Quantitative Soil Properties Research Articles

Evaluation of Effects of Wetland Conversion on Quantitative Soil Properties and Water Quality in Namutumba District, Uganda

Evaluation of Effects of Wetland Conversion on Quantitative Soil Properties and Water Quality in Namutumba District, Uganda

Visual soil examination and evaluation in the sub-humid and semi-arid regions of Kenya

Soil quality is indicated by the interaction of physical, chemical and biological soil properties. The importance of physical properties, for example soil structure, lies in the fact that they enhance chemical and biological soil functions. Consequently, periodic assessment of structural quality is an important aspect of soil quality management. Quantitative soil properties can be used as indirect indicator parameters for soil structural changes. However, measuring these properties is not applicable, especially for smallholder farmers who cannot afford to pay for laboratory tests. This study contributes to the validation of visual field assessments by comparing the performance of such methods on ‘tropical’ soils. The study was conducted across two regions with contrasting soil types and land use, in the sub-humid with clay Nitiosl and semi-arid with sandy loam Cambisol locations of Kenya. At both locations, visual methods were tested on soils under cropland and under natural forests (NF). Under the cropland, evaluation sites were selected from researcher and farmer managed sites. Visual scores from visual soil assessment (VSA), visual evaluation of soil structure (VESS) and visual evaluation of soil structure using the core (coreVESS) were correlated with soil physical and chemical properties measured in the laboratory. Under the clay Nitisol, absolute values of Pearson r between VSA scores and laboratory measured soil properties ranged from 0.84 to 0.54, for VESS, they varied between 0.75 and 0.37 while for coreVESS, they ranged from 0.84 to 0.60. For the sandy loam Cambisol, absolute Pearson r values between laboratory measured soil properties and VSA scores ranged from 0.83 to 0.29, the r values were between 0.88 and 0.45 for VESS and between 0.81 and 0.40 for coreVESS. From the obtained correlations, we concluded that the visual methods tested are capable of distinguishing structural quality due to different land use and are therefore suitable for assessing soil structural quality of tropical soils in Africa. Management thresholds were determined using bulk density (BD). The target value for good soil quality (Sq<2) for the Kibugu Nitisol was BD = 0.0012*SOC+0.6476 (r = 0.71; SOC is soil organic carbon), while the trigger and remediation values were 0.93 Mg m−3 (Sq = 2) and 0.99 Mg m−3 (Sq = 3), respectively. In the absence of SOC data, the target mean BD for Sq<2 is 0.79 Mg m−3. For Machang’a Cambisol, the target, trigger and remediation values were 1.48 Mg m−3 (Sq = 2), 1.56 Mg m−3 (Sq = 3) and 1.64 Mg m−3 (Sq = 4), respectively.

Mapping soil properties at multiple depths from disaggregated legacy soil maps in the Brittany region, France

Digital Soil Mapping (DSM) is increasingly needed to improve existing soil information and derive soil property maps at the suitable spatial resolution for sustainable soil landscape management. However, predicting several soil properties while preserving specific pedological process is a great challenge, particularly when only coarse soil information is available over large areas. Spatial disaggregation seems to be an effective technique to extract pedological information by downscaling the original information to produce soil maps at finer resolutions. In a previous study, legacy soil maps of Brittany (France) were disaggregated at a 50 m spatial resolution using the DSMART (Disaggregation and Harmonization of Soil Map Units Through Resampled Classification Trees) algorithm and pedological knowledge. The present study had two main objectives: (i) assess the preservation of the relationships between soil properties when soil properties are estimated at standard depths by applying the equal-area spline method on soil data at pedon scale, and (ii) combine disaggregated soil maps and spline-function results to estimate spatial patterns of nine soil properties for six regular soil-depth intervals down to 200 cm across Brittany, an area of 27,040 km2. To this end, soil properties were first generated for standard soil-depth intervals using spline functions. Then, for mapping soil properties at the six standard depths, weighted mean of each soil attribute was calculated for each grid cell from reference soil-property values of the three most probable predicted soil types. Their associated probabilities of occurrence were used as weights. To assess the ability of spline functions to preserve soil-property relationships, multiple statistical analyses were performed using original and splined soil datasets. Bivariate and multivariate analysis highlighted that spline functions preserved soil-property relationships. Derived digital soil maps showed strong spatial patterns: SOC and silt contents generally decreased with depth, while sand content and coarse fragment percentage consistently increased with depth. In addition, experimental semivariogram analysis of SOC content showed high spatial variability over short distances for all soil-depth intervals except the deepest (100–200 cm), while silt content showed high semivariance for the deepest soil layers. This study can be considered an example of harmonization to common output specifications, which generates a geo-database of quantitative soil properties that describe lateral and vertical soil variation for regular depth intervals. These predictions can be incorporated into environmental models to help decision makers manage landscapes.

Assessment of different digital soil mapping methods for prediction of soil classes in the Shahrekord plain, Central Iran

Error indices have been a major focus on evaluation of digital soil maps but the uncertainty of maps has been gravely overlooked. The main aim of this study was to compare the uncertainty and error of decision tree (DT), random forest (RF), artificial neural network (ANN) and support vector machine (SVM) algorithms for different hierarchies of the Soil Taxonomy (ST) and the World Reference Base (WRB) systems in the Shahrekord plain, Iran. A number of 120 soil profiles were sampled and described under a stratified random scheme. Two types of data were used, including expert knowledge from soil scientists familiar with the study area (e.g., qualitative and quantitative soil properties maps), and auxiliary parameters. Accuracy and uncertainty of the algorithms were evaluated by overall accuracy (OA) and confusion index (CI). The algorithm performance demonstrated that, as the taxonomic levels increased (i.e., from order to family level or reference soil groups (RSGs) to second level), the OA decreased while the CI increased. This is possibly due to consideration of more comprehensive/detailed soil information (i.e., higher number of classes) at lower levels of both classification systems. Despite the proximity of OA values in both RF and DT algorithms, the DT algorithm was more efficient due to its lower uncertainty value. At upper taxonomic levels, the SVM algorithm performed better in comparison with other algorithms, whereas the DT algorithm was better when there was a higher number of classes. The results confirmed that both uncertainty and algorithm accuracy need to be taken into account when ascertaining the accuracy of various algorithms for prediction of soil classes. Furthermore, the type of algorithm and classification system, the frequency of soil classes at each taxonomic level, as well as the spatial distribution of soils in a given region are very important criteria to predict soil classes.

Editorial for pedometrics 2017 special issue

Editorial for pedometrics 2017 special issue

The merits of the Visual Evaluation of Soil Structure method (VESS) for assessing soil physical quality in the remote, undeveloped regions of the Amazon basin

The Visual Evaluation of Soil Structure (VESS) is a straightforward and practical method for characterising and scoring soil structural and physical quality, ideally suited to evaluate and monitor soil degradation in remote and undeveloped areas. The research presented here tested for the first time the feasibility of using VESS in the Amazon basin, under the specific land uses and soils (Oxisol and “Terra Preta de Índio”) of the region, and its relation with quantitative soil properties commonly used as indicators of soil physical quality. The evaluated areas, which had never been subjected to mechanisation, chemical fertilisation nor tillage, were “Terra Preta de Índio”/Anthropogenic Dark Earth; Regenerating Forest; Slash and Burn; Pasture; and Pristine Forest. The results showed that the quantitative properties were less sensitive at revealing signs of degradation than VESS and that VESS brought to light evidence of historic land use change and limitations to crop productivity. VESS was significantly correlated with soil resistance to penetration. However, VESS had difficulty capturing surface sealing, but the hands on approach to VESS allowed the user to identify these problems, despite not being listed in the reference chart. Overall, VESS was a more integrated soil quality indicator, providing more information about different soil functions than the quantitative properties, it was also a more practical method to perform making it ideal for tracking soil degradation and structural quality in similarly challenging situations. However, more research is required to fully enable VESS to capture structural quality in ‘sandified’ soils, caused by the slash and burn method widely used in the Amazon region.

The Dokuchaev hypothesis as a basis for predictive digital soil mapping (on the 125th anniversary of its publication)

Predictive digital soil mapping is widely used in soil science. Its objective is the prediction of the spatial distribution of soil taxonomic units and quantitative soil properties via the analysis of spatially distributed quantitative characteristics of soil-forming factors. Western pedometrists stress the scientific priority and principal importance of Hans Jenny’s book (1941) for the emergence and development of predictive soil mapping. In this paper, we demonstrate that Vasily Dokuchaev explicitly defined the central idea and statement of the problem of contemporary predictive soil mapping in the year 1886. Then, we reconstruct the history of the soil formation equation from 1899 to 1941. We argue that Jenny adopted the soil formation equation from Sergey Zakharov, who published it in a well-known fundamental textbook in 1927. It is encouraging that this issue was clarified in 2011, the anniversary year for publications of Dokuchaev and Jenny.

Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel

The data acquired from the hyperspectral airborne sensor DAIS-7915 over Izrael Valley in northern Israel was processed to yield quantitative soil properties maps of organic matter, soil field moisture, soil saturated moisture, and soil salinity. The method adopted for this purpose was the Visible and Near Infrared Analysis (VNIRA) approach, which yields an empirical model for predicting the soil property in question from both wet chemistry and spectral information of a representative set of samples (calibration set). Based on spectral laboratory data that show a significant capability to predict the above soil properties and populations using the VNIRA strategy, the next step was to examine this feasibility under a hyperspectral remote sensing (HSR) domain. After atmospherically rectifying the DAIS-7915 data and omitting noisy bands, the VNIRA routine was performed to yield a prediction equation model for each property, using the reflectance image data. Applying this equation on a pixel-bypixel basis revealed images that described spatially and quantitatively the surface distribution of each property. The VNIRA results were validated successfully from a priori knowledge of the area characteristics and from data collected from several sampling points. Following these examinations, a procedure was developed in order to create a soil property map of the entire area, including soils under vegetated areas. This procedure employed a random selection of more than 80 points along non-vegetated areas from the quantitative soil property images and interpolation of the points to yield an isocontour map for each property. It is concluded that the VNIRA method is a promising strategy for quantitative soil surface mapping, furthermore, the method could even be improved if a better quality of HSR data were used.

Combining soil maps with interpolations from point observations to predict quantitative soil properties : Heuvelink, G B M; Bierkens, M F P GeodermaV55, N1/2, Oct 1992, P1–15

Combining soil maps with interpolations from point observations to predict quantitative soil properties : Heuvelink, G B M; Bierkens, M F P GeodermaV55, N1/2, Oct 1992, P1–15

Combining soil maps with interpolations from point observations to predict quantitative soil properties

A heuristic method is presented to predict quantitative soil properties by combining information from a soil map with information derived from additional point observations. This is done by taking a weighted average of soil map predictions and predictions obtained from kriging the observations. The weights are chosen such that the accuracies of the individual prediction methods are taken into account. A case study on the mapping of the mean highest water table in a Dutch polder area is used to test the method. Validation shows that combining the information from a 1:50,000 soil map with a kriged map produces a more accurate map than when either the soil map or the kriged map are used separately.

Holocene stratigraphy in flood frequency analysis

The risk of rare catastrophic flood events is extremely difficult to evaluate by using conventional flood frequency analyses. The true return period may be so long compared to gaging station or historical records that the stratigraphic record of the Holocene (0–10,000 years B.P.) may be the best method for assessing the risk of outstanding floods. Several geologic methods applicable to flood frequency analysis are (1) tree ring analysis of floodplain trees destroyed or scarred by floodwaters, (2) radiocarbon dating of organics in deposits (landforms) eroded by recent floods, (3) radiocarbon dating of organic materials in older flood deposits, (4) pollen analysis and radiocarbon dating of organic materials in lenses of fine‐grained alluvium buried by coarse flood deposits, (5) radiocarbon dating of soil horizons or organic material in slack‐water deposits, (6) radiocarbon or archaeological dating of soils developed in older flood deposits, and (7) dating of landforms eroded or buried by floods based on changes in quantitative soil properties versus time. Using one or more of the above geologic techniques, return periods of some recent catastrophic floods in the United States are: 1976 Big Thompson River, Colorado, 5000 years (?); 1964 Klamath River, California, 100 years (?); 1972 Western Run, Maryland, 2100 years (?); and 1972 Elm Creek, Texas, 400 years (?).