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Semivariogram models and parameters for the soil properties.
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Spatial variability of soil physical properties in a cultivated field such as; bulk density (BD), penetration resistance (PNT), saturated hydraulic conductivity (Ks), field capacity (FC) and permanent wilting point (PWP), were determined by geostatistical method. While BD values varied between 1.12 and 1.41 g cm-3, PNT resistance (0.66 to 1.88 MPa)...
Contexts in source publication
Context 1
... statistics for the soil physical properties in 15 cm soil depth of the cultivated field are given in Table 1. In 15 cm soil depth of the cultivated field, while bulk density values varied between 1.12 and 1.41 g cm -3 , PNT resistance varied between 0.66 and 1.88 MPa (Table 1) To evaluate the spatial variability of the soil physical properties, the exponential model for clay content, BD, PWP, and the spherical model for PNT, Ks and FC were selected with their biggest r 2 values and the smallest reduced sums of squares (RSS) values using the GS+ 9 package program ( Table 2). The semivariograms of the soil properties indicated that the range in spatial correlation varied among soil properties (Figure 2). ...Context 2
... strong spatial dependency of soil properties is related to structural intrinsic factors such as texture, parent material and mineralogy, and weak spatial dependency is related to random extrinsic factors such as plowing, fertilization and other soil management practices ( Zheng et al., 2009). The ratios of nugget/sill in the soil physical properties, except BD, were less than 25% in Table 2. Therefore, spatial dependence for these soil properties was strong. ...Similar publications
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Citations
... Despite the fact that texture is an inherent soil property, management practices may have contributed indirectly to the changes in particle size distribution particularly in the surface layers as result of removal of soil by sheet and rill erosions, and mixing up of the surface and the subsurface layers during continuous tillage activities (Tilahun, 2007). It can also be stated that the effect of soil tillage on soil particle size by Gülser et al. (2016) reported that heterogeneity and variation of soil physical parameters in a field due to soil plowing should be taken into consideration for a successful agricultural management. Table 3.2 shown the saturation percentage of the soil profile (PO1) ranges from 56 to 73% whereas in profile (PO2) it ranges from 64 to 78% and in both site this indicates the high water holding capacity of the soil. ...
The objectives of this study were to characterize the soil physical and chemical properties and to classify the soil in order to assess the soil water and crop management systems under rain fed and irrigated agriculture in soils of the farm of Faculty of Agriculture as data base for experimental research studies in the farm of the Faculty of Agriculture, Upper Nile University.. Two soil profiles were excavated in the study area to a depth of 150 cm and fourteen soil samples were collected. The samples were analyzed in the laboratory for their physical and chemical properties. Physical properties results showed that the bulk density of the soil is high (1.7 to 1.9 g ∕ cm 3) with low pore space (30% to 37%). The infiltration rate is very low to low (0.8 to 1.2 c ̸ hr.). The saturated hydraulic conductivity is very slow to slow (0.7 to 1, 9 cm ̸ hr.) with high available water (23% to 38%). The chemical results indicate that the soil is neutral to slightly alkaline in reaction (pH 7.3 to pH 8.3) non-saline (ECe 2.6 to 3.4 dSm-1), non-sodic (SAR 1.4 to 8.3), non-calcareous, poor in organic matter (1.7 to 2.4%) and nitrogen content (0.05 to 0.09%). The CEC-values are very high (79 to 87%) and this is due to the high clay content and its Smectitic nature as well. The soil was classified as Typic Haplustertst, very fine, Smectitic, super active, hyper thermic according to Soil Survey Staff (2010). The main limitation in this soil is its deficiency in nitrogen, organic matter and phosphorus well. Therefore, it is necessary to add manures or N-fertilizers for sustainable crop yield. The clay content may cause problems of water management and workability of this soil which require good management.
... The semivariogram models were adjusted using the GS + 7.0 program (Gamma Design Software, 2012). The choice of the best model adjusted to the semivariograms was based on the coefficient of determination (R2), according to Gülser et al. (2016) and Chen et al. (2020). In general, 30 data pairs is considered minimum number of pairs for obtaining a reliable estimate of the variogram at any lag (ZIRSCHKY, 1985). ...
... Such as soil formation factors and soil management practices (CAMBARDELLA et al., 1994). Similar results can also be verified in previous research (OZGOZ et al., 2011;GLENDELL et al., 2014;GÜLSER et al., 2016). ...
Soils in areas under grazing undergo physical changes, of greater or lower intensity, by the action of animal trampling. Thus, the objective of this study was to evaluate the spatial dependence of the physical attributes and associated soil functionalities under trampling by cattle on rotational grazing in the region of Pernambuco, Brazil. The research was conducted at the Experimental Farm of the Federal Rural University of Pernambuco, Agreste region of the State of Pernambuco, Brazil. In the field, a paddock of 0.25 ha was demarcated and four crossbred dairy cattle (Holstein-Zebu) with an average weight of 450 kg were used, managed in a rotational grazing system that included 7 days of occupation and 28 days for pasture rest. Undisturbed soil samples were collected before the first passage of the animals in the paddock and at the end of each pasture rest period, i.e., at 0, 35, 70 and 105 days of study, in a 6 × 14 m mesh, which totalled 32 sampling points. At each point, bulk density (BD), total porosity (TP), water content (W), soil penetration resistance (PR) at depths of 0.00-0.10 and 0.10-0.20 m and the soil saturated hydraulic conductivity (K 0) were determined. The data obtained were submitted to descriptive, geostatistical and multivariate statistical analysis (canonical discriminant analysis). After a period of 105 days of grazing, at both evaluated depths, an increase in the average values of PR and BD and a reduction in the values of TP, K 0 and W was observed. Normal distribution was observed in all variables studied. The physical attributes of the soil presented a coefficient of variation between low and medium and strong spatial dependence in both grazing periods. The canonical multivariate analysis indicated a reduction in the soil physical quality, especially after the animals' second passage in the paddock.
... The CV was low for organic matter (Table 1) is likely due to the combination of a low amount of organic material intake and high temperatures that accelerate the decomposition of organic matter. Organic matter (9.13%) was found to be less variable in the field based on the CV values of the soil properties (Gulser et al., 2016). Peter-Jerome et al., (2022) report that, in comparison to the soil fertility ratings suggested by Esu, (1991), the available phosphorus content and organic carbon content are significantly lower. ...
... Bulk density of all the pedons ranged from 1.01 to 1.75 Mg m and increased with depth which may be ascribed −3 to progressive compaction due to filling of pores by eluvial materials, lower organic matter, and less aggregation. It is well recognized that the variation in bulk densit is due to y differences in soil texture, organic matter content and management practices (Gülser et al 2016). Bulk density was negatively and significantly correlated with clay content (r = -0.60) ...
Nine pedons representing the typical landforms of micro-watersheds in Chhachhrauli block of Yamunanagar, Haryana were exposed to determine the interrelationships and variability of soil characteristics. The colour of pedons was yellowish brown with dominant hue of 10YR. Soil structure was weak to strong, fine to medium, sub angular blocky across the pedons. The consistency of different pedons varied from non-sticky non-plastic to sticky plastic and soil texture varied from sand to loam with predominance of sand than clay in all the pedons. Bulk density, particle density and available water content across all the pedons varied from 1.01 to 1.75 Mg m−3, 2.50 to 2.65 Mg m−3 and 1.01 to 16.38%, respectively. The pH ranged from 4.20 to 8.30 across all the profiles indicating acidic to alkaline nature of the soils. The soils were low to high in soil organic carbon (SOC). In all soils, SOC varied from 0.06 to 2.54 per cent and was higher in surface horizons than subsurface horizons. The CaCO3 content in pedons 1, 3, 4, 5 and 6 was <1% suggesting nearly complete decalcification in these soils. The exchangeable complex of the soils was dominated by Ca followed by Mg, Na and K. Broadly the physiography and land use considerably influenced the value of total exchangeable bases. Relatively higher content of nutrients was observed in the surface horizons compared to subsurface horizons. The results demonstrated that, in general, varying SOC concentrations are associated with varying slope positions of the pedons, highlighting the significance of landform location in regulating soil water content was well as the SOC concentration. The geomorphic location of each pedon across the watersheds has influenced strongly the movement of solutes and therefore soil development. The soils of the study area were classified as sandy, mixed, hyperthermic, typic Ustipsamments (Pedon 1), fine loamy Ustochrepts (Pedon 2, 3, 5, 6, 7, 8 and 9) and coarse loamy Ustorthents (Pedon 4).
... Vasu et al. [23] used the Kriging method in West Bengal, India, for characterization and mapping the soil fertility factors. Gülser et al. [24] used block Kriging to generate the physical properties map of some Turkish soils. Kriging and co-Kriging interpolation methods were used to generate surface maps of spatial variability of soil Physico-chemical properties in Babylon, Iraq [25]. ...
This study aims to assess the soil quality under different land uses by utilizing technique for order preference by similarity to the ideal solution method (TOPSIS) and geostatistical Technique in the Northern part of Sohag Governorate, Egypt. Various soil parameters such as sand, silt, clay content, CEC, ESP OC, ECe, pH, and CaCO3 were determined. Afterward, the geostatistics approach using ordinary kriging interpolation and semivariogram was applied to produce a spatialized and detailed map for each soil parameter. Spherical, Exponential, Gaussian, and J-Bessel geostatistical models were used to define the spatial variability of soil properties based on RMS, MSE, and RMSSE. Based on the TOPSIS method, the soil quality index (SQI) and its ranking under land use types in the study area were calculated. The results of SQI ranged from 38.75% to 55.82% and 27.53% to 52.72%, and 5.75% to 26.73% for old cultivated, new cultivated, and desert soil, respectively. The SQI was classified into three regions. The first has a fair quality index and covers 56.48% (403.91 km 2) of the total geographical area (TGA). The soils of this region 17 are located mainly in old cultivated soils and some new ones. The second region was observed in some newly reclaimed soils and desert soils and extended over an area of about 27.75% (198.45 km 2). These soils have low values of favorable studied indicators, leading to negative effects on the SQI that are defined as poor. The third region is very poor quality, covers about 15.77% (112.78 km 2) of TGA, and is located mainly in desert soils with low beneficial and high non-beneficial studied indicators. Finally, the results indicate that the integration of TOPSIS and geostatistical technique allow for an accurate and practical assessment of the SQI.
... cm 3 cm -3 . In another study, Gülser et al. (2016) collected soil samples from 0-100 cm and found large spatial variability in FC, ranging from 0.12-0.57 cm 3 cm -3 . ...
Highlights
Among six manufacturer calibrations, the default calibration resulted in the largest errors.
Sensor performance was negatively affected by higher clay content and salinity.
Sensor-based approaches to estimating field capacity were inconsistent and spatially variable.
Abstract. Maintaining the economic and environmental sustainability of crop production requires optimizing irrigation management using advanced technologies such as soil water sensors. In this study, the performance of a commercially available multi-sensor capacitance probe was evaluated under irrigated field conditions across western Oklahoma. The effects of clay content and salinity on sensor performance were investigated too. In addition, the field capacity (FC) of soil cores collected at study sites was determined in the laboratory. These laboratory FC values were used to assess the performance of two sensor-based approaches for estimating FC: the days to reach laboratory FC after major watering events and the percentile of collected sensor readings that represented laboratory FC. The results showed that among the six calibrations provided by the manufacturer, the default and silty clay loam calibrations produced the largest and smallest soil water content errors, respectively. Errors generally increased with clay and salinity, except for the heavy clay calibration, which showed improved performance with increasing clay content. The default and sand calibrations were more sensitive to increases in clay and salinity compared to other calibrations. In the case of sensor-based FC, on average, one to three days were required to reach laboratory FC, with a large range of one to nine days. The percentiles representing laboratory FC had an average of 56% and a range of 3%-97%. Overall, the sensor-based approaches produced inconsistent and highly variable estimates of FC. Keywords: Calibrations, Clay content, Irrigation scheduling, Salinity, Sensor accuracy, Soil water threshold.
... Determining the spatial variation pattern of physical and chemical soil properties via different interpolation models allows predicting the value of the investigated soil properties at any point in the research area with the lowest error. Therefore, the distribution maps obtained as a result of the interpolation analysis of soil properties allow the most appropriate planning and management decisions related to land management for the research area (Öztaş, 1996;Özyazıcı et al., 2015;Gülser et al., 2016;Çelik and Dengiz, 2017;Alaboz et al., 2020). In soil science, many studies have been commonly utilized some interpolation methods such as ordinary, simple, universal kriging (Taşan and Demir, 2017;Arslan et al., 2018;Celilov and Dengiz, 2019;Şimşek et al., 2020;Alaboz et al., 2020), inverse distance weighting (IDW) (with different power levels) (Özyazıcı et al., 2015;Çelik and Dengiz, 2017;Sancan and Karaca, 2017;Taşan and Demir, 2017;Arslan et al., 2018;Celilov and Dengiz, 2019;Alaboz et al., 2020;Şenol et al., 2020), and radial basis function (RBF) (Taşan and Demir, 2017;Arslan et al., 2018;Celilov and Dengiz, 2019;Özdemir et al., 2019;Alaboz et al., 2020) to determine distribution of soil physical and chemical properties. ...
... Determining the spatial variation pattern of physical and chemical soil properties via different interpolation models allows predicting the value of the investigated soil properties at any point in the research area with the lowest error. Therefore, the distribution maps obtained as a result of the interpolation analysis of soil properties allow the most appropriate planning and management decisions related to land management for the research area (Öztaş, 1996;Özyazıcı et al., 2015;Gülser et al., 2016;Çelik and Dengiz, 2017;Alaboz et al., 2020). In soil science, many studies have been commonly utilized some interpolation methods such Moreover, they reported that these interpolation methods have been successfully used to determine distribution maps of soil properties. ...
This study aimed to predict the live weight from morphological characteristics of Karakaş seep by using Ridge, LASSO, Adaptive LASSO, Naive Elastic Net, and Elastic Net and to make the variable selection to reduce the model complexity.
... BD, particle-size distribution and SMC varies spatially and temporally like other soil variables. Many researchers (Gülser et al. 2016;Nebo et al. 2020) reported variability of these soil variables is inherent in nature due to geologic and pedologic soil forming factors, but it may also vary due to the wetting and drying cycles (Dörner et al. 2009), tillage operations (Tesfahunegn et al. 2011), grazing (Krümmelbein et al. 2009) and changes in land uses (Saglam and Dengiz 2012). Reynolds et al. (2007) and Guo et al. (2020) showed that BD and SMC varied due to seasonal climatic conditions, management practices, crop development, topography and soil properties. ...
... Many geostatistical analysis techniques have been used for spatial interpolation of soil physical properties, such as ordinary kriging (OK), co-kriging and universal kriging (Liao et al. 2013;Santra et al. 2017;Saleh 2018). As the OK technique normally performs better and is easy to apply, it has been widely used in spatial mapping (Owji et al. 2014;Tripathi et al. 2015;Gülser et al. 2016;Reza et al. 2016;Seyedmohammadi et al. 2019). Based on a best-fit model criterion, only one model was selected for kriging having lowest SSE. ...
The bulk density (BD), particle-size distribution and soil moisture content (SMC) are important properties that govern the physical and mechanical behaviours of the soil and also provide crucial information on the pedogenesis of soils. A total of 153 soil profiles were randomly selected and studied from part of Sepahijala district, Tripura, Northeastern India. The horizon-wise soil property data were converted to fixed-depth data for 0–20, 20–40, 40–60, 60 − 80 and 80–100 cm of soil depth. The result showed that the mean values of BD 1.51, 1.60, 1.59, 1.55 and 1.55 Mg m−3; sand 37.7, 38.1, 36.9, 36.0, and 37.3%; silt 29.1, 26.8, 28.0, 28.3 and 27.2%; clay 33.2, 35.1, 35.1, 35.7 and 35.5%; and SMC 21.4, 20.2, 21.2, 21.3 and 19.4% in the 0 − 20, 20 − 40, 40 − 60, 60 − 80 and 80 − 100 cm depths, respectively. The values of CV for soil variables ranged from 9.4 to 70.0% for all the depths. The CVs indicated that BD had lowest variability, while sand and SMC showed high variability in all the depths. Stein, circular, spherical, exponential and Gaussian models were found to be the best fit for semivariograms of different physical properties for different soil depths. The nugget to sill ratios of the empirical semivariograms for all the layers of BD, clay and SMC indicated moderate spatial dependence, except the 80 − 100 cm of BD and clay and 20 − 40 and 60 − 80 cm of SMC, which showed strong spatial dependence. Soil physical properties exhibited different distribution pattern. Spatial distribution maps of BD showed that except surface layer, the BD is lower in the southern part of the study area, and decreasing towards the northern parts in all the depths may be due to higher clay content in this region. The spatial distribution of SMC closely followed the distribution pattern of sand and clay contents. The developed maps prepared in this study may be useful to farmers, planners and decision-makers for improving soil–water management.
... In the light of this information, it is possible to interpret the beta mode model as making a more reliable prediction than the GBP mode model. (Öztaş, 1996;Özyazıcı et al., 2015;Gülser et al., 2016;Çelik and Dengiz, 2017;Alaboz et al., 2020). In soil science, many studies have been commonly utilized some interpolation methods such Moreover, they reported that these interpolation methods have been successfully used to determine distribution maps of soil properties. ...
