL algorithm.•Similar to transfer learning, a localized PLSR model is derived from regional ...model.•Localized models perform better than the spiked-regional model.•Spiking affects the regression coefficients, but not the loading of PLSR model.
An increasing number of soil spectral libraries are being developed at larger extents, including at national, continental, and global scales. However, the prediction accuracy of these libraries was often fairly poor when used on local scales. This study evaluates different strategies to improve the model accuracy of a regional spectral library for soil organic carbon prediction in four different local target areas. In total, five strategies using the Partial least squares regression (PLSR) were compared, including the use of local, spiked-regional, spiked-subset-regional and two localized models (memory based learning (MBL) and localized PLSR). MBL derives a new local prediction model based on a subset of the regional spectra similar to the new sample to be predicted. A localized PLSR calibrates a linear regression model using projected scores of the local samples derived from a pre-trained regional PLSR model. Validation results showed that the performances of the spiked models were not much better than those derived from the local and localized models. With >20 local samples, the localized PLSR model performed better than both the local and spiked-regional models. MBL model achieved similar performance to the localized PLSR model. Nevertheless, the accuracy of the models was heavily affected by both the spectral similarity of the data and the statistics of the predictand. Therefore, we recommend localizing the prediction models. Our results also suggest that spiking affected the regression coefficients of the PLSR models but not the loadings, which enabled the compression of spectra data into informative PLS scores. If the local spectra have low similarity to the regional spectral library, building a local spectral library would be more beneficial, assuming the sample size is large enough (>30).
Legacy soil maps typically consist of a tessellation of polygon soil map unit delineations where the map units consist of a defined assemblage of soil classes assumed to exist in more-or-less fixed ...proportions. There are several limitations in this kind of mapping approach that relate to the original intent of the soil survey, the effect of mapping scale, and the nature of soil polygon boundaries. Yet perhaps a more fundamental limitation is the fact that most of the time, the soil classes that comprise the soil map units are not mapped individually: in effect their spatial distributions are unknown beyond the qualitative indications given in the accompanying soil map unit report.
Spatial disaggregation of soil map units attempts to map the spatial distribution of the individual soil classes that comprise a legacy soil map. We developed an approach called “Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees” (DSMART). DSMART samples the polygons of a legacy soil map and uses classification trees to generate a number of realisations of the potential soil class distribution. The realisations are then used to estimate the probability of occurrence of the individual soil classes. These estimates are mapped as raster grids, which can overcome some of the limitations of mapping scale and polygon boundaries inherent in the original legacy soil map.
We demonstrate the DSMART approach on a legacy soil map from the former Dalrymple Shire in central Queensland, Australia. We mapped the estimated probability of occurrence of the 71 soil classes in the legacy soil map, as well as the most probable soil class, second-most-probable soil class and the degree of confusion between them as determined by a confusion index. Validation on 285 observed soil profiles indicated that for 48.4% of the validation profiles, the observed soil class was identified in the top three most probable soil classes.
•We developed DSMART, an algorithm for disaggregating legacy soil map units.•We used DSMART to map estimates of probability of occurrence of soil classes.•The most probable and second-most-probable soil classes were derived and mapped.•DSMART can disaggregate all soil classes in a legacy soil map simultaneously.
The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be ...efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (latent Dirichlet allocation) to find patterns in a large collection of text corpora. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that (a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries,
(b) the reviewed publications can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, neural networks, SVM, support vector machines), spectroscopy, modelling (classes), crops, physical, and modelling (continuous),
and (c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships.
From these findings, we found research gaps, in particular, about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.
Soil organic carbon (SOC) is a key element of agroecosystems functioning and has a crucial impact on global carbon storage. At the landscape scale, SOC spatial variability is strongly affected by ...natural and anthropogenic processes and linear anthropogenic elements (such hedges or ditches). This study aims at mapping SOC stocks distribution in the A-horizons for a depth up to 105cm, at a high spatial resolution, for an area of 10km2 in a heterogeneous agricultural landscape (North-Western France). We used a data mining tool, Cubist, to build rule-based predictive models and predict SOC content and soil bulk density (BD) from a calibration dataset at 8 standard layers (0 to 7.5cm, 7.5 to 15cm, 15 to 30cm, 30 to 45cm, 45 to 60cm, 60 to 75cm, 75 to 90cm and 90 to 105cm). For the models calibration, 70 sampling locations were selected within the whole study area using the conditioned Latin hypercube sampling method. Two independent validation datasets were used to assess the performance of the predictive models: (i) at landscape scale, 49 sampling locations were selected using stratified random sampling based on a 300-m square grid; (ii) at hedge vicinity, 112 sampling locations were selected along transects perpendicular to 14 purposively chosen hedges. Undisturbed samples were collected at fixed depths and analysed for BD and SOC content at each sampling location and continuous soil profiles were reconstructed using equal-area splines. Predictive environmental data consisted in attributes derived from a light detection and ranging digital elevation model (LiDAR DEM), geological variables, land use data and a predictive map of A-horizon thickness. Considering the two validation datasets (at landscape scale and hedge vicinity), root mean square errors (RMSE) of the predictions, computed for all the standard soil layers (up to a depth of 105cm), were respectively 7.74 and 5.02gkg−1 for SOC content, and 0.15 and 0.21gcm−3 for BD. Best predictions were obtained for layers between 15 and 60cm of depth. The SOC stocks were calculated over a depth of 105cm by combining the prediction of SOC content and BD. The final maps show that the carbon stocks in the soil below 30cm accounted for 33% of the total SOC stocks on average. The whole method produced consistent results between the two predicted soil properties. The final SOC stocks maps provide continuous data along soil profile up to 105cm, which may be critical information for supporting carbon policy and management decisions.
•We model SOC content and BD in 3D in a complex agricultural landscape.•Linear anthropogenic landscape elements have a great influence on soil properties.•We use a LiDAR DEM at 2m resolution to access the micro-topography.•Our method combines Latin hypercube sampling, depth function and machine learning.•The deep SOC stocks count in average for 33% of the total SOC stocks.
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