Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

Reza, S. K.; Baruah, Utpal; Sarkar, D.; Singh, Surendra

doi:10.1007/s12517-016-2474-y

Cited by 29 publications

(13 citation statements)

References 25 publications

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“…This low variability, particularly for the pH (H2O) attribute, can occur, according to Reza et al (2017), because the values of this attribute are presented in a log scale of the concentration of protons in the soil solution. Polo et al (2010), Reza et al (2016), andShukla et al (2016), in their studies, verified the low CV of the pH (H2O), when compared with those of other soil properties, thereby corroborating this work. From the 0.1-0.2 m depth range analysis, all the attributes showed average variation, except for the attributes, Al, P, K, Cu, and Zn, which varied significantly.…”

Section: Observing the Variability Of The Data For The Soil Properties Analyzed By The Coefficient Of Va Riation (%Cv)supporting

confidence: 70%

Spatialization of Granulometric and Chemical Characteristics in Irrigated Hydromorphic Soil

Águas¹,

Frazão²,

Nascimento³

et al. 2021

JAS

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Lowlands represent a significant portion of agricultural areas in the world. Thus, understanding the spatial variability of the chemical and granulometric characteristics of hydromorphic soils can contribute to improving soil management and fertility. The objective of this work was to characterize spatial dependence, spatialized chemical attributes, granulometry, and the ideal number of samples in irrigated hydromorphic soil. Soil sampling was conducted in a grid of 10x10 m, in the layers of 0–0.1 and 0.1–0.2 m, totalling 432 composite samples. The evaluated attributes are as follows: pH in water, H+Al, SOM, Ca, Mg, Al, P, K, CTC, V%, Cu, Fe, Zn, Mn, clay, silt, and sand. The texture of the area was classified as loamy–sandy and free in the 0–0.1 and 0.1–0.2 m layers, respectively. The coefficient of variation of the attributes ranged from 2.71% (0–0.1m) to 149.07% (0.1–0.2m). All the attributes studied exhibited moderate to strong spatial dependence. The sample grid with a sampling interval of 20 m referring to the 0–0.1 m layer proved to be adequate. The ideal number of simple samples per composite is 19 for granulometry and macronutrients, and 28 for the micronutrients in the 0–0.1 m layer.

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Section: Observing the Variability Of The Data For The Soil Properties Analyzed By The Coefficient Of Va Riation (%Cv)supporting

confidence: 70%

Spatialization of Granulometric and Chemical Characteristics in Irrigated Hydromorphic Soil

Águas¹,

Frazão²,

Nascimento³

et al. 2021

JAS

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“…The nugget-sill ratio of SI in the area was 0.23 (23 %), which showed intense spatial autocorrelations according to Table 2. Geostatistical analysis uses the semivariogram to quantify the spatial variation of a regionalized variable and derives important parameters used for kriging spatial interpolation (Reza et al, 2016). Different semivariogram functions (Circular, Spherical, Tetraspherical, Pentaspherical, Exponential, Gaussian) were evaluated to select the best fit with the data.…”

Section: Resultsmentioning

confidence: 99%

Comparison of different interpolation techniques in determining of agricultural soil index on land consolidation projects

Uyan

2019

International Journal of Engineering and Geosciences

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Land consolidation (LC) is a tool to improve the processing efficiency of agricultural area and the promotion of rural development same time an indispensable application for the promotion of sustainable agriculture. In order to achieve the reallocation process after LC, determining the correct of soil index (SI) for each of the agricultural parcels is very important for the success of LC projects. Nowadays, interpolation methods are extensively applied in the mapping processes to estimate the SI at unsampled sites. The objective of this study was to evaluate and compare the performance of three interpolation methods for the agricultural SI values maps with GIS technology for LC projects. The SI data were determined from 132 observation points. Three spatial interpolation methods Ordinary Kriging (OK), Inverse Distance Weighted (IDW), and Radial Basis Functions (RBFs) were utilized for modeling the agricultural SI values. The results indicated that all methods provided a high prediction accuracy of the mean concentration of SI.In this study, although the best performed interpolation method was the OK, the results showed that the performance differed slightly among three methods. Results show that all the methods present a good performance in the estimation with RMSE (root mean square error) and ME (mean error) close to 0%.

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“…There was SSD on soil pH and OC; moderate spatial dependence (MSD) on Olsen-P (Panday et al, 2018), OC (Costa et al, 2015;Panday et al, 2018), and pH (Panday et al, 2018). Weak spatial dependence on soil Olsen-P (Ozgoz et al, 2013;Costa et al, 2015) and OC (Reza et al, 2016) and SSD on Olsen-P (Kingsley et al, 2019) were also mentioned. According to Wang et al (2009) and Fu et al (2014), a strong and MSD of soil parameters are due to soil internal factors, including parent materials, soil texture, geography, and plants.…”

Section: Evaluation Of the Geostatistical Interpolation Methodsmentioning

confidence: 93%

Variability modeling and mapping of soil properties for improved management in Ethiopia

Tiruneh

Alemayehu

Meshesha

et al. 2023

Agrosystems Geosci & Env

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Managing soils for improved agricultural production requires information on soil fertility status. Our objective was to map for better soil management in Ethiopia and determine their spatial correlation at a separation distance of 29 m. We collected 82 soil samples (0–20 cm depth) at 560 ha of land and determined pH, Olsen extractable phosphorus (Olsen‐P), and organic carbon (OC). We then interpolated between sample points (ordinary kriging‐OK and distance weighting‐IDW [inverse distance weighting]) to evaluate spatial dependence. Olsen‐P ranged from 2.68–42 mg/kg and exhibited high variability with a coefficient of variation (CV) ≥35%. Conversely, soil pH showed low variability (CV ≤ 15%) and ranging from 4.84 to 6.81. Soil OC content varied from 0.81% to 3.17%. The IDW (R2 = 0.86; RMSE = 0.019) outperformed the OK. The semivariogram results indicate a strong dependence for pH and OC for spherical, exponential, and Gaussian models, while moderately spatially auto correlated for Olsen‐P for all models. The IDW and OK predict the spatial variability of the pH (moderately acidic), Olsen‐P (low), and OC (very low) contents. The soil maps may help to improve soil management alternatives, increase crop productivity, and secure environmental quality.

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Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

Cited by 29 publications

References 25 publications

Spatialization of Granulometric and Chemical Characteristics in Irrigated Hydromorphic Soil

Spatialization of Granulometric and Chemical Characteristics in Irrigated Hydromorphic Soil

Comparison of different interpolation techniques in determining of agricultural soil index on land consolidation projects

Variability modeling and mapping of soil properties for improved management in Ethiopia

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