Predictive mapping of soil organic carbon in Northeast Algeria

Boubehziz, Sana; Khanchoul, Kamel; Benslama, Mohamed; Benslama, Abdelraouf; Marchetti, A.; Francaviglia, Rosa; Piccini, Chiara

doi:10.1016/j.catena.2020.104539

Cited by 41 publications

(26 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, including auxiliary environmental variables could increase the interpolation accuracy of various soil parameters [36]. Future research is planned to implement independent predictors of soil parameters in order to implement high potential methods in spatial interpolation, such as regression kriging and machine learning [37].…”

Section: Discussionmentioning

confidence: 99%

The Effect of Soil Sampling Density and Spatial Autocorrelation on Interpolation Accuracy of Chemical Soil Properties in Arable Cropland

et al. 2021

View full text Add to dashboard Cite

Knowledge of the relationship between soil sampling density and spatial autocorrelation with interpolation accuracy allows more time- and cost-efficient spatial analysis. Previous studies produced contradictory observations regarding this relationship, and this study aims to determine and explore under which conditions the interpolation accuracy of chemical soil properties is affected. The study area covered 823.4 ha of agricultural land with 160 soil samples containing phosphorus pentoxide (P2O5) and potassium oxide (K2O) values. The original set was split into eight subsets using a geographically stratified random split method, interpolated using the ordinary kriging (OK) and inverse distance weighted (IDW) methods. OK and IDW achieved similar interpolation accuracy regardless of the soil chemical property and sampling density, contrary to the majority of previous studies which observed the superiority of kriging as a deterministic interpolation method. The primary dependence of interpolation accuracy to soil sampling density was observed, having R2 in the range of 56.5–83.4% for the interpolation accuracy assessment. While this study enables farmers to perform efficient soil sampling according to the desired level of detail, it could also prove useful to professions dependent on field sampling, such as biology, geology, and mining.

show abstract

Section: Discussionmentioning

confidence: 99%

The Effect of Soil Sampling Density and Spatial Autocorrelation on Interpolation Accuracy of Chemical Soil Properties in Arable Cropland

et al. 2021

View full text Add to dashboard Cite

show abstract

“…At the same time, the differences between different land use types in urban areas have become more and more significant after long-term human development and transformation. This phenomenon leads to an effective increase or decrease in SOC content in different areas, which greatly impacts the spatial distribution of SOC (Boubehziz et al, 2020;Barreto et al, 2021). Therefore, exploring the influence of land use types on the spatial distribution of SOC, and determining the of SOC stock (SOCS), forms an important foundation for eco-city construction based on original urban planning, especially for greening construction, environmental protection, and the rational development of land resources (Yan et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Application of land use modes in the spatial prediction of soil organic carbon in urban green spaces

Guo¹,

Liu²,

Gao³

et al. 2022

Int. Agrophys.

View full text Add to dashboard Cite

A b s t r a c t . The challenge of predicting soil organic carbon distribution accurately has received great attention in order to support urban green space soil management during climate change. This study compared four geostatistical methods: kriging combined with land use, ordinary kriging, inverse distance weighting and radial basis function, to predict the spatial distribution patterns of soil organic carbon content and soil organic carbon density in the Xiong'an New Area, estimate organic carbon stocks, and assess the role of land use types in the spatial prediction of soil organic carbon stocks. The results showed that the soil organic carbon content decreased with increasing soil depth, and was significantly affected by different land use types (p<0.05). The correlation coefficient values of kriging combined with land use were on average 0.229 higher than those of other methods. The root mean squared error and the mean absolute error of kriging combined with land use were on average 0.148 and 0.139 lower than those of the other methods. Kriging combined with land use has a greater advantage over other methods in predicting the spatial distribution of soil organic carbon content, and also the spatial distribution of soil organic carbon density and the spatial distribution of soil organic carbon, the prediction results of the four interpolation methods were similar. The average soil organic carbon density was 2085 Gg (0-30 cm) and 1363 Gg (30-60 cm). In conclusion, land use type clearly influences the spatial distribution of soil organic carbon in urban areas, and by using land use type as auxiliary data, we can obtain a more accurate spatial distribution of soil organic carbon and predict the total storage capacity of the soil. This study may result in significant advances in the spatial prediction of soil organic carbon for urban areas. K e y w o r d s: geostatistics, soil organic carbon, spatial distribution, urban land use, Xiong'an New Area

show abstract

“…Geographic information systems (GIS) are not limited to the field of geography per se [1], but have found broad application in agriculture [2][3][4][5], forestry [6,7], pedology [8], ecology [9], environmental studies [10][11][12][13], botany, biology, landscape planning [14], land consolidation projects [15] and nature conservation. The interconnection of GIS with other diverse scientific disciplines is necessary in the development of methodological procedures and terminology for working with spatial soil data [16].…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of the Accuracy of Interpolation Methods in Crafting Maps of Physical and Hydro-Physical Soil Properties

Igaz

Šinka

Варга³

et al. 2021

Water

View full text Add to dashboard Cite

The goal of this study was the spatial processing and showcasing selected soil properties (available water capacity, total organic carbon content and the content of clay fraction <0.001 mm) in the Nitra River Basin (Slovakia) via the usage and the subsequent evaluation of the quality of applied interpolation methods (Spline, inverse distance weighting (IDW), Topo to Raster). The results showed the possibilities of “conversion” of point information obtained by field research as well as research in the laboratory into a spatial expression, thus providing at least relevant estimation of the soil properties even in localities not directly covered by soil sampling. Based on the evaluation and mutual comparison of the accuracy of the used interpolation methods (by using the so-called cross-validation and trust criteria), the most favorable results were achieved by the Spline method in the GRASS GIS environment, and in the ArcGIS environment. When comparing the measured and estimated values of given soil properties at control points, the interpolated values classified as very accurate up to accurate prevailed in the verification dataset. Qualitatively less favorable (but still acceptable) were the results obtained with Topo to Raster (ArcGIS) interpolation method. On the contrary, the Spline method in the ArcGIS environment turned out to be the least accurate. We assume that this is most likely not only a consequence of insufficient density of points (resources), but also an inappropriate implementation of the method into the ArcGIS environment.

show abstract

Predictive mapping of soil organic carbon in Northeast Algeria

Cited by 41 publications

References 47 publications

The Effect of Soil Sampling Density and Spatial Autocorrelation on Interpolation Accuracy of Chemical Soil Properties in Arable Cropland

The Effect of Soil Sampling Density and Spatial Autocorrelation on Interpolation Accuracy of Chemical Soil Properties in Arable Cropland

Application of land use modes in the spatial prediction of soil organic carbon in urban green spaces

The Evaluation of the Accuracy of Interpolation Methods in Crafting Maps of Physical and Hydro-Physical Soil Properties

Contact Info

Product

Resources

About