Estimating Bulk Density Research Articles

Bulk density prediction while drilling vertical complex lithology using artificial intelligence

Because of porosity and mineralogy disparity, rocks have a broad range of bulk densities, a critical property that has an important role in rock characterization. Practically, the density of rock is estimated either via wireline logging or logging while drilling tools. However, these techniques are not always accessible, making the door open for using different predicting methods such as empirical correlations. The existing empirical correlations have substantial restrictions, which limited their accuracy and reliability. This work targets developing various artificial intelligence models to predict bulk density for complex lithology during drilling (i.e., at real-time). The artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques are used with the mechanical drilling parameters as inputs. This study uses a vertical well with 2912 data points from various lithologies including sand, carbonate, and shale to develop the models. Besides, a new empirical correlation for bulk density is developed based on the optimized ANN technique. A different dataset from the same tested field was used to validate the developed models. The obtained outcomes demonstrated that both ANN- and ANFIS-based models estimated the rock density with high fitting accuracy. The ANFIS approach results correlation coefficient (R) values of 0.99 with an average absolute percentage error (AAPE) values of 0.83 and 0.92% in training and testing processes. Whereas the ANN approach is slightly outperformed, as described by the highest R values of 0.99 and the lowest AAPE of 0.77 and 0.93% for training and testing processes. Also, a new empirical model for bulk density estimation was derived from the developed, optimized ANN-based model, which is applicable to the extracted weights and biases. The validation process indicates the robustness and reliability of the developed models with R values of 0.99 and AAPE of 0.92 and 0.96% for ANN- and ANFIS-based models, respectively. The outcomes of this work develop models with high accuracy for real-time prediction of bulk density, which can help in rock description in an adequate method with less cost, time, and errors.

Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements

Abstract. Knowledge of sea-ice thickness and volume depends on freeboard observations from satellite altimeters and in turn on information of snow mass and sea-ice density required for the freeboard-to-thickness conversion. These parameters, especially sea-ice density, are usually based on climatologies constructed from in situ observations made in the 1980s and earlier while contemporary and representative measurements are lacking. Our aim with this paper is to derive updated sea-ice bulk density estimates suitable for the present Arctic sea-ice cover and a range of ice types to reduce uncertainties in sea-ice thickness remote sensing. Our sea-ice density measurements are based on over 3000 km of high-resolution collocated airborne sea-ice and snow thickness and freeboard measurements in the western Arctic Ocean in 2017 and 2019. Sea-ice bulk density is derived assuming isostatic equilibrium for different ice types. Our results show higher average bulk densities for both first-year ice (FYI) and especially multi-year ice (MYI) compared to previous studies. In addition, we find a small difference between deformed and possibly unconsolidated FYI and younger MYI. We find a negative-exponential relationship between sea-ice bulk density and sea-ice freeboard and apply this parameterisation to one winter of monthly gridded CryoSat-2 sea-ice freeboard data. We discuss the suitability and the impact of the derived FYI and MYI bulk densities for sea-ice thickness retrievals and the uncertainty related to the indirect method of measuring sea-ice bulk density. The results suggest that retrieval algorithms be adapted to changes in sea-ice density and highlight the need of future studies to evaluate the impact of density parameterisation on the full sea-ice thickness data record.

TOI-1201 b: A mini-Neptune transiting a bright and moderately young M dwarf

We present the discovery of a transiting mini-Neptune around TOI-1201, a relatively bright and moderately young early M dwarf (J ≈ 9.5 mag, ~600–800 Myr) in an equal-mass ~8 arcsecond-wide binary system, using data from the Transiting Exoplanet Survey Satellite, along with follow-up transit observations. With an orbital period of 2.49 d, TOI-1201 b is a warm mini-Neptune with a radius of Rb = 2.415 ± 0.090 R⊕. This signal is also present in the precise radial velocity measurements from CARMENES, confirming the existence of the planet and providing a planetary mass of Mb = 6.28 ± 0.88 M⊕ and, thus, an estimated bulk density of 2.45−0.42+0.48 g cm−3. The spectroscopic observations additionally show evidence of a signal with a period of 19 d and a long periodic variation of undetermined origin. In combination with ground-based photometric monitoring from WASP-South and ASAS-SN, we attribute the 19 d signal to the stellar rotation period (Prot = 19–23 d), although we cannot rule out that the variation seen in photometry belongs to the visually close binary companion. We calculate precise stellar parameters for both TOI-1201 and its companion. The transiting planet is anexcellent target for atmosphere characterization (the transmission spectroscopy metric is 97−16+21) with the upcoming James Webb Space Telescope. It is also feasible to measure its spin-orbit alignment via the Rossiter-McLaughlin effect using current state-of-the-art spectrographs with submeter per second radial velocity precision.

Modeling sub-boreal forest canopy bulk density in Minnesota, USA, using synthetic aperture radar and optical satellite sensor data

BackgroundNational estimates of canopy bulk density (CBD; kg m−3) for fire behavior modeling are generated and supported by the LANDFIRE program. However, locally derived estimates of CBD at finer scales are preferred over national estimates if they exist, as the absolute accuracy of the LANDFIRE CBD product is low and varies regionally. Active sensors (e.g., lidar or radar) are better suited for this task, as passive sensors are ill equipped to detect differences among key vertical fuel structures, such as coniferous surface fuels (≤2 m high) and canopy fuels above this threshold—a key categorical fuel distinction in fire behavior modeling. However, previous efforts to map CBD using lidar sensor data in the Superior National Forest (SNF) of Minnesota, USA, yielded substandard results. Therefore, we use a combination of dormant-season synthetic aperture radar (SAR) and optical satellite sensor data to (1) expand detectability of coniferous fuels among mixed forest canopies to improve the accuracy of CBD modeling and (2) better understand the influence of surface fuels in this regard. Response variables included FuelCalc output and indirect estimates of maximum burnable fuel based on canopy gap fraction (CGF) measured at ground level and 2 m above ground level.ResultsSAR variables were important predictors of CBD and total fuel density (TFD) in all independent model calibrations with ground data, in which we define TFD as the sum of CBD and primarily live coniferous surface fuel density (SFD) 0 to 2 m above ground. Exploratory estimates of TFD appeared biased to the presence of sapling-stage conifer fuel on measures of CGF at the ground level. Thus, modeling efforts to calibrate SFD with satellite sensor data failed. Both CGF-based and FuelCalc-based field estimates of CBD yielded close unity with satellite-calibrated estimates, although substantial differences in data distributions existed. Estimates of CBD from the widest CGF zenith angle range (0 to 38°) correlated best with FuelCalc-based CBD estimates, while both resulted in maximum biomass values that exceeded those considered typical for the SNF. Model results from the narrowest zenith angle range (0 to 7°) produced estimates of CBD that were more in line with values considered typical. LANDFIRE’s estimates of CBD were weakly, but significantly (P = 0.05), correlated to both narrow- and wide-angle CGF-based estimates of CBD, but not with FuelCalc-based estimates.ConclusionsThe combined use of field estimates of CBD, based on indirect measures of CGF according to Keane et al. (Canadian Journal of Forest Research 35:724–739, 2005), with SAR and optical satellite sensor data demonstrates the potential of this method for mapping CBD in the Upper Midwest, USA. Results suggested that the presence of live, coniferous surface fuels neither confounds remote detection nor precludes mapping of CBD in this region using SAR satellite sensor data, as C- and L-band idiosyncrasies likely limit the visibility of these smaller understory fuels from space. Nevertheless, research using direct measures of burnable SFD for calibrations with SAR satellite sensor data should be conducted to more definitively answer this remote detection question, as we suspect substantial bias among measures of CGF from ground level when estimating SFD as the difference between TFD and CBD.

Ephemeris and hazard assessment for near-Earth asteroid (101955) Bennu based on OSIRIS-REx data

Small bodies such as the near-Earth asteroid Bennu drift in their orbit due to thermal radiation forces (the Yarkovsky effect). Ground-based observations have indicated a nonzero probability of Bennu impacting Earth, depending on how its orbit evolves. Thus, among the goals of the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) mission to Bennu were to precisely measure the Yarkovsky effect and refine the impact hazard assessment for this body. Here we address these objectives. Using OSIRIS-REx spacecraft tracking data, we derive meter-level constraints on the distance between Earth and Bennu from January 2019 to October 2020. While these data greatly improve the knowledge of the trajectory of Bennu, they also require an unprecedented fidelity for the modeling of an asteroid’s trajectory. In particular, special care is needed to take into account the contribution of 343 small-body perturbers and the uncertainty in their masses. Radiation effects such as the Poynting–Robertson drag, so far only considered for interplanetary dust dynamics, now become a consideration for modeling the trajectory of a 500-m asteroid such as Bennu. By employing a thermophysical model based on OSIRIS-REx’s characterization of Bennu, we estimate a semimajor axis drift of −284.6±0.2 m/yr (signal-to-noise ratio ∼1400) at epoch 2011 January 1 caused by the Yarkovsky effect. The largest source of modeling error is solar wind drag, which may lower the magnitude of the semimajor axis drift from the Yarkovsky effect by up to 0.16 m/yr. The Yarkovsky-related semimajor axis drift varies by roughly ±1 m/yr as the orbit of Bennu evolves due to planetary perturbations from 1900 to 2135. The Yarkovsky thermophysical model proves to be extremely accurate by predicting a bulk density estimate within 0.1% of that estimated through gravity science analysis. Compared to the information available before the OSIRIS-REx mission, the knowledge of the circumstances of the scattering Earth encounter that will occur in 2135 improves by a factor of 20, thus allowing us to rule out many previously possible impact trajectories. However, there remain some impact trajectories compatible with the data. Prior to the spacecraft encounter, the overall impact probability through 2200 was 3.7×10−4 (1 in 2700). As a result of our analysis, the cumulative impact probability through 2300 becomes 5.7×10−4 (1 in 1750) and the most significant individual impact solution is for September 2182, with an impact probability of 3.7×10−4 (1 in 2700). Both Bennu and (29075) 1950 DA have a Palermo scale value of −1.42 and share the distinction as the currently most hazardous object in the asteroid catalog.

Assessing new sensor‐based volume measurement methods for high‐throughput bulk density estimation in the field under various soil conditions

AbstractSoil bulk density (BD) is a key soil property in soil science. BD is measured at the scale of the soil horizon with conventional methods: core sampling and rubber balloon. Regardless of the method, BD measurement in the field is cumbersome and time consuming, especially in stony soils, and new, less invasive methods have emerged, but their measurement quality needs to be compared with conventional methods. The photogrammetric technique (SfM) consists of the reconstruction of a given scene in 3D from multi‐view photographs. The aim of the present work is to assess the SfM as a rapid, accurate method of measuring the volume of undisturbed soil for BD measurements, regardless of the soil depth and stone content. Ten soil horizons were investigated from various types of soils with different soil properties, especially texture (10 to 48% of clay) and stone content (0 to 73%). The bulk density of each horizon was measured with a reference method (core sampling), an excavation method (rubber balloon) and two recently developed sensor‐based methods: SfM and a lightweight flash LiDar. For the sensor‐based methods, an automated post‐processing method was developed to reduce the human operating time. The BDs measured from the SfM were significantly similar to those measured from core sampling. At this stage, the lightweight flash LiDar is not sufficient to measure the BD with high accuracy. Finally, we recommend the use of SfM to measure BD regarding its robustness in varying soil conditions, especially stony soils, and we discuss the potential of the method regarding the recent advances in its field use with smartphones.Highlights Soil bulk density (BD) is a key soil property in soil science The photogrammetric technique and flash Lidar were applied to measure BD in the field The photogrammetric technique and flash Lidar were compared to classical methods The flash lidar is not sufficient to measure the BD with high accuracy The photogrammetric technique is recommended especially in stony soils

Mass and Density of Asteroid (16) Psyche

Abstract We apply our novel Markov Chain Monte Carlo (MCMC)–based algorithm for asteroid mass estimation to asteroid (16) Psyche, the target of NASA’s eponymous Psyche mission, based on close encounters with 10 different asteroids, and obtain a mass of (1.117 ± 0.039) × 10−11 M ⊙. We ensure that our method works as expected by applying it to asteroids (1) Ceres and (4) Vesta, and find that the results are in agreement with the very accurate mass estimates for these bodies obtained by the Dawn mission. We then combine our mass estimate for Psyche with the most recent volume estimate to compute the corresponding bulk density as (3.88 ± 0.25) g cm−3. The estimated bulk density rules out the possibility of Psyche being an exposed, solid iron core of a protoplanet, but is fully consistent with the recent hypothesis that ferrovolcanism would have occurred on Psyche.

Effect of particle size distribution, drying and milling technique on explosibility behavior of olive pomace waste

The Mediterranean area is responsible for about 98% of the olive oil worldwide production, with 900 million olive trees occupying 10 million hectares. However, the processing of 100 kg of olives leads to the production of 40 kg of wastes, mainly constituted by olive pomace, which is potentially recoverable as energetic or material source. In general, in the past 20 years, the exploitation of olive pomace has increased, but along with it, the need for further information about its chemical-physical characterization and the related hazard in industry. Thus, a risk analysis assessment was conducted. When pelletized or in chunks, olive pomace does not pose any greater hazard than a pile of woody material, but when pulverized, it might become dangerous. Two parallel series of experiments were carried out at Dalhousie University (Lab 1) and at Polytechnic of Turin (Lab 2) using the same olive pomace sample, according to slightly different experimental procedures. Olive pomace dust explosibility and flammability parameters were measured: minimum ignition energy (MIE), minimum ignition temperature (MIT), maximum pressure rise rate ((dP/dt)max and KSt), maximum pressure (Pmax), and minimum explosible concentration (MEC). Moreover, the chemical and physical characterization of olive pomace was carried out: moisture content, particle size analysis, Scanning Electronic Microscope (SEM) investigation, thermo-gravimetric analysis (TGA), solid-state Nuclear Magnetic Resonance (NMR), mass spectrometry, calorific value, and bulk density estimation. Different thermal behaviors were observed according to the sieving/grinding pre-treatment. As concern flammability tests, samples seemed not to be sensitive to electric arc ignition (a value of MIE could not be measured), while coarser samples demonstrated higher ignition sensitivity to hot environment sources (MIT furnace) than finer ones. On the other hand, explosion violence parameters were enhanced by decreasing the particle size, while peak pressures were significantly influenced by the heat of combustion and the moisture content. Finally, a new test was developed to quantify the propensity of the raw material to produce fines by abrasion. It is defined “Abrasion by Rolling Test” (ART). The properties of the fines produced were measured as well.

Impact of bulk density estimation in mine planning

ABSTRACT The incorporation of bulk density uncertainty for Mineral Resource and Mineral Reserves (MRMR) steps is of great importance for successful mine planning process. In practice, most mining projects use an average density value for each lithological type of the deposit. However, density determinations require special attention as they directly affect the conversion of volumes to tonnages and ore grades to metal contents. The objective of this study is to measure and minimize the risks associated with the MRMR stages, including the density variability in mine planning. Traditional interpolation techniques in geostatistics were used to represent density spatial distribution in a block model, similarly to those used for grade estimation. This approach was developed and applied at a Brazilian iron ore mine. The results indicate MRMR are underestimated if density uncertainty is not considered. Mine design changes were observed, including 5% impact on the Life-of-mine (LOM) and 2% in Net Present Value (NPV).

Effects of Moisture Content and Compaction Pressure on Bulk Density of Rye

HighlightsRye grain compaction was measured for three different moisture contents (8%, 12%, and 16% wet basis) at five different compaction pressures (7, 14, 34, and 55 kPa)Bulk densities were found to be statistically significantly dependent (p < 0.0001) on both the moisture content and applied pressure.Compacted bulk densities increased with increasing applied pressure for all moisture contents.Abstract. Bulk density of agricultural grains is needed to determine the quantity of grain in storage structures and to calculate grain pressures. The objective of this study was to investigate the effects of moisture content and applied pressure on bulk density of rye grain at moisture contents and pressures typical of those seen in storage structures. Rye compaction was measured for three moisture contents (8%, 12%, and 16% wet basis) at four compaction pressures (7, 14, 34, and 55 kPa) using a square box (based on the design used by Thompson and Ross, 1983). Data from the compaction tests were used to calculate the bulk densities for the three moisture contents and four compaction pressures. The bulk densities were found to be significantly dependent (p <0.0001) both on moisture contents and the pressure applied. Bulk densities varied with increasing moisture content as has been observed in similar studies for rye and other agricultural grains such as wheat and soybeans. These results provide guidance for estimating bulk density of rye in bins and other storage structures. Keywords: Grain compaction, Grain storage, Kernel rearrangement, Kernel elasticity.

Airborne lidar provides reliable estimates of canopy base height and canopy bulk density in southwestern ponderosa pine forests

Accurate estimates of canopy base height (CBH) and canopy bulk density (CBD) are critical inputs for many fire modeling and simulation programs. Commonly used LANDFIRE estimates of CBH and CBD are only available at 30 m resolution and have relatively low accuracy, and traditional field-based estimates can lead to inaccurate spatial estimates by assuming averages across spatial extents. Discrete-return airborne light detection and ranging (lidar) has become increasingly common in forestry applications, with demonstrated success in measuring canopy metrics and forest structure across broad landscapes and varied forest types. However, few studies have investigated the potential of airborne lidar for estimating these metrics in southwestern forests. This study developed an approach for estimating CBH and CBD using airborne lidar data and evaluated the accuracy of estimates at 20 m resolution. We also evaluated the predicted accuracy between managed and unmanaged forest stands. We employed a quantile-based method to derive CBH and utilized the Fire and Fuels Extension of the Forest Vegetation Simulator to estimate CBD from a lidar-derived tree list. Our results indicate that airborne lidar produced more accurate estimates of both CBH (R2 = 0.4; RMSE = 1.25 m) and CBD (R2 = 0.76; RMSE = 0.021 kg−3) as compared to LANDFIRE and other regional remote-sensing based estimates. We also report that airborne lidar is more accurate at estimating CBH in unmanaged stands versus managed stands, but CBD estimates maintain similar accuracy regardless of management history. Compared to methods developed in other regions, our approach resulted in relatively low R2 values, but our RMSE values were found to be similar or slightly improved. Our approach for deriving both metrics are conceptually and computationally simple making them especially valuable for use by land managers and ecologists alike. This study provides clear evidence that airborne lidar can be used to derive broad-scale, fine-resolution fuel data layers, which are commonly required in fire modeling and forest management activities and decision-making.

Variations of porosity in intermediate-sized lunar impact basins

The Moon is cratered with hundreds of well-preserved impact basins, and shared bombardment history with Earth makes lunar basins a good proxy for the processes affecting early Earth. Basin-forming impact events alter the bulk density and porosity of the targeted crust, and measurement of these quantities offers a means by which we can investigate the mechanisms of basin formation. In this work, we calculate the bulk density of the crust in the vicinity of 50 basins with diameters of 170–360 km—a size range that has been neglected by previous studies—with the goal of understanding the deformational mechanisms present during impact events. Our bulk density estimates are derived from gravity data from NASA's Gravity Recovery and Interior Laboratory (GRAIL) mission and topography data from the Lunar Orbiter Laser Altimeter (LOLA) using a new technique similar to the Nettleton method in terrestrial gravimetry. By making assumptions about crustal compositions across the Moon, we can furthermore estimate porosity in and around the basins. Low porosity is observed in the interior of 76% of the basins studied, and this trend becomes more apparent when basins in the Feldspathic Highlands Terrane are observed alone. Overall, impact events appear to decrease porosity inside basins and increase porosity outside basins, suggesting the prevalence of impact melting within the basins and fracturing and ejecta deposition in the exteriors. Our results are seemingly inconsistent with previous studies that measured high porosity values inside basins from residual Bouguer anomalies. However, these disparate results may be reconciled if impact melting processes are confined to the uppermost kilometers of the lunar crust and are underlain by heavy impact-induced fracturing.

Understanding Asteroid 16 Psyche’s composition through 3D impact crater modeling

Asteroid 16 Psyche is the largest M-type (metallic) Main Belt Asteroid (MBA). Radar albedo data indicate Psyche’s surface is rich in metallic content, but estimates for Psyche’s bulk structure vary widely, with bulk density estimates ranging from 1.4 ± 0.3 g/cm3 to 4.5 ± 1.4 g/cm3 and porosity estimates ranging from 30% to 70%. A forthcoming NASA discovery mission will visit Psyche. Narrowing the range of estimated compositions of Psyche prior to mission launch can help to determine the necessary tools for asteroid analysis. Psyche has two large impact structures in its Southern hemisphere, with estimated diameters from 50 km to 70 km and estimated depths up to 6.4 km. In this work, we present results from 2D and 3D hydrocode simulations of the largest of these impact structures. Through quantitative comparison of the simulated crater dimensions with measured values, our models suggest that Psyche is largely composed of porous, metallic material. In addition, to match the crater dimensions, our simulations indicate that the angle of impact was significantly altered from vertical by 45–60 degrees.

The sensitivity of cosmogenic radionuclide analysis to soil bulk density: Implications for soil formation rates

AbstractImproving our knowledge of soil formation is critical so that we can better understand the first‐order controls on soil thickness and more effectively inform land‐management decisions. Cosmogenic radionuclide analysis has allowed soil scientists to more accurately constrain the rates at which soils form from bedrock. In such analysis, the concentration of an isotope, such as Beryllium‐10, is measured from a sample of bedrock. Because this concentration is partly governed by the lowering of the bedrock‐soil interface, a cosmogenic depth‐profile model can be fitted to infer the bedrock and surface lowering rates compatible with the measured concentrations. Given that the bedrock‐soil interface is shielded by soil, the cosmic rays responsible for the in‐situ production of the radionuclide are attenuated, with attenuation rates dependent on the density profile of this soil. Many studies have assumed that soil bulk density is either equal to that of the bedrock or constant with depth. The failure to acknowledge the variations in soil bulk density means that cosmogenically derived soil formation rates previously published may be under‐ or overestimates. Here, we deploy a new model called “CoSOILcal” to a global compilation of cosmogenic analyses of soil formation and, by making use of estimated bulk density profiles, recalculate rates of soil formation to assess the sensitivity to this important parameter. We found that where a soil mantle >0.25 m overlies the soil‐bedrock interface, accounting for the soil bulk density profile brings about a significantly slower rate of soil formation than that previously published. Moreover, the impact of using bulk density profiles on cosmogenically derived soil formation rates increases as soil thickens. These findings call into question the accuracy of our existing soil formation knowledge and we suggest that future cosmogenic radionuclide analysis must consider the bulk density profile of the overlying soil.Highlights The effect of heterogeneities in soil bulk density on cosmogenically derived soil formation rates is unknown. Soil formation rates are recalculated using a new model to analyse the effect of density variations. Accounting for density in soils >0.25 m thickness brings about significantly slower soil formation rates. Measuring soil bulk density is essential when cosmogenically deriving soil formation rates.

Landuse and soil property effects on infiltration into Alfisols in the Lower Mississippi River Valley, USA

Unsustainable, agriculturally related practices of water usage have caused increasing groundwater depletion in the Alluvial Aquifer in the Lower Mississippi River Valley (LMRV) of eastern Arkansas. To avoid further depletion, one method of decreasing drawdown of the Alluvial Aquifer would be to adopt agricultural management practices that increase surface infiltration, which would increase the amount of water that could potentially recharge the aquifer. The objective of this study was to evaluate surface water infiltration and infiltration-related properties in fine-textured, loessial and alluvial Alfisols under different land managements in the LMRV Delta region of eastern Arkansas. Falling-head, double-ring infiltration measurements (n = 105) were conducted as a completely random experimental design between November 2015 and July 2016 in six current major landuses: native prairie, deciduous forest, coniferous forest, Conservation Reserve Program (CRP) grassland, conventional-tillage (CT) agriculture, and no-tillage (NT) agriculture. The overall infiltration rate for the 20-min measurement interval for the deciduous forest (1.17 mm min−1) was 6.7 times greater (P < .05) than that for the other five landuses, which did not differ and averaged 0.17 mm min−1. Overall infiltration rate was positively correlated (P < .05) with soil organic matter (SOM), total C, and total N contents and C:SOM ratio, while negatively correlated (P < .05) with estimated bulk density and extractable soil Na and Mg contents in the top 10 cm. Restoration of highly erodible agricultural lands in the LMRV should consider reforestation activities with deciduous species to contribute to improved infiltration capacity that can contribute to potentially greater groundwater recharge.

The Performance of the DES Sensor for Estimating Soil Bulk Density under the Effect of Different Agronomic Practices

The estimation of soil wet bulk density (ρn) and dry bulk density (ρb) using the novel digital electromechanical system (DES) has provided information about important parameters for the assessment of soil quality and health with a direct application for agronomists. The evaluation of the DES performance is particularly appropriate for different tillage methods, mulching systems, and fertilizers used to increase soil fertility and productivity, but currently, there is a lack of information, particularly in the arid areas in underdeveloped countries. Therefore, the main aim of this study was the application of a novel digital electromechanical system (DES) to evaluate bulk density, wet (ρn) and dry (ρb), under different soil treatments according to the variations in thermal efficiencies (ηth), microwave penetration depths (MDP), and specific energy consumption (Qcon) in an experimental area close to Baghdad (Iraq). The experimental design consisted of 72 plots, each 4 m2. The agronomic practices included two different tillage systems (disc plough followed by a spring disk and mouldboard plough followed by a spring disk) and twelve treatments involving mulching plastic sheeting combined with fertilizers, to determine their effect on the measured soil ρn and ρb and the DES performance in different soils. The results indicated that soil ρn and ρb varied significantly with both the tillage systems and the mulching systems. As expected, the soil ρn and ρb, MDP, and Qcon increased with an increase in the soil depth. Moreover, the tillage, soil mulching, and soil depth value significantly affected ηth and Qcon. A strong relationship was identified between the soil tillage and MDP for different soil treatments, leading to the changes in soil ρb and the soil dielectric constant (ε’).

Soil Organic Carbon Across Mexico and the Conterminous United States (1991–2010)

AbstractSoil organic carbon (SOC) information is fundamental for improving global carbon cycle modeling efforts, but discrepancies exist from country‐to‐global scales. We predicted the spatial distribution of SOC stocks (topsoil; 0–30 cm) and quantified modeling uncertainty across Mexico and the conterminous United States (CONUS). We used a multisource SOC dataset (&gt;10 000 pedons, between 1991 and 2010) coupled with a simulated annealing regression framework that accounts for variable selection. Our model explained ~50% of SOC spatial variability (across 250‐m grids). We analyzed model variance, and the residual variance of six conventional pedotransfer functions for estimating bulk density to calculate SOC stocks. Two independent datasets confirmed that the SOC stock for both countries represents between 46 and 47 Pg with a total modeling variance of ±12 Pg. We report a residual variance of 10.4 ±5.1 Pg of SOC stocks calculated from six pedotransfer functions for soil bulk density. When reducing training data to define decades with relatively higher density of observations (1991–2000 and 2001–2010, respectively), model variance for predicted SOC stocks ranged between 41 and 55 Pg. We found nearly 42% of SOC across Mexico in forests and 24% in croplands, whereas 31% was found in forests and 28% in croplands across CONUS. Grasslands and shrublands stored 29 and 35% of SOC across Mexico and CONUS, respectively. We predicted SOC stocks &gt;30% below recent global estimates that do not account for uncertainty and are based on legacy data. Our results provide insights for interpretation of estimates based on SOC legacy data and benchmarks for improving regional‐to‐global monitoring efforts.

Estimating bulk density in leguminous grains with different traits using color parameters from digital images combined with artificial neural networks

Dry grains from leguminous species, such as soybeans (Glycine max L.), common beans (Phaseolus vulgaris L.), chickpeas (Cicer arietinum L.) and corn (Zea mays L.), are regularly consumed for human nutrition. This paper showed the possibility of estimating bulk density as quality parameter of 4 different dry grains (soybeans, common beans, chickpeas and corn) in a same model using the average values of color descriptors from digital images combined with an artificial neural network, with low computational costs. These food products are good sources of carbohydrates, protein and dietary fiber, and they possess significant amounts of vitamins and minerals and a high energetic value. Estimation of the physicochemical properties of grains is challenging due to variations in shape, texture, and size and because the grain colors appear similar to the naked eye. In this work, an analytical method was developed based on digital images converted into ten color scale descriptors combined with a neural model to provide an accurate parameter for grain quality control with a low computational cost. The bulk densities of four type of grains, i.e., soybeans, beans, chickpeas and corn, were predicted using numerical data represented by the average values of color histograms of a ten color scale (red - R, green - G, blue - B, hue - H, saturation - S, value - V, relative RGB and luminosity - L) from digital images combined with artificial neural networks (ANNs). The reference bulk densities were empirically measured. A very good correlation between the reference values and values predicted by the ANN was achieved, and with a single ANN developed for the four grains, a correlation coefficient of 0.98 was observed for the test set. Moreover, the relative errors were between 0.01 and 5.6% for the test set.

The influence of prescribed burning and wildfire on lidar-estimated forest structure of the New Jersey Pinelands National Reserve

Prescribed burning is a common land management tool used to reduce fuels, emulate the effects of wildfire and increase heterogeneity in fire-prone ecosystems. However, the forest structure created by prescribed burning is likely to be dissimilar to that produced by wildfire. We used three-dimensional estimates of canopy bulk density (CBD) from lidar data to explore the relationship between fire type, number of burns and fuel structure/forest structure in the New Jersey Pinelands National Preserve, USA. We found that in areas of previous prescribed fires, as the number of fires increased, the understorey (1–2m) exhibited a slight decrease in CBD, while the upper canopy (15–23m) had higher values of CBD for ≥4 fires, though these differences were not statistically significant. However, an increasing number of wildfires was associated with a statistically significant increase in CBD in the mid-storey (3–7m) and a decrease in CBD in the canopy (≥8m). These results have important implications for forest resource managers because they indicate that prescribed burning reduces ladder fuels that lead to torching and crown fires, but it does not replicate the structure created by wildfire.

Systematic tests for study of tire tractive performance on soft soil: Part II – Parameterization of terramechanics model and tire model

The parameterization of terramechanics model for the tire off-road traction dynamics modeling relies exclusively on the plate-sinkage test and shear test (not using a tire as the shear tool), according to the existing literature. However, the plate-soil interaction cannot be considered the same as the tire-soil interaction (Plackett, 1985). Part II of this paper investigates whether the tire-soil test can be used to parameterize terramechanics model. The parameterization of the modified Bekker’s model, the Magic Formula tire model, and a bulk density estimation model, using the data from the series of tire and soil tests presented in Part I of this paper (He et al., 2019), is discussed in detail herein. The parameterization approach presented herein can be used to develop tire off-road dynamics models.