Search

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]

• 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]

• 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]

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]

• Does combining proximal sensor always bring synergy for predicting soil texture? • Vis-NIR, NixPro TM , and pXRF data were tested for soil texture predictions. • PXRF provided key information to create accurate prediction models. • Smoothed Vis-NIR data preprocessing and dry NixPro TM condition positively influenced the results. • Soil texture and textural classes were accurately predicted by at least one approach. Soil texture is a primary variable influencing many soil chemical-physical-biological processes, providing important information for decision-making regarding sustainable soil management. The standard traditional methods for determining soil texture, however, are performed manually and are time-consuming, costly, and generate chemical wastes. As an alternative, portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared spectroscopy (Vis-NIR) have been increasingly used worldwide to predict soil attributes. Other sensors (e.g., NixPro TM color sensor) are also promising, but less evaluated to date. Thus, investigations towards proximal sensor data fusion for prediction of soil textural separates (clay, silt, and total, coarse, and fine sand contents) and soil textural classes (loam, loamy sand, etc) in tropical soils are rare. Therefore, this study aimed to evaluate proximal sensor data for predicting soil particle size fractions and soil textural classes (both Family particle size classes and USDA soil texture triangle) via random forest algorithm in tropical regions. A total of 464 soil samples were collected from A (n = 208) and B (n = 256) horizons in Brazil. Soil samples were submitted to laboratory analyses for soil texture and proximal sensor (pXRF, Vis-NIR, and NixPro TM) scanning. Samples were randomly split into 70% for modeling and 30% for validation. The best approach varied according to the predicted attribute; however, pXRF data were key information for soil texture prediction accuracy. The best results delivered highly accurate predictions via the aforementioned proximal sensors for rapid assessment of soil texture (total sand R2 = 0.84, RMSE = 7.60%; silt 0.83, 6.11%; clay 0.90, 5.64%; coarse sand 0.87, 6.30%; fine sand 0.82, 5.27%). Categorical prediction accuracy for soil textural classes (Family particle size classes, overall accuracy = 0.97, Kappa index = 0.95; USDA soil texture triangle, 0.83, 0.73) was enhanced when the predictions were made by soil order sub-datasets. Smoothed Vis-NIR preprocessing and dry NixPro TM color data positively influenced the results. The results reported here represent alternatives for reducing costs and time needed for evaluating soil texture, supporting agronomic and environmental strategies in Brazilian conditions. Further works should extend the results of this study to temperate regions to corroborate the conclusions presented herein regarding the fusion of these three proximal sensors. [ABSTRACT FROM AUTHOR]

Soil cation exchange capacity (CEC) is an important measure of soil fertility, owing to the large number of plant essential nutrients which are taken up by plants as cations. However, current methods for evaluating soil CEC are arduous and require analysis in a laboratory. Portable X-ray fluorescence (PXRF) spectrometry is a proximal sensing technique which provides elemental data in-situ, in seconds. This study examined the potential of using PXRF for soil CEC prediction by evaluating 450 soil samples from California and Nebraska, USA representing a wide variety of soil textures found in active farm fields. Multiple linear regression was applied to a modeling dataset to establish the relationship between lab-determined CEC and PXRF elemental data. A second model also included auxiliary input data (soil clay, pH, organic matter) as potential modeling variables. Both models were shown to perform similarly, with the auxiliary input model providing slightly higher R 2 (0.926 vs. 0.908) and slightly lower RMSEs (2.236 vs. 2.498) compared to pure elemental data models. Independent validation datasets were compelling for both pure elemental models (0.904) and auxiliary input models (0.953). Summarily, PXRF was able to predict soil CEC accurately, thereby minimizing the need for lab-based CEC data for many applications. [ABSTRACT FROM AUTHOR]

This work sought to predict soil microbiological attributes based on soil fertility and texture, elemental contents determined by portable X-ray fluorescence spectrometry, and terrain attribute data with and without addition of season (dry or rainy) and phytophysiognomy as auxiliary predictors. Soil samples were collected in both seasons in four phytophysiognomies. Analyses for prediction of basal soil respiration, microbial biomass carbon, metabolic quotient, and microbial quotient were performed. Terrain attributes, total elemental concentrations obtained by portable X-ray fluorescence spectrometry, soil fertility and texture as well as phytophysiognomy and season were used as predictor variables. Prediction models were created via conditional random forest algorithm and validated with leave-one-out cross-validation through coefficient of determination (R2), root mean square error, mean absolute error and ratio percent deviation. The best results were delivered when phytophysiognomy and season were included as predictors. Metabolic quotient, microbial quotient, microbial biomass carbon and basal soil respiration achieved the best prediction using only soil fertility and texture data (R2 = 0.79, 0.66, 0.65, 0.91, respectively). Predictions of basal soil respiration and metabolic quotient using only terrain data achieved R2 values of 0.91 and 0.73, respectively. Elemental concentrations determined by portable X-ray fluorescence spectrometry reasonably predicted two microbiological attributes. It is possible to adequately predict these four microbiological attributes both locally and spatially through terrain and soil properties data. We encourage further investigations on prediction of these and other microbiological attributes under different environmental conditions and at shorter spatial and temporal scales. • Microbiological attributes were accurately predicted from soil and terrain data. • Phytophysiognomy and season (dry or rainy) data improved predictions. • Soil fertility and texture best predicted microbiological attributes. • Terrain data accurately predicted basal soil respiration and metabolic quotient. • Spatial variability map of microbiological attributes enhanced data visualization. [ABSTRACT FROM AUTHOR]

• A setup containing a smartphone and a dark chamber was used for predicting soil texture. • Soil images were acquired using the setup and analyzed via computer vision, RF, and CNN models. • Both RF and CNN showed high prediction accuracy for clay and sand, with moderate prediction accuracy for silt. • Image-extracted color features showed the maximum influence on the RF model performance. • An Android app using the calibrated CNN model was able to predicted soil textural values with satisfactory accuracy. The rapid and non-invasive prediction of soil sand, silt, and clay is becoming increasingly attractive given the laborious nature of traditional soil textural analysis. This study proposed a novel and cheap setup comprising a smartphone, a custom-made dark chamber, and a smartphone application for predicting soil texture of the dried, ground, and sieved samples. The image acquisition system was used to capture triplicate images from 90 mineral soil samples, representing a wide textural variability from sand to clay. Local features, color features, and texture features were extracted from the cropped images and subsequently used in different combinations to predict laboratory-measured clay, silt, and sand via random forest (RF) and convolutional neural network (CNN) algorithms. Results indicated high prediction accuracy for clay (R2 = 0.97–0.98) and sand (R2 = 0.96–0.98) and moderate prediction accuracy for silt (R2 = 0.62–0.75) using both algorithms. Color features outperformed all other image-extracted features and showed the maximum influence on RF model performance. The better performance of the color features can be attributed to the color features of mineral matter and soil organic matter (SOM). An Android-based smartphone application based on the calibrated CNN model was able to predict and return soil textural values. These results exhibited the potential of the proposed system as a proximal sensor for rapid, cost-effective, and eco-friendly soil textural analysis using computer-vision and deep learning. More research is warranted to augment the setup design, develop a standalone mobile application, and measure the impacts of soil moisture and high SOM on the model prediction performance to extend the approach for on-site prediction of soil texture. [ABSTRACT FROM AUTHOR]

Traditional methods of soil chemical analysis are time consuming, costly, and generate chemical waste. Proximal sensors, such as portable X-ray fluorescence (pXRF) spectrometry, may help to overcome these issues since they have been shown to produce accurate predictions of many soil properties. However, such processes need to be further investigated in Brazilian soils. This work aimed to assess the influence of soil management and mineralogy on elemental composition of soils and predict exchangeable Al3+, Ca2+, Mg2+, and available K+, and P contents from pXRF data alone and associated with soil texture through machine learning algorithms [stepwise generalized linear models (SGLM), and random forest (RF)] in soils of the Brazilian Coastal Plains (BCP). A total of 285 soil samples were collected from the A (n = 123) and B (n = 162) horizons and subjected to laboratory analyses and pXRF scans. Samples were randomly separated into 70% for modeling and 30% for validation. Soil mineralogy and management mainly influenced Al, and Ca and K total content, respectively. In general, the inclusion of the auxiliary input data of soil texture did not change the predictive power of the models. The best results highlight a considerable promise of pXRF technique for rapidly assessing exchangeable Ca2+ (RMSE = 176.3 mg kg−1, R2 = 0.71), Mg2+ (37.7 mg kg−1, 0.60), and available K+ (27.46 mg kg−1, 0.67). The algorithms could not generate reliable models to predict exchangeable Al3+ (30.6 mg kg−1, 0.47) and available P (19.9 mg kg−1, 0.14). In sum, pXRF can be used to reasonably predict soil fertility properties in the BCP soils. Further studies may extend predictions to other soil properties. • pXRF elemental data was used to available/exchangeable soil nutrients. • Soil textural data as auxiliary input data minimally improved predictive algorithms. • Random forest algorithm provided optimal prediction of soil parameters. [ABSTRACT FROM AUTHOR]