Using Indicator Kriging Technique for Soil Salinity and Yield Management

Eldeiry, Ahmed A.; Garcia, Luis A.

doi:10.1061/(asce)ir.1943-4774.0000280

Cited by 15 publications

(8 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimated value in the unsampling sites by Indicator Kriging indicates the probability that the values are more or less than a specified cut-off. Indicator Kriging has been used in soil science, which included the quality of soils (Smith et al, 1993;Halvorson et al, 1996;Oyedele et al;, soil degradation (Diodato and Ceccarelli, 2004;Eldeiry and Garcia, 2011), as well as environmental risk of heavy metals (Goovaerts, 1997;Van Meirvenne and Goovaerts, 2001;Chu et al, 2010). Furthermore, the application of IK also spread into hydrology and water resource science, ecology, including groundwater pollution risk, potentiality of water resources (Lee et al, 2008;Jang, 2013).…”

Section: Introductionmentioning

confidence: 99%

Identifying sources and hazardous risks of heavy metals in topsoils of rapidly urbanizing East China

Yang

et al. 2016

J. Geogr. Sci.

View full text Add to dashboard Cite

With rapid economic and social development, soil contamination arising from heavy metals has become a serious problem in many parts of China. We collected a total of 445 samples (0-20 cm) at the nodes of a 2 km×2 km grid in surface soils of Rizhao city, and analyzed sources and risk pattern of 10 heavy metals (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb and Zn). The combination of Multivariate statistics analysis and Geostatistical methods was applied to identify the sources and hazardous risk of heavy metals in soils. The result indicated that Cr, Ni, Co, Mn, Cu, and As were mainly controlled by parent materials and came from natural sources. Cd and Hg originated from anthropogenic sources. Pb and Zn, belonging to different groups in multivariate analysis, were associated with joint effect of parent materials and human inputs. Ordinary Kriging and Indicator Kriging suggested that single element and elements association from the same principal components had similar spatial distribution. Through comprehensive assessment on all elements, we also found the high risk areas were located in the populated urban areas and western study area, which could be attributed to the higher geological background in the western part and strong human interference in the eastern part.

show abstract

Section: Introductionmentioning

confidence: 99%

Identifying sources and hazardous risks of heavy metals in topsoils of rapidly urbanizing East China

Yang

et al. 2016

J. Geogr. Sci.

View full text Add to dashboard Cite

show abstract

“…For example, variation on wine grape yield (total harvested weight) has been shown to vary 8-to 10-fold between areas within a single field (Bramley and Hamilton 2004). Numerous studies have investigated different soil and topographic influences on the spatial uniformity of crop yield for a variety of crops (Elms et al 2001;Kravchenko et al 2005;Eldeiry and Garcia 2011).…”

Section: Introductionmentioning

confidence: 99%

Center Pivot Sprinkler Distribution Uniformity Impacts on the Spatial Variability of Evapotranspiration

Howes

Ellenson

Hoffmann

et al. 2015

J. Irrig. Drain Eng.

View full text Add to dashboard Cite

Understanding variable evapotranspiration (ET) throughout a field can help maximize yield on a per-acre basis, as well as assist with proper irrigation scheduling. The results from this study indicate that irrigation system distribution uniformity (DU) has a significant effect on the uniformity of ET during water-stressed periods. The study site involved intensely managed forage (alfalfa and winter grain hay) irrigated by center pivots being supplied with reclaimed water near Palmdale, California. During spring and early summer 2007 the center pivots were operating under deficit irrigation. In 2010, after the installation of reservoirs, water was applied to meet full evapotranspiration (ET c ) demands. Using remote sensing of actual evapotranspiration, the variability in ET c for the same pivots with the same crop was quantified. During the non-water-stressed period, ET uniformity was significantly better than during the water-stressed period (2007). The difference in uniformity was found to be attributable to irrigation system distribution uniformity. For the 540 ha used in this study, irrigation system DU was found to explain 55% of the ET nonuniformity during deficit irrigation. A method to predict the nonuniformity in ET as a result of irrigation system DU and water-stress level is presented.

show abstract

“…For characterizing subsoil salinity, electromagnetic (EM) induction data (e.g. EM38) are an option because many studies have demonstrated how apparent electrical conductivity (EC a ) can be used to measure (Triantafilis et al ., ) and then to map spatial variability in salinity with depth (Eldeiry & Garcia, ; Li et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China

Guo

Shi

et al. 2013

Soil Use and Management

View full text Add to dashboard Cite

In coastal China, there is an urgent need to increase land for agriculture. One solution is land reclamation from coastal tidelands, but soil salinization poses a problem. Thus, there is need to map saline areas and identify appropriate management strategies. One approach is the use of digital soil mapping. At the first stage, auxiliary data such as remotely sensed multispectral imagery can be used to identify areas of low agricultural productivity due to salinity. Similarly, proximal sensing instruments can provide data on the distribution of soil salinity. In this study, we first used multispectral QuickBird imagery (Bands 1–4) to provide information about crop growth and then EM38 data to indicate relative salt content using measurements of apparent soil electrical conductivity (ECa) in the horizontal (ECh) and vertical (ECv) modes of operation. Second, we used a fuzzy k‐means (FKM) algorithm to identify three salinity management zones using the normalized difference vegetation index (NDVI), ECh and ECv/ECh. The three identified classes were statistically different in terms of auxiliary and topsoil properties (e.g. soil organic matter) and more importantly in terms of the distribution of soil salinity (ECe) with depth. The resultant three classes were mapped to demonstrate that remote and proximally sensed auxiliary data can be used as surrogates for identifying soil salinity management zones.

show abstract

Using Indicator Kriging Technique for Soil Salinity and Yield Management

Cited by 15 publications

References 46 publications

Identifying sources and hazardous risks of heavy metals in topsoils of rapidly urbanizing East China

Identifying sources and hazardous risks of heavy metals in topsoils of rapidly urbanizing East China

Center Pivot Sprinkler Distribution Uniformity Impacts on the Spatial Variability of Evapotranspiration

Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China

Contact Info

Product

Resources

About