Assessment of temporal and spatial variability of soil salinity in a coastal saline field

Shi, Zhou; Li, Y.; Wang, R. C.; Makeschine, Franz

doi:10.1007/s00254-005-0035-x

Cited by 18 publications

(23 citation statements)

References 24 publications

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“…Moreover, cokriging method had the most accuracy for prediction of the SAR by means of the auxiliary variable of Na + and based on RMSE criterion. These results are similar to the fi ndings of Meul and van Meirvenne (2003), Shi et al (2005), Sokoti et al (2007) and Ayoubi et al (2007). They recognized that the cokriging method had the most superiority to other methods to predict soil properties as well.…”

Section: Resultssupporting

confidence: 82%

“…Shao et al (2006) by using of geostatistics techniques and kriging method determined the spatial distribution of the soil nutrients in Hebei province, China. Shi et al (2005) provided the salinity distribution maps in a coastal saline area in China using geostatistics methods. They reported that for prediction of SAR and EC, the cokriging method was the most suitable one.…”

Section: Resultsmentioning

confidence: 99%

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Mapping of Spatial Distribution of Soil Salinity and Alkalinity in a Semi-arid Region

Keshavarzi¹,

Sarmadian²

2012

Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation

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Mapping of Spatial Distribution of Soil Salinity and Alkalinity in a Semi-arid Region. Spatial variability of salinity and alkalinity is important for site-specifi c management since they are the most important factors infl uencing soil quality and agricultural production. Geostatistical methods provide a means to study the heterogeneous nature of spatial distributions of soil salinity and alkalinity. The present study was carried out to evaluate the accuracy of different spatial interpolation methods including kriging, cokriging and IDW methods for prediction of spatial distribution of salinity (EC) and sodium adsorption ratio (SAR) in soils of Ziaran region in Qazvin province, Iran. The tracking of the soil profi les was done using a Garmin eTrex-H model global positioning system (GPS) receiver. Sampling was done with stratifi ed random method and sixty soil samples from 0 to 15 cm depth were collected. After data normalization, the variograms were developed. For selecting the best model for competing on experimental variograms, the lower RSS value was used. Experimental variograms were fi tted to spherical and exponential models. The best model for interpretative was selected by means of cross validation and error evaluation methods, such as RMSE method. The sum of Ca 2+ + Mg 2+ and Na + concentration which were highly correlated with soil salinity and sodium adsorption ratio, respectively, are used as auxiliary parameters in this study. The results showed that kriging and cokriging methods were better than IDW method for prediction of EC and SAR. Finally, the soil EC and SAR maps were prepared, using different spatial interpolation methods in GIS environment.

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Section: Resultssupporting

confidence: 82%

Section: Resultsmentioning

confidence: 99%

Mapping of Spatial Distribution of Soil Salinity and Alkalinity in a Semi-arid Region

Keshavarzi¹,

Sarmadian²

2012

Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation

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“…The ratio of nugget variance to sill expressed in percentages (C 0 /C+C 0 ) can be regarded as a criterion for classifying the spatial dependence of the soil parameters. If this ratio is less than 25%, then the variable has strong spatial dependence (Shi et al, 2005). The pH, EC, OC, N, P, K, Zn and Cu showed the values between 25% and 75% considered as moderate spatial dependence, and the values greater than 75% have weak spatial dependence as shown in table, S Fe and Mn have moderate spatial structure.…”

Section: Spatial Variabilitymentioning

confidence: 99%

Spatial variability of Soil Nutrients Using Geospatial Techniques: A case study in soils of Sanwer Tehsil of Indore district of Madhya Pradesh

Tagore

Bairagi

Sharma

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A study was conducted to explore the spatial variability of major soil nutrients in a soybean grown region of Malwa plateau. From the study area, one hundred sixty two surface soil samples were collected by a random sampling strategy using GPS. Then soil physico-chemical properties i.e., pH, EC, organic carbon, soil available nutrients (N, P, K, S and Zn) were measured in laboratory. After data normalization, classical and geo-statistical analyses were used to describe soil properties and spatial correlation of soil characteristics. Spatial variability of soil physico-chemical properties was quantified through semi-variogram analysis and the respective surface maps were prepared through ordinary Kriging. Exponential model fits well with experimental semi-variogram of pH, EC, OC, available N, P, K, S and Zn. pH, EC, OC, N, P, and K has displayed moderate spatial dependence whereas S and Zn showed weak spatial dependence. Cross validation of kriged map shows that spatial prediction of soil nutrients using semi-variogram parameters is better than assuming mean of observed value for any un-sampled location. Therefore it is a suitable alternative method for accurate estimation of chemical properties of soil in un-sampled positions as compared to direct measurement which has time and costs concerned.

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“…However, these areas are so vulnerable to climatic changes because of poor drainage system and the intrusion of seawater (Datta et al, 2013;Lv et al, 2013). Due to periodic tidal activity, the salinity changes seasonally (Shi et al, 2005;Sun et al, 2012) which makes the soil complicated to manage and unsuitable for sustainable use. High salinity and pH in the coastal saline soils might be the two main limiting factors to enzyme activities.…”

Section: Introductionmentioning

confidence: 99%

Soil enzymes as indicators of saline soil fertility under various soil amendments

Liu

Zhang

Wang

et al. 2017

Agriculture, Ecosystems & Environment

162

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Assessment of temporal and spatial variability of soil salinity in a coastal saline field

Abstract: The online version of the original article can be found at http:// dx

Cited by 18 publications

References 24 publications

Mapping of Spatial Distribution of Soil Salinity and Alkalinity in a Semi-arid Region

Mapping of Spatial Distribution of Soil Salinity and Alkalinity in a Semi-arid Region

Spatial variability of Soil Nutrients Using Geospatial Techniques: A case study in soils of Sanwer Tehsil of Indore district of Madhya Pradesh

Soil enzymes as indicators of saline soil fertility under various soil amendments

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