scholarly journals Spatial distribution and correlation of soil properties in a field: a case study

2011 ◽  
Vol 48 (No. 10) ◽  
pp. 425-432
Author(s):  
L. Borůvka ◽  
H. Donátová ◽  
K. Němeček
Keyword(s):  
Spatial Distribution ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Clay Content ◽  
Total Porosity ◽  
Organic Carbon Content ◽  
Agricultural Field ◽  
Spatial Correlations ◽  
Fine Silt ◽  
Silt Content

Analysis of spatial distribution and correlation of soil properties represents an important outset for precision agriculture. This paper presents an analysis of spatial distribution and mutual correlations, both classical and spatial, of soil properties in an agricultural field in Klučov. Clay and fine silt content, pH, organic carbon content (C<sub>org</sub>), moisture (Q), total porosity (Pt), capillary porosity (P<sub>c</sub>), and coefficients of aggregate vulnerability to fast wetting (K<sub>v1</sub>), to slow wetting and drying (K<sub>v2</sub>), and to mechanical impacts (K<sub>v3</sub>) were determined. Semivariogram ranges from 206 m (clay content) to 1120 m (K<sub>v3</sub>) were detected. Many relationships between soil properties were spatially based. Fine silt content and Corg&nbsp;proved to be the most important soil properties controlling all the three aggregate vulnerability coefficients, which was not clear for K<sub>v2</sub>&nbsp;from classical correlation only. Determined spatial correlations and similarities in spatial distribution may serve as groundwork in delineation of different zones for site-specific management.

scholarly journals Eucalyptus reforestation induces soil water repellency

Soil Research ◽  
2015 ◽  
Vol 53 (2) ◽  
pp. 168 ◽  
Author(s):  
L. L. Walden ◽  
R. J. Harper ◽  
D. S. Mendham ◽  
D. J. Henry ◽  
J. B. Fontaine
Keyword(s):  
Soil Properties ◽  
Soil Water ◽  
Water Movement ◽  
Computer Modelling ◽  
Clay Content ◽  
Water Repellency ◽  
Water Repellent ◽  
Organic Carbon Content ◽  
Soil Water Repellency ◽  
Hydrophobic Compounds

There is an increasing interest in eucalypt reforestation for a range of purposes in Australia, including pulp-wood production, carbon mitigation and catchment water management. The impacts of this reforestation on soil water repellency have not been examined despite eucalypts often being associated with water repellency and water repellency having impacts on water movement across and within soils. To investigate the role of eucalypt reforestation on water repellency, and interactions with soil properties, we examined 31 sites across the south-west of Western Australia with paired plots differing only in present land use (pasture v. plantation). The incidence and severity of water repellency increased in the 5–8 years following reforestation with Eucalyptus globulus. Despite this difference in water repellency, there were no differences in soil characteristics, including soil organic carbon content or composition, between pasture and plantation soils, suggesting induction by small amounts of hydrophobic compounds from the trees. The incidence of soil water repellency was generally greater on sandy-surfaced (<10% clay content) soils; however, for these soils 72% of the pasture sites and 31% of the plantation were not water repellent, and this was independent of measured soil properties. Computer modelling revealed marked differences in the layering and packing of waxes on kaolinite and quartz surfaces, indicating the importance of interfacial interactions in the development of soil water repellency. The implications of increased water repellency for the management of eucalyptus plantations are considered.

scholarly journals Impact of cement industries dust on soil properties in Bhatapara,Chhattisgarh

2021 ◽  
Vol 23 (2) ◽  
pp. 209-214
Author(s):  
Keyword(s):  
Soil Properties ◽  
Wind Direction ◽  
Clay Content ◽  
Carbonate Content ◽  
Organic Carbon Content ◽  
Cement Dust ◽  
West Wind ◽  
South West ◽  
Designed Experiment ◽  
East West

The present investigation was carried around cement industries at Bhatapara during 2017-18, to study the different soil properties as affected by the dust of cement Industries in Bhatapara Chhattisgarh. Two hundred fifty six composite soil samples were taken from around the cement industries i.e., from eight radiant wind directions viz., North, South, East, West, Northeast, Northwest, Southeast and Southwest in clockwise manner at the distances 0.5, 1, 2, and 3 km from the surface (0-15 cm) and sub-surface (15-30 cm) soil depths. Statistical analysis was done in 3-factors factorial designed experiment using CRD and the effect of cement dust on soil properties were also correlated with wind directions (X1), distances(X2) and soil depths (X3). Thephysico-properties of soils showed a significant increase in sand and silt in south-west and west wind directions. Significant increase in clay content was also observed in west and south-wind direction. An increase in pH, electrical conductivity and calcium carbonate content in soil (0-15 cm) soil up to 0.5 km distance in the southwest wind direction was also observed. Organic carbon content in soils also increased significantly with increase in distance at surface soilin the southwest wind direction.

Soil properties: their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks

2021 ◽  
Author(s):  
Liang Zhong ◽  
Xi Guo ◽  
Zhe Xu ◽  
Meng Ding
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Soil Properties ◽  
Convolutional Neural Networks ◽  
Soil Texture ◽  
Precision Agriculture ◽  
Organic Carbon Content ◽  
Spectral Library ◽  
Deep Convolutional Neural Networks ◽  
Large Soil

&lt;p&gt;Soil, as a non-renewable resource, should be monitored continuously to prevent its degradation and promote sustainable agricultural management. Soil spectroscopy in the visible-near infrared range is a fast and cost-effective analytical technique to predict soil properties. The use of large soil spectral libraries can reduce the work needed to reliably estimate soil properties and obtain robust models capable of widespread applicability. Deep learning is apt for big data analysis, and this approach could herald a profound change in the way we model soil spectral data generally. Accordingly, we explored the modeling potential of deep convolutional neural networks (DCNNs) for soil properties based on a large soil spectral library. The European topsoil dataset provided by the Land Use/Cover Area frame Survey (LUCAS) was used without any pre-processing of spectra or covariates added. Two 16-layer DCNN models (ResNet-16 and VGGNet-16) were successfully used to make regression predictions of seven soil properties and classification predictions of soil texture into four groups and 12 levels. Our results showed that the ResNet-16 and VGGNet-16 models produced highly accurate predictions for most soil properties, being superior to either a shallow convolutional neural network and&amp;#160;traditional machine learning approaches. Soil organic carbon content, nitrogen content, cation exchange capacity, pH, and calcium carbonate content were well predicted, having a ratio of performance to deviation (RPD)&amp;#160;&gt; 2.0. Soil potassium content was adequately predicted (1.4 &amp;#8804; RPD&amp;#160;&amp;#8804; 2.0) and phosphorous content was poorly predicted (RPD&amp;#160;&lt; 1.4). The overall classification accuracy of soil texture was 0.749&amp;#160;(four groups) and 0.566&amp;#160;(12 levels). The position of feature wavelengths differed among the soil properties, for which multiple characteristic peaks were common. This study fully demonstrates the modeling potential of deep learning with soil hyperspectral data, which could bring us closer to achieving precision agriculture.&lt;/p&gt;

scholarly journals Spatial variability of saturated hydraulic conductivity and its links with other soil properties at the regional scale

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Boguslaw Usowicz ◽  
Jerzy Lipiec
Keyword(s):  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Organic Carbon ◽  
Spatial Variability ◽  
Soil Properties ◽  
Regional Scale ◽  
Total Porosity ◽  
The Other ◽  
Study Region ◽  
The Mean

AbstractSaturated hydraulic conductivity (K) is a key property for evaluating soil water movement and quality. Most studies on spatial variability of K have been performed soil at a field or smaller scale. Therefore, the aim of this work was to assess (quantify) the spatial distribution of K at the larger regional scale in south-eastern Poland and its relationship with other soil properties, including intrinsic sand, silt, and clay contents, relatively stable organic carbon, cation exchange capacity (CEC) and temporally variable water content (WC), total porosity (FI), and dry bulk density (BD) in the surface layer (0–20 cm). The spatial relationships were assessed using a semivariogram and a cross-semivariogram. The studied region (140 km2) with predominantly permeable sandy soils with low fertility and productivity is located in the south-eastern part of Poland (Podlasie region). The mean sand and organic carbon contents are 74 and 0.86 and their ranges (in %) are 45–95 and 0.002–3.75, respectively. The number of individual samples varied from 216 to 228 (for K, WC, BD, FI) to 691 for the other soil properties. The best fitting models were adjusted to the empirical semivariogram (exponential) and the cross-semivariogram (exponential, Gaussian, or linear) used to draw maps with kriging. The results showed that, among the soil properties studied, K was most variable (coefficient of variation 77.3%) and significantly (p < 0.05) positively correlated with total porosity (r = 0.300) and negatively correlated with soil bulk density (r = – 0.283). The normal or close to the normal distribution was obtained by natural logarithmic and root square transformations. The mean K was 2.597 m day−1 and ranged from 0.01 up to 11.54 m day−1. The spatial autocorrelation (range) of K in the single (direct) semivariograms was 0.081° (8.1 km), while it favourably increased up to 0.149°–0.81° (14.9–81 km) in the cross-semivariograms using the OC contents, textural fractions, and CEC as auxiliary variables. The generated spatial maps allowed outlining two sub-areas with predominantly high K above 3.0 m day−1 in the northern sandier (sand content > 74%) and less silty (silt content < 22%) part and, with lower K in the southern part of the study region. Generally, the spatial distribution of the K values in the study region depended on the share of individual intrinsic textural fractions. On the other hand, the ranges of the spatial relationship between K and the intrinsic and relatively stable soil properties were much larger (from ~ 15 to 81 km) than between K and the temporally variable soil properties (0.3–0.9 km). This knowledge is supportive for making decisions related to land management aimed at alteration of hydraulic conductivity to improve soil water resources and crop productivity and reduce chemical leaching.

scholarly journals The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

2021 ◽  
Vol 10 (4) ◽  
pp. 243
Author(s):  
Azamat Suleymanov ◽  
Evgeny Abakumov ◽  
Ruslan Suleymanov ◽  
Ilyusya Gabbasova ◽  
Mikhail Komissarov
Keyword(s):  
Spatial Distribution ◽  
Soil Properties ◽  
Soil Nutrients ◽  
Precision Agriculture ◽  
Large Scale ◽  
Soil Nutrient ◽  
Steppe Zone ◽  
Shuttle Radar Topography Mission ◽  
Coefficient Of Determination ◽  
Support Vector

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

scholarly journals Wavelet geographically weighted regression for spectroscopic modelling of soil properties

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yongze Song ◽  
Zefang Shen ◽  
Peng Wu ◽  
R. A. Viscarra Rossel
Keyword(s):  
Organic Carbon ◽  
Soil Properties ◽  
Geographically Weighted Regression ◽  
Near Infrared ◽  
Linear Models ◽  
Spatial Information ◽  
Clay Content ◽  
Weighted Regression ◽  
Organic Carbon Content ◽  
Least Squares Regression

AbstractSoil properties, such as organic carbon, pH and clay content, are critical indicators of ecosystem function. Visible–near infrared (vis–NIR) reflectance spectroscopy has been widely used to cost-efficiently estimate such soil properties. Multivariate modelling, such as partial least squares regression (PLSR), and machine learning are the most common methods for modelling soil properties with spectra. Often, such models do not account for the multiresolution information presented in the vis–NIR signal, or the spatial variation in the data. To address these potential shortcomings, we used wavelets to decompose the vis–NIR spectra of 226 soils from agricultural and forested regions in south-western Western Australia and developed a wavelet geographically weighted regression (WGWR) for estimating soil organic carbon content, clay content and pH. To evaluate the WGWR models, we compared them to linear models derived with multiresolution data from a wavelet decomposition (WLR) and PLSR without multiresolution information. Overall, validation of the WGWR models produced more accurate estimates of the soil properties than WLR and PLSR. Around 3.5–49.1% of the improvement in the estimates was due to the multiresolution analysis and 1.0–5.2% due to the integration of spatial information in the modelling. The WGWR improves the modelling of soil properties with spectra.

scholarly journals Influence of Organic Mulches on Soil Physico-Chemical Properties and Maize (Zea Mays L.) Crop Performance.

2018 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
Edward A. Ampofo
Keyword(s):  
Soil Properties ◽  
Crop Production ◽  
Management Practice ◽  
Germination Rate ◽  
Block Design ◽  
Chemical Properties ◽  
Total Porosity ◽  
Organic Carbon Content ◽  
Maize Production ◽  
Randomized Block Design

Application of organic mulching is soil management practice that seeks to improve soil moisture conservation, increase soil fertility and improve crop production. The study was carried out to quantify the effect of different organic mulches on some soil properties at three crop stages and maize production under coastal savanna condition. Four treatments of mulch; maize stover (MS), dry grass straw (GS), palm frond (PF) mulches at 3 Mg ha-1 each and no-mulch (NM) (control) with three replications were laid out in a complete randomized block design. After two consecutive cropping seasons, different organic mulches had no significant effect on the examined soil properties at the seed emergence stage. However, at both tasseling and harvest stages, the differences of bulk density, total porosity, organic carbon content and macro-nutrients (NPK) among the treatments were significant and were in the order of GS > MS > PF > NM. The germination rate was in the order of NM (91.0%) > MS (89.9%) > GS (87.9%) > PF (86.8%). The effect of mulches on both the plant height and the LAI was in the order of GS > MS > PF > NM. The increase in grain yield over the control were GS= 23 %, MS= 16 % and PF =15 % while that of the WUE relative to the control were 155 %, 122 % and 58 % for GS, MS and PF respectively. Dry grass mulch could be used to improve soil properties and achieve higher maize production in the study area.

scholarly journals Understanding Fields by Remote Sensing: Soil Zoning and Property Mapping

2020 ◽  
Vol 12 (7) ◽  
pp. 1116 ◽  
Author(s):  
Onur Yuzugullu ◽  
Frank Lorenz ◽  
Peter Fröhlich ◽  
Frank Liebisch
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Soil Property ◽  
Clay Content ◽  
Agricultural Systems ◽  
Property Prediction ◽  
High Agreement ◽  
New Era ◽  
Field Management ◽  
Agronomic Yield

Precision agriculture aims to optimize field management to increase agronomic yield, reduce environmental impact, and potentially foster soil carbon sequestration. In 2015, the Copernicus mission, with Sentinel-1 and -2, opened a new era by providing freely available high spatial and temporal resolution satellite data. Since then, many studies have been conducted to understand, monitor and improve agricultural systems. This paper presents results from the SolumScire project, focusing on the prediction of the spatial distribution of soil zones and topsoil properties, such as pH, soil organic matter (SOM) and clay content in agricultural fields through random forest algorithms. For this purpose, samples from 120 fields were investigated. The zoning and soil property prediction has an accuracy greater than 90%. This is supported by a high agreement of the derived zones with farmer’s observations. The trained models revealed a prediction accuracy of 94%, 89% and 96% for pH, SOM and clay content, respectively. The obtained models for soil properties can support precision field management, the improvement of soil sampling and fertilization strategies, and eventually the management of soil properties such as SOM.

scholarly journals Spatial Variability of Saturated Hydraulic Conductivity and its Links with other Soil Properties at the Commune Scale

2021 ◽  
Author(s):  
Boguslaw Usowicz ◽  
Jerzy Lipiec
Keyword(s):  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Organic Carbon ◽  
Spatial Variability ◽  
Soil Properties ◽  
Bulk Density ◽  
Soil Water ◽  
Total Porosity ◽  
The Other ◽  
The Mean

Abstract Saturated hydraulic conductivity (SHC) is a key property for evaluating soil water movement and quality. Most studies on spatial variability of SHC have been performed soil at a field or smaller scale. Therefore, the aim of this work was to assess (quantify) the spatial distribution of SHC at the commune scale and its relationship with other soil properties, including intrinsic sand, silt, and clay contents, relatively stable organic carbon, cation exchange capacity (CEC), dynamic water content (WC), total porosity (FI), and dry bulk density (BD) in the surface layer (0–20 cm). The spatial relationships were assessed using a semivariogram and a cross-semivariogram. The studied commune (140 km2) with predominantly permeable sandy soils with low fertility and productivity is located in the south-eastern part of Poland (Podlasie region). The mean sand and organic carbon contents are 74 andobablyctknąć, czy o to chodzid mniej znacznie mniejszed? ? 0.86 and their ranges (in %) are 45-95 and 0.002-3.75, respectively. The number of individual samples varied from 216–228 (for SHC, WC, BD, FI) to 691 for the other soil properties. The best fitting models were adjusted to the empirical semivariogram (exponential) and the cross-semivariogram (exponential, Gaussian, or linear) used to draw maps with kriging. The results showed that, among the soil properties studied, SHC was most variable (coefficient of variation 77.3%) and significantly (p <0.05) positively correlated with total porosity (r = 0.300) and negatively correlated with soil bulk density (r = –0.283). The mean SHC was 2.597 m day–1 and ranged from 0.01 up to 11.54 m day–1. The spatial autocorrelation (range) of SHC in the single (direct) semivariograms was 0.081° (8.1 km), while it favourably increased up to 0.149–0.81° (14.9–81 km) in the cross-semivariograms using the OC contents, textural fractions, and CEC as auxiliary variables. The generated spatial maps allowed outlining two sub-areas with predominantly high SHC above 3.0 m day–1 in the northern sandier (sand content >74%) and less silty (silt content <22%) part and, with lower SHC in the southern part of the commune. Generally, the spatial distribution of the SHC values in the commune area depended on the share of individual intrinsic textural fractions. On the other hand, the ranges of the spatial relationship between SHC and the intrinsic and relatively stable soil properties were much larger (from ~15 to 81 km) than between SHC and the dynamic soil properties (0.3-0.9 km). This knowledge is supportive for making decisions related to land management aimed at reduction of hydraulic conductivity and chemical leaching and improvement of soil water resources and crop productivity.

Reactions of copper with soil affecting its availability to plants. I. Effect of soil type and time

Soil Research ◽  
1980 ◽  
Vol 18 (4) ◽  
pp. 447 ◽  
Author(s):  
RF Brennan ◽  
JW Gartrell ◽  
AD Robson
Keyword(s):  
Organic Carbon ◽  
Soil Properties ◽  
Carbon Content ◽  
Soil Type ◽  
Clay Content ◽  
Organic Carbon Content ◽  
The Difference ◽  
Western Australian

The effect of moist incubation on the availability of applied copper to wheat was examined in a range of Western Australian soils. Incubating soil with copper reduced its availability relative to freshly applied copper by up to 70%. The availability of copper to wheat plants decreased with increasing time of incubation up to 120 days. The extent of the decline in availability differed among soils. The difference did not appear to be specifically related to any one of the following soil properties-pH, organic carbon content, clay content, free sesquioxide content and levels of total and extractable copper.

Sign in / Sign up

Export Citation Format

Share Document