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Portable X‐ray fluorescence (PXRF) spectrometry is a proximal sensing technique whereby low‐power X‐rays are used to make elemental determinations in soils. The technique is rapid, portable, and provides multi‐elemental analysis with results generally comparable to traditional laboratory‐based techniques. Elemental data from PXRF can then be either used directly for soil parameter assessment (e.g., total Ca, total Fe) or as a proxy for predicting other soil parameters of interest (e.g., soil cation‐exchange capacity [CEC], soil reaction, soil salinity) via simple or multiple linear regression. Importantly, PXRF does have some limitations that must be considered in the context of soil analysis. Those notwithstanding, PXRF has proven effective in numerous, agronomic, pedological, and environmental quality assessment applications. [ABSTRACT FROM AUTHOR]

Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement. [ABSTRACT FROM AUTHOR]

Precise estimation of Sea Surface Salinity (SSS) is of prime importance to know marine physical and biochemical processes. In this manuscript we have utilized the Random Forest (RF) and Support Vector Regression (SVR) to estimate the SSS concentration. Particularly, we have utilized the global SSS values provided by the Copernicus Marine Services. We have also extracted the Aqua MODIS and Sentinel-3 data from the Google Earth Engine platform. The complete range of SSS varies approximately from 6 psu to 38 psu which enables the global application of the dataset. We have also studied the importance of different wavelength bands and their significance to SSS using the RF model. It is found that wavelengths around 400 nm have their highest significance due to the sensitiveness to Chromophoric Dissolved Organic Matter (CDOM) and other related constituents. For the regression analysis we have also utilized the Laplacian kernel in the SVR. It is found that the SVR with Laplace kernel outperforms the RF and SVR with Radial Basis Function (RBF) kernel. The trained algorithms are used to estimate the SSS content over the Bay of Bengal and the Arabian Sea regions which show interesting variations in SSS content due to diverse climatological conditions. [ABSTRACT FROM AUTHOR]

Abstract Soil base saturation percentage (BSP) plays an important role in the assessment of soil taxonomic classification and soil fertility. Conventionally, soil BSP measurement methods are fraught with many drawbacks such as being laborious, time consumptive, destructive to the samples, and can lead to the underestimation of true cation exchange capacity (CEC). Recently, proximal sensors such as portable X-ray fluorescence (PXRF) spectrometry have proven to be effective for rapid physicochemical analysis of soils. In this study, we proposed and examined a PXRF-based method to predict BSP using 300 soil samples from the active agricultural lands in six states across the USA; Colorado, California, Minnesota, Nebraska, Oklahoma, and Texas. An Olympus Vanta series PXRF analyzer was employed to measure Mg, Ca, and K for BSP prediction. Results were validated using four different multivariate models [generalized additive model (GAM), multiple linear regression (MLR), random forest (RF), regression tree (RT)] via R 3.5.1. Predictive model performance was assessed via root mean squared error (RMSE), coefficient of determination (R2), residual prediction deviation (RPD), the ratio of performance to interquartile (RPIQ) range, and bias. While predicting BSP from PXRF quantified elements, models exhibited R2, RMSE (%), and RPDs as follows: GAM = 0.58, 9.0, 1.6; MLR = 0.45, 10.4, 1.4; RF = 0.62, 8.7, 1.6; RT = 0.68, 7.9, 1.8, respectively. Soil cation exchange capacity was also predicted using a similar approach, with similar and moderate predictive performance; GAM produced R2, RMSE (cmol c kg−1), and RPD of 0.69, 5.6, 1.8, respectively, relative to laboratory data. This study showed that the PXRF elements can be used to predict BSP with fair accuracy for the range of agricultural soils examined. As such, further study and enhancement of the approach outlined herein on a wider array of soils is warranted. Highlights • Soil base saturation percentage was predicted via PXRF. • Na was negligible in agricultural soils. • Regression tree was optimal for BSP prediction. [ABSTRACT FROM AUTHOR]

Abstract In this study, elemental data from portable X-ray fluorescence (PXRF) spectrometry was used to test the efficiency of four machine learning techniques (random forest; linear and nonlinear support vector machine; classification and regression tree) for distinguishing three land use types in India based upon scans of mineral surface (0–20 cm) soil. Results showed similar performance among the four tested algorithms, with classification accuracy of a randomly selected validation set ranging from 83% to 91%. The classification and regression tree was favored based upon simple "IF AND THEN" rules which make classification of the data simple. In sum, PXRF data was shown highly effective at differentiating land use types in India. Future work should focus on a larger number of land use classification types and possible combination of PXRF data with complimentary proximal sensing datasets (e.g., visible near infrared spectroscopy). Highlights • Elemental data from PXRF can be used for land classification. • Classification accuracy was between 83% and 91%. • Classification and regression tree analysis gave optimal land classification. [ABSTRACT FROM AUTHOR]

Abstract The quality and precision of spatial soil information varies tremendously worldwide, owing to differential access to technology, laboratory facilities, and adequate funding. Some countries offer detailed soil maps, but in developing countries such as Romania, Bulgaria, and others from Eastern Europe, such data are limited. Visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) is a proximal and in situ sensing approach, which can quickly provide quantitative data reflecting multiple soil physicochemical properties. If in situ VisNIR DRS is shown effective in Eastern Europe, it would greatly enhance the collection of high quality soil data in support of mapping, environmental modeling, soil and water resource assessment, and precision agriculture. The present study was conducted on a soil library (500 samples) collected from mineral surface soils of Romania and Bulgaria. The samples covered a wide range of soil types and land use. VisNIR spectra were acquired on all samples with a portable spectroradiometer and then soil mineralogy was established via standard laboratory methods. The entire dataset was split into three subsets for two different external parameter orthogonalization (EPO) approaches; using a combination of field-moist intact cores and processed samples (EPO1) and only processed samples (EPO2) to remove the moisture signature from moist spectra of both moist intact and ground samples to improve clay predictions. Results indicated that using EPO1 and EPO2 it was possible to remove 5% and 29% of the error introduced by the wet/ in situ measurements, respectively. However, the complex interactions between both soil moisture and mineral composition on the spectra possibly prevented EPO from fully removing the in situ effect on spectra when applied to a sample space with high variability in mineral composition. In situ VisNIR DRS shows important advantages over traditional laboratory approaches, allowing for more analytical density and less expensive soil information, with minimal reduction in data quality. The use of in situ VisNIR DRS with the EPO has excellent potential for providing data needed to support optimized soil resource mapping and land management decisions in Eastern Europe. Highlights • EPO removed interference from VisNIR spectra. • EPO was effective despite diverse soil mineralogy. • Re -wetted legacy samples successfully predicted soil clay content. [ABSTRACT FROM AUTHOR]

Compost is a valuable organic amendment which affords substantive fertility to soils where applied. A common component of compost fertility is cation exchange capacity (CEC), which has traditionally been determined via standard wet chemistry laboratory methods. This research utilized portable X-ray fluorescence (PXRF) spectrometry to evaluate 74 compost samples from the USA and Canada. PXRF elemental data were used for predicting compost CEC via random forest (RF) regression. Comparison between laboratory-determined vs. PXRF predicted CEC produced the following relationships: R2=0.90, RMSE = 5.41 meq 100 g−1 (model calibration) and R2=0.60, RMSE = 8.07 meq 100 g−1 (model validation). A key advantage of this technique is that the same data used for CEC prediction can also yield insight into other compost parameters of interest such as heavy metal content, plant essential nutrient content, salinity, and pH. Taken collectively, the PXRF approach can provide rapid, on-site analysis of compost which was previously not feasible with conventional methods. Our initial study has established the viability of PXRF for compost CEC determination, with further development on a wider array of feedstocks suggested for future study. [ABSTRACT FROM AUTHOR]

Compost salinity is an ongoing concern for compost producers, especially with certain feedstocks and in arid or semiarid regions. Current testing protocols call for sampling and testing ex-situ via 1:5 (w/v) slurries via electrical conductance. For this research an alternate approach has been proposed, the use of portable X-ray fluorescence (PXRF) spectrometry. Adapting methods developed for soil and water salinity analysis via PXRF, elemental data was used as a proxy for the prediction of compost salinity. In total, 74 compost samples were scanned with PXRF followed by traditional laboratory analysis. Results indicated a strong correlation between the datasets (R 2 0.80; RMSE 1.04 dS m −1 ), similar to findings for soil and water salinity. Furthermore, using the same elemental dataset, compost pH was reasonably predicted (R 2 0.63; RMSE 0.35). PXRF has the benefit of being able to be conducted in-situ or in the laboratory. And, multiple chemical parameters of interest can potentially be predicted from the same dataset. In conclusion, PXRF shows promise for rapid, in-situ salinity determination of composted products. [ABSTRACT FROM AUTHOR]

Diffuse reflectance spectroscopy (DRS) has shown its potential as a feasible, rapid and non-invasive soil characterization tool. Nevertheless, the use of whole VisNIR spectra in DRS models often incorporates different disruptive and masking effects, eventually producing inefficient model predictions. Thus the careful choice of informative spectral variables is a significant step toward producing robust and useful DRS-based models. This study evaluated the feasibility of combining variable indicator-based DRS outputs and geostatistical interpolations to rapidly produce soil spatial variability maps for six soil properties [sand, clay, silt, total carbon (TC), total nitrogen (TN) and loss-on-ignition organic matter (LOI)]. A total of 300 samples were collected from three catenas of Transylvanian Plain, Romania. First derivative spectra were used to calculate Pearson's correlation coefficient ( r ), biweight midcorrelation ( bicor ), mutual information based adjacency ( AMI ), variable importance in the projection ( VIP ), and their combinations. This variable indicator suite was combined with an ordered predictor selection (OPS) method to choose the optimum number of spectral variables (NSV). This method was tested with partial least squares regression (PLSR) and support vector regression (SVR) with independent validation. Results indicated that the variable indicator-based SVR model yielded superior predictability relative to full-spectrum PLSR model for all soil parameters. Moreover, both PLSR and SVR optimal models used the identical best variable indicators. While AMI appeared as the best indicator for four soil attributes (clay, TN, TC and LOI), bicor was selected as the best indicator for sand and silt. Spatial variability mapping using optimal SVR model outputs satisfactorily demonstrated management and landscape dynamics across the catenas like the place of manure stockpiles. Summarily, the results of this study indicated that a successful combination of OPS-based variable indicators and their subsequent incorporation into DRS-based chemometric models can potentially improve model predictions that can be further combined with geostatistical interpolation methods to produce spatial maps of soil properties. [ABSTRACT FROM AUTHOR]

Proximal sensors have rapidly developed in-situ analysis of soil and sediment. Two systems in particular, portable X-ray fluorescence (PXRF) spectrometry and visible near infrared diffuse reflectance spectroscopy (VisNIR DRS), have gained widespread acceptance for agronomic, pedological, and environmental quality assessment, but have scarcely been utilized to investigate mine tailings in South Africa. This study investigated 419 samples across four mines in South Africa using both PXRF and VisNIR DRS. Specifically, the PXRF was used to provide elemental data for comparison to X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDAX) coupled with scanning electron microscopy (SEM) for confirmation of tailings mineralogy. Next, PXRF data was used to model first derivative (1D) VisNIR reflectance spectra. Results revealed many satisfactory calibrations (R 2 > 0.70) relative to PXRF analysis with VisNIR DRS predictive models showing fair (RPD 1.4–2.0) to stable, accurate (RPD > 2.0) prediction of multiple elements. Use of better data partitioning methods and considering the target variability is likely to improve model accuracy further. Summarily, this study has established the effectiveness of using VisNIR DRS for the evaluation of mine tailings, justifying further application development with mine-specific models. [ABSTRACT FROM AUTHOR]

The importance of unknown substance identification in forensic science is vital to implementation or exclusion of criminal charges against an offender. While traditional laboratory measures include the use of gas chromatography/mass spectroscopy, an alternate method has been proposed to efficiently perform presumptive analyses of unknown substances at a crime scene or at airport security points. The use of portable X-ray fluorescence (PXRF) and visible near infrared diffuse reflectance spectroscopy (DRS) to determine elemental composition was applied to pharmaceutical medications (n=83), which were then categorized into 21 classifications based on their active ingredients. Each pharmaceutical was processed by standard laboratory procedures and scanned with both PXRF and DRS. Lastly, the datasets obtained were compared using multivariate statistical analyses. The aforementioned devices indicate that differentiation of unknown substances is clearly demonstrated among the samples with 73.49% DRS classification accuracy. Thus, the approach shows promise for future development as a rapid analytical technique for unknown pharmaceutical substances and/or illicit narcotics. [ABSTRACT FROM AUTHOR]

Secondary CaCO3 is commonly found in soils of arid and semiarid regions in variable states of development as Bk, Bkk, Bkkm, Ck, or, more rarely, Ak horizons. Historically, a qualitative scale featuring various stages of development has been applied when evaluating carbonate-laden soils. By contrast, this study used portable X-ray fluorescence spectrometry to determine the soil Ca concentration of 75 soil samples from four US states in relation to developmental stage, as determined independently by five pedologists from the USDA-NRCS Soil Survey Staff. Although they were experienced, the evaluators unanimously agreed on the carbonate development stage on only 22.6% of the samples while evaluating the samples ex situ. Portable X-ray fluorescence-determined Ca content generally increased from Development Stage I through VI for intact aggregates as well as ground soil samples. The widest variation in Ca content was found in Stage III for both conditions. No substantive differences in Ca content were observed between Stages V and VI. A strong positive correlation was observed between the Ca content of intact aggregates vs. ground soil samples (r = 0.89). Both support vector machine classification and interpretable rules were used to classify secondary carbonate development stages using total Ca concentrations for intact aggregates and ground soil. Using scans of both conditions offers stronger predictive ability than either condition independently. Portable X-ray fluorescence provides an important analytical tool available to field soil scientists for evaluation of soils containing Ca as part of CaCO3. [ABSTRACT FROM AUTHOR]

Rapid and cost-effective analysis of soil solid As phases would be an invaluable tool in studying polluted soils and predicting soil As mobility. Analysis of soil solid As phases has commonly used sequential extraction; however, the approach is time consuming, destructive, and costly. Several studies have established the viability of using visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) for elemental data analysis of soil, sediment, and other matrices. This pilot study used VisNIR DRS spectral data for rapidly predicting total As and five different solid As phases (Mg, PO 4 , Ox, HCl and org pools). A total of 200 surface soil (0–15 cm) samples were collected from arable lands surrounding a polluted landfill site and scanned via VisNIR DRS. The raw reflectance spectra were preprocessed using three spectral transformations for predicting soil total As and five extracted pools using partial least squares regression (PLSR). Quantitatively, better accuracy was produced by PO 4 (Validation R 2 = 0.72, RPIQ = 3.39) and org (Validation R 2 = 0.93, RPIQ = 4.81) pools along with total As (Validation R 2 = 0.88, RPIQ = 3.54) using the first derivative of original reflectance values. Both qualitative spectral analysis and PLSR coefficients indicated that prediction of soil As and its phases were dependent on their close association with spectrally active soil organic matter, clay minerals and Fe/Al-oxides. [ABSTRACT FROM AUTHOR]

Soil arsenic (As) contamination by anthropogenic and industrial activities is a problem of global concern. This pilot study demonstrates the feasibility of adapting the diffuse reflectance spectroscopy (DRS) approach using the visible near infrared (VisNIR) spectra for detecting soil As pollution. Further, spatial variability of soil As contamination was evaluated combining DRS based predictions and two geostatistical algorithms. The raw reflectance spectra were preprocessed using three spectral transformations for predicting soil As contamination using three multivariate algorithms. Quantitatively, better accuracy was produced by the elastic net-first derivative model (R 2 = 0.97, residual prediction deviation = 6.32, RPIQ = 7.33, RMSE = 0.24 mg kg − 1 ). The prediction of soil As was dependent on the close association between soil As and spectrally active soil organic matter and Fe-/Al-oxides. Moreover, the As pollution risks hotspots were reasonably identified using ordinary kriging and indicator kriging interpolations based on DRS predicted As values. [ABSTRACT FROM AUTHOR]

Rapid, in-situ elemental water analysis would be an invaluable tool in studying polluted and/or salt-impacted waters. Analysis of water salinity has commonly used electrical conductance (EC); however, the identity of the elements responsible for the salinity are not revealed using EC. Several studies have established the viability of using portable X-ray fluorescence (PXRF) spectrometry for elemental data analysis of soil, sediment, and other matrices. However, the accuracy of PXRF is known to be affected while scanning moisture-laden soil samples. This study used PXRF elemental data in water samples to predict water EC. A total of 256 water samples, from 10 different countries were collected and analyzed via PXRF, inductively coupled plasma atomic emission spectroscopy (ICP-AES), and a digital salinity bridge. The PXRF detected some elements more effectively than others, but overall results indicated that PXRF can successfully predict water EC via quantifying Cl in water samples (validation R 2 and RMSE of 0.77 and 0.95 log μS cm −1 , respectively). The findings of this study elucidated the potential of PXRF for future analysis of pollutant and/or metal contaminated waters. [ABSTRACT FROM AUTHOR]

Lithologic discontinuity identification can be arduous and erroneous in instances where distinct morphological differences between parent materials are absent. Often, investigators must wait for laboratory data to help differentiate parent materials via physicochemical properties. This study used visible near-infrared diffuse reflectance spectroscopy (VisNIR DRs) and portable X-ray fluorescence (PXRF) spectrometry for establishing parent material differentia more quickly. Ten pedons containing 135 samples were scanned in situ in the united states, Italy, and Hungary, morphologically described by trained pedologists, then sampled for standard laboratory characterization. Compared with laboratory data and/or morphologically described discontinuities, PXRF data were associated with large, abrupt changes in standardized PXRF differences of elements (DEs), noted in data plots as DE maxima and minima-areas of likely discontinuity. standardized VisNIR DRs calculated differences (CDs) in reflectance spectra (350-2500 nm) were also associated with discontinuities based on CD reflectance maxima and minima. Notably within both types of data plots, lithologic discontinuities were not well captured by the proximal sensors when CD or DE values fell in the data plot midsection (i.e., not at maxima or minima within the data plots). Across the pedons evaluated, PXRF was more useful for detecting discontinuities than VisNIR DRs. summarily, PXRF showed good alignment with morphologically established discontinuities in eight out of 10 pedons, while VisNIR DRs showed good alignment in only five pedons. Both PXRF and VisNIR DRs provided useful information for lithologic discontinuity recognition, especially in soils with nondescript morphology. [ABSTRACT FROM AUTHOR]

Using 108 petroleum contaminated soil samples, this pilot study proposed a new analytical approach of combining visible near-infrared diffuse reflectance spectroscopy (VisNIR DRS) and portable X-ray fluorescence spectrometry (PXRF) for rapid and improved quantification of soil petroleum contamination. Results indicated that an advanced fused model where VisNIR DRS spectra-based penalized spline regression (PSR) was used to predict total petroleum hydrocarbon followed by PXRF elemental data-based random forest regression was used to model the PSR residuals, it outperformed (R 2 = 0.78, residual prediction deviation (RPD) = 2.19) all other models tested, even producing better generalization than using VisNIR DRS alone (RPD's of 1.64, 1.86, and 1.96 for random forest, penalized spline regression, and partial least squares regression, respectively). Additionally, unsupervised principal component analysis using the PXRF + VisNIR DRS system qualitatively separated contaminated soils from control samples. Capsule Fusion of PXRF elemental data and VisNIR derivative spectra produced an optimized model for total petroleum hydrocarbon quantification in soils. [ABSTRACT FROM AUTHOR]

Soil total carbon (TC) and total nitrogen (TN) both play critical roles in soil health and ecosystem dynamics. The former is involved in soil structural formation, atmospheric carbon sequestration, and improved soil tilth while the latter is a plant essential element which is often deficient in agronomic production systems. Traditionally, both TC and TN were limited to laboratory determination using techniques such as wet or dry combustion, ion sensing electrodes, loss on ignition, or via chemical assays. These techniques, while generally accurate, require extensive soil sampling, laboratory analysis, and are inherently destructive to the sample analyzed. An approach which could quantify both TC and TN in situ would result in considerable time and cost savings and provide the analyst with the ability to capture more data for a given field of interest. Portable x-ray fluorescence (PXRF) and visible near infrared (VisNIR) diffuse reflectance spectrometry were used to scan 675 soil samples in a laboratory with diverse physicochemical properties from three states of the USA, then compared via random forest (RF) regression and penalized spline regression (PSR) to TC and TN data obtained through traditional laboratory analysis (Dumas method high temperature combustion). Results clearly demonstrated that merging the PXRF and VisNIR datasets improved the power of predictive models by improving the residual prediction deviation (RPD) and R 2 statistics. Using synthesized (PXRF + VisNIR) models, independent validation data produced quality predictive statistics for soil TC (RPD = 2.90; R 2 = 0.88 via PSR) and TN (RPD = 2.99; R 2 = 0.89 via RF). Both proximal sensing techniques were also used to independently predict TC and TN, with results less robust than the synthesized approach. The general order of optimal prediction can be summarized as PXRF + VisNIR > VisNIR > PXRF. In conclusion, the use of synthesized proximal data from PXRF and VisNIR was shown to be a solid, stable predictor of soil TC and TN with widespread agronomic and environmental science applications. [ABSTRACT FROM AUTHOR]

Highlights: [•] We modeled compost enzymatic activity with VisNIR DRS spectra. [•] We examined 7 spectral pretreatments and 6 multivariate models. [•] Spectral separations were found for different compost types. [•] Artificial neural network was best for assessing compost enzymatic activity. [•] VisNIR DRS is promising for rapidly quantifying compost enzymatic activity. [ABSTRACT FROM AUTHOR]

Abstract: Oil spills occur across large landscapes in a variety of soils. Visible and near-infrared (VisNIR, 350–2500nm) diffuse reflectance spectroscopy (DRS) is a rapid, cost-effective sensing method that has shown potential for characterizing petroleum contaminated soils. This study used DRS to measure reflectance patterns of 68 samples made by mixing samples from two soils with different clay content, three levels of organic carbon, three petroleum types and three or more levels of contamination per type. Both first derivative of reflectance and discrete wavelet transformations were used to preprocess the spectra. Three clustering analyses (linear discriminant analysis, support vector machines, and random forest) and three multivariate regression methods (stepwise multiple linear regression, MLR; partial least squares regression, PLSR; and penalized spline) were used for pattern recognition and to develop the petroleum predictive models. Principal component analysis (PCA) was applied for qualitative VisNIR discrimination of variable soil types, organic carbon levels, petroleum types, and concentration levels. Soil types were separated with 100% accuracy and levels of organic carbon were separated with 96% accuracy by linear discriminant analysis using the first nine principal components. The support vector machine produced 82% classification accuracy for organic carbon levels by repeated random splitting of the whole dataset. However, spectral absorptions for each petroleum hydrocarbon overlapped with each other and could not be separated with any clustering scheme when contaminations were mixed. Wavelet-based MLR performed best for predicting petroleum amount with the highest residual prediction deviation (RPD) of 3.97. While using the first derivative of reflectance spectra, penalized spline regression performed better (RPD=3.3) than PLSR (RPD=2.5) model. Specific calibrations considering additional soil physicochemical variability and integrating wavelet-penalized spline are expected to produce useful spectral libraries for petroleum contaminated soils. [Copyright &y& Elsevier]

• Compost organic matter was characterized with a color sensor. • Compost organic matter content was differentiated by principal components analysis. • Accurate compost organic matter quantification is possible in seconds. Composted materials serve as an effective soil nutrient amendment. Organic matter in compost plays an important role in quantifying composted materials overall quality and nutrient content. Measuring organic matter content traditionally takes considerable time, resources, and various laboratory equipment (e.g., oven, muffle furnace, crucibles, precision balance). Much like the quantitative color indices (e.g., sRGB R, sRGB G, sRGB B, CIEL*a* b*) derived from the low-cost NixPro2 color sensor have proven adept at predicting soil organic matter in-situ , the NixPro2 color sensor has the potential to be effective for predicting organic matter in composted materials without the need for traditional laboratory methods. In this study, a total of 200 compost samples (13 different compost types) were measured for organic matter content via traditional loss-on-ignition (LOI) and via the NixPro2 color sensor. The NixPro2 color sensor showed promising results with an LOI-prediction model utilizing the CIEL*a* b* color model through the application of the Generalized Additive Model (GAM) algorithm yielding an excellent prediction accuracy (validation R2 = 0.87, validation RMSE = 4.66 %). Moreover, the PCA scoreplot differentiated the three lowest organic matter compost types from the remaining 10 compost types. These results have valuable practical significance for the compost industry by predicting compost organic matter in real time without the need for laborious, time-consuming methods. [ABSTRACT FROM AUTHOR]

• SOC of 371 soil samples was predicted via Nix, PXRF-Rb, and texture. • Combining Nix color parameters and texture showed the best prediction of SOC. • Using PXRF-Rb as a proxy for clay could not produce satisfactory SOC prediction. • Color variables b* and a* extracted by Nix appeared influential in predicting SOC. The optimum ecosystem functioning is reliant on soil organic carbon (SOC) content which is traditionally measured in the laboratory via a cumbersome wet-chemistry method. This preliminary research tested whether a combination of Nix color sensor and portable X-ray fluorescence (PXRF) spectrometer data with soil texture data can improve soil SOC prediction accuracy relative to using them independently. A total of 371 samples representing diverse soil texture and SOC content were collected from three different ecoregions of eastern India: coastal saline zone, red and lateritic zone, and Gangetic alluvial zone. All dried, ground, and sieved samples were scanned via Nix and PXRF and random forest (RF) regression was used to predict soil SOC with different combinations of data. Soils were grouped into nine textural classes while soil SOC content exhibited substantial variability (0.08–2.26%). Comparing soil SOC with texture (sand + silt + clay), satisfactory prediction accuracy was observed (validation R2 = 0.70). Combining Nix extracted color parameters with texture substantially improved the model performance, producing the validation determination coefficient of 0.81. In contrast, PXRF-Rb, as a proxy of soil clay content was unable to achieve satisfactory prediction performance (R2 = 0.24), indicating the heterogeneity in soil mineralogical composition. The RF variable importance plot using Nix alone identified redness (a*) and yellowness to blueness (b*) as influential predictors, manifesting the impact of red color from Fe and Al-oxides and their significant negative correlation with soil SOC (r = −0.62 and −0.57 for a* and b*, respectively). These color parameters were again identified by the RF variable importance plot of (Texture + Nix)-model, implying that the SOC prediction improvement may be linked with the Nix sensor's capability of extracting useful information in the visible range. Summarily, a combination of Nix color variables and texture data was adept at predicting soil SOC in lieu of traditional laboratory analysis. The robustness of the (Texture + Nix)-based SOC prediction model can be augmented by incorporating more soil samples representing all 12 soil textural classes and variable SOC content, showing all possible soil colors in the pedogenic environment. [ABSTRACT FROM AUTHOR]

• Data from a simple color sensor and VisNIR DRS were combined to predict soil OC. • Variables measured by the color sensor showed negative and non-linear effects on OC. • PLSR produced satisfactory OC prediction using DRS spectra alone. • The combined model outperformed model from either sensor. Contemporary soil characterization is increasingly dependent upon proximal sensor data whereby high sample throughput and low-cost analysis are realized. Recent research studies have shown that combined sensor platforms generally offer greater predictive model stability and increased accuracy than the use of sensors in isolation. In this study, data from an inexpensive ($350 USD) Nix Pro color sensor, which measures the true color of an object by using red, blue, and green filters, was used with diffuse reflectance spectroscopy (DRS) (590–2490 nm) to predict soil organic carbon (OC) content in highly disturbed, landfill soils of India ex-situ. Generalized additive model (GAM) and partial least squares regression (PLSR) were applied to model DRS and Nix Pro data, respectively, both independently and by combining model predictions using a bilinear regression. Results showed that the combined model outperformed either sensor independently where the 30% external test set achieved a validation R2 of 0.95, residual prediction deviation (RPD) of 4.54, and the ratio of performance to interquartile range of 6.25 relative to laboratory-measured OC data. In contrary, the GAM-OC model using Nix Pro data alone and the PLSR-OC model using DRS data alone exhibited validation R2 values of 0.58 and 0.81, respectively. In sum, the addition of the inexpensive Nix Pro sensor substantively improved the prediction of soil OC relative to the use of DRS in isolation. Future studies should evaluate the effectiveness of such an approach on a wider variety of soil types (e.g., colors), its effectiveness in-situ under variable moisture conditions, and in possible combination with other proximal sensing platforms. [ABSTRACT FROM AUTHOR]

• A field-portable acoustic sensing device was evaluated for GMC measurements. • Acoustic velocity in soils exhibited the impact of soil texture and compaction. • LME model showed an increase in the acoustic velocity with a decrease in GMC. • Models fitted for individual LME-based clusters produced satisfactory R2 values. For the last couple of decades, the idea of soil water measurement has gained widespread importance due to the spiraling crisis of global water resources. While several methods are being commonly used for soil water measurement, most of them do not satisfy all the essential requirements for water monitoring under field conditions including rapid on-site measurement and cost-effectiveness. This study was conducted to evaluate the efficacy of a novel field-portable acoustic sensing device on soil moisture measurements. The conventional Brutsaert's model was followed involving the continuous wave zero-crossing method to measure the acoustic velocity and considering the phase shift due to the amplifier and ambient temperature. The gravimetric moisture content (GMC) was calibrated using the acoustic velocity for 70 samples representing seven textural classes and variable soil organic carbon (OC). Results indicated the monotonous reduction of the acoustic velocities of p-wave with increasing GMC. In general, soils with higher clay content reflected higher acoustic velocities. However, sandy soils with high OC content were less prone to compaction and produced comparatively higher acoustic velocities than soils with higher clay content. A linear mixed effect (LME) model indicated that one unit increase in the acoustic velocity resulted in a 0.086% decrease in GMC. Moreover, LME parameters-based K-means clustering further highlighted the influence of soil texture for controlling the acoustic velocity. The use of LME justified the use of GMC as the target instead of the degree of saturation. Linear and quadratic models fitted for individual LME-based clusters produced R2 values ranging from 0.80 to 0.88 and 0.82–0.88, respectively. Moreover, combined linear (R2 = 0.61; RMSE = 52.12 m/s) and quadratic (R2 = 0.64; RMSE = 50.48 m/s) models incorporating all 70 samples via bootstrapping produced comparable accuracies for GMC prediction, but yielded inferior results than the individual cluster-based models. In conclusion, the acoustic sensing device showed its potential for soil water content monitoring, leading to efficient irrigation planning. More research is warranted for texture-wise calibration development incorporating a wide assortment of soil types and on-site evaluation. [ABSTRACT FROM AUTHOR]

Coal is an important natural resource for global energy production. However, certain types of coal (e.g., lignite) often contain abundant sulfur (S) which can lead to gaseous sulfur dioxide (SO 2) emissions when burned. Such emissions subsequently create sulfuric acid (H 2 SO 4), thus causing highly acidic rain which can alter the pH of soil and surface waters. Traditional laboratory analysis (e.g., dry combustion) is commonly used to characterize the S content of lignite, but such approaches are laborious and expensive. By comparison, proximal sensing techniques such as portable X-ray fluorescence (PXRF) spectrometry, visible near infrared (VisNIR) spectroscopy, and optical sensors (e.g., NixPro) can acquire voluminous data which has been successfully used to elucidate fundamental chemistry in a wide variety of matrices. In this study, four active lignite mines were sampled in North Dakota, USA. A total of 249 samples were dried, powdered, then subjected to laboratory-based dry combustion analysis and scanned with the NixPro, VisNIR, and PXRF sensors. 75% of samples (n = 186) were used for model calibration, while 25% (n = 63) were used for validation. A strong relationship was observed between dry combustion and PXRF S content (r = 0.90). Portable X-ray fluorescence S and Fe as well as various NixPro color data were the most important variables for predicting S content. When using PXRF data in isolation, random forest regression produced a validation R2 of 0.80 in predicting total S content. Combining PXRF + NixPro improved R2 to 0.85. Dry combustion S + PXRF S and Fe correctly identified the source mine of the lignite at 55.42% via discriminant analysis. Adding the NixPro color data to the PXRF and dry combustion data, the location classification accuracy increased to 63.45%. Even with VisNIR reflectance values of 10–20%, spectral absorbance associated with water at 1940 nm was still observed. Principal component analysis was unable to resolve the mine source of the coal in PCA space, but several NixPro vectors were closely clustered. In sum, the combination of the NixPro optical sensor with PXRF data successfully augmented the predictive capability of S determination in lignite ex-situ. Future studies should extend the approach developed herein to in-situ application with special consideration of moisture and matrix efflorescence effects. • Combined PXRF and NixPro sensors accurately quantified S in lignite. • NixPro inclusion to discriminant analysis boosted classification accuracy. • Despite weak reflectance, VisNIR identified moisture in lignite. [ABSTRACT FROM AUTHOR]

Heavy metal contamination of water is a burning global issue, and enormous importance has been given on easily applicable watertreatment methods namely adsorption. However, search for an efficient and reusable adsorbent is ever-evolving. In this study, activated carbon from highly ligno-cellulosic jute stick (JS-AC), the agricultural by-product of jute (Corchorus olitorius), was evaluated as a new adsorbent for removing cadmium (Cd) from the aqueous medium. The direct and interaction effects of four independent adsorption factors namely, initial Cd(II) concentration (20–100 mg/L), adsorbent dose (0.5–1 g/L), solution pH (3–7), and time (30–180 min) were investigated by applying response surface methodology (RSM). A 24 Box-Behnken matrix model with 3 centre points indicated that the maximum % Cd(II) removal was achieved at the initial concentration, dose, pH and time of 60.87 mg/L, 0.5 g/L, 7, and 30 min, respectively. The adsorption equilibrium was investigated by applying Freundlich, Langmuir, Temkin, and Jovanoic isotherm models while Langmuir isotherm exhibited the best fit (R2 = 0.99). The maximum Cd(II) adsorption capacity value of JS-AC was 73.53 mg/g. Finally, JS-AC was regenerated with 0.1M HNO3 as the desorption medium and reused for five successive cycles. This study indicated that jute stick, the under-utilised agro-residue of a cash crop, could be converted into a valuable product like JS-AC which offers immense application potential as an efficient and reusable adsorbent to treat Cd(II) contaminated aqueous medium before releasing in the environment. [ABSTRACT FROM AUTHOR]

This study evaluated a novel smartphone-based soil image segmentation technique and subsequent machine learning (ML) optimization methodology with a set of soil images for rapidly predicting soil organic matter (SOM) with minimal soil processing. A smartphone and a custom-made box were used to capture images for 90 soil samples, collected from three different agroclimatic zones of West Bengal, India under three different illumination conditions. To offset the impact of variable illumination, the reflectance component of the image was recovered by removing the illumination from the image. Further, to deceive the ML model without distorting the soil image, an adversarial image was generated by adding Gaussian noise to the image. A Tree-based Pipeline Optimisation Tool was used to find an optimum ML stacking scheme using six different ML models. Model validation statistics indicated that reflectance image-extracted sub-colour space could predict SOM with reasonable accuracy (R2 = 0.88, RMSE = 0.28%) using original images in stack one. Moreover, the sub-colour space using perturbed images in stack one could sense noise, worsening the model validation (R2 = 0.79, RMSE = 0.36%). Conversely, seven out of eight tested colour spaces in stack two were unable to sense the image noise, producing higher validation performance than the original images. The proposed smartphone-based image acquisition setup combined with the computer vision and ML pipeline produced an important advance in affordable optical tool-based SOM prediction with significant time and cost savings. More research is warranted to extend this approach by incorporating field images of variable soil types taken under variable illuminations. • Smartphone captured soil images were used to predict SOM. • The image reflectance was recovered to remove variable illumination. • Adversarial images were generated by adding Gaussian noise to the original images. • SOM prediction model produced good validation accuracy using original images. • Inclusion of perturbed images was able to detect noise. [ABSTRACT FROM AUTHOR]

Filamentous fungi native to heavy metals (HMs) contaminated sites have great potential for bioremediation, yet are still often underexploited. This research aimed to assess the HMs resistance and Hg remediation capacity of fungi isolated from the rhizosphere of plants resident on highly Hg-contaminated substrate. Analysis of Hg, Pb, Cu, Zn, and Cd concentrations by X-ray spectrometry generated the ecological risk of the rhizosphere soil. A total of 32 HM-resistant fungal isolates were molecularly identified. Their resistance spectrum for the investigated elements was characterized by tolerance indices (TIs) and minimum inhibitory concentrations (MICs). Clustering analysis of TIs was coupled with isolates’ phylogeny to evaluate HMs resistance patterns. The bioremediation potential of five isolates’ live biomasses, in 100 mg/L Hg2+ aqueous solution over 48 h at 120 r/min, was quantified by atomic absorption spectrometry. New species or genera that were previously unrelated to Hg-contaminated substrates were identified. Ascomycota representatives were common, diverse, and exhibited varied HMs resistance spectra, especially towards the elements with ecological risk, in contrast to Mucoromycota-recovered isolates. HMs resistance patterns were similar within phylogenetically related clades, although isolate specific resistance occurred. Cladosporium sp., Didymella glomerata, Fusarium oxysporum, Phoma costaricensis, and Sarocladium kiliense isolates displayed very high MIC (mg/L) for Hg (140–200), in addition to Pb (1568), Cu (381), Zn (2092–2353), or Cd (337). The Hg biosorption capacity of these highly Hg-resistant species ranged from 33.8 to 54.9 mg/g dry weight, with a removal capacity from 47% to 97%. Thus, the fungi identified herein showed great potential as bioremediators for highly Hg-contaminated aqueous substrates. [ABSTRACT FROM AUTHOR]

Novel technologies help to monitor the environmental impact of human activities, but tests involving datasets from several countries, encompassing a large variability of soil properties, are still scarce. This study utilized proximal sensors to predict soil organic carbon (OC) and soil texture of samples from Brazil, France, India, Mozambique, and United States. A total of 1749 samples were analyzed by portable X‐ray fluorescence (pXRF) spectrometry and visible near‐infrared diffuse reflectance spectroscopy. Sand (R2 = 0.89), silt (0.87), and clay (0.84) predictions were very accurate, despite contrasting climates, soil parent materials, and weathering degrees. Soil OC predictions were similarly successful (0.74) using samples from five countries. pXRF was the optimal sensor for soil texture predictions. The addition of international data may improve local models. Proximal soil sensing can be successfully used with a multinational soil database offering a clean, rapid, and accurate alternative to estimate soil texture and OC with international datasets. Core Ideas: Soil properties can be predicted via proximal sensors using multinational datasets.This study encompassed soil samples from Brazil, France, Mozambique, India, and United States.Soil organic carbon and texture were accurately predicted via proximal sensors.The broad application of proximal sensors to aid soil characterization is encouraged. [ABSTRACT FROM AUTHOR]

We examined the impact of three different sample preparation methods on bulk soil geochemistry data obtained from a hand‐held, portable X‐ray fluorescence (pXRF) spectrometer. We generated data from a soil core recovered from the surface, downward into unaltered loess, and into a buried soil at a site in eastern Iowa. Samples were scanned (i) directly from field‐moist soil cores; (ii) after drying, grinding, and being loosely massed in plastic cups; and (iii) as pressed‐powder pellets. Data derived using these methods were compared with data obtained from a standard benchtop X‐ray fluorescence (XRF) unit. Generally, the results indicated that data from pressed powder pellets often provide the best correlation to benchtop XRF data, although the results were sometimes element or compound specific. Calcium oxide, Fe2O3, and K2O generally provided the strongest correlations between pXRF‐ and XRF‐reported values; SiO2 data were more problematic. Field‐moist pXRF scans generally underestimated element concentrations, but the correlations between pXRF and benchtop XRF measurements were greatly improved after applying pXRF‐derived calibration standards. In summary, although element/compound data provided by pXRF showed significant relationships to benchtop XRF data, the results are improved with proper sample preparation (i.e., drying, grinding, pressing) and usually by calibrating the pXRF data against known standards. [ABSTRACT FROM AUTHOR]

Motivated by a multivariate calibration problem from a soil characterization study, we proposed tractable and robust variants of penalized signal regression (PSR) using a class of non-convex Huber-like criteria as the loss function. Standard methods may fail to produce a reliable estimator, especially when there are heavy-tailed errors. We present a computationally efficient algorithm to solve this non-convex problem. Simulation and empirical examples are extremely promising and show that the proposed algorithm substantially improves the PSR performance under heavy-tailed errors. [ABSTRACT FROM AUTHOR]

[Display omitted] • 332 soil sample were collected from six LULC types of Eastern Indian Himalayas. • Soil physicochemical attributes perfectly classified LULC types using three ML techniques. • Soil hyperspectral data produced good LULC classification using SVM algorithm. • RF algorithm selected the influential soil and spectral variables for LULC classification. • The tested approach can be used for LULC classification of national archived soil samples. Information on periodic land use/land cover (LULC) changes are imperative for regional agricultural planning and policymaking. In this study, 332 soil samples were collected across five LULC types (coal mine degraded land, grassland, shifting cultivation or Jhum, plantation, and upland agriculture) of Eastern Indian Himalayas and subsequently characterized via traditional laboratory protocols and hyperspectral sensor. The soil physicochemical data and spectral data were separately used to classify six LULC types using three machine learning (ML) algorithms [support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN)]. Results indicated that the change in LULC directly impacted the dynamics of soil properties. The principal component analysis highlighted the interrelationships between the suite of soil physicochemical properties in classifying soil samples from different LULC types. All three ML algorithms exhibited that the physicochemical properties of the soil can perfectly classify LULC types. Spectral data from the hyperspectral sensor also demonstrated good classification accuracy. Overall, SVM performed better than RF and KNN, producing 93% and 78% classification accuracy using soil properties and spectral data, respectively. Moreover, the RF algorithm could select the influential soil and spectral variables for classifying LULC types. In the future, the approach tested herein can be used to classify several LULC types into the archived national soil spectral database. More research is needed to include a wide range of soil data and more LULC classes for a comprehensive classification using advanced deep learning tools. [ABSTRACT FROM AUTHOR]

In this study, a novel chromotropic acid-based color development method was proposed for quick estimation of soil nitrate (NO3−). The method utilized a 3D printed device integrated with the rear-end camera of a smartphone and a stand-alone application called SMART NP. By analyzing the mean Value (V) component of the sample's image, the SMART NP provides instant predictions of soil NO3− levels. The limit of detection was calculated as 0.1 mg L−1 with a sensitivity of 0.26 mg L−1. The device showed a % bias of 0.9% and a precision of 1.95%, indicating its reliability. Additionally, the device-predicted soil NO3− data, combined with kriging interpolation, showcased spatial variability in soil NO3− levels at the regional level. The study employed a Gaussian model of variogram for kriging, and the high Nugget/Sill ratio indicated low spatial autocorrelation, emphasizing the impact of management factors on the spatial distribution of soil NO3− content in the study area. Overall, the imaging device, along with geostatistical interpolation, provided a comprehensive solution for the rapid assessment of spatial variability in soil NO3−content. [ABSTRACT FROM AUTHOR]

Abstract The role of subsoil to store stable carbon (C) has become an important research topic in recent decades. In doing so, pools of subsoil C are compared to surface soil. The present study identified differences in mineral associated C (as separated by density fractionation) and aggregate occluded C in surface and subsurface soils. It also considered the impact of submerged rice as well as upland non-rice ecology on soil C stability. Results indicated that subsoil had a significantly greater amount of soil microaggregates as well as microaggregates within macroaggregates compared to surface soils. Subsequently, subsoil also had higher microaggregate occluded C, inaccessible to microorganisms. The higher 'ratio of C present in the heavy density fraction with C present in bulk soils' suggested that more C was strongly associated with the subsoil mineral fraction. Besides, spectroscopic analysis of functional groups indicated higher stability of subsoil aggregate occluded C than the aggregate C of surface soils. Summarily, results indicated that subsoil is a potential niche for recalcitrant C storage. Contrariwise, submerged rice ecology resulted in marginally higher soil aggregation compared to upland non-rice soils. Crop ecology did not influence aggregate occluded C dynamics and mineral-C association. As soil physical and aggregation dynamics differ substantively in puddled rice soils and upland non-rice soils, more research is warranted before making a strong conclusion. Graphical abstract Unlabelled Image Highlights • C pools were studied for surface and subsoils of rice and non-rice ecologies. • Microaggregate formation and microaggregate occluded C were higher in subsoil. • Spectroscopic study inferred higher stability of subsoil aggregate occluded C. • Recalcitrant subsoil C was further confirmed by high soil mineral-C association. • No decisive outcome confirmed the impact of crop ecology on C stability. [ABSTRACT FROM AUTHOR]

Abstract Playas are common natural occurrences in the Southern High Plains of Texas and act as sources of runoff in rural and urban settings and provide habitat for migratory waterfowl. Rural and urban playas in Lubbock County, TX were geochemically compared in order to optimize management for ecological functionality. Soil texture, specifically sand and clay, and organic matter concentration influenced geochemical properties such as trace element concentration, pH, and electrical conductivity. Rural playas contained more clay, organic matter, trace elements, and electrical conductivity than urban playas. Urban playas had a higher pH and roughly twice the sand content of rural playas. The prevalence of sand in urban playas likely influences groundwater recharge dynamics and potentially disqualifies these lakes as Vertisols owing to their sandy nature. The soil mineralogy between urban and rural playas were also different. Quartz and muscovite were the dominant minerals in urban and rural playas, respectively. The differing soil properties between rural and urban playas signify the necessity of unique management strategies. Highlights • Physicochemical comparisons were made between urban and rural playas. • Urban playas featured higher sand content and pH than rural playas. • Rural playas featured higher organic matter and heavy metals. [ABSTRACT FROM AUTHOR]

Abstract Parent material (PM) type is crucial for understanding the distribution of soils across the landscape. However, such information is not available at a detailed scale in Brazil. Thus, portable X-ray fluorescence (pXRF) spectrometry can aid in PM characterization by measuring elemental concentrations. This work focused on mapping soil PM (specifically variations of phyllite) using pXRF data and evaluating which soil horizon (A, B, or C) provides optimal PM identification in the Brazilian Cerrado. A total of 120 soil samples were collected from A, B, and C horizons across the study area as well as associated PMs; all were subjected to pXRF analysis. Artificial neural network, support vector machine, and random forest were used to model and predict PMs through pXRF data to the entire area. The nine maps (3 soil horizons data × 3 algorithms) generated for PM prediction were validated through overall accuracy, Kappa coefficient, producer's, and user's accuracy. The most accurate PM maps were obtained by using C horizon information (overall accuracy of 0.87 and Kappa coefficient of 0.79) via support vector machine algorithm. Land use dramatically influenced the results. In sum, pXRF data can be successfully used to predict soil PMs by robust algorithms. Specifically, V, Ni, Sr, and Pb were optimal for predicting PM regardless of land use. Highlights • Soil parent material can be accurately predicted via pXRF analysis of soils. • First time detecting parent material variability in area with unique parent rock. • Land use affects PM predictions, with less effect when using soil C horizon data. • V, Ni, Sr, and Pb produced good PM predictions due to their high stability in soils. • SVM and RF algorithms outperformed ANN in most predictions. [ABSTRACT FROM AUTHOR]

Agriculture is central to the Indian economy and suffers from widespread operational inefficiencies that could be corrected by the use of digital agriculture technologies (DA). We review and synthesise available literature concerning digital agriculture in India and anticipate its transformative potential in the coming decade. Although the initial growth of DA was more conspicuous in the downstream sectors and high-value crops, reaching smallholder farmers upstream is slowly emerging despite significant obstacles such as small fragmented holdings, inadequate data infrastructure and public policy, and unequal access to digital infrastructure. Agri-tech enables innovation at many locations within value chains, and a steady shift is occurring in change from individual farms to the whole value chain. Technology in the sector is progressing from information and communication technology-based solutions to Internet of Things and artificial intelligence–machine learning-enabled services. India's public policy shows signs of a longstanding investment and collaboration in the sector, with an explicit focus on data infrastructure development. We find smallholder predominance, diversity in production systems, the predominance of commodity crops, proximity to urban markets, and public policy as the major factors of DA's success in India. A stocktake of the available technologies and their applications by the public sector, tech giants, information technology leaders and agri-food tech startups in India strongly indicates a digital transformation of Indian agriculture. However, given the federal structure of governance and agriculture being a state (province) subject, we need to wait to see how DA policies are rolled out and taken up across the country. We try to determine whether digital agriculture technologies can correct widespread operational inefficiencies in Indian agriculture.. We observe digital agriculture's transition from individual farms to the whole value chain despite infrastructural and policy obstacles. India's public policy shows signs of a longstanding investment and collaboration in the sector. [ABSTRACT FROM AUTHOR]

A rapid method for in-situ elemental composition analysis of metal-laden water would be indispensable for studying polluted water. Current analytical lab methods to determine water quality include flame atomic absorption spectrometry (FAAS), atomic absorption spectrophotometry (AAS), electrothermal atomic absorption spectrometry (EAAS), and inductively coupled plasma (ICP) spectroscopy. However only two field methods, colorimetry and absorptiometry, exist for elemental analysis of water. Portable X-ray fluorescence (PXRF) spectrometry is an effective method for elemental analysis of soil, sediment, and other matrices. However, the accuracy of PXRF is known to be affected while scanning moisture-laden soil samples. This study sought to statistically establish PXRF’s predictive ability for various elements in water at different concentrations relative to inductively coupled plasma atomic emission spectroscopy (ICP-AES). A total of 390 metal-laden water samples collected from leaching columns of mine tailings in South Africa were analyzed via PXRF and ICP-AES. The PXRF showed differential effectiveness in elemental quantification. For the collected water samples, the best relationships between ICP and PXRF elemental data were obtained for K and Cu (R 2  = 0.92). However, when scanning ICP calibration solutions with elements in isolation, PXRF results indicated near perfect agreement; Ca, K, Fe, Cu and Pb produced an R 2 of 0.99 while Zn and Mn produced an R 2 of 1.00. The utilization of multiple PXRF (stacked) beams produced stronger correlation to ICP relative to the use of a single beam in isolation. The results of this study demonstrated the PXRF's ability to satisfactorily predict the composition of metal-laden water as reported by ICP for several elements. Additionally this study indicated the need for a “Water Mode” calibration for the PXRF and demonstrates the potential of PXRF for future study of polluted or contaminated waters. [ABSTRACT FROM AUTHOR]

As countries of Eastern Europe look to advance their agricultural markets through large scale agronomic production, high resolution mapping of soil resources will be essential. Portable X-ray fluorescence (PXRF) spectrometry and diffuse reflectance spectroscopy (DRS) are non-invasive, proximal sensing techniques which provide quantitative data germane to physicochemical soil properties in seconds. While these techniques have been widely used to characterize individual soil samples, sample sets, or variability across individual fields, less work has been done at the catena scale (even less so in Eastern Europe), where variability due to topographic differences substantively affects a wide number of soil properties. The present study was conducted on three catenas of the Transylvanian Plain, Romania, each with 100 sampling points randomly established in ArcGIS. Laboratory analysis (particle size analysis, total carbon, total nitrogen, soil organic matter) was conducted at Texas Tech University, USA. Following Savitzky–Golay first derivative transformation, DRS spectra were used to predict soil physicochemical parameters of interest via support vector regression. The whole dataset was randomly divided into a 70% training (n = 210) and 30% test set (n = 90). Across all catenas, a combined PXRF + DRS approach showed better parameter prediction relative to either sensor independently as evidenced by higher R 2 , lower RMSE, higher RPD, and higher RPIQ values. For each parameter, the 100 points per catena were used as input data to develop a PXRF + DRS predictive model, and the output data from each model was kriged using ArcGIS 10.3.1. Spatial analysis strongly reflected management and landscape dynamics across the catenas. Combined proximal sensor approaches show considerable advantages over traditional laboratory approaches, allowing for high sample throughput, greater analytical density, and less expensive data, with minimal fall off in data quality. The combined PXRF + DRS approach showed excellent potential for providing the data needed to support optimized soil resource mapping and land management decisions in Eastern Europe or worldwide. [ABSTRACT FROM AUTHOR]

The organic fraction of soils is critically important to soil health and optimal ecosystem functioning. Traditional analysis of soil organic horizons (O horizons) has been dependent upon laboratory-based instrumentation. Simultaneously, the use of proximal sensors such as portable X-ray fluorescence (PXRF) spectrometry along with visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) has gained popularity for providing rapidly acquired spectral and elemental data useful for soil physicochemical property quantification. However, PXRF and VisNIR DRS have mostly been applied to the assessment of mineral soils. This preliminary study evaluated 136 organic laden soil samples (most aptly described as upland, non-saturated O horizons) using both laboratory based instrumentation (CN analyzer) and proximal sensors to evaluate total carbon (TC) and total nitrogen (TN). Results revealed that combining model outcomes using model fusion improved TC and TN prediction accuracies relative to using an individual instrument (PXRF or VisNIR DRS) or model averaging with improvements in root mean square error (RMSE) on the order of 10–47% and 10–67% for TC and TN, respectively. Partial least squares + random forest (PLS + RF) approaches emerged as the best model for predicting both TC and TN in organic laden soil samples. These results suggest that the strong predictive applications of proximal sensors extensively documented on mineral soils, may show similar promise for determination of a wide number of physicochemical properties on organic soil matrices, yet further exploration with a larger and more diverse dataset is recommended. [ABSTRACT FROM AUTHOR]

[Display omitted] • Relations between sediment connectivity and LULC dynamics are mapped. • Existing spatial LULC patterns were coupled with CA-Markov chain and GeoWEPP model. • Future LULC dynamism showed 32 % shift due to human influence in the catchment. • Expansion of agricultural lands near high connectivity areas were observed. • Modelled sediment yield increased by 59% during 2001–20 and 97% during 2020–40. The soil erosion and sediment yield on the Chota Nagpur Plateau, India have been greatly affected over the years by ever increasing anthropogenic influences and associated land-use/land-cover (LULC) changes. This study coupled a CA-Markov model with the Geospatial Interface for Water Erosion Prediction Project (GeoWEPP) model along with sediment connectivity index (IC) to estimate the decadal-scale soil erosion and sediment yield changes in a representative catchment of Chota Nagpur Plateau for 2001–2040 period. This LULC- Erosion-IC modeling approach in the Konar catchment has revealed (i) concerning patterns of conversions of bare lands and grasslands to agricultural fields and (ii) dominance of the new agricultural lands in the areas with higher connectivity. Results indicated that around 11 % and 13 % of the total catchment area undergoing agriculture will be in the highly erodible and highly connected category respectively by 2040. The corresponding mean erosion rates showed a significant increase for agricultural lands [from 26 to 35 T/Ha/Y], built up areas [from 43 to 48 T/Ha/Y], and bare lands [from 30 to 36 T/Ha/Y] during the time period of 2001 to 2040. The evaluation of mean connectivity values showed future expansions of forests (through agro-forestry, i.e. ∼2400 ha) with low connectivity by 2040. A strong linkage between increased future sediment yield and changing LULC patterns (around +6 % and +8 % of the built up area and agricultural lands and −5% and −9% of bare lands and grasslands respectively) can be observed by 2040. These changes are directly attributed to the sediment yield in the region with approximately 59 % and 97 % increase during 2001–2020 and 2020–2040 periods respectively. This study provided a good understanding of general trends in erosion and sediment yield in the Chota Nagpur Plateau and the influence of ongoing efforts in agro-forestry components and land use change dynamics. [ABSTRACT FROM AUTHOR]

• Soils from three agro-climatic zones were classified using MSCC and Nix. • Soil OC was predicted using MSCC and Nix in different combinations. • Both non– standardized and MSCC-standardized Nix values were used. • Results indicated the reproducibility and potential accuracy of the Nix sensor. • Nix + MSCC produced satisfactory surface OC prediction. Optimal soil management depends on rapid and frequent monitoring of key soil properties, which are conventionally measured in the laboratory using laborious wet-chemistry protocols. The Nix color sensor has recently exhibited promise for predicting several soil properties using soil color. This study evaluated the relationship between the Munsell Soil Color Chart (MSCC) color values of dry and ground surface soil samples to those reported by the Nix color sensor with (Nix STD) and without MSCC standardization (Nix NON-STD) to classify 371 samples collected from three contrasting soil types, collected from three agro-climatic zones (coastal saline zone, red and laterite zone, and Gangetic alluvial zone) and to predict soil organic carbon (OC) using different multivariate data mining algorithms. Comparing the CIEL*a*b* color values reported by the MSCC and the Nix STD , an acceptable mean color difference (ΔE* ab) value of 5.20 was obtained, indicating the potential accuracy of the Nix sensor. Principal component analysis efficiently clustered the soil types using the RGB variables extracted from the MSCC color chips in tandem with the Nix STD /Nix NON-STD data. Both classification tree and linear support vector machine algorithms perfectly classified all three contrasting soil types using Nix NON-STD data alone. Besides, the combination of the MSCC and the Nix NON-STD datasets produced the best OC prediction (R2 = 0.66) via random forest (RF) algorithm and indicated the potential of Nix in digital soil morphometrics. In most of the RF models, redness (a*), yellowness (b*), and yellow (Y) variables appeared influential, presumably because of their negative correlation with OC in red and laterite soils. More research is warranted to measure the impacts of variable soil moisture and other confounding soil morphological features on the soil classification and OC prediction performance to extend the approach for classifying soil types and predicting OC in-situ. [ABSTRACT FROM AUTHOR]

A common theme emerging from nutrient omission trials conducted extensively across sub-Saharan Africa has been the large variability in yield response to applied nutrients. Yet, little is known about the factors associated with this variability. Therefore, the objectives of this review were to (1) synthesize the available data from nutrient omission trials of maize and rice and quantify spatial variability in yield responses and the probability of attaining specified yield targets; (2) identify the genotypic, environmental, and nutrient management factors associated with yield responses; and (3) provide insights and actionable information for setting priorities for future nutrient management research and development. Here, we provide distributions and expected values of yield response and agronomic efficiencies of nitrogen (N), phosphorus (P), and potassium (K) to serve as benchmarks for improving fertilizer recommendations. We also show that with the combined application of NPK fertilizer, rain-fed maize and rice yields can be raised from the current yields of ~2 Mg ha−1 to 4 Mg ha−1. Yield responses to N, P, and K were in the ratio of 11:6:1 in maize and 13:7:5 in rice, probably arising from differences in uptake requirements and growing environments. K yield responses were 2–3 times higher in rice than in maize. Explorative analysis using machine learning algorithms provided further insights into the possible interaction of agroecology, soil type, and exchangeable cations on the spatial variability in yield responses to N, P, and K in maize and rice. We recommend future research to address site-specific interactions between the applied and indigenous soil nutrients, focusing on optimizing application rates of K, other macronutrients and micronutrients for sustainable intensification of maize and rice production. This study highlights the critical need for balanced fertilization to optimize the productivity and nutrient use efficiencies in rice and maize production in smallholder farming systems in SSA. [ABSTRACT FROM AUTHOR]

Indian soils are inherently poor in quality due to the warm climate and erosion. Conversion of land uses like forests to croplands and faulty management practices in croplands further cause soil degradation. This study aimed to understand the extent of these impacts in a small representative part of eastern India, covering Himalayan terai and nearing alluvial plains. Soils were collected from (i) forests, (ii) croplands (under agricultural practices for more than 50–60 years) and (iii) converted lands (converted from forests to croplands or tea gardens over the past 15–20 years). Different soil quality indicators were assessed and soil quality index (SQI) was generated to integrate, scale and allot a single value per soil. Results indicated that continuous organic matter deposition and no disturbances consequence the highest presence of soil carbon pools, greater aggregation and maximum microbial dynamics in forest soils whereas high application of straight fertilizers caused the highest available nitrogen and phosphorus in cropland soils. The SQI scorebook indicated the best soil quality under forests (x¯ 0.532), followed by soils of converted land (x¯ 0.432) and cropland (x¯ 0.301). Comparison of the SQI spatial distribution with land use and land cover confirmed the outcome. Possibly practices like excessive tillage, high cropping intensity, no legume in crop rotations, cultivation of heavy feeder crops caused degraded soil quality in croplands. This study presented an example of soil quality degradation in India due to land use change and faulty management practices. Such soil degradation on a larger scale may affect future food security. [ABSTRACT FROM AUTHOR]

In this work, harvested mushroom substrate (HMS) has been explored for the first time through a comprehensive optimization study for the green synthesis of silver nanoparticles (AgNPs). A multiple response central composite design with three parameters: pH of the reaction mixture, temperature, and incubation period at three distinct levels was employed in the optimization study. The particle size of AgNPs, UV absorbance, and the percentage of Ag/Cl elemental ratio were considered as the response parameters. For each response variable examined the model used was found to be significant (P < 0.05). The ideal conditions were: pH 8.9, a temperature of 59.4 °C, and an incubation period of 48.5 h. The UV–visible spectra of AgNPs indicated that the absorption maxima for AgNP–3 were 414 nm, 420 for AgNPs–2, and 457 for AgNPs-1. The XRD analysis of AgNPs-3 and AgNPs-2 show a large diffraction peak at ∼38.2°, ∼44.2°, ∼64.4°, and ∼77.4°, respectively, which relate to the planes of polycrystalline face-centered cubic (fcc) silver. Additionally, the XRD result of AgNPs–1, reveals diffraction characteristics of AgCl planes (111, 200, 220, 311, 222, and 400). The TEM investigations indicated that the smallest particles were synthesized at pH 9 with average diameters of 35 ± 6 nm (AgNPs–3). The zeta potentials of the AgNPs are −36 (AgNPs–3), −28 (AgNPs–2), and −19 (AgNPs–1) mV, respectively. The distinct IR peak at 3400, 1634, and 1383 cm−1 indicated the typical vibration of phenols, proteins, and alkaloids, respectively. The AgNPs were further evaluated against gram (+) strain Bacillus subtilis (MTCC 736) and gram (−) strain Escherichia coli (MTCC 68). All of the NPs tested positive for antibacterial activity against both bacterial strains. The study makes a sustainable alternative to disposing of HMS to achieve the Sustainable Development Goals (SDGs). [Display omitted] • Harvested Mushroom Substrate utilized for the synthesis of silver nanoparticles. • The synthesis process was further optimized with central composite design. • PH > 9 yielded better morphology of synthesized silver nanoparticles. • The average size of the silver nanoparticles at pH > 9 was 35 ± 6 nm. • Highest bactericidal activity of silver nanoparticles were observed in 6.96 ± 0.03 ppm. [ABSTRACT FROM AUTHOR]

The color of plant leaves can be assessed qualitatively by color charts or after processing of digital images. This pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves. In order to assess its performance, a color-dependent parameter (SPAD index) was used as the dependent variable, since there is a strong correlation between SPAD index and greenness of plant leaves. A total of 1,872 fresh and intact leaves from 13 crops were analyzed using a SPAD-502 meter and scanned using the Nix™ Pro color sensor. The color was assessed via RGB and CIELab systems. The full dataset was divided into calibration (70% of data) and validation (30% of data). For each crop and color pattern, multiple linear regression (MLR) analysis and multivariate modeling [least absolute shrinkage and selection operator (LASSO), and elastic net (ENET) regression] were employed and compared. The obtained MLR equations and multivariate models were then tested using the validation dataset based on r, R2, root mean squared error (RMSE), and mean absolute error (MAE). In both RGB and CIELab color systems, the Nix™ Pro color sensor was able to differentiate crops, and the SPAD indices were successfully predicted, mainly for mango, quinoa, peach, pear, and rice crops. Validation results indicated that ENET performed best in most crops (e.g., coffee, corn, mango, pear, rice, and soy) and very close to MLR in bean, grape, peach, and quinoa. The correlation between SPAD and greenness is crop-dependent. Overall, the Nix™ Pro color sensor was a fast, sensible and an easy way to obtain leaf color directly in the field, constituting a reliable alternative to digital camera imagery and associated image processing. • For the first time, the Nix™ Pro color sensor has been used to obtain the color of plant leaves. • The color parameters of plant leaves can be easily, accurate, and instantaneously obtained using the Nix™ Pro color sensor. • A color-dependent parameter (SPAD index) was satisfactory predicted based on Nix color parameters. • Nix provided to be a powerful in-field sensor to assess the nutritional and physiological status of plants in future works. [ABSTRACT FROM AUTHOR]

Salt affected soils are pervasive in semiarid and arid regions worldwide. Traditionally, soil salinity has been measured via electrical conductivity (EC). This study evaluated the feasibility of using three different methods for prediction of surface soil salinity, namely visible near infrared diffuse reflectance spectroscopy (VisNIR DRS), portable x-ray fluorescence (PXRF) spectrometry, and remote sensing (RS). Two saline playas were evaluated in West Texas, USA featuring 91 and 74 soils collected via random stratified sampling at 0–5 cm and representing a wide variety of soil salinity from high levels inside the playa bottoms to lower levels on the annulus and surrounding uplands. Samples were subjected to PXRF and VisNIR DRS scanning under laboratory conditions, and compared to Landsat spectral data and traditional laboratory analyses of salinity (e.g., 1:5 v/v suspensions). Results showed a broad range of EC (1:5) (0.028 to 43.41 dS m − 1 ). Derived from PXRF, both Cl and S were significantly and positively correlated with log 10 transformed EC (1:5). VisNIR partial least squares prediction models produced strong residual prediction deviations (RPDs) of 2.49–2.91. Validation statistics of Savitzky–Golay support vector regression outperformed all other VisNIR models tested with an RPD of 3.1. The model using Landsat band reflectance alone produced lowest prediction accuracy (RPD = 1.27). While the performance of each technique produced variable success independently, combining the three techniques produced the highest predictability (RPD = 3.35). Given that, laboratory determination of EC (1:5) is time consuming and all three types of data (VisNIR DRS, PXRF, and RS) are being quick and easy to collect, their synthesis in predictive models offers excellent potential for providing soil salinity measurements comparable to standard, laboratory derived data. Furthermore, remotely sensed data can potentially be used to map topsoil salinity across large areas with suitable calibrations. [ABSTRACT FROM AUTHOR]

Precision agriculture provides detailed information on the spatial variability of soil properties, including nutrient content, allowing for local-specific decision making. Recently, proximal sensors have been used to accurately predict soil properties, contributing to reduce costs of conventional wet-chemistry analyses for soil characterization. However, further investigations on this approach in tropical soils are needed. This work aimed to use portable X-ray fluorescence (pXRF) spectrometry data for prediction of exchangeable Ca2+ and available K+ and P contents in soils of a highly heterogeneous tropical area and evaluating its practical applications. 90 samples from soil A horizon were collected in a regular grid design, and analyzed through pXRF and for nutrient contents. Such data were split into modeling (63 samples) and validation (27 samples) datasets. Linear regression (LR), polynomial regression (PR), power regression (PwR) and stepwise multiple linear regression (SMLR) were tested for predictions. The models were used to spatially represent nutrient contents across the area and to compare the practical effects of varying regression models. PXRF elemental data provided reliable predictions of exchangeable Ca2+ and available P via SMLR and PwR, respectively, reaching root mean square errors (RMSE) of 5.66 cmolc dm−3 for Ca2+ and 9.13 mg dm−3 for P. Available K+ predictions were not successful. Different models yielded contrasting maps showing the classes of soil fertility across the area, drawing attention to the importance of testing multiple prediction models and using the best one for precision agriculture. Fusion of data from different proximal sensors may enhance available K+ predictions. [ABSTRACT FROM AUTHOR]

Summary: Surface soil moisture plays a key role in regulating a variety of processes associated with water and energy, especially evaporation. Most prevailing methods measure soil water content only for soil blocks or layers of a certain depth, rather than directly on the soil surface. Diffuse reflectance spectroscopy (DRS) provides a way to approximate surface soil water content by utilizing surface reflectance to quantify soil water. The direct measurement of surface water content could potentially improve the understanding and modeling accuracy of the processes associated with surface water dynamics. The present study investigated the reflectance variations in one artificially constructed sample set, one set of natural soil cores, and one set of natural surface soils, with various moisture levels. Results showed that the reflectance decreased non-linearly with an increase in soil moisture at single wavelengths for all three sample sets. The models derived from partial least square (PLS) regression with segmented cross-validation using natural logarithm transformed wave bands or full spectra provided accurate and stable prediction for all moisture ranges studied. Although it is still problematic to construct a single, universal model for surface soil moisture, segmented models based on soil property classes (i.e. texture, color) are able to accurately predict surface soil water content from measured reflectance. Specific calibrations for soils with similar soil texture or color are expected to provide promising prediction of surface soil moisture. [ABSTRACT FROM AUTHOR]

Data heterogeneity has become a challenging problem in modern data analysis. Classic statistical modeling methods, which assume the data are independent and identically distributed, often show unsatisfactory performance on heterogeneous data. This work is motivated by a multivariate calibration problem from a soil characterization study, where the samples were collected from five different locations. Newly proposed and existing signal regression models are applied to the multivariate calibration problem, where the models are adapted to handle such spatially clustered structure. When compared to a variety of other methods, e.g. kernel ridge regression, random forests, and partial least squares, we find that our newly proposed varying-coefficient signal regression model is highly competitive, often out-performing the other methods, in terms of external prediction error. • We propose a new varying-coefficient signal regression approach that outperforms several other Multivariate Calibration approaches. • We model clustered or spatial data ito improve performance based on external prediction error. • We overview Penalized Signal Regression, then propose a new extension. • We use a rich spectral dataset soil samples from four U.S. states and the country of France and provide a combined analysis. • Our methods aim for the dual role of quality external prediction and interpretability through statistical models. [ABSTRACT FROM AUTHOR]