Proximal Soil Research Articles

Assessment of laboratory VIS-NIR-SWIR setups with different spectroscopy accessories for characterisation of soils from wildfire burns

Thousands of hectares of Mediterranean forests are burned every year. Fires affect all the landscape components. They trigger erosion processes, which can have catastrophic consequences. Thus, detection and post-fire monitoring of soil properties is of great importance. Changes in soil caused by the fire can be detected by proximal soil sensing. In this context, the study evaluates the applicability of laboratory experimental setups for spectral analysis of burnt soils. Three setups of Analytical Spectral Device (ASD) FieldSpec®4 spectroradiometer with different spectroscopy accessories (external integrating sphere, illuminator lamp and contact probe) were used for measurement of reflectance spectra and evaluation of soil organic matter (SOM) in 82 soil samples from wildfire burns in Aragon, Northern Spain. No statistically significant differences were detected between values obtained by different setups. Lower reflectances registered with integrating sphere are probably due to the fact that the internal cavity of the device is not perfectly spherical because of the existence of multiple port windows. Measurements with Illuminator lamp and contact probe were more stable and corresponding calibration models for SOM built using partial least square regression combined with step-down variable selection algorithm (SA-PLSR) demonstrated acceptable predictive ability (0.75 ≤ R2V ≤ 0.81; 2.00 ≤ RPD ≤ 2.55). The coefficients are ∼10% higher than those obtained with the integrating sphere. The study demonstrated feasibility of using Visible – Near InfraRed – Short Wave InfraRed (VIS-NIR-SWIR) spectroscopy for monitoring post-fire evolution of burnt soils and showed that the choice of the appropriate accessory (e.g., Illuminator lamp) improves the reliability of SOM estimations.

Soil physical property estimation from soil strength and apparent electrical conductivity sensor data

Proximal soil sensing is an attractive approach for quantifying soil properties, but many currently available sensors do not respond to a single soil property. For example, soil strength and apparent electrical conductivity (ECa) sensor measurements are significantly affected by soil texture, bulk density (BD), and water content (WC). The objective of this study was to explore the potential for estimating soil texture, BD, and WC using combinations of sensor-based soil strength and ECa data obtained from sites with varying soil physical properties. Data collected from three research sites in Missouri included on-the-go horizontal soil strength at five depths up to 0.5 m on a 0.1-m interval, cone index measurements at the same depths, ECa measured by a Veris 3100, and depth-dependent, laboratory-determined soil properties. An ECa model inversion approach was used to generate layer EC values corresponding to the depth increments of the other variables. Fits of models using EC to estimate WC were variable (R2 = 0.31–0.79). Best fitting BD estimation models (R2 = 0.11–0.55) generally included EC, but soil strength was included in fewer than half of the models. BD model fits were improved considerably by adding lab-measured WC to the model (R2 = 0.30–0.86), suggesting the need for a WC sensor. Soil clay texture fraction models based on EC and WC fit well (R2 = 0.80–0.93). This study showed the potential of combining data from multiple mobile proximal sensors to estimate important soil physical properties.

Sensor mapping of Amazonian Dark Earths in deforested croplands

Amazonian Dark Earths (ADEs) are fertile soils for agricultural production as well as important archaeological resources for understanding the pre-Columbian past of the Neotropical lowland rainforest. ADEs are threatened by expanding land exploitation and there is a need to develop efficient approaches to soil mapping and analysis for documenting these soils. In this paper we assess the potential of satellite remote sensing and proximal soil sensing to map, predict and monitor ADEs in land affected by agro-industrial development. We use instruments based on portable x-ray fluorescence (PXRF) and electromagnetic induction (EMI) as well as high-resolution satellite data (Spot 6) for detailed soil surveys at a 10-ha ADE site now mainly used for soybean production on the Belterra Plateau, Pará, Brazil. We predict the regional occurrence of ADE in a c. 250km2 test area centred on the known ADE site São Francisco using satellite data. Multivariate adaptive regression splines models were parameterised for predictions of soil organic carbon (SOC), cation exchange capacity (CEC), phosphorus (P) and depth of the A horizon in ADEs from sensor data – both from individual sensors and in sensor combinations. Combining sensors gave the best validation results: the highest modelling efficiencies (E) were 0.70 (SOC), 0.88 (CEC) and 0.74 (for both P and A depth). The most powerful single proximal sensor outputs in the predictions were Sr from the PXRF data and magnetic susceptibility (MSa) as measured by the EMI instrument. In the regional satellite based model we located 17 previously unrecorded ADE sites >2ha. Ground control checks showed that 10 out of 11 sites were correctly classified. We conclude that these sensors are useful in studies of ADE in deforested cropland and provide new opportunities for detailed studies of the archaeological record.

Field‐Scale Mapping of Soil Carbon Stock with Limited Sampling by Coupling Gamma‐Ray and Vis‐NIR Spectroscopy

Core Ideas Proximal sensors have been used for high‐detail mapping of soil C stock at field scale. Laboratory Vis‐NIR spectroscopy allowed an increase in the number of data points. Topsoil spatial variability maps were obtained using field gamma‐ray spectroscopy. Only conventional laboratory analysis was used to calibrate the model (one sample/ha). High‐precision mapping of important soil services, such as soil organic C stocks, is basic for monitoring the effects of different soil management regimes and the effectiveness of agricultural policies. Proximal soil sensing methods have been often used in the last decades to limit costs, field work, and time and to obtain reliable and accurate maps. We tested the combined use of two proximal sensors, visible–near‐infrared (Vis‐NIR) and passive γ‐ray spectrometers, to obtain highly detailed maps of C stocks of the topsoil (CS30, 0–30 cm) of nine pairs of fields in western Sicily using a limited number of sampling sites per field for traditional laboratory analysis (about one sample per hectare). Laboratory Vis‐NIR diffuse reflectance spectroscopy allowed the number of data points per field to be increased, at the same time reducing the costs for laboratory analysis. The predictive model had a coefficient of determination (R2) of 0.77 and an error (RMSE) of 0.67 kg m−2. Data points predicted by Vis‐NIR on the fine earth (<2 mm) and corrected for gravel content (CS30pred) were interpolated within each field using geographically weighted multiple regression and two sets of covariates: (i) digital elevation model derivatives, such as elevation, slope, plan and profile curvature, and topographic wetness index; and (ii) elevation and γ‐ray total counts maps. Validation of 36 independent data points showed that the second method provided greater accuracy than the first. In particular, residual prediction deviation (RPD) showed a mean value of 2.19; however, three pairs of fields showed high error and low RPD. This methodology provides a cost‐effective tool to interpolate C stocks within arable fields, limiting laboratory analysis. The accuracy of the CS30pred maps allows monitoring of the effects of agricultural management and/or soil erosion on the soil C pool.

Proximal Soil Sensing - A Contribution for Species Habitat Distribution Modelling of Earthworms in Agricultural Soils?

BackgroundEarthworms are important for maintaining soil ecosystem functioning and serve as indicators of soil fertility. However, detection of earthworms is time-consuming, which hinders the assessment of earthworm abundances with high sampling density over entire fields. Recent developments of mobile terrestrial sensor platforms for proximal soil sensing (PSS) provided new tools for collecting dense spatial information of soils using various sensing principles. Yet, the potential of PSS for assessing earthworm habitats is largely unexplored. This study investigates whether PSS data contribute to the spatial prediction of earthworm abundances in species distribution models of agricultural soils.Methodology/Principal FindingsProximal soil sensing data, e.g., soil electrical conductivity (EC), pH, and near infrared absorbance (NIR), were collected in real-time in a field with two management strategies (reduced tillage / conventional tillage) and sandy to loam soils. PSS was related to observations from a long-term (11 years) earthworm observation study conducted at 42 plots. Earthworms were sampled from 0.5 x 0.5 x 0.2 m³ soil blocks and identified to species level. Sensor data were highly correlated with earthworm abundances observed in reduced tillage but less correlated with earthworm abundances observed in conventional tillage. This may indicate that management influences the sensor-earthworm relationship. Generalized additive models and state-space models showed that modelling based on data fusion from EC, pH, and NIR sensors produced better results than modelling without sensor data or data from just a single sensor. Regarding the individual earthworm species, particular sensor combinations were more appropriate than others due to the different habitat requirements of the earthworms. Earthworm species with soil-specific habitat preferences were spatially predicted with higher accuracy by PSS than more ubiquitous species.Conclusions/SignificanceOur findings suggest that PSS contributes to the spatial modelling of earthworm abundances at field scale and that it will support species distribution modelling in the attempt to understand the soil-earthworm relationships in agroecosystems.

Microscope-based computer vision to characterize soil texture and soil organic matter

Characterization and quantification of soil properties are important for the optimum use and management our soil. While Soil texture is an important factor for decision making in agriculture, civil engineering and other industries, soil organic matter (SOM) is the backbone of soil health and quality and also affects a range of other soil properties and processes. Traditional methods for estimating these soil properties are time consuming and laborious. This paper discloses the design and development of a new cost effective in situ computer vision-based sensor system to estimate soil texture and SOM. A small and inexpensive hand-held microscope was used to develop an image acquisition system. Images acquired in laboratory with variable texture and SOM were processed using means of geospatial data analysis based computer vision algorithm. Simple linear regression predictive relationships were developed to estimate soil texture and SOM using various computed parameters of acquired imagery. Predicted sand (coefficients of determination, R2 = 0.63) and SOM (R2 = 0.83) were in good agreement with the laboratory measurement. These correspond to root mean square error of 84.7 g sand kg−1 of soil and 0.11 log (SOM %) for soils exhibiting the range of % sand from 3.4 to 59.3 and % SOM from 5.5 to 72.8, respectively. Low cost and the portability of the acquisition system and the computer vision algorithm developed in this study suggested suitability for its use in both laboratory and field conditions and shows promise as a proximal soil sensor.

What is the evidence that point sources of anthropogenic effluent increase antibiotic resistance in the environment? Protocol for a systematic review.

Herein we describe a protocol for a systematic review of the evidence on whether point sources of anthropogenic effluent are associated with an increase in antibiotic resistance in the adjacent environment. The review question was based on the Population, Exposure, Comparator, Outcome, Study Design (PECOS) framework as follows: Is the prevalence or concentration of antibiotic resistant bacteria or resistance genes (O) in soil, water, air or free-living wildlife (P) higher in close proximity to, or downstream from, known or suspected sources of anthropogenic effluent (E) compared to areas more distant from or upstream from these sources (C)? A comprehensive search strategy was created to capture all relevant, published literature. Criteria for two stages of eligibility screening were developed to exclude publications that were not relevant to the question, and determine if the study used a design that permitted estimation of an association between a source and levels of resistance. A decision matrix was created for assessment of risk of bias to internal validity due to sample selection bias, information bias, and confounding. The goal of this protocol is to provide a method for determining the state of knowledge about the effect of point sources on antibiotic resistance in the environment.

Predicting total dissolved salts and soluble ion concentrations in agricultural soils using portable visible near-infrared and mid-infrared spectrometers

Soil salinization is the primary obstacle to sustainable agricultural development in arid regions. Because total dissolved salts and soluble ion content are the primary indicators of the degree of soil salinization, their accurate estimation is essential to the determination of appropriate soil salinization remediation techniques, irrigation regimes, and the agricultural distribution layout. A total of 261 soil samples were collected from agricultural fields in the province of Xinjiang, China. A portable Fourier transform (FT) mid-infrared (MIR) spectrometer (4000–600 cm−1) and a visible near-infrared (VNIR) field spectrometer (350–2500 nm) were used to obtain soil spectra. We subsequently used partial least-square regression (PLSR) and support vector machine (SVM) algorithms to establish models in VNIR, MIR, and VNIR–MIR regions. The main objectives of this study are (i) to investigate the possibility of using spectroscopic techniques to predict total dissolved salts and soluble ion content; (ii) to compare the prediction accuracy of these soil properties in the VNIR, MIR, and VNIR–MIR spectral regions; (3) to compare the prediction accuracy with linear and nonlinear algorithms. Our findings demonstrated that spectroscopic techniques are a promising way to predict total dissolved salts and soluble ion content. Good predictions were obtained for total dissolved salts content, HCO 3 − , SO 4 2 − and Ca2+, satisfactory for Mg2+, Cl−, and Na+, but poor for K+. This work demonstrates the potential of portable VNIR and MIR spectrometers as proximal soil sensors for more efficient soil analysis and acquisition of soil salinity information.

Studies on Soil Microarthropods Associated With Mechanic Workshops in Benin City

This study assessed the abundance of soil microarthropods in the soil in proximity to three auto mechanic workshops in Benin City, Nigeria, to determine the effect of petroleum hydrocarbon wastes on these organisms. A Berlese funnel was used to collect the microarthropods from soil samples collected from one mechanic workshop located in each of the three local government areas within Benin City: Oredo (site 1), Egor (site 2), and Ikpoba-Okha (site 3). A Control Site soil sample was also collected from an area where no mechanic workshops were present. Inspection of the soil samples revealed a total number of 198 soil microarthropods belonging to two classes (Insecta and Acarina), eight families, and 10 species: Achegozetes longisetosus, Dermatophagoides pteronyssinus, Eremaeus columbianus, Eremaeus oblongus, Euzetes globules, Isotomurus palustris, Phlebotomus duboscqi, Solenopsis invicta, Hydrachna magnicutata, and an unidentified species were recorded. S. invicta was the most abundant, whereas the least abundant were E. columbianus, E. oblongus, and E. globules. Although there were no significant differences (p > 0.05) in mean temperature, pH, or soil moisture content across the study sites, there was a significant difference (p 0.05) in microarthropod abundance across sites 1 through 3, abundance varied significantly (p < 0.05) between sites 1 through 3 collectively when compared with the abundance observed in samples from the Control Site. The implication of this survey is that the indiscriminate dumping of spent oils onto the soil has adverse effects on both the environment and the abundance of soil microarthropods.

Effects of Soil Surface Roughness on Soil Reflectance Measured in Laboratory and Outdoor Conditions

Various remote sensing methods, including diffuse and contact reflectance spectroscopy (DRS and CRS, respectively) as well as proximal soil sensing (PSS), have been widely used for obtaining information about the soil properties. The influence of soil surface roughness on soil spectra obtained with a FieldSpecPro (ASD) spectroradiometer using three measurement techniques was investigated. The PSS spectra in outdoor (PSS-O) and laboratory (PSS-L) conditions were collected and then compared with the spectra of the same soil recorded using Muglight (ML) adapter. PSS measurements of each soil sample were taken at three surface roughness levels: the lowest roughness level, representing the rolled surface, the second representing a moderately rough (MR) surface of medium-sized clods and aggregates, and the third level with the highest soil surface roughness, where the clods and aggregates, with size beyond their assessed mean size for a given soil. The roughness indices height standard deviation (HSD) and tortuosity ( $\mathbf{T}_\mathbf{3D}$ ) were closely related to the size of the aggregates and clods forming the soil surface. The ML spectra of ground samples were more strongly correlated with the PSS-L spectra than with the PSS-O spectra, and mean coefficient of determination ( $\mathbf{R}^\mathbf{2}$ ) for the spectra obtained at three levels of roughness was 0.99 and 0.95, respectively. With the increase of surface roughness, the differences between the level of ML and both PSS-L and PSS-O spectra increased. The relative difference (RD) calculated for PSS-L and PSS-O spectra in relation to ML spectra was better explained by the $\mathbf{T}_\mathbf{3D}$ ( $\mathbf{R}^{\mathbf{2}} = \mathbf{0.82}$ ), than the HSD ( $\mathbf{R}^{\mathbf{2}} = \mathbf{0.68}$ ).

Analysing the proximal gamma radiometry in contrasting Mediterranean landscapes: Towards a regional prediction of clay content

The analysis of γ-radiometric data from proximal soil sensing measurements can be used to predict key soil properties for environmental and agricultural issues. These predictions require prior calibrations that use a large sampling of field data that could be significantly reduced if regional predictions can be established. However, such predictions may be challenging for Mediterranean regions because of the highly contrasted soils and specific geopedological characteristics (sedimentary rocks, low weathering, etc.). Our study focuses on the regional prediction of a key soil property (clay content), within highly contrasting geopedological groups (GPG) from the Languedoc region. The aims of the research were (i) to understand the relationship between γ-radiometric data and the clay content within the different complex GPGs and (ii) to predict the clay content from γ radiometry with a regional calibration based on the GPG as an auxiliary variable. A proximal soil survey using the γ radiometer, called “The Mole”, was conducted in 8 areas of the Languedoc region. The plots were selected over different GPGs that are representative of the soil diversity and materials according to their weathering intensity and igneous components. In total, 242 sites were sampled based on a random grid cell for soil laboratory measurements. The soil texture and stoniness were analysed, and the particle size fractions were separated for mineral identification via X-ray diffraction. The activity of 232Th [1 to 71bq/kg] and 40K [40 to 771bq/kg] exhibits the largest range between the different GPGs, which spans the range of values measured in previous European studies. The relationship between the clay content and 232Th is significant at a regional level [R2=0.60; RMSE=42g/kg]. The residuals are significantly distributed according to the different GPGs. A new linear model based on the 232Th activity and the GPGs improves the prediction of the clay content [R2=0.72; RMSE=35g/kg]. Our study showed that ground γ radiometry can be used to predict the clay content within a complex Mediterranean sedimentary context. The 232Th radionuclide is the best predictor. The disturbance of the relationship between the 232Th and the clay content corresponds to the fine earth mineralogy type and the proportion of igneous pebbles.

Moisture insensitive prediction of soil properties from VNIR reflectance spectra based on external parameter orthogonalization

Moisture is the single most important factor that affects soil reflectance spectra, particularly for field applications. Interest in using soil VNIR spectral libraries, which are commonly based on dry ground soils, to predict soils in the intact field-moist condition (in situ VNIR) is growing. External parameter orthogonalization (EPO) has been proposed as a useful method that links dry ground VNIR models to field moist scans. The goal of this study is to test EPO on a wider set of soil properties and four different modeling techniques, namely, Partial Least Squares Regression (PLS), Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM). We selected and scanned 352 archived soil samples from Nebraska, USA, among which 185 samples were used to develop dry ground models and the remaining 167 samples were rewetted to eight different moisture levels for EPO development and testing. Two methods to determine optimum number of EPO components, model-coupled cross validation (Model-Coupled-CV) and Wilk's Λ were also compared. The results showed that EPO minimized the variability of soil spectra induced by moisture. Results suggest a preference for the Wilk's Λ method over Model-Coupled-CV for determining the number of EPO components g, as it produced smoother transformed spectra and more parsimonious models. Among the eight soil properties tested, EPO caused significant improvements for soil Organic Carbon (OC), Inorganic Carbon (IC), and Total Carbon (TC) prediction, marginal improvement for sand and clay, and no improvement for pH, Mehlich-3 Phosphorus, and Cation Exchange Capacity. The failed EPO for the latter three properties is attributable to the poor initial dry-ground models that EPO was built upon. For OC, IC, and TC, EPO coupled effectively with all four modeling methods, with ANN and SVM outperforming the other two slightly. This adds flexibility to the implementation of EPO in predicting field moist soils. As there are increasing demands of spatially-explicit soil data in many disciplines, EPO would be an important essential part for the future in situ VNIR based proximal soil sensing technology.

Heavy metal and metalloid mobilisation and rates of contamination of water, soil and bottom sediments in the Chiprovtsi mining district, Northwestern Bulgaria

Geochemical studies of seasonally collected mine, stream and drinking waters, bottom sediments (mine and stream) and soil samples from all mining sections were carried out in order to assess the rates of pollution in the immediate proximity to underground mining facilities and related waste rock dumps. The determined concentrations of studied elements in water (As, Pb, Cu, Zn and Sb) show spatial distribution corresponding to ore mineralisation in different sections. Arsenic concentrations show gradual decrease in west-east direction, whereas Pb concentrations peak in the central and eastern sections. Arsenic and, to a lesser extent, Pb proved to be major pollutants in mine and surface waters, as well as in bottom sediments and soils. Detailed geochemical study of soils revealed strong spatial relation with host rocks and ore mineralogy. Comparisons with state guidelines for harmful elements revealed that alluvial and meadow soils in close proximity to waste dumps contain As, Pb, Cu, Zn and Cd above maximum permissible levels. It was also found that, compared to other Bulgarian and world alluvial (fluvisol) soils and the upper continental crust, the soils in Chiprovtsi mining district are enriched in Te, Re, W, Pd, Au, Ag, Mo, Ti, Mn, Co, Se, Sb, Bi and Cs. Since the processes of weathering and oxidation of mine waste remaining in the area continue naturally, the pollution with As and Pb will presumably carry on with decreasing effect.

История изучения и основные направления развития методов оценки и картографирования засоленности почв аридных и семиаридных территорий

The national and foreign literature was reviewed in terms of milestones and prospects of development of soil salinity assessment and mapping in arid regions. The contribution of the department of salt-affected soils of V.V. Dokuchaev Soil Science Institute into the development of methods for assessment and mapping of salt-affected soils in Russia and Central Asia during the second half of the XXth century was illustrated. The analysis of the state of assessment and inventory of salt-affected soils in Russia by the beginning of the XXIst century has shown that the latest data given in the published literature on properties and distribution of salt-affected soils are outdated as they are based on the 30-year-old studies. Undoubtedly, these data should be revised with the use of modern methods and approaches widely adopted worldwide. A uniform guideline for the assessment of salt-affected soils of Russia should be developed with the due account for international approaches. Such a guideline will be necessary for inventory and creation of database of salt-affected soils compatible with international databases. The methodology of mapping and inventory of salt-affected soils based on the uniform approach to the assessment of salt-affected soils and specified methodology of large-scale mapping with the use of remote sensing data and proximal soil sensing should be developed.

Wheat yield prediction using machine learning and advanced sensing techniques

Understanding yield limiting factors requires high resolution multi-layer information about factors affecting crop growth and yield. Therefore, on-line proximal soil sensing for estimation of soil properties is required, due to the ability of these sensors to collect high resolution data (>1500 sample per ha), and subsequently reducing labor and time cost of soil sampling and analysis. The aim of this paper is to predict within field variation in wheat yield, based on on-line multi-layer soil data, and satellite imagery crop growth characteristics. Supervised self-organizing maps capable of handling existent information from different soil and crop sensors by utilizing an unsupervised learning algorithm were used. The performance of counter-propagation artificial neural networks (CP-ANNs), XY-fused Networks (XY-Fs) and Supervised Kohonen Networks (SKNs) for predicting wheat yield in a 22ha field in Bedfordshire, UK were compared for a single cropping season. The self organizing models consisted of input nodes corresponded to feature vectors formed from normalized values of on-line predicted soil parameters and the satellite normalized difference vegetation index (NDVI). The output nodes consisted of yield isofrequency classes, which were predicted from the three trained networks. Results showed that cross validation based yield prediction of the SKN model for the low yield class exceeded 91% which can be considered as highly accurate given the complex relationship between limiting factors and the yield. The medium and high yield class reached 70% and 83% respectively. The average overall accuracy for SKN was 81.65%, for CP-ANN 78.3% and for XY-F 80.92%, showing that the SKN model had the best overall performance.

Baseline estimates of soil organic carbon by proximal sensing: Comparing design-based, model-assisted and model-based inference

For baselining and to assess changes in soil organic carbon (C) we need efficient soil sampling designs and methods for measuring C stocks. Conventional analytical methods are time-consuming, expensive and impractical, particularly for measuring at depth. Here we demonstrate the use of proximal soil sensors for estimating the total soil organic C stocks and their accuracies in the 0–10cm, 0–30cm and 0–100cm layers, and for mapping the stocks in each of the three depth layers across 2837ha of grazing land. Sampling locations were selected by probability sampling, which allowed design-based, model-assisted and model-based estimation of the total organic C stock in the study area. We show that spectroscopic and gamma attenuation sensors can produce accurate measures of soil organic C and bulk density at the sampling locations, in this case every 5cm to a depth of 1m. Interpolated data from a mobile multisensor platform were used as covariates in Cubist to map soil organic C. The Cubist map was subsequently used as a covariate in the model-assisted and model-based estimation of the total organic C stock. The design-based, model-assisted and model-based estimates of the total organic C stocks in the study area were similar. However, the variances of the model-assisted and model-based estimates were smaller compared to those of the design-based method. The model-based method produced the smallest variances for all three depth layers. Maps helped to assess variability in the C stock of the study area. The contribution of the spectroscopic model prediction error to our uncertainty about the total soil organic C stocks was relatively small. We found that in soil under unimproved pastures, remnant vegetation and forests there is good rationale for measuring soil organic C beyond the commonly recommended depth of 0–30cm.

Use of geophysical survey as a predictor of the edaphic properties variability in soils used for livestock production

The spatial variability in soils used for livestock production (i.e. Natraquoll and Natraqualf) at farm and paddock scale is usually very high. Understanding this spatial variation within a field is the first step for site-specific crop management. For this reason, we evaluated whether apparent electrical conductivity (ECa), a widely used proximal soil sensing technology, is a potential estimator of the edaphic variability in these types of soils. ECa and elevation data were collected in a paddock of 16 ha. Elevation was negatively associated with ECa. Geo-referenced soil samples were collected and analyzed for soil organic matter (OM) content, pH, the saturation extract electrical conductivity (ECext), available phosphorous (P), and anaerobically incubated Nitrogen (Nan). Relationships between soil properties and ECa were analyzed using regression analysis, principal components analysis (PCA), and stepwise regression. Principal components (PC) and the PC-stepwise were used to determine which soil properties have an important influence on ECa. In this experiment elevation was negatively associated with ECa. The data showed that pH, OM, and ECext exhibited a high correlation with ECa (R2=0.76; 0.70 and 0.65, respectively). Whereas P and Nan showed a lower correlation (R2=0.54 and 0.11 respectively). The model resulting from the PC-stepwise regression analysis explained slightly more than 69% of the total variation of the measured ECa, only retaining PC1. Therefore, ECext, pH and OM were considered key latent variables because they substantially influence the relationship between the PC1 and the ECa (loading factors&gt;0.4). Results showed that ECa is associated with the spatial distribution of some important soil properties. Thus, ECa can be used as a support tool to implement site-specific management in soils for livestock use.

Comment on “Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review” by Horta et al.

) of sites around the world, soils are heavilycontaminated with a wide variety of chemicals, both inorganic and or-ganic. It has been clear for sometime that,regrettably, the overwhelm-ing majority of these sites cannot be reclaimed, because the extent ofthe endeavor is beyond the financial means of most countries. Never-theless, to protect human populations and prevent ecotoxicologicalrisks, it is crucial to thoroughly characterize the extent of the contami-nation at existing sites. Current analytical methods to assess the levelof chemical contamination of soils are deemed too onerous, and signif-icantlycheaperalternativesarebeingactivelysought.Inthatrespect,inan article published recently in Geoderma, Horta et al. (2015) suggestthat “current tiered investigation approaches and sampling strategiescan be improved by using new technologies such as proximal sensing.Design of sampling can be aided by on-the-go proximal soil sensing;and expedited by subsequent adaptive spatially optimal sampling andprediction procedures enabled by field spectroscopic methods and ad-vanced geostatistics. Field deployment of portable visible & near infra-red [wavelength 400–2500 nm] (Vis–NIR) and X-ray fluorescence(PXRF) spectroscopies will require special calibration approaches butshowhugepotentialforsynergisticuse.”Wefeel,however,thatthisup-beat perspective is unfounded, for various reasons, and that, in anyevent, the maps that would result from using this proposed synergisticmethod would not be as useful as claimed by Horta et al. (2015).Theideathattheuse ofVis–NIRcould advantageouslyreplace time-consuming and expensive analytical laboratory techniques in the mon-itoring of soil contamination with trace metals has been promotedimplicitly or explicitly by a number of researchers for several years(e.g., Kemper and Sommer, 2002; Bray et al., 2009; Wu et al., 2011;Shi et al., 2014), even though, intrinsically, Vis–NIR cannot “see” thesechemicalelementsinsoilsatall,unlesstheirconcentrationisexception-allyhigh(N4000mgkg

Data fusion techniques for delineation of site-specific management zones in a field in UK

Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geostatistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied field. The loading coefficients for TC, pH and TN of the first factor were highest and positive. This means that the first factor can be assumed as a synthetic indicator of soil fertility. The overall spatial association between the yield classes and MZs was about 40 %, which reveals that more than 50 % of the yield variation can be attributed to more dynamic factors than soil parameters, such as agro-meteorological conditions, plant diseases and nutrition stresses. Nevertheless, multivariate geostatistics proved to be an effective approach for site-specific management of agricultural fields.

Use of A Portable Camera for Proximal Soil Sensing with Hyperspectral Image Data

In soil proximal sensing with visible and near-infrared spectroscopy, the currently available hyperspectral snapshot camera technique allows a rapid image data acquisition in a portable mode. This study describes how readings of a hyperspectral camera in the 450–950 nm region could be utilised for estimating soil parameters, which were soil organic carbon (OC), hot-water extractable-C, total nitrogen and clay content; readings were performed in the lab for raw samples without any crushing. As multivariate methods, we used PLSR with full spectra (FS) and also combined with two conceptually different methods of spectral variable selection (CARS, “competitive adaptive reweighted sampling” and IRIV, “iteratively retaining informative variables”). For the accuracy of obtained estimates, it was beneficial to use segmented images instead of image mean spectra, for which we applied a regular decomposing in sub-images all of the same size and k-means clustering. Based on FS-PLSR with image mean spectra, obtained estimates were not useful with RPD values less than 1.50 and R2 values being 0.51 in the best case. With segmented images, improvements were marked for all soil properties; RPD reached values ≥ 1.68 and R2 ≥ 0.66. For all image data and variables, IRIV-PLSR slightly outperformed CARS-PLSR.