Prediction of Soil Particle Size Fraction using Geographically Weighted Regression and Random Forest

The development of spatial modeling for soil properties has progressed in recent decades. This responds to the growing demand for land spatial data and exact soil property prediction for agronomical reasons, particularly in precision farming, in order to speed up precision agricultural activities. In this regards a comparison of the GWR and RF models was carried out in order to determine which model is the best at forecasting surface soil texture and how dependable each model is at doing so. The purpose of this research is to get the best model in predicting particle soil fraction (PSF). 50 topsoil samples were collected from several locations in the Kalikonto Watershed, Indonesia, and the soil PSF (sand, silt, and clay) in the upper 10 cm varied. The LMV, slope, and elevation were calculated using DEM data and utilized as predictor variables. As a result, the weighting of the GWR model has a considerable impact on the final model, and all other factors have a major effect on the PSF determination. The RF, on the other hand, looks to be superior than the GWR variants. The RF model outperformed the other models in every PSF variable. This study reveals that topsoil quality and terrain attributes are linked, which may be assessed using field measurements and model projections. More research is needed to generate more efficient input parameters that will help with soil variability precision and accuracy of soil map products.

Black Carbon and Its Effect on Carbon Sequestration in Soil

The properties of black carbon (BC) are described very differently in the literature, even when determined by the same methodological procedure. To clarify this discrepancy, BC was investigated in the clay Cambisols of southern Bohemia, Czech Republic, in groups of soils with lower and higher deposition of its atmospheric fallout. The BC determination was performed according to a modified method of Kuhlbusch and Crutzen (1995). The amount of the free light fraction, the occluded light fraction of soil organic matter and its ratio, the amount of heavy soil fraction DF, and its soil organic matter DFOM were determined. Other soil characteristics were identified. It was found that there are two very different types of BC in soils. Historical BC from biomass fires, and new, anthropogenic, from the furnace and transport fumes. Historical BC has a significant effect on the organic matter of the heavy soil fraction, on the ratio of the free and occluded soil organic matter fraction, and the number of water-resistant soil aggregates. Anthropogenic BC does not have this effect. Because this form of BC is not significantly stabilized by the colloidal mineral fraction, it is necessary to take general data on BC’s high stability and resistance to mineralization in the soil with circumspection.

Distribution of nickel (Ni) in peatland situated alongside mineral soil derived from ultrabasic rocks

<p>Detailed studies of Ni distribution in peat that is influenced by Ni-rich soil derived from ultrabasic rocks are still limited. The objective of this study was to reveal the characteristics of Ni in peat from Morowali (Central Sulawesi Province, Indonesia) at several depths and distances from the boundary of the ultrabasic mineral soil. Peat was sampled from depths of 0–30, 30–60, and 60–90 cm at distances of 100, 200, 300, 400, 500, and 600 m from the border of the ultrabasic mineral soil in March 2018. Ni characteristics were examined through their total, exchangeable, water-soluble, and adsorbed distributions. The relationships between Ni and some peat chemical properties such as pH; cation exchange capacity; macronutrient contents of K, Ca, and Mg; and micronutrient contents of Fe, Cu and Zn were also observed. The high Ni content in peat at the study transect is caused by an accumulation of Ni transported from elevated areas of mineral soil. Most Ni in peat is bonded to the soil organic exchange complexes. Accumulation of the mineral soil fraction in the peat surface is indicated at distances of 100–400 meters from the ultrabasic mineral soil. Ni distribution in peat at the study transect is mainly governed by a combination of Fe, pH, organic material, water content, peat depth, and distance from ultrabasic mineral soil.</p>

Nanoclay and Its Importance

Clays are the one of the most important minerals and have numerous applications in nanotechnology, helps in improvise the product quality, cost effective and protect the environment from pollution. This review explained about the key characters of nanoclay particles and classification of nanoclay based on the sheets arrangements in their structural unit called layer. Nano clay major groups are kaoline-serpentine, smectite, mica, vermiculite, pyrophyllite talc and chlorite. The physicho-chemical and morphological properties of halloystite and mantmorillonite clay represents the 1:1 and 2:1 layer groups respectively. Nano clays are the group which is naturally present in the soil fraction of clay and most important nano clay material present in the soil are montmorillonite and allophone. Montmorillonite is a characteristically crystalline, phyllosilicate and hydrous silicate layer. Organo clays are the organically modified forms of the montmorillonite and formed from quaternary ammonium ions intercalation process and which have been used in inks, rheomodifiers, cosmetics, greases, as a additives in paints and also used in controlled release of drugs in delivery systems. Largest usage of nanoclaysis being practiced in polymer-clay nanocomposites. Organo clays are most importantly using in water treatment and pollution control. Allophane is formed by weathering of volcanic ash; it is non crystalline alluminium silicate derivative. Agricultural lands in Chile mostly formed by the allophane clay fraction. It is most suitable for enzyme mobilization. It also very useful in abortion of phenolic compounds, mill effluent colours and phosphates from waste water.

Using high-resolution UAV spectral images to disentangle soil, shade, and tree contributions to satellite vegetation indices in sparse dry forests

&lt;p&gt;Remote sensing (RS) techniques have great potentials for earth surface monitoring. Nevertheless, for most low to moderate resolution satellites, the problem of mixed pixels with information from the vegetation of interest and the background surfaces can cause large biases in signals and also in their interpretations. This is especially so in low-density forests and semi-arid ecosystems. Ground-level multispectral instruments reduce these effects by measuring at close range to the canopy. However, little work has been published on partitioning the contributions from vegetation and the background elements for both approaches.&lt;/p&gt;&lt;p&gt;This work was motivated by the observed mismatch between data for the same ecosystem from Landsat 8 satellite and Skye radiometer installed on a flux tower in a low-density semi-arid pine forest from 2013-2019. Data from both sources showed similar seasonal patterns in NDVI, but large differences in the reflectance bands. This was most prominent in the NIR reflectance, which showed an opposite seasonal cycle in the two sensors. Thus, similar changes in NDVI were produced by different signals. We hypothesized that the different contributions of the surface components (canopy, shaded areas, and exposed soil) in the footprint areas of the two sensors can explain, and can help correct, these differences. &amp;#160;&lt;/p&gt;&lt;p&gt;Multispectral images with a spatial resolution of 5 cm were captured monthly using an Unmanned Aerial Vehicle (UAV) from April 2018 to November 2019. Reflectance-based algorithms were developed to identify and estimate the fraction and reflectance from the canopy, shaded areas, and open soil. This information was, in turn, applied in the equivalent nadir-viewing satellite pixel. For the tower-based Skye footprint, the same quantities were calculated from its 90&amp;#176; angle of view and the 3D canopy data.&lt;/p&gt;&lt;p&gt;The results showed a canopy fraction of 45% and 95% in the Landsat 8 and Skye footprints. The remaining soil fraction showed a similar seasonal cycle in NDVI as the canopy, but different in the NIR reflectance. The partition between exposed and shaded soil was related to the sun angle, with the exposed soil having a NIR seasonal cycle opposite to that of the vegetation (correlating with soil moisture), and shaded soil having a weak NIR signal variably diluting the overall pixel NIR signal. Differences in the red reflectance were smaller with less effects on the seasonal NDVI cycles.&lt;/p&gt;&lt;p&gt;The results demonstrated firstly, that accounting for the fractional contributions of the surface components can reconcile differences between satellite and ground-based RS. Secondly, vegetation indices such as NDVI obtained by satellite RS in low-density forests can provide misleading information, despite its apparent correlation with certain vegetation variables.&lt;/p&gt;

Estimating maximum fine-fraction organic carbon in UK grasslands

Soil organic carbon (SOC) sequestration across agroecosystems worldwide can contribute to mitigate the effects of climate change by reducing levels of atmospheric CO2. Stabilisation of organic carbon (OC) in the fine soil fraction (< 20 µm) is considered an important long-term store of SOC, and the saturation deficit (difference between measured OC and estimated maximum OC in the fine fraction) is frequently used to assess SOC sequestration potential following the linear regression equation developed by Hassink (1997). However, this approach is often taken without any assessment of the fit of the equation to the soils being studied. The statistical limitations of linear regression have previously been noted, giving rise to the proposed use of boundary line (BL) analysis and quantile regression (QR) to provide more robust estimates of maximum SOC stabilisation. The objectives of this work were to assess the suitability of the Hassink (1997) equation to estimate maximum fine-fraction OC in UK grassland soils of varying sward ages and to evaluate the linear regression, boundary line and quantile regression methods to estimate maximum fine-fraction OC. A chronosequence of 10 grasslands was sampled, in order to assess the relationship between sward age (time since the last reseeding event) and the measured and predicted maximum fine-fraction OC. Significantly different regression equations show that the Hassink (1997) equation does not accurately reflect maximum fine-fraction OC in UK grasslands when determined using the proportion of the fine soil fraction (< 20 µm, %) and measured fine-fraction OC (g C per kg soil). The QR estimate of maximum SOC stabilisation was almost double that of the linear regression and BL analysis (0.89 ± 0.074, 0.43 ± 0.017 and 0.57 ± 0.052 g C per kg soil, respectively). Sward age had an inconsistent effect on the measured variables and potential maximum fine-fraction OC. Fine-fraction OC across the grasslands made up 4.5 % to 55.9 % of total SOC, implying that there may be either high potential for additional C sequestration in the fine fraction of these soils or that protection in aggregates is predominant in these grassland soils. This work highlights the need to ensure that methods used to predict maximum fine-fraction OC reflect the soil in situ, resulting in more accurate assessments of carbon sequestration potential.

Spatio-Temporal Characteristics of Drought Events and Their Effects on Vegetation: A Case Study in Southern Tibet, China

Frequent droughts in a warming climate tend to induce the degeneration of vegetation. Quantifying the response of vegetation to variations in drought events is therefore crucial for evaluating the potential impacts of climate change on ecosystems. In this study, the standardized precipitation index (SPI) was calculated using the precipitation data sourced from the China Meteorological Forcing Dataset (CMFD), and then the drought events in southern Tibet from 1982 to 2015 were identified based on the SPI index. The results showed that the frequency, severity, and intensity of drought events in southern Tibet decreased from 1982 to 2015, and the highest frequency of drought was found between 1993 and 2000. To evaluate the impact of drought events on vegetation, the vegetation characteristic indexes were developed based on the normalized difference vegetation index (NDVI) and the drought characteristics. The assessment of two drought events showed that the alpine grasslands and alpine meadows had high vegetation vulnerability (AI). The assessment of multiple drought events showed that responses of vegetation to drought were spatially heterogeneous, and the total explain rate of environmental factors to the variations in AI accounted for 40%. Among the many environmental factors investigated, the AI were higher at middle altitudes (2000–3000 m) than low altitudes (<2000 m) and high altitudes (3000–4500 m). Meanwhile, the silt soil fraction in the upper soil layer (0–30 cm) had the greatest positive correlation with AI, suggesting that areas with a high silt soil fraction were more sensitive to drought. The relative contribution rates of environmental factors were predicted by a multivariate linear regression (MLR) model. The silt soil fraction was found to make the greatest relative contribution (23.3%) to the changes in AI.

Testing water fluxes and storage from two hydrology configurations within the ORCHIDEE land surface model across US semi-arid sites

Plant activity in semi-arid ecosystems is largely controlled by pulses of precipitation, making them particularly vulnerable to increased aridity that is expected with climate change. Simple bucket-model hydrology schemes in land surface models (LSMs) have had limited ability in accurately capturing semi-arid water stores and fluxes. Recent, more complex, LSM hydrology models have not been widely evaluated against semi-arid ecosystem in situ data. We hypothesize that the failure of older LSM versions to represent evapotranspiration, ET, in arid lands is because simple bucket models do not capture realistic fluctuations in upper-layer soil moisture. We therefore predict that including a discretized soil hydrology scheme based on a mechanistic description of moisture diffusion will result in an improvement in model ET when compared to data because the temporal variability of upper-layer soil moisture content better corresponds to that of precipitation inputs. To test this prediction, we compared ORCHIDEE LSM simulations from (1) a simple conceptual 2-layer bucket scheme with fixed hydraulic parameters and (2) an 11-layer discretized mechanistic scheme of moisture diffusion in unsaturated soil based on Richards equations, against daily and monthly soil moisture and ET observations, together with data-derived estimates of transpiration / evapotranspiration, T∕ET, ratios, from six semi-arid grass, shrub, and forest sites in the south-western USA. The 11-layer scheme also has modified calculations of surface runoff, water limitation, and resistance to bare soil evaporation, E, to be compatible with the more complex hydrology configuration. To diagnose remaining discrepancies in the 11-layer model, we tested two further configurations: (i) the addition of a term that captures bare soil evaporation resistance to dry soil; and (ii) reduced bare soil fractional vegetation cover. We found that the more mechanistic 11-layer model results in a better representation of the daily and monthly ET observations. We show that, as predicted, this is because of improved simulation of soil moisture in the upper layers of soil (top ∼ 10 cm). Some discrepancies between observed and modelled soil moisture and ET may allow us to prioritize future model development and the collection of additional data. Biases in winter and spring soil moisture at the forest sites could be explained by inaccurate soil moisture data during periods of soil freezing and/or underestimated snow forcing data. Although ET is generally well captured by the 11-layer model, modelled T∕ET ratios were generally lower than estimated values across all sites, particularly during the monsoon season. Adding a soil resistance term generally decreased simulated bare soil evaporation, E, and increased soil moisture content, thus increasing transpiration, T, and reducing the negative bias between modelled and estimated monsoon T∕ET ratios. This negative bias could also be accounted for at the low-elevation sites by decreasing the model bare soil fraction, thus increasing the amount of transpiring leaf area. However, adding the bare soil resistance term and decreasing the bare soil fraction both degraded the model fit to ET observations. Furthermore, remaining discrepancies in the timing of the transition from minimum T∕ET ratios during the hot, dry May–June period to high values at the start of the monsoon in July–August may also point towards incorrect modelling of leaf phenology and vegetation growth in response to monsoon rains. We conclude that a discretized soil hydrology scheme and associated developments improve estimates of ET by allowing the modelled upper-layer soil moisture to more closely match the pulse precipitation dynamics of these semi-arid ecosystems; however, the partitioning of T from E is not solved by this modification alone.

MODELING OF POTATO MILL SEPARATION PROCESSES IN A DRUM SEPARATOR

In order to successfully enter Ukraine into the western markets, it is necessary to ensure the competitiveness of its products, which is achieved through the complex mechanization of technological processes, reduction of labor costs, increase in yield and quality of the products obtained. The most common method of mechanized harvesting of potatoes is the erosion of the tuber layer with its subsequent destruction and the separation of tubers from heap, which contains vegetable impurities, soil tubers and stones. The most difficult is the separation from the tubers of solid soil clumps and stones. Attempts have been made to overcome this problem by placing potatoes on well-sown sandy soils with the help of special agrotechnical techniques that reduce, to some extent, the number of tubers. However, such measures are local, not sufficiently reliable and significantly complicate technology. About 25% of the area occupied by potatoes is heavily clogged with stones, the size of which is close to the size of tubers, and about 40% of the planted potatoes are placed on soils prone to significant lumps. Accordingly, the purpose of the study was to conduct a comparative analysis of technologies and machines for potato harvesting, to develop a model of the process of separation of potato pile in a drum separator. The researches were carried out by technological and structural analysis of technologies and machines for potato harvesting. During the research, the methods of comparison and mathematical modeling of technological processes were used. The information base of the research was the works of Ukrainian and foreign scientists on technologies and machines for potato harvesting. On the basis of the comparative analysis of technologies and machines for potato harvesting, the main processes affecting the agrotechnical indices of the harvesting technique have been identified, a model of the process of separation of potato pile in a drum separator has been developed, which includes the following basic processes: model of sifting of a through fraction of soil from the top layer of pile; heap mixing model; model of destruction of soil lumps on the separator; model kinematic analysis of potato pile in a drum separator; model of sifting of the passable soil fraction from the bottom layer of the pile; model of damage to tubers and other structural and kinematic parameters of the working bodies of the drum separator.

Estimating maximum mineral associated organic carbon in UK grasslands

Soil organic carbon (SOC) sequestration across agroecosystems worldwide can contribute to mitigate the effects of climate change by reducing levels of atmospheric CO2. Mineral associated organic carbon (MAOC) is considered an important long-term store of SOC and the saturation deficit (difference between measured MAOC and estimated maximum MAOC) is frequently used to assess SOC sequestration potential following the linear regression equation developed by Hassink (1997). However, this approach is often taken without any assessment of the fit of the equation to the soils being studied. The statistical limitations of linear regression have previously been noted, giving rise to the proposed use of boundary line (BL) analysis and quantile regression (QR) to provide more robust estimates of maximum SOC stabilisation. The objectives of this work were to assess the suitability of the Hassink (1997) equation to estimate maximum MAOC in UK grassland soils of varying sward ages and to evaluate the linear regression, BL and QR methods to estimate maximum MAOC. A chronosequence of 10 grasslands was sampled, in order to assess the relationship between sward age (time since last reseeding event) and current and predicted maximum MAOC. Significantly different regression equations show that the Hassink (1997) equation does not accurately reflect maximum MAOC in UK grasslands when determined using the proportion of fine soil fraction and current MAOC. The QR estimate of maximum SOC stabilisation was almost double that of linear regression and BL analysis (0.89 ± 0.074, 0.43 ± 0.017 and 0.57 ± 0.052 g C kg−1 soil, respectively). Sward age had an inconsistent effect on the measured variables and potential maximum MAOC. MAOC across the grasslands made up 4.5 to 55.9 % of total SOC, implying that there may be either high potential for additional C sequestration in the mineral fraction of these soils, or stabilisation in aggregates is predominant in these grassland soils. This work highlights the need to ensure that methods used to predict maximum MAOC reflect the soil in situ, resulting in more accurate assessments of carbon sequestration potential.