Geostatistical-based index for spatial variability in soil properties

Neto, Edemar Appel; Seidel, Enio Júnior; Oliveira, Marcelo Silva de

doi:10.36783/18069657rbcs20200086

Cited by 4 publications

(2 citation statements)

References 17 publications

(34 reference statements)

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“…Moreover, with fewer data, the OK and IDW models failed to capture the SOC fluctuation. Soil properties change with time and space due to inherent and external influences (Abreu et al., 2003; Apple Neto et al., 2020; Marins et al., 2018; Reichert et al., 2020). Understanding soil quality is critical for optimizing fertilizer use, increasing agricultural output, and reducing environmental risks (Guan et al., 2017; Panday et al., 2018; Tesfahunegn et al., 2011).…”

Section: Introductionmentioning

confidence: 99%

Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

Tiruneh,

Hanjagi,

Mumtaz

et al. 2024

Soil Science Soc of Amer J

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A precise soil organic carbon (SOC) content estimate is crucial soil quality parameter for agricultural produce and ecological safety. Moreover, geospatial modeling of SOC is critical when there are limited laboratory equipment and chemical reagents for soil analysis. This study used geostatistics—ordinary kriging (OK) and inverse distance weighting (IDW)—to map SOC in Libokemkem area, Northwest Ethiopia, for improved SOC management. About 107 soil samples were obtained from the plow layer at a 20‐cm depth and SOC was determined. Statistical Package for Social Sciences version 24.0 was used to generate descriptive statistics, and geostatistical analysis was also performed on the data using ArcGIS platform. The coefficient of determination (R2) and root mean square error (RMSE) derived from the validation of the predicted maps were used to assess the models. The results revealed homogeneity (coefficient of variation < 10%), low (0.12%–1.74%), and optimal (1.74%–4.06%) mean levels of SOC in study area. The OK showed an R2 of 0.74 and an RMSE of 13%, and the IDW revealed an R2 of 0.69 and an RMSE of 14%. The semivariogram results indicate a moderate dependence for SOC with stable, circular, spherical, exponential, and Gaussian models. We conclude that the sustainable monitoring of SOC is significant in enhancing soil quality. However, further study considering all drivers of spatial variability for SOC in the study and other soil sampling approaches improving performance of the prediction models is needed.

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Section: Introductionmentioning

confidence: 99%

Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

Tiruneh,

Hanjagi,

Mumtaz

et al. 2024

Soil Science Soc of Amer J

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“…Soil properties vary with time (Awe et al 2015a(Awe et al , b, 2020aBarbosa et al 2019;Reichert et al 2020) and space (Secco et al 2009;Marins et al 2018;Appel Neto et al 2020) due to the cumulative effect of biophysical and chemical factors. Understanding of soil properties' spatial variability (Silva et al 2003;Guan et al 2017) and developing site-specific recommendations (Guan et al 2017;Rosemary et al 2017) are vital for optimizing nutrient use, improving crop productivity, and minimizing environmental hazards (Panday et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability modeling of soil fertility for improved nutrient management in Northwest Ethiopia

et al. 2021

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In the study, two geostatistical methods-Ordinary Kriging (OK) and Least Distance Weighting (IDW)-were utilized to predict the spatial variability and distribution of soil properties for improved nutrient management in the Fogera plain, Northwest Ethiopia. In an area of 5646 ha, 60 composite soil samples were collected at a 0-20-cm soil depth and analyzed for soil pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av.P), exchangeable calcium (Ca 2+ ), potassium (K + ), sodium (Na + ), and magnesium (Mg 2+ ), electrical conductivity (EC), and cation exchange capacity (CEC) using standard analytical procedures. The data were then incorporated into a GIS database and semi-variogram, and geostatistical analyses were performed with ArcGIS software version 10.5. Descriptive statistical treatments were applied using IBM Statistical Packages for Social Sciences (SPSS) software version 24. The performance of interpolation methods was assessed using the mean error (ME), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R 2 ) extracted from cross-validation of predicted maps. Analysis of different semi-variogram models depicts different degrees of spatial dependence. An exponential model is detected for the soil pH, CEC, OC, and TN; a spherical one for EC and Ca 2+ ; a moderate one for soil K + ; and a weak spatial dependence for soil av. P. This result demonstrates a high spatial continuity and dependence between adjacent soil samples. The inverse distance weighting (IDW) and ordinary kriging (OK) models well described the variation of all soil fertility parameters except organic carbon (OC) and available phosphorus (av. P), which had low NSE (≤ 50%) for both IDW and OK methods. Consequently, the generated maps revealed that the spatial variability of soil properties was adequate to predict the values of soil fertility indicators in the non-sampled locations within the study area and similar regions. The geostatistical-based soil fertility maps will be helpful for farmers, researchers, and policymakers to improve soil management methods, optimize fertilization strategies, and enhance crop productivity. By means of the methodological approach applied, we have succeeded in demonstrating the strong spatial dependence of the studied soils; however, a particular attention to the implementation of site-specific soil management practices in the Fogera plain is essential.

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Spatial variability of soil chemical properties under different land-uses in Northwest Ethiopia

et al. 2021

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The understanding of the spatial variation of soil chemical properties is critical in agriculture and the environment. To assess the spatial variability of soil chemical properties in the Fogera plain, Ethiopia, we used Inverse Distance Weighting (IDW), pair-wise comparisons, descriptive analysis, and principal component analysis (PCA). In 2019, soil samples were collected at topsoil (a soil depth of 0–20 cm) from three representative land-uses (cropland, plantation forestland, and grazing lands) using a grid-sampling design. The variance analysis for soil pH, available phosphorus (avP), organic carbon (OC), total nitrogen (TN), electrical conductivity (EC), exchangeable potassium (exchK), exchangeable calcium (exchCa), and cation exchange capacity (CEC) revealed significant differences among the land-uses. The highest mean values of pH (8.9), avP (32.99 ppm), OC (4.82%), TN (0.39%), EC (2.28 dS m−1), and exchK (2.89 cmol (+) kg-1) were determined under grazing land. The lowest pH (6.2), OC (2.3%), TN (0.15%), and EC (0.11 dS m−1) were recorded in cultivated land. The PCA result revealed that the land-use change was responsible for most soil chemical properties, accounting for 93.32%. Soil maps can help identify the nutrient status, update management options, and increase productivity and profit. The expansion of cultivated lands resulted in a significant decrease in soil organic matter. Thus, soil management strategies should be tailored to replenish the soil nutrient content while maintaining agricultural productivity in the Fogera plain.

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Geostatistical-based index for spatial variability in soil properties

Cited by 4 publications

References 17 publications

Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

Spatial variability modeling of soil fertility for improved nutrient management in Northwest Ethiopia

Spatial variability of soil chemical properties under different land-uses in Northwest Ethiopia

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