Modeling and mapping the spatial variability of soil micronutrients in the Tigris basin

Gökmen, Veysel; Sürücü, Abdulkadir; Budak, Mesut; Bilgili, Ali Volkan

doi:10.1016/j.jksus.2023.102724

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Geostatistical techniques have a substantial role in examining how soil fertility qualities vary in other areas [20]. These techniques have been applied in a wide range of disciplines, and the most prominent one is in the study of soil variability [16], [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Characterizing, Predicting, and Mapping Soil Available Phosphor, pH, and Electrical Conductivity in Rubber Plantation Based on Three Different Methods of Ordinary Kriging in GIS Environment

Wijayanto,

Nareswari,

Saputra

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Soils in rubber plantations have unique characteristics due to their prolonged and excessive uses. These practices have changed soil qualities, production, and the geographical distribution of soil conditions. For this reason, research on the spatial analysis of soil attributes in rubber plantations is essential. Although it is acknowledged that there are proven methods for accounting for geographical variability, their use in rubber plantations is still somewhat restricted. Evidence also demonstrates ongoing debate and variation in the findings on the capacity of methods to predict spatial variability. Therefore, the primary goal of this study is to examine how well various approaches perform when using ordinary kriging to analyze spatial variability of three soil properties: Soil Available Potassium (SAP), pH, and Electrical Conductivity. The methodology employed in this work includes (a) grid sampling for data collection, (b) interpolation using Ordinary kriging with three methods (exponential, spherical, and Gaussian), (c) mapping, and (d) evaluation. The findings of this study demonstrate that semi-variogram analysis using three distinct methods yields somewhat varied outcomes with accuracy in higher order from SAP, soil EC, and soil pH. The results of this study also show that the different methods have unique characteristics when representing spatial structure. These findings suggested that the number of samples and the selection of interpolation techniques are essential factors in studying these three soil properties and determining the accuracy of the results.

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Section: Introductionmentioning

confidence: 99%

Characterizing, Predicting, and Mapping Soil Available Phosphor, pH, and Electrical Conductivity in Rubber Plantation Based on Three Different Methods of Ordinary Kriging in GIS Environment

Wijayanto,

Nareswari,

Saputra

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Optimizing sunflower production through the use of GIS-based soil fertility management strategy

Nungula,

Massawe,

Chappa

et al. 2024

Cogent Food & Agriculture

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Modeling the Spatial Variability of Soil Nutrients - A Case from Soil Health Card Project, India

Velamala,

Pant

2024

International Journal of Environment and Geoinformatics

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Ascertaining and mapping soil nutrient data is crucial for governments to maintain soil health on farmlands. As part of the soil health card project, a total of 329 geo-referenced soil samples were collected from Thaticherla village, Anantapur mandal, Andhra Pradesh, India. These samples were analyzed for various soil properties such as soil pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), available phosphorus (P), available potassium (K), available sulphur (S), DTPA extractable micronutrients (Fe, Mn, Zn, Cu), and hot water-soluble boron (B) at a depth of 0 to 15 cm. The results showed high variability (>35%) in coefficients of variation in Cu, EC, Zn, and B. The findings indicated positive correlation between Zn and Mn; N and OC; and OC and Zn. The data underwent logarithmic and Box-Cox transformations to achieve normalization. The ordinary kriging method was employed to analyze the spatial variability. The findings revealed that exponential model was appropriate for B, Fe, Mn, Zn, and OC; Gaussian for K; J-Bessel for N; K-Bessel for Cu, P, and S; stable for EC and rational quadratic for pH, respectively. The analysis showed a strong to weak spatial dependency. In the study area, the spatial variability maps exhibited deficiencies of 97%, 96% and 40% for N, OC and Zn, respectively. Therefore, it is urgent to apply suitable manures and fertilizers in the study area to address these issues. The study area exhibited significant variation in spatial patterns, emphasizing the importance of implementing field-specific plans for soil health and environmental management.

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Modeling and mapping the spatial variability of soil micronutrients in the Tigris basin

Cited by 3 publications

References 41 publications

Characterizing, Predicting, and Mapping Soil Available Phosphor, pH, and Electrical Conductivity in Rubber Plantation Based on Three Different Methods of Ordinary Kriging in GIS Environment

Characterizing, Predicting, and Mapping Soil Available Phosphor, pH, and Electrical Conductivity in Rubber Plantation Based on Three Different Methods of Ordinary Kriging in GIS Environment

Optimizing sunflower production through the use of GIS-based soil fertility management strategy

Modeling the Spatial Variability of Soil Nutrients - A Case from Soil Health Card Project, India

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