Regional Soil Mapping Using Multi-Grade Representative Sampling and a Fuzzy Membership-Based Mapping Approach

High-resolution wetness index mapping: A useful tool for regional scale wetland management

In the initial phase of a national project to map clay, sand and soil organic matter (SOM) content in arable topsoil in Sweden, a study area in south-west Sweden comprising about 100 000 ha of arable land was assessed. Models were created for texture, SOM and two estimated variables for lime requirement determination (target pH and buffering capacity), using a data mining method (multivariate adaptive regression splines). Two existing reference soil datasets were used: a grid dataset and a dataset created for individual farms. The predictor data were of three types: airborne gamma-ray spectrometry data, digital elevation from airborne laser scanning, and legacy data on Quaternary geology. Validations were designed to suit applicability assessments of prediction maps for precision agriculture. The predictor data proved applicable for regional mapping of topsoil texture at 50 × 50 m2 spatial resolution (root mean square error: clay = 6.5 %; sand = 13.2 %). A novel modelling strategy, 'Farm Interactive', in which soil analysis data for individual farms were added to the regional data, and given extra weight, improved the map locally. SOM models were less satisfactory. Variable-rate application files for liming created from derived digital soil maps and locally interpolated soil data were compared with 'ground truth' maps created by proximal sensors on one test farm. The Farm Interactive methodology generated the best predictions and was deemed suitable for adaptation of regional digital soil maps for precision agricultural purposes.

Efficient sampling strategies expedite behavioral data collection. While multiple studies have evaluated sampling strategies for core behaviors in cattle, few have focused on social interactions. To identify sampling strategies that accurately captured cattle social behaviors and brush use feedlot steers (n = 3 pens; 9 steers/pen) were observed from 8:00 to 17:00. Average bout duration (sec), total duration per day (sec), and bout frequency were recorded for allogrooming, bar licking, tongue rolling, and brush utilization. Frequency was recorded for headbutting and mounting. Data was extracted from continuous observation datasets using eight different sampling strategies and the results subsequently compared. Differences among sampling strategies were evaluated using a non-parametric One-Way ANOVA Kruskal-Wallis Test. Pearson correlation evaluated the strength of association between a specific sampling strategy and continuous observations. Bout duration for allogrooming (P > 0.65), bar licking (P > 0.60), tongue rolling (P > 0.99), brush use (P > 0.99), and mounting frequency (P > 0.70) did not differ from continuous observations. Tongue rolling (r2 > 0.95, P <0.0001) and brush use (r2 > 0.70, P < 0.0003) were best captured when cattle were observed from 08:00 to 14:00. When cattle were continuously observed from 08:00 to 14:00 or for 15 minutes every 30 minutes, allogrooming (P > 0.2) (frequency, duration), bar licking (P > 0.95) (frequency, duration), brush use (P > 0.1) (frequency, duration), heat butt (P > 0.30) (frequency), or tongue rolling (P > 0.30) (frequency, duration) did not differ from continuous observations. Observing cattle for 15 minutes every 30 minutes yielded the highest accuracy for all behavioral metrics and was considered the most effective strategy for comprehensively evaluating cattle social behavior (r2 > 75; P < 0.05). These results provide insight into accurate and efficient sampling strategies that expedite social behavior data collection in cattle and will facilitate efficient generation of new knowledge regarding cattle social behaviors.

Semi-automatic disaggregation of a national resource inventory into a farm-scale soil depth class map

• Improved estimation of karst soil thickness at the landform scale. • Rocky desertification indexes are key factors in predicting soil thickness. • Quantile regression forests effectively models soil thickness variation. Soil thickness is a basic feature of the earth’s land surface. Accurately representing the spatial distribution of soil thickness is important for various models of earth surface processes. However, mapping soil thickness in karst landforms is highly uncertain. To address this challenge, this study analyzed the correlation between 906 soil thickness measurements and 376 environmental characteristics in a typical karst landscape covering 54,000 km 2 . We employed a quantile regression forests (QRF) approach to estimate soil thickness and evaluate the associated uncertainty in the predicted results. We found that, like other regional scale soil mapping models, climate and topographic data were key factors influencing soil thickness. Specific for karst landscapes, we found that the characteristics of karst rocky desertification play a key role in predicting soil thickness. The rocky desertification information indexes (RIs), which use exposure rate of bedrock to represent the degree of karst rocky desertification, showed relatively high importance in the variable importance assessment. The developed model explained 40 % of the spatial variability of soil thickness across the study area, with an RMSE (37.3 cm) of 50–60 % of the mean thickness. This indicates that the model, and environmental factors evaluated within, explained a little less than half of the spatial variability. The prediction results reveal the distribution pattern of soil thickness at both regional and local scales within karst landforms. Thick soil was commonly found in low-lying landscape features like depressions and foothills, whereas areas with steep slopes, ridges, and peaks tended to have thin soil, following a typical toposequence. In areas with relatively deep soil or severe rocky desertification, the uncertainty of predicting soil thickness is relatively high. The results of uncertainty analysis, as a supplement to the prediction results, have improved the usability of the predictions to a certain extent. This study has, to some extent, addressed the challenges of predicting soil thickness in karst areas and has also provided transferable methods for other complex regions.

Riverbed hydraulic conductivity (Kr) was measured along one river reach in four tributaries of the Murray–Darling Basin (MDB) in south-eastern Australia. Two techniques were trialled: in-river falling-head tests in high Kr sediments, and laboratory evaporation tests on intact riverbed cores for low Kr sediments. In-river falling-head tests were conducted using two types of permeameter: a steel-base permeameter or a stand-pipe permeameter. Kr was found to range from 10–10 to 10–3 m s–1, corresponding to a range in riverbed sediment textures from clay to silty gravels, respectively. Although the within-reach variability in Kr was also large, in general the river reaches could be divided in two groups, those with a low Kr (&lt;10–8 m s–1) or a high Kr (&gt;10–5 m s–1). The low Kr reach (Billabong Creek) was a clay-lined bed, whereas the others had silty sand or silty gravel beds. Thus, regional-scale assessments of Kr in the MDB could be made using a stratified sampling process in which reaches would be first classified into low or high Kr classes, and then Kr measurements made in a subsample of low and high Kr reaches. This would be an improvement over the current practice whereby riverbed Kr is estimated either from regional soil maps or through the calibration of groundwater models.

Compressive sampling (CS), or Compressed Sensing , has generated a tremendous amount of excitement in the signal processing community. Compressive sampling, which involves non-traditional samples in the form of randomized projections, can capture most of the salient information in a signal with a relatively small number of samples, often far fewer samples than required using traditional sampling schemes. Adaptive sampling (AS), also called Active Learning , uses information gleaned from previous observations (e.g. , feedback) to focus the sampling process. Theoretical and experimental results have shown that adaptive sampling can dramatically outperform conventional (non-adaptive) sampling schemes. This paper compares the theoretical performance of compressive and adaptive sampling for regression in noisy conditions, and it is shown that for certain classes of piecewise constant signals and high SNR regimes both CS and AS are near optimal. This result is remarkable since it is the first evidence that shows that compressive sampling, which is non-adaptive, cannot be significantly outperformed by any other method (including adaptive sampling procedures), even in the presence of noise. The performance of CS schemes for signal detection is also investigated.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Much older soils data and soils information lies idle in libraries and archives and is largely unused, especially in developing countries like Kenya. We demonstrated the usefulness of a stepwise approach to bring legacy soils data ‘back to life’ using the 1980 Reconnaissance Soil Map of the Busia Area (quarter degree sheet No. 101) in western Kenya as an example. Three studies were conducted by using agronomic information, field observations, and laboratory data available in the published soil survey report as inputs to several digital soil mapping techniques. In the first study, the agronomic information in the survey report was interpreted to generate 10 land quality maps. The maps represented the ability of the land to perform specific agronomic functions. Nineteen crop suitability maps that were not previously available were also generated. In the second study, a dataset of 76 profile points mined from the survey report was used as input to three spatial prediction models for soil organic carbon (SOC) and texture. The three predictions models were (i) ordinary kriging, (ii) stepwise multiple linear regression, and (iii) the Soil Land Inference Model (SoLIM). Statistically, ordinary kriging performed better than SoLIM and stepwise multiple linear regression in predicting SOC (RMSE = 0.02), clay (RMSE = 0.32), and silt (RMSE = 0.10), whereas stepwise multiple linear regression performed better than SoLIM and ordinary kriging for predicting sand content (RSME = 0.11). Ordinary kriging had the narrowest 95% confidence interval while stepwise multiple linear regression had, the widest. From a pedological standpoint, SoLIM conformed better to the soil forming factors model than ordinary kriging and had a narrower confidence interval compared to stepwise multiple linear regression. In the third study, rules generated from the map legend and map unit descriptions were used to generate a soil class map. Information about soil distribution and parent material from the map unit polygon descriptions were combined with six terrain attributes, to generate a disaggregated fuzzy soil class map. The terrain attributes were multiresolution ridgetop flatness (MRRTF), multiresolution valley bottom flatness (MRVBF), topographic wetness index (TWI), topographic position index (TPI), planform curvature, and profile curvature. The final result was a soil class map with a spatial resolution of 30 m, an overall accuracy of 58% and a Kappa coefficient of 0.54. Motivated by the wealth of soil agronomic information generated by this study, we successfully tested the feasibility of delivering this information in rural western Kenya using the cell phone-based Soil Explorer app (https://soilexplorer.net/). This study demonstrates that legacy soil data can play a critical role in providing sustainable solutions to some of the most pressing agronomic challenges currently facing Kenya and most African countries.

The need for improved soil inventory information in the province of British Columbia (BC), Canada, was addressed using a random forest (RF) classifier that was informed using legacy soil data. RF models were prepared for 110 ecodistrict subdivisions of BC, and predictions were subsequently assembled into a final soil parent material map mosaic covering the entire province. The ecodistricts are part of a framework for ecosystem classification in BC and in Canada, and delineate areas with relatively homogeneous biophysical and climatic conditions. Training areas for predicting soil parent materials were identified using single-component polygons from legacy terrain, soil, and ecosystem maps. For parent material mapping, we intersected training points amalgamated from all legacy surveys with a suite of 18 topographic covariates derived from a 100-m digital elevation model (DEM). For each ecodistrict, two versions of the resulting training dataset were submitted to the RF classifier. A ‘balanced’ dataset contained equal numbers of training data points for all parent material classes representing all legacy data derived from single-component polygons. A ‘constrained’ dataset was also derived where conditions were imposed on selected topographic attributes of the training points to reflect known geomorphic processes and to ensure consistent mapping criteria were applied across multiple legacy soil survey projects. RF predictions of soil parent material resulted in 100-m gridded class maps for BC that incorporate expert knowledge extracted from legacy soil inventories.

Comparisons were made of soil and geologic mapping in regions of mafic and ultramafic lithology. Samples from 39 locations were collected and analyzed for weak acid‐extractable Ca and Mg. Identification of parent materials was facilitated by mineralogical examination of the &lt; 20‐µm fraction of the subsoil by x‐ray diffraction techniques. Comparisons and analyses indicate that some serpentine soil units have been mapped over nonserpentine mafic rock. The Ca/Mg ratios of serpentine soils differ significantly at the 0.01 level from those soils formed from nonserpentine mafic rocks. In areas where mafic and serpentinitic parent materials occur in association, weak acid‐extractable Ca/Mg ratios can be used as an aid in distinguishing soils formed from these parent materials. In such areas, soils with Ca/Mg ratios &lt; 0.10 have a 98% probability of having formed from serpentinitic rather than from nonserpentine mafic parent materials.

The new approach for the updation of the traditional paper soil maps was provided. It is based on the imitation modeling of the traditional expert-based approaches for soil mapping. The result of the probe of our approach for soil mapping on the example of test site in the center of european part of Russia was given. The open-source script program IMSOIL was developed for soil maps updation on the R programming language. In the comparison to the others in our approach the expert-based knowledge of soil geography are also incorporated with the results of statistical data analysis. The updated soil map had much better consistency with the up-to-date soil forming factors data than the traditional paper soil map. The suggested approach of the imitation of soil mapping in geoinformational system includes using the good quality soil geography data and quantitative rules for soil mapping. The models in the form of decision trees can be analyzed by an expert, and the improvements to the covariates and obtained models can be made by him. The form of the decision trees of the soil mapping rules allowed to save up traditional qualitative description of the soil geography in the formalised representation and to integrate it with the unambiguous quantitative rules for soil mapping. They are based on the satellite data and thematic maps. Such a method allows automatically extracting relationships of the soils with the soil forming factors. The updation of the soil maps is accompanied by the probability map.

<tex>$k$</tex>-Regret Querying is a well studied problem to query a dataset <tex>$D$</tex> for a small subset <tex>$S$</tex> of size <tex>$k$</tex> with the minimal regret ratio for unknown utility functions. In this paper, we point out some issues in <tex>$k$</tex> - Regret Querying, including the assumption of non-negative dataset and the lack of shift invariance. Known algorithms for <tex>$k$</tex> - Regret Querying are limited in scope and result quality, and are based on the assumption of non-negative data. We introduce a new problem definition called k-pleased querying for dealing with the shift variance issue, and propose a strategy of random sampling of the utility functions. This strategy is based on a study of the theoretical guarantee of the sampling approach. We also introduce a dimensionality reduction strategy, an improved greedy algorithm, and a study of other utility function sampling methods. All of our solutions can handle negative data. Theoretically, we derive a guarantee on the approximation attained by our sampling algorithm. Experimental results on numerous real datasets show that our proposed method is effective even with a small number of samples and small values of <tex>$k$</tex>.

We developed a comprehensive, gridded Global Soil Dataset for use in Earth System Models (GSDE) and other applications. The GSDE provides soil information, such as soil particle‐size distribution, organic carbon, and nutrients, and quality control information in terms of confidence level at 30″ × 30″ horizontal resolution and for eight vertical layers to a depth of 2.3 m. The GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. We used a standardized data structure and data processing procedures to harmonize the data collected from various sources. We then used a soil type linkage method (i.e., taxotransfer rules) and a polygon linkage method to derive the spatial distribution of the soil properties. To aggregate the attributes of different compositions of a mapping unit, we used three mapping approaches: the area‐weighting method, the dominant soil type method, and the dominant binned soil attribute method. The data set can also be aggregated to a lower resolution. In this paper, we only show the vertical and horizontal variations of sand, silt and clay contents, bulk density, and soil organic carbon as examples of the GSDE. The GSDE estimates of global soil organic carbon stock to the depths of 2.3, 1, and 0.3 m are 1922.7, 1455.4, and 720.1 Gt, respectively. This newly developed data set provides more accurate soil information and represents a step forward to advance earth system modeling.

Whilst some of the assays used for serological screening of post-mortem blood samples from deceased tissue donors in some countries have been specifically validated by the manufacturer for this purpose, a significant number of those currently in use globally have not. Although specificity has previously been considered a problem in the screening of such samples, we believe that ensuring sensitivity is more important. The aim of this study was to validate a broader range of assays for the screening of post-mortem blood samples from deceased tissue donors. Six microplate immunoassays currently in use within National Health Service Blood and Transplant (NHSBT) for the screening of blood, tissue and stem cell donations were included. Representative samples from confirmed positive donors were titrated in screen negative post-mortem samples in parallel with normal pooled negative serum to determine if there was any inhibition with the post-mortem samples. There were no significant differences seen (P<0.005) between the dilution curves obtained for the positive samples diluted in post-mortem samples and normal pooled sera. Although small numbers of samples were studied, it can be surmised that the post-mortem blood samples from deceased tissue donors, collected according to United Kingdom guidelines, are a suitable substrate for the assays evaluated. No diminution of reactivity was seen when dilution with sera from deceased donors was compared to dilution using pooled serum from live donors. In the absence of genuine low titre positive post-mortem samples, the use of samples spiked with various levels of target material provides a means of qualifying serological screening assays used by NHSBT for the screening of post-mortem blood samples from deceased tissue donors.

Functional digital soil mapping: A case study from Namarroi, Mozambique