Modelling of soil texture and its verification with related soil properties

Soil Research ◽  
2018 ◽  
Vol 56 (4) ◽  
pp. 421 ◽  
Author(s):  
M. Shahadat Hossain ◽  
G. K. M. Mustafizur Rahman ◽  
M. Saiful Alam ◽  
M. Mizanur Rahman ◽  
A. R. M. Solaiman ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Soil Texture ◽  
Cation Exchange Capacity ◽  
Soil Property ◽  
Exchange Capacity ◽  
Strong Correlations ◽  
Soil Maps ◽  
Hypothetical Data ◽  
Digital Soil Maps

Soil texture is an independent and innate soil property and other dynamic soil properties such as electrical conductivity (EC), organic carbon (OC) content and cation exchange capacity (CEC) are mostly dependent on it. An attempt was made to develop a model for numerically simulating soil texture and also to construct relationships of the developed model with other soil properties. Hypothetical data of particle size distribution and our data were used to justify and validate the newly defined indices. Scatter diagrams showed good association between the indices and hypothetical data of soil separates. Moreover, similar trends were observed between the line charts of USDA soil textural class codes and the indices. Strong correlations (r = 0.78–0.96) were found between the indices and soil separates (sand, silt and clay) for our data. However, the indices demonstrated moderate correlations (r = –0.34 to –0.55) with EC and OC of the soils. Strong nonlinear relationships were found between CEC and the three indices (R2 = 0.699, R2 = 0.732 and R2 = 0.672 (all P < 0.001). Furthermore, the variability of EC, OC and CEC within a single USDA textural class and customised textural index groups were described using the developed model. The developed indices showed excellent fitness for simulation of soil texture and demonstrated an extended applicability in terms of their relationships with soil properties related to soil texture, which will help in constructing digital soil maps.

scholarly journals Mobile Proximal Sensing with Visible and Near Infrared Spectroscopy for Digital Soil Mapping

Soil Systems ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 40
Author(s):  
Masakazu Kodaira ◽  
Sakae Shibusawa
Keyword(s):  
Soil Properties ◽  
Regression Models ◽  
Near Infrared ◽  
Soil Property ◽  
Coefficient Of Determination ◽  
Clay Loam ◽  
Soil Maps ◽  
Local Regression ◽  
Predicted Values ◽  
Digital Soil Maps

The objective of this study was to estimate multiple soil property local regression models, confirm the accuracy of the predicted values using visible near-infrared subsurface diffuse reflectance spectra collected by a mobile proximal soil sensor, and show that digital soil maps predicted by multiple soil property local regression models are able to visualize empirical knowledge of the grower. The parent materials in the experimental fields were light clay, clay loam, and sandy clay loam. The study was conducted in Saitama Prefecture, Japan. To develop local regression models for the 30 chemical and 4 physical properties, a total of 231 samples were collected; to evaluate accuracy of prediction, 65 samples were collected. The local regression models were developed using 2nd derivative pretreatment by the Savitzky–Golay algorithm and partial least squares regression. The local regression models were evaluated using the coefficient of determination (R2), residual prediction deviation (RPD), range error ratio (RER), and the ratio of prediction error to interquartile range (RPIQ). The R2 accuracy of the 34 local regression models was 0.81 or higher. In the predicted values for 65 unknown samples, the local regression models could ‘distinguish between high and low’ for 3 of the 34 soil properties, but were ‘not useful’ as absolute quantitative values for the other 31 soil properties. However, it was confirmed that the predicted values followed the transition in measured values, and thus that the developed 34 regression models could be used for generating digital soil maps based on relative quantitative values. The grower changed the ridge direction in the field from east–west to north–south just looking at the digital soil maps.

scholarly journals Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3936 ◽  
Author(s):  
Tibet Khongnawang ◽  
Ehsan Zare ◽  
Dongxue Zhao ◽  
Pranee Srihabun ◽  
John Triantafilis
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Cation Exchange ◽  
Cation Exchange Capacity ◽  
Three Dimensional ◽  
Exchange Capacity ◽  
Inversion Algorithm ◽  
Three Dimensional Mapping ◽  
Em Induction ◽  
Leave One Out

Most cultivated upland areas of northeast Thailand are characterized by sandy and infertile soils, which are difficult to improve agriculturally. Information about the clay (%) and cation exchange capacity (CEC—cmol(+)/kg) are required. Because it is expensive to analyse these soil properties, electromagnetic (EM) induction instruments are increasingly being used. This is because the measured apparent soil electrical conductivity (ECa—mS/m), can often be correlated directly with measured topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m) clay and CEC. In this study, we explore the potential to use this approach and considering a linear regression (LR) between EM38 acquired ECa in horizontal (ECah) and vertical (ECav) modes of operation and the soil properties at each of these depths. We compare this approach with a universal LR relationship developed between calculated true electrical conductivity (σ—mS/m) and laboratory measured clay and CEC at various depths. We estimate σ by inverting ECah and ECav data, using a quasi-3D inversion algorithm (EM4Soil). The best LR between ECa and soil properties was between ECah and subsoil clay (R2 = 0.43) and subsoil CEC (R2 = 0.56). We concluded these LR were unsatisfactory to predict clay or CEC at any of the three depths, however. In comparison, we found that a universal LR could be established between σ with clay (R2 = 0.65) and CEC (R2 = 0.68). The LR model validation was tested using a leave-one-out-cross-validation. The results indicated that the universal LR between σ and clay at any depth was precise (RMSE = 2.17), unbiased (ME = 0.27) with good concordance (Lin’s = 0.78). Similarly, satisfactory results were obtained by the LR between σ and CEC (Lin’s = 0.80). We conclude that in a field where a direct LR relationship between clay or CEC and ECa cannot be established, can still potentially be mapped by developing a LR between estimates of σ with clay or CEC if they all vary with depth.

scholarly journals Mapping Soil Properties to Advance the State of Spatial Soil Information for Greater Food Security on US Tribal Lands

2021 ◽  
Vol 1 ◽  
Author(s):  
Bryan Fuentes ◽  
Amanda J. Ashworth ◽  
Mercy Ngunjiri ◽  
Phillip Owens
Keyword(s):  
Food Security ◽  
High Resolution ◽  
Soil Properties ◽  
Cation Exchange ◽  
Cation Exchange Capacity ◽  
Soil Property ◽  
Exchange Capacity ◽  
Soil Survey ◽  
Soil Information ◽  
Tribal Lands

Knowledge, data, and understanding of soils is key for advancing agriculture and society. There is currently a critical need for sustainable soil management tools for enhanced food security on Native American Tribal Lands. Tribal Reservations have basic soil information and limited access to conservation programs provided to other U.S producers. The objective of this study was to create first ever high-resolution digital soil property maps of Quapaw Tribal Lands with limited data for sustainable soil resource management. We used a digital soil mapping (DSM) approach based on fuzzy logic to model the spatial distribution of 24 soil properties at 0–15 and 15–30 cm depths. A digital elevation model with 3 m resolution was used to derive terrain variables and a total of 28 samples were collected at 0–30 cm over the 22,880-ha reservation. Additionally, soil property maps were derived from Gridded Soil Survey Geographic Database (gSSURGO) for comparison. When comparing properties modeled by DSM to those derived from gSSURGO, DSM resulted in lower root mean squared error (RMSE) for percent clay and sand at 0–15 cm, and cation exchange capacity, percent clay, and pH at 15–30 cm. Conversely, gSSURGO-derived maps resulted in lower RMSE for cation exchange capacity, pH, and percent silt at the 0–15 cm depth, and percent sand and silt at the 15–30 cm depth. Although, some of the soil properties derived from gSSURGO had lower RMSE, spatial soil property patterns modeled by DSM were in better agreement with the topographic complexity and expected soil-landscape relationships. The proposed DSM approach developed property maps at high-resolution, which sets the baseline for production of new spatial soil information for Quapaw Tribal soils. It is expected that these maps and future versions will be useful for soil, crop, and land-use decisions at the farm and Tribal-level for increased agricultural productivity and economic development. Overall, this study provides a framework for developing DSM on Tribal Lands for improving the accuracy and detail of soil property maps (relative to off the shelf products such as SSURGO) that better represents soil-forming environments and the spatial variability of soil properties on Tribal Lands.

scholarly journals Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

Soil Systems ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 52
Author(s):  
Gustavo M. Vasques ◽  
Hugo M. Rodrigues ◽  
Maurício R. Coelho ◽  
Jesus F. M. Baca ◽  
Ricardo O. Dart ◽  
...  
Keyword(s):  
Soil Properties ◽  
Ordinary Kriging ◽  
Soil Property ◽  
Clay Content ◽  
Exchange Capacity ◽  
Organic C ◽  
Covariate Data ◽  
Independent Validation ◽  
Soil Sensors ◽  
Dense Grid

Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps.

Co-composting of distillery and winery wastes with sewage sludge

2007 ◽  
Vol 56 (2) ◽  
pp. 187-192 ◽  
Author(s):  
M.A. Bustamante ◽  
C. Paredes ◽  
R. Moral ◽  
J. Moreno-Caselles ◽  
M.D. Pérez-Murcia ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Sewage Sludge ◽  
Cation Exchange ◽  
Cation Exchange Capacity ◽  
Exchange Capacity ◽  
Total N ◽  
Organic C ◽  
Mediterranean Countries ◽  
Feasible Option ◽  
Total Organic C

The winery and distillery wastes (grape stalk and marc (GS and GM, respectively), wine lees (WL) and exhausted grape marc (EGM)) are produced in great amounts in the Mediterranean countries, where their treatment and disposal are becoming an important environmental problem, mainly due to their seasonal character and some characteristics that make their management difficult and which are not optimised yet. Composting is a treatment widely used for organic wastes, which could be a feasible option to treat and recycle the winery and distillery wastes. In this experiment, two different piles (pile 1 and 2) were prepared with mixtures of GS, GM, EG and sewage sludge (SS) and composted in a pilot plant by the Rutgers static pile composting system. Initially, GS, GM and EGM were mixed, the pile 1 being watered with fresh collected vinasse (V). After 17 days, SS was added to both piles as a nitrogen and microorganisms source. During composting, the evolution of temperature, pH, electrical conductivity, total organic C, total N, humic acid-like C and fulvic acid-like C contents, C/N ratio, cation exchange capacity and germination index of the mixtures were studied. The addition of V in pile 1 produced higher values of temperature, a greater degradation of the total organic C, higher electrical conductivity values and similar pH values and total N contents than in pile 2. The addition of this effluent also increased the cation exchange capacity and produced a longer persistence of phytotoxicity. However, both piles showed a stabilised organic matter and a reduction of the phytotoxicity at the end of the composting process.

Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

2020 ◽  
Author(s):  
Nada Mzid ◽  
Stefano Pignatti ◽  
Irina Veretelnikova ◽  
Raffaele Casa
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Central Italy ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Field Scale ◽  
Umbria Region ◽  
Digital Soil Maps

&lt;p&gt;The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).&lt;/p&gt;

scholarly journals Land Suitability Classification for Current Land Use in Akkar Plain- Tartous Governorate

2018 ◽  
Vol 27 (2) ◽  
pp. 89-105
Author(s):  
Safwan A. Mohammed Safwan A. Mohammed
Keyword(s):  
Land Use ◽  
Cation Exchange ◽  
Soil Texture ◽  
Cation Exchange Capacity ◽  
Soil Analysis ◽  
Exchange Capacity ◽  
Land Suitability ◽  
Land Evaluation ◽  
Limiting Factors ◽  
Physical Factors

Land evaluation is one of the most important tools for integrated land use management for sustainable agricultural and land use planning. The aim of this study is to evaluate the land suitability for current land use in akkar plain- Tartous Governorate. Depending on the elevation and land use, nine soil profiles representing the main physiographic units have been chosen. Soil samples were collected for conducting some chemical and physical analyses such as: soil texture (sand%, silt% and clay%), the content of organic matter OM, Cation Exchange Capacity CEC (cmol(+)/kg -1clay). The results of the soil analysis showed that the soil texture was Clay, and the pH values were between 7.13-8.5. Furthermore, The cation exchange capacity were ranging from (12-33) (cmol(+)/kg -1clay). Results of land evaluation showed that the limiting factors either fertility factors such as high pH in the villages of Beit-kamouna, Majdaloun-albaher and Dier-hbash, or physical factors such as shallowness depth of soil. The study concluded that the suitability class ranged from S2 to N2, which emphasis the importance of reconsidering the type of land use in the study area.

scholarly journals SOIL PROPERTIES AND NUTRIENT ELEMENT STATUS OF COCONUT LEAVES UNDER DEFFERENT CROPPING PATTERNS

CORD ◽  
1988 ◽  
Vol 4 (01) ◽  
pp. 34
Author(s):  
Doah Dekok Tarigans
Keyword(s):  
Organic Matter ◽  
Soil Properties ◽  
Cation Exchange ◽  
Cation Exchange Capacity ◽  
Block Design ◽  
Exchange Capacity ◽  
Cropping Pattern ◽  
Nutrient Element ◽  
Cropping Patterns ◽  
Significant Difference

This study was conducted to investigate the effects of six co­conut cropping patterns on the soil properties and nutrient element status of coconut leaves. The experiments were carried out from August 1984 to May 1985 in Silang, Cavite, Philippines. Data on‑soil properties and nutrient element starus of coconut leaves were statistically analyzed in Randomized Block Design with three replications. Six cropping patterns in coconut with four species of perennial crops as intercrops, namely: banana, papaya, coffee and pineapple were used in this study. The organic matter, pH and cation exchange capacity of the soils did not differ significantly with cropping pattern although intensively cropped farms tended to have higher organic matter' and cation exchange capacity values. Nitrogen, phosphorus and potassium in the top soil were significantly higher in most intensive intercropped farms, but calcium and magnesium did not vary significantly. Moisture content, waterholding capacity, bulk density and particle density of the soil did not show significant difference with cropping patterns. Likewise, the number of bacteria, fungi and actinomycetes in the soil remained statistically the same. Leaf nitrogen and calcium, in­creased while potassium decreased with intensity of cropping. Phosphorus and magnesium showed no definite trend.

scholarly journals Earthworm Community In Relation To Soil Properties And Metals In Hebei Province, North China

2021 ◽  
Author(s):  
Yue Yin ◽  
Kun Wang ◽  
Miaomiao Chen ◽  
Xiaoquan Mu ◽  
Bo Li ◽  
...  
Keyword(s):  
Soil Properties ◽  
Cation Exchange Capacity ◽  
North China ◽  
Exchange Capacity ◽  
Hebei Province ◽  
Species Dominance ◽  
Soil Variables ◽  
High Metal ◽  
Key Variables ◽  
Total Potassium

Abstract In this study, we examined the influence of soil properties (pH, total potassium (TK), available potassium (AK), total nitrogen (TN), total phosphorus (TP), available potassium (AP), cation exchange capacity (CEC), and soil organic carbon (SOC)), and metals (Cd, Pb, Cu, and Zn) on the density, diversity, and species composition of earthworms in the Hebei Province, North China. In total, 535 earthworms were collected from 20 sites in the study area, and belonged to three families, six genera, and ten species. Amynthas hupeiensis (39.4%) and Drawida gisti (37.8%) were the dominant species. The correlations between soil variables and earthworm composition determined using redundancy analysis indicated that SOC, TK, and AK enhanced earthworm density (total, adult, and juvenile) and species (A. hupeiensis and D. gisti) abundances. Earthworm composition remained unaffected by the metals (Cd and Pb) in the uncontaminated sites; in contrast, species were absent in areas with high metal concentrations (S19 and S20). Soil TN content was negatively and positively related to Shannon and Peilou indexes (p<0.05), respectively, indicating that TN may be pivotal in influencing earthworm diversity and species evenness. Overall, the soil properties such as K, SOC, and TN were the key variables affecting earthworm density, diversity, and species dominance.

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