Abstract
In arid regions such as the Jordan valley, salinity and sodicity are major constraints to soil quality and crop production. Accurate spatial determination of sodicity and salinity at field scale is a challenge, which can limit the effectiveness of management strategies. Interpolation techniques are used to derive maps to estimate the extent of the areas affected by sodicity and devise appropriate management plans. Nevertheless, different methods may draw different pictures. The main objectives of this study are to compare two interpolation techniques: 1. empirical Bayesian (EBK) and 2. disjunctive kriging (DK) to spatially predict soil salinity and sodicity in intensively used agricultural soils. Surface and subsurface samples were collected from randomly selected agricultural fields and analyzed for salinity (ECe) and sodicity (sodium adsorption ratio -SARe and exchangeable sodium percentage -ESP). Both EBK and DK methods revealed serious soil salinization and sodification problems in the middle and southern parts of the Jordan Valley. Salinity (ECe) maps showed that about 34% of the total area has salinity < 4, 12% < 8, 7% < 16, and 46% exceeds 16 dS m−1. For sodicity (ESP), 44% < 10, 18% < 15, and 37% > 15. Surface soils had higher salinity and sodicity levels than subsurface soils. The average values of surface soils were ECe (15.7 dS m−1), SARe (9.8), and ESP (15.5), compared with ECe (7.4 dS m−1), SARe (7.5), and ESP (13.1) for subsurface soils. Smoother and less patchy predictions were generated using DK compared to EBK. However, EBK had higher accuracy than DK in spatially predicting and addressing the uncertainty inherent in soil salinity and sodicity. This investigation gives important fundamental steps for developing site-specific reclamation techniques to manage and sustain agriculture in these regions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data are available upon special request to the corresponding author.
References
Abdennour MA, Douaoui A, Bradai A et al (2019) Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria. Spanish J Soil Sci. https://doi.org/10.3232/SJSS.2019.V9.N2.04
Abdennour MA, Douaoui A, Piccini C et al (2020) Predictive mapping of soil electrical conductivity as a Proxy of soil salinity in south-east of Algeria. Environ Sustain Indic. https://doi.org/10.1016/j.indic.2020.100087
Al Kuisi M, El-Naqa A, Hammouri N (2006) Vulnerability mapping of shallow groundwater aquifer using SINTACS model in the Jordan valley area Jordan. Environ Geol. https://doi.org/10.1007/s00254-006-0239-8
Bilgili AV (2013) Spatial assessment of soil salinity in the Harran Plain using multiple kriging techniques. Environ Monit Assess. https://doi.org/10.1007/s10661-012-2591-3
Bishop MP, Young BW, Huo D, Chi Z (2020) Spatial analysis and modeling in geomorphology. In: Reference module in earth systems and environmental sciences
Blake GR, Hartge KH (1986) Bulk density. Methods of soil analysis. John Wiley & Sons, Ltd., Hoboken, pp 363–375
Bouhout S, Haboubi C, Haboubi K et al (2023) Spatial variability of nitrate leaching and risk assessment of nitrate contamination in the Ghiss-Nekor alluvial aquifer system (Northeastern Morocco) through Disjunctive Kriging. Sci African. https://doi.org/10.1016/J.SCIAF.2023.E02009
Camana F, Deutsch C V (2019) The Nugget effect. Gl
Cambardella CA, Moorman TB, Novak JM et al (1994) Field-scale variability of soil properties in central iowa soils. Soil Sci Soc Am J 58:1501–1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x
Douaoui AEK, Nicolas H, Walter C (2006) Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma. https://doi.org/10.1016/j.geoderma.2005.10.009
ESRI (2020) ArcGIS Pro v2.6.7. In: [Software]
ESRI (2021) ESRI ArcGIS Pro. In: Redlands, CA, USA
Eswaran H, Lal R, Reich PF (2019) Land degradation: An overview. In: Response to land degradation
FAO & ITPS (2015) Status of the World’s Soil Resources (SWSR)—Main Report. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, Rome, Italy The. Intergov Tech Panel Soils
Ferreira CSS, Seifollahi-Aghmiuni S, Destouni G, et al (2022) Soil degradation in the European Mediterranean region: processes, status and consequences. Sci Total Environ. 805
Gangwar P, Singh R, Trivedi M, Tiwari RK (2019) Sodic soil: Management and reclamation strategies. In: Environmental Concerns and Sustainable Development: Volume 2: Biodiversity, Soil and Waste Management. pp 175–190
Gao J (2021) Fundamentals of spatial analysis and modelling
Gee GW, Bauder JW (1986) Particle size analysis. In: Klute, A. (ed),. Method of soil Analysis, part 1: Physical and Mineralogical Methods. In: Soil Science Society of America, Madison, Wisconsin USA
Gharaibeh MA, Albalasmeh AA, El Hanandeh A (2021a) Estimation of saturated paste electrical conductivity using three modelling approaches: Traditional dilution extracts; saturation percentage and artificial neural networks. Catena 200:105141. https://doi.org/10.1016/j.catena.2020.105141
Gharaibeh MA, Albalasmeh AA, Pratt C, El Hanandeh A (2021b) Estimation of exchangeable sodium percentage from sodium adsorption ratio of salt-affected soils using traditional and dilution extracts, saturation percentage, electrical conductivity, and generalized regression neural networks. Catena 205:105466. https://doi.org/10.1016/j.catena.2021.105466
Goovaerts P (1999) Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89:1–45. https://doi.org/10.1016/S0016-7061(98)00078-0
Grekousis G (2020) Spatial analysis methods and practice, 1st edn. Cambridge University Press, New York, NY
Gribov A, Krivoruchko K (2020) Empirical Bayesian kriging implementation and usage. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.137290
Gupta RK, Abrol IP (1990) Salt-affected soils: their reclamation and management for crop production. pp 223–288
Hammouri N, Al-Amoush H, Al-Raggad M, Harahsheh S (2014) Groundwater recharge zones mapping using GIS: a case study in Southern part of Jordan Valley, Jordan. Arab J Geosci. https://doi.org/10.1007/s12517-013-0995-1
Hamzehpour N, Eghbal MK, Bogaert P et al (2013) Spatial prediction of soil salinity using kriging with measurement errors and probabilistic soft data. Arid L Res Manag. https://doi.org/10.1080/15324982.2012.724144
Hilal A, Bangroo SA, Kirmani NA et al (2024) Geostatistical modeling—a tool for predictive soil mapping. Remote Sens Precis Agric. https://doi.org/10.1016/B978-0-323-91068-2.00011-4
Hoffman GJ (1981) Guidelines for reclamation of salt-affected soils. In: Proc. InterAmerican salinity and water management technology conference
Islam KI (2023) Predicting areal extent of groundwater contamination through geostatistical methods exploration in a data-limited rural basin. Groundw Sustain Dev 23:101043. https://doi.org/10.1016/J.GSD.2023.101043
Juan P, Mateu J, Jordan MM et al (2011) Geostatistical methods to identify and map spatial variations of soil salinity. J Geochem Explor. https://doi.org/10.1016/j.gexplo.2010.10.003
Kalambukattu JG, Kumar S (2021) Geospatial technology in salt-affected land assessment and reclamation. In: Kalambukattu JG (ed) Modern cartography series. Elsevier, Amsterdam
Keshavarzi A, Tuffour HO, Brevik EC, Ertunç G (2021) Spatial variability of soil mineral fractions and bulk density in Northern Ireland: assessing the influence of topography using different interpolation methods and fractal analysis. Catena. https://doi.org/10.1016/j.catena.2021.105646
Krivoruchko K (2012) Empirical Bayesian Kriging. ESRI Press Fall, California
Krivoruchko K (2004) Introduction to modeling spatial processes using geostatistcal analyst. Esri
Krivoruchko K (2011) Spatial statistical data analysis for GIS users spatial statistical data analysis for GIS users. analysis
Krivoruchko K, Gribov A (2019) Evaluation of empirical Bayesian kriging. Spat Stat. https://doi.org/10.1016/j.spasta.2019.100368
Leila E, Hamed R (2020) Salinity assessment of groundwater for irrigation to prevent soil salinization. Paiguan Jixie Gongcheng Xuebao J Drain Irrig Mach Eng. https://doi.org/10.3969/j.issn.1674-8530.19.0098
Lin X, Wang Z, Li J (2022) Spatial variability of salt content caused by nonuniform distribution of irrigation and soil properties in drip irrigation subunits with different lateral layouts under arid environments. Agric Water Manag. https://doi.org/10.1016/j.agwat.2022.107564
Lucke B, Ziadat F, Taimeh A (2013) The soils of Jordan. In: Ababsa M (ed) Atlas of Jordan. Presses de l’Ifpo, Paris
Mailappa AS (2023) Determination of gypsum requirement of soil. In: Mailappa AS (ed) Experimental soil fertility and biology. CRC Press, Boca Raton
Moyeed RA, Papritz A (2002) An empirical comparison of kriging methods for nonlinear spatial point prediction. Math Geol. https://doi.org/10.1023/A:1015085810154
Munyati C, Sinthumule NI (2021) Comparative suitability of ordinary kriging and Inverse Distance Weighted interpolation for indicating intactness gradients on threatened savannah woodland and forest stands. Environ Sustain Indic. https://doi.org/10.1016/j.indic.2021.100151
Navidi MN, Seyedmohammadi J (2022) Mapping and spatial analysis of soil chemical effective properties to manage precise nutrition and environment protection. Int J Environ Anal Chem. https://doi.org/10.1080/03067319.2020.1746775
Odeh IOA, Onus A (2008) Spatial analysis of soil salinity and soil structural stability in a semiarid region of New South Wales, Australia. Environ Manage. https://doi.org/10.1007/s00267-008-9100-z
Ofori S, Puškáčová A, Růžičková I, Wanner J (2021) Treated wastewater reuse for irrigation: pros and cons. Sci Total Environ 760:144026
Oliver AM, Webster R (2015) Basic steps in geostatistics: the variogram and kriging. 2211-808X, vol 1. Springer, Cham
Qadir M, Quillérou E, Nangia V et al (2014) Economics of salt-induced land degradation and restoration. Nat Resour Forum 38:282–295. https://doi.org/10.1111/1477-8947.12054
Reeve RC, Peterson DF, Allison LE (1948) Reclamation of Saline-alkali Soils by Leaching: Delta Area, Utah. Utah Agricultural Experiment Station
Richards LA. (1954) Diagnosis and improvement of saline and alkali soils. Agriculture Handbook No. 60
Samsonova VP, Blagoveshchenskii YN, Meshalkina YL (2017) Use of empirical Bayesian kriging for revealing heterogeneities in the distribution of organic carbon on agricultural lands. Eurasian Soil Sci. https://doi.org/10.1134/S1064229317030103
Seyedmohammadi J, Esmaeelnejad L, Shabanpour M (2016) Spatial variation modelling of groundwater electrical conductivity using geostatistics and GIS. Model Earth Syst Environ. https://doi.org/10.1007/s40808-016-0226-3
Shahid SA, Zaman M, Heng L (2018) Introduction to soil salinity, sodicity and diagnostics techniques. Guideline for salinity assessment mitigation and adaptation using nuclear and related techniques. Springer International Publishing, Cham, pp 1–42
Shi J, Wang H, Xu J et al (2007) Spatial distribution of heavy metals in soils: A case study of Changxing. Environ Geol, China. https://doi.org/10.1007/s00254-006-0443-6
Thomas A, Aryal J (2021) Spatial analysis methods and practice: describe–explore–explain through GIS. J Spat Sci. https://doi.org/10.1080/14498596.2021.1955816
UNCCD (2017) The global land outlook, 1st edn. UNCCD, Bonn
Ungureanu N, Vlăduț V, Voicu G (2020) Water scarcity and wastewater reuse in crop irrigation. Sustain 12:9055
Walter C, McBratney AB, Douaoui A, Minasny B (2001) Spatial prediction of topsoil salinity in the chelif valley, Algeria, using local ordinary kriging with local variograms versus whole-area variogram. Aust J Soil Res 39:259–272. https://doi.org/10.1071/SR99114
Wang L, Hu P, Zheng H et al (2023) Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images. Geoderma. https://doi.org/10.1016/j.geoderma.2022.116321
Webster R, Oliver MA (2008) Geostatistics for environmental scientists, 2nd edn. Wiley, Hoboken
Weil R, Brady N (2017) The nature and properties of soils, 15th edn. Wiley, Hoboken
Wicke B, Smeets E, Dornburg V et al (2011) The global technical and economic potential of bioenergy from salt-affected soils. Energy Environ Sci. https://doi.org/10.1039/c1ee01029h
Wood G, Oliver MA, Webster R (1990) Estimating soil salinity by disjunctive kriging. Soil Use Manag. https://doi.org/10.1111/j.1475-2743.1990.tb00817.x
Yates SR, Warrick AW, Myers DE (1986) Disjunctive Kriging: 1. Overview of estimation and conditional probability. Water Resour Res. https://doi.org/10.1029/WR022i005p00615
Zhang H, Ouyang Z, Jiang P et al (2022) Spatial distribution patterns and influencing factors of soil carbon, phosphorus, and C: P ratio on farmlands in southeastern China. Catena. https://doi.org/10.1016/j.catena.2022.106409
Zhao Y, Wang Y, Zhang X (2021) Spatial and temporal variation in soil bulk density and saturated hydraulic conductivity and its influencing factors along a 500 km transect. Catena. https://doi.org/10.1016/j.catena.2021.105592
Acknowledgements
This research was funded by the Deanship of Research, Jordan University of Science and Technology, Irbid, Jordan (Grant Number 50/2016).
Funding
Deanship of Research, Jordan University of Science and Technology, Irbid, Jordan, (Grant Number 50/2016)
Author information
Authors and Affiliations
Contributions
M.A.G wrote the initial draft, N.M. prepared spatial maps and wrote part of the methodology, A.A.A., C.P., and A.E. critically reviewed the manuscript, C.P. and A.E. suggestions, improvements, and interpretation of data in the text.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Conflict of interest
The authors declare that they have no known conflict of financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gharaibeh, M.A., Albalasmeh, A.A., Moos, N. et al. A comparative analysis to forecast salinity and sodicity distributions using empirical Bayesian and disjunctive kriging in irrigated soils of the Jordan valley. Environ Earth Sci 83, 238 (2024). https://doi.org/10.1007/s12665-024-11537-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-024-11537-x