Apparent Conductivity Research Articles

Effects of soil ECa and LiDAR-derived leaf area on yield and fruit quality in apple production

Spatio-temporal estimation of tree canopy geometry in three-dimensional space (3D) was carried out using a LiDAR scanner in commercial apple production (Malus × domestica Borkh. ‘Gala’/M9) at 55, 85, 115, 140 days after bud break (DABB) for two years. Leaf area (LA) was measured by defoliating trees to calibrate corresponding LiDAR-based points per tree (PPT). Estimation of LA was improved when points of woody parts were removed from PPT, resulting in leave-one-out cross validation adjacent coefficient of determination (R2adj,CV) of 0.92 and root mean squared error (RMSECV) of 4.52%. Spatio-temporal LA was obtained for each tree (n = 4506) showing mean values of 6.25 m2 and 7.15 m2 with high standard deviation of 3.64 m2 and 2.83 m2 in 2018 and 2019, respectively. The growth rate of foliage was calculated with sigmoid growth function quantifying the full development of canopies at DABB105 and DABB95 in 2018 and 2019, respectively. Apparent soil electrical conductivity (ECa) and LA were correlated with fruit size. Accordingly, k-nearest neighbour models were built to predict fruit quality at harvest from first year data, validated on second year data. Based on geoposition and ECa, classification accuracy for fruit size in the test set validation was 48.1%, whereas classification with geopositioning and LA resulted in 67.9% accurate classification. Results highlight the spatio-temporal variation of canopy growth considering a high sample number. Furthermore, results support the future use of LA data instead or in addition to soil data in decision support systems aimed at optimising orchard management practices and, particularly, quantifying the impact of orchard management on fruit size.

Management Zones in Pastures Based on Soil Apparent Electrical Conductivity and Altitude: NDVI, Soil and Biomass Sampling Validation

The intensification of the Montado mixed ecosystem (agro–silvo–pastoral) is a current endeavor in the context of promoting the sustainability of extensive livestock production in the Mediterranean region. Increased pasture productivity and extensive animal production involves the use of technologies to monitor spatial variability and to implement differentiated management of pasture grazing, fertilization or soil amendment. An intermediate step should lead to the identification and demarcation of areas with similar characteristics (soil and/or crop development), known as homogeneous management zones (HMZ) to implement site-specific management strategies. In this study, soil apparent electrical conductivity (ECa) and altimetry surveys were carried out in six experimental pasture fields with a non-contact electromagnetic induction sensor (EM38) associated with a Global Navigation Satellite System (GNSS) receiver. These ECa and topographic maps were used in geostatistical analyses for designing and establishing final classification maps with three HMZ (less, intermediate and more potential). The normalized difference vegetation index (NDVI), obtained from a proximal optical sensor, and soil and biomass sampling were used to validate these HMZ. From a practical perspective, these HMZ are the basis for preparation of fertilizer prescription maps and use of variable rate technology (VRT) in a Precision Agriculture project.

Frequency domain electromagnetic induction imaging: An effective method to see inside a capped landfill

The frequency-domain electromagnetic (FDEM) methods are a powerful tool for evaluating the impact caused on natural environments by anthropic facilities such as landfills. Noninvasive FDEM rapidly investigates large areas with no impact on the system. This is essential in case of capped landfills, as the impermeable liner represents a strong limitation for the use of all others direct and indirect investigation methods. This technique allows the propagation of the EM fields and collection of subsurface response below the liner thus representing the only effective solution both for static imaging and time-lapse monitoring of the processes that take place into the waste deposits. Traditionally, electromagnetic data are visualized as apparent electrical conductivity (ECa) maps that give practically no information about the variation of the conductivity with depth because ECa is only the equivalent conductivity of a homogeneous soil that would give the same measured response along depth. More recent approaches allow for an inversion of data thus providing clear information on the thickness of the investigated subsurface layers. The need for building a 3D electromagnetic model is crucial in the context of the urban waste landfill characterization, where leachate induces strong anomalies in electrical conductivity, which in turn causes a nonlinear model of the EMI response. A rigorous EMI inversion approach has been tested at a closed landfill in Southern Italy. The inverted model provided detailed information unattainable with other methods, by corroborating the assumption that electromagnetic measurements represent the best technique to characterize closed systems such as capped landfills.

Seasonal changes in thermal process based on thermal response test of borehole heat exchanger

The ground-source heat pump (GSHP) system is one of the most energy-efficient cooling and heating systems popularly used worldwide. The subsurface thermal properties, especially the apparent thermal conductivity, are applied in designing an optimal system. A thermal response test (TRT) is usually conducted once to estimate the apparent thermal conductivity. However, seasonal changes in the apparent thermal conductivity obtained from the TRT have not been sufficiently discussed. Subsurface conditions such as groundwater flow and temperature distribution can vary seasonally because of natural or artificial causes. Therefore, to optimally design a GSHP system, an evaluation of the variation of apparent thermal conductivity caused by seasonal changes in subsurface conditions such as groundwater flow and temperature distribution is imperative. We performed TRT with temperature monitoring three times in January, May, and October, and analyzed the results using numerical simulation. The apparent thermal conductivity in January (winter season) was estimated to be approximately 10%–15% greater than that in May and October owing to the rapid groundwater flow in the partial aquifer, assumed to be because of pumping well operation for snow melting. To evaluate the rate of increase of the apparent thermal conductivity caused by the partial groundwater flow, numerical simulations were performed using a three-dimensional single borehole heat exchanger model. It was found that the parameter of dispersivity plays an important role in the numerical simulation of the thermal process with groundwater flow, and the rate of increase of thermal conductivity has a linear relationship with the mass flow rate, regardless of the partial aquifer thickness and dispersivity. This finding may aid in the estimation of apparent thermal conductivity when considering seasonal changes in groundwater.

Spatial variability of soil attributes by an electromagnetic induction sensor: A framework of multiple fields assessment under Brazilian soils

Soil apparent electrical conductivity (EC a ) to survey soil has been demonstrated to be a valuable tool for mapping the soil attributes. Whereas the correlation between EC a and soil attributes is well documented, the practical usage of EC a data to characterise soil properties on individual fields or across many fields is still not recognised in Brazil. Including EC a data from several fields could increase the capacity to characterise soil properties compared to a within-field approach. The objective of this study was to deliver a framework to allow the characterisation of soil spatial variability using EC a data from multiple fields. The ability of EC a to map the content and variability of soil attributes was assessed in six fields, totalling 412 ha and 2000 soil samples. The results indicate a significant correlation between EC a and selected soil properties. EC a data is useful to predict and characterise the content and variability of soil attributes according to EC a classes. Sites with relatively low EC a values presented greater spatial variability of those attributes, which requires more intensive sampling for proper spatial characterization. The amplitude of variation of EC a was directly correlated to clay content ( R 2 = 0.95), SOM ( R 2 = 0.65) and CEC ( R 2 = 0.76) ranges, where 1.0 mS m −1 corresponded to 1.5 g kg −1 , 0.12 g dm −3 and 0.25 mmol c dm −3 , respectively. The spatial variability of those attributes remained stable over different sampling times. The significant correlation of EC a readings to soil attributes indicates a potential framework to manage soil fertility in multiple fields. • ECa are an excellent alternative to establish fertility zones. • ECa can be used to guide soil sampling processes. • Low ECa classes presented greater spatial variability. • High ECa values have higher soil fertility levels. • Spatial patterns were temporally maintained.

Detection of soil pipe network by geophysical approach: Electromagnetic induction (EMI) and electrical resistivity tomography (ERT)

AbstractStudying soil pipes is a methodological challenge that needs improvement in detection methods in order to better recognize the role of piping erosion in land degradation and hillslope hydrology. This study explores electromagnetic induction (EMI) and electrical resistivity tomography (ERT) in order to identify soil pipes. The study was conducted in a mountainous area (the Bieszczady Mountains, SE Poland) under a temperate climate, where pipes develop in silty‐clayey soils. In the plot area, eight profiles were measured by the conductivity meter at different depths and then interpolated to present apparent electrical conductivity (ECa). Also, six ERT profiles were carried out using the Wenner‐Schlumberger electrode configuration. The ECa values measured by EMI are not very diversified, suggesting its lower sensitivity to changes in the ECa, whereas the ECa values measured by ERT are characterized by greater fluctuation, that is, better detection possibilities. ERT has revealed soil pipes as zones of higher electrical resistivity (ER >268 Ωm) than their surroundings (characterized below pipes by ER <105 Ωm) underlying the air filling of pipes (ER >427 Ωm), whereas EMI has revealed its higher sensitivity to water content. The EMI results have shown the lowering of the water table in the lower part of the slope, perhaps because of the drainage by a complex pipe network. EMI allows quick measurements of ECa providing information on water content, and thus indirectly soil pipes, but, it cannot delineate individual pipes. Only the integration of geophysical methods supported by field recognition provides an effective method to detect soil pipes.

Electrolytic manganese residue disposal based on basic burning raw material: Heavy metals solidification/stabilization and long-term stability

Solidification/stabilization (S/S) is an option for the treatment of electrolytic manganese residue (EMR). Basic burning raw material (BRM) could successfully solidify/stabilize EMR, though heavy metals S/S mechanism and long-term stability remain unclear. Herein, Mn2+ and NH4+ S/S behavior, hydrated BRM and S/S EMR characterization, Mn2+ long-term leaching behavior, phase and morphology changes for long-term leaching were discussed in detail to clarify these mechanisms. Mn2+ and NH4+ leaching concentrations as well as pH value in S/S EMR were respectively 0.02 mg/L, 0.68 mg/L and 8.75, meeting the regulations of Chinese standard GB 8978–1996. Long-term stability of EMR was significantly enhanced after S/S. Mn2+ leaching concentration, Mn2+ migration, Mn2+ cumulative release, Mn2+ apparent diffusion coefficient and conductivity of EMR reduced to 0.05 mg/L, 5.5 × 10−6 mg/(m2·s), ~ 9 mg/m2, 6.30 × 10−15 m2/s and 435 μs/cm. Mechanism studies showed that the hydration of BRM forms OH−, calcium silicate hydrate gels (C-S-H) and ettringite. Therefore, during S/S process, NH4+ was escaped as NH3, Mn2+ was solidified/stabilized as tephroite (Mn2SiO4), johannsenite (CaMnSi2O6) and davreuxite (MnAl6Si4O17(OH)2), and Pb2+, Cu2+, Ni2+, Zn2+ were solidified/stabilized by C-S-H and ettringite via substitution and encapsulation. This study provides a good choice for EMR long-term stable storage.

Delineation of Nitrate Reduction Hotspots in Artificially Drained Areas through Assessment of Small-Scale Spatial Variability of Electrical Conductivity Data.

Identification of nitrate reduction hotspots (NRH) can be instrumental in implementing targeted strategies for reducing nitrate loading from agriculture. In this study, we aimed to delineate possible NRH areas from soil depths of 80 to 180 cm in an artificially drained catchment by utilizing electrical conductivity (EC) values derived by the inversion of apparent electrical conductivity data measured by an electromagnetic induction instrument. The NRH areas were derived from the subzones generated from clustering the EC values via two methods, unsupervised ISODATA clustering and the Optimized Hot Spot Analysis, that highly complement each other. The clustering of EC values generated three classes, wherein the classes with high EC values correspond to NRH areas as indicated by their low redox potential values and nitrate (NO3−) concentrations. Nitrate concentrations in the NRH were equal to 13 to 17% of the concentrations in non-NRH areas and occupied 26% of the total area of the drainage catchments in the study. It is likely that, with the identification of NRH areas, the degree of nitrogen reduction in the vadose zone may be higher than initially estimated at the subcatchment scale.

Thermophysical Characteristics of a Cold Compressed Granular Medium: An Experimental Investigation

To our knowledge, very few models have described the thermal behavior of granular media or powders as a function of the mechanical stresses to which they are subjected. In recent years, many researchers have expressed great interest in establishing laws that can show the relationship between the apparent thermal conductivity and the mechanical behavior of granular media. This paper presents an experimental study of heat transfer enhancement using granular blocks. A number of experiments were carried out in the test section of a wind tunnel where velocity and temperature measurements were performed. The velocity profiles permitted us to have an idea on the flow structure in the presence of granular blocks. The temperature measurements under various operating conditions (various Reynolds numbers and granular blocks structures) led us to the conclusion that using granular blocks can improve the thermal transfer in comparison with the case of a smooth channel and this enhancement is strongly dependent on the geometrical and thermo-physical properties of these blocks.

Fractal nonlocal thermoelasticity of thin elastic nanobeam with apparent negative thermal conductivity

This study treats a new model of nonlocal fractal thermoelasticity beam theory for nanomaterials characterized by an apparent negative thermal conductivity which occurs in shaped graded materials. The model is based on the new concept of "product-like fractal measure" introduced by Li and Ostoja-Starzewski in their formulation of fractal continuum media with fractal dimension The basic setups of the theory are formulated, yet, we have restricted our analysis for the static case. The effects of fractal dimension on field variables, in particular the lateral deflection and the temperature increment of the resonator were analyzed analytically and graphically. It was observed that the physics of the thin elastic nanobeam is affected by the numerical value of the fractal dimension and the characteristic lengths of the theory. Comparable scenarios observed in complex material structures are obtained and discussed.

Frequency-Domain Electromagnetic Mapping of an Abandoned Waste Disposal Site: A Case in Sardinia (Italy)

For decades, bad practices in municipal and industrial waste management have had negative environmental impacts, generating high health risks for people and the environment. The use of badly designed, not engineered, and not well-operated landfills has, around the world, produced a large number of potentially contaminated sites, for which there are urgent needs to assess the actual risk and to proceed, in case, with reclamation activities. One of these sites, an abandoned waste disposal site located near a Site of Community Importance on the central-eastern coast of Sardinia (Italy), is the subject of the case history described in this work. As a part of a multi-method geophysical characterisation, a frequency-domain electromagnetic (FDEM) mapping survey was carried out with the specific aim of detecting the presence of buried materials (waste) and of delineating the lateral extent of the landfill by identifying the electrical conductivity anomalies produced, for the most part, by the conductive waste fill. Using an EM31 device in the vertical-dipole configuration, at a height of 0.9 m above the ground, both quadrature and in-phase electromagnetic responses were collected over a 7-hectare area with elevation varying between 6 m and 2.8 m above sea level. After removing the measurements identified as data coming from any recognisable surface man-made features within the survey area or near its perimeter, the filtered quadrature response (expressed as apparent conductivity) ranged from 5.5 mS/m to about 188.6 mS/m. All values are beyond the low induction number (LIN) condition and valid for the classical EM31 mapping, thus requiring advanced data processing. To obtain undistorted, meaningful, and interpretable high-resolution maps, measured data have been processed to correct the bias, introduced by the nonlinearity of the device, as a function of height above ground and the topography. The comparative analysis of the apparent conductivity map, obtained by the properly processed EM31 data and some aerial photos that clearly documented the site history, has allowed unequivocal delineation of the landfill extent, in good agreement with the results obtained with other geophysical methods (not described in this paper) and with the ground truthing data provided by three boreholes, which were core-drilled at the end of the study at three locations selected on the basis of the apparent conductivity map.

Use of two relative depths of the soil apparent electrical conductivity to define experimental blocks with spatial regression models

Aim of study: Our main objective was to take advantage of the ECa information that the EM38-MK2 sensor records simultaneously at two relative depths for modeling using spatial regression and the subsequent blocking of the conductivity estimate values, incorporating elevation. Area of study: A 23.1-ha field located in the municipality of Puerto López (Meta, Colombia). Material and methods: A series of georeferenced data (15438) was collected from the EM38-MK2 sensor, through which the ECa was obtained at two depths, a spatial aggregation was performed using a grid of 40 m ´ 40 m (167 grid cells), to provide data in Lattice form, the centroid of the cells was determined as the new representative spatial coordinates, to adjust a Spatial Autoregression Model (SAC), and then define the blocks from the predictions of the adjusted model. Main results: The adjusted model has a comparative purpose with the usual proposals for delimiting management zones separately, so it was convenient to incorporate in the model a 3D weighting matrix relating the two relative depths recorded by the EM38MK2 sensor. By mapping the surface layer with the predictions of the SAC model, two distinguishable blocks were delimited in its ECa and management zone analyst (MZA), which can be suitable for experimentation or agricultural management. Research highlights: These results can be adopted to define the shape and dimension of the blocks in the context of experimental design so that with adequate blocking, the effect of spatial dependence associated with the physicochemical properties of soils related to ECa can be mitigated or suppressed.

Synthesis, antibacterial activity, and enzymatic decomposition of bio-polyurethane foams containing propolis

Polyurethane foam (PUF) is formed by reacting sugar, propolis, and polyisocyanate, and then blowing with CO2, formed by reacting polyisocyanate with water. An optimum foaming rate of 1440% was found for 15 wt.% of sugar and 20 wt.% of propolis, and the synthesis and cell structure of polyurethane were analyzed by scanning electron microscopy (SEM) and Fourier transform infrared (FT-IR) spectroscopy. Under the optimized conditions, the apparent density and thermal conductivity of the PUF-containing propolis were 0.04 – 0.147 g/mL and 0.028 – 0.029 W/(mK), respectively. The bacterial reduction rate was 92.7% in the PUF containing 20 wt.% propolis. PUF can be broken down by enzymatic reactions in water using invertase. The degradation reaction of invertase was kinetically analyzed. This study showed that eco-friendly polymers can be synthesized using sugar and propolis and decomposed through antibacterial activity and enzymatic green recycling.

Spatial Variability of Soil Properties and Its Effect on Maize Yields within Field—A Case Study in Hungary

To better understand the potential of soils, understanding how soil properties vary over time and in-field is essential to optimize the cultivation and site-specific technologies in crop production. This article aimed at determining the within-field mapping of soil chemical and physical properties, vegetation index, and yield of maize in 2002, 2006, 2010, 2013, and 2017, respectively. The objectives of this five-year field study were: (i) to assess the spatial and temporal variability of attributes related to the maize yield; and (ii) to analyse the temporal stability of management zones. The experiment was carried out in a 15.3 ha research field in Hungary. The soil measurements included sand, silt, clay content (%), pH, phosphorous (P2O5), potassium (K2O), and zinc (Zn) in the topsoil (30 cm). The apparent soil electrical conductivity was measured in two layers (0–30 cm and 30–90 cm, mS/m) in 2010, in 2013, and in 2017. The soil properties and maize yields were evaluated in 62 management zones, covering the whole research area. The properties were characterized as the spatial-temporal variability of these parameters and crop yields. Classic statistics and geostatistics were used to analyze the results. The maize yields were significantly positively correlated (r = 0.62–0.73) with the apparent electrical conductivity (Veris_N3, Veris_N4) in 2013 and 2017, and with clay content (r = 0.56–0.81) in 2002, 2013, and 2017.

Relationships between Soil Electrical Conductivity and Sentinel-2-Derived NDVI with pH and Content of Selected Nutrients

Site-specific crop management demands maps which present the content of the main macronutrients. Such maps are prepared based on optimized soil sampling within management zones, which should be quite homogenous according to nutrient content, especially the content of potassium and phosphorus. Delineation of management zones is very often conducted using soil apparent electrical conductivity (EC) or other variables related to soil condition, including satellite-derived vegetation indices. In this study conducted in North-Western Poland, relationships between soil electrical conductivity and the satellite-derived normalized difference vegetation index (NDVI) of various crops (wheat, barley, and rapeseed) with soil pH and content of P, K, and Mg were evaluated. Strong relationships were observed between NDVI of cereals with potassium content in soil. Correlation coefficients for wheat ranged from 0.37 to 0.60 for average potassium content for three years and from 0.05 to 0.63 for barley. Stronger relationships were observed for the years 2018 and 2019 when NDVI was based on Sentinel-2 data, while weaker for year 2017 when Landsat 8 NDVI was used. Relationships between EC and macronutrients content were similar to those observed with NDVI. Satellite-derived NDVI of cereals can be used as a variable for the delineation of within-field management zones. The same relationships were much weaker and not consistent for winter rapeseed.

Unraveling Heat Transport and Dissipation in Suspended MoSe2 from Bulk to Monolayer.

Understanding heat flow in layered transition metal dichalcogenide (TMD) crystals is crucial for applications exploiting these materials. Despite significant efforts, several basic thermal transport properties of TMDs are currently not well understood, in particular how transport is affected by material thickness and the material's environment. This combined experimental-theoretical study establishes a unifying physical picture of the intrinsic lattice thermal conductivity of the representative TMD MoSe2 . Thermal conductivity measurements using Raman thermometry on a large set of clean, crystalline, suspended crystals with systematically varied thickness are combined with ab initio simulations with phonons at finite temperature. The results show that phonon dispersions and lifetimes change strongly with thickness, yet the thinnest TMD films exhibit an in-plane thermal conductivity that is only marginally smaller than that of bulk crystals. This is the result of compensating phonon contributions, in particular heat-carrying modes around ≈0.1THz in (sub)nanometer thin films, with a surprisingly long mean free path of several micrometers. This behavior arises directly from the layered nature of the material. Furthermore, out-of-plane heat dissipation to air molecules is remarkably efficient, in particular for the thinnest crystals, increasing the apparent thermal conductivity of monolayer MoSe2 by an order of magnitude. These results are crucial for the design of (flexible) TMD-based (opto-)electronicapplications.

Using machine learning to predict optimal electromagnetic induction instrument configurations for characterizing the shallow subsurface

Abstract. Electromagnetic induction (EMI) is used widely for hydrological and other environmental studies. The apparent electrical conductivity (ECa), which can be mapped efficiently with EMI, correlates with a variety of important soil attributes. EMI instruments exist with several configurations of coil spacing, orientation, and height. There are general, rule-of-thumb guides to choose an optimal instrument configuration for a specific survey. The goal of this study was to provide a robust and efficient way to design this optimization task. In this investigation, we used machine learning (ML) as an efficient tool for interpolating among the results of many forward model runs. Specifically, we generated an ensemble of 100 000 EMI forward models representing the responses of many EMI configurations to a range of three-layer subsurface models. We split the results into training and testing subsets and trained a decision tree (DT) with gradient boosting (GB) to predict the subsurface properties (layer thicknesses and EC values). We further examined the value of prior knowledge that could limit the ranges of some of the soil model parameters. We made use of the intrinsic feature importance measures of machine learning algorithms to identify optimal EMI designs for specific subsurface parameters. The optimal designs identified using this approach agreed with those that are generally recognized as optimal by informed experts for standard survey goals, giving confidence in the ML-based approach. The approach also offered insight that would be difficult, if not impossible, to offer based on rule-of-thumb optimization. We contend that such ML-informed design approaches could be applied broadly to other survey design challenges.

Enhanced sintering and electrical properties of proton-conducting electrolytes through Cu doping in BaZr0.5Ce0.3Y0.2O3-δ

To obtain BaZrO3-based electrolyte materials with both good sinterability and high proton conductivity, a Cu-doped B-site BaZr0.5Ce0.3Y0.2-xCuxO3-δ (BZCYCux; x = 0, 0.05, 0.1) system is designed and synthesized. The effects of Cu doping on the crystal structure, sintering and electrical properties of BZCYCux are systematically investigated. The BZCYCu0.05 sample demonstrates the highest sintering density and total conductivity among the studied materials. BZCYCu0.05 can be densified at 1250 °C, which is 300 °C lower than that for unmodified BZCY. The calculated results suggest that the increased conductivity in BZCYCu0.05 benefits from a high apparent specific grain-boundary conductivity, which originates from a small space-charge potential and a large clean part of impurities in the electrolyte. A synthetic analysis based on impedance spectroscopy and distribution of relaxation time further confirms that BZCYCu0.05 is more conductive. A single cell built using the BZCYCu0.05 (∼46 μm) electrolyte shows an excellent power density of 258 mW cm−2 at 700 °C. These results indicate that BZCYCu0.05 is a potential proton conductor for use in protonic ceramic fuel cells.

Corn grains drying using direct-fired furnace with wood chips: Performance, quality and polycyclic aromatic hydrocarbons

The objective of this work was to evaluate the drying performance, the quality of the corn grain and the contamination by polycyclic aromatic hydrocarbons (PAHs) using a direct-fired furnace fed automatically by wood chips. Samples were collected at five-minute intervals, 12 samples before drying and 12 samples after drying. The following experimental evaluations were carried out: dryer – performance and fuel consumption; grains – moisture content, germination, electrical conductivity, apparent specific mass, mass of a thousand grains, color and PAHs. The dryer showed an average efficiency of 83.61%. The average consumption of wood chips was 0.3932 kg of chips per kilogram of evaporated water. Differences were observed in the grains after drying for the characteristics of mass of a thousand grains, apparent specific mass, germination and electrical conductivity. The average concentrations of PAHs in corn grains were higher than the maximum values ​​allowed by the European Union legislation.

Can soil health explain grain quality? A case study of a corn field in Texas

AbstractStudies show a strong relationship between soil health and crop yield, but those relating soil health and grain quality are limited. We studied the relationship between soil health and grain protein and oil content from a corn (Zea mays L.) field in Texas. Protein and oil content data were collected in the field with a CropScan monitor. Soil health values were measured at 202 locations using the Haney Soil Health Tool. We first mapped protein and oil content using apparent electrical conductivity (ECa) and 14 terrain attributes as predictors, and we then quantified the relationship with data from sample locations. Soil health was positively correlated with protein and oil content, but the relationship was rather weak. Soil health accounted for up to 13% of the variability in protein (p < .001) and between 2 and 17% in oil content (p < .1) depending on soil map unit. Their spatial distribution was mostly influenced by elevation, ECa, and wetness index. We do not recommend estimating grain protein and oil content with the Haney Soil Health Tool; however, we suggest investigating such relationship across different soil and agronomic conditions for further verification.