Mapping trace element deficiency by cokriging from regional geochemical soil data: A case study on cobalt for grazing sheep in Ireland

Lark, R. M.; Ander, E. Louise; Cave, Mark; Knights, K.V.; Glennon, Mairead; Scanlon, Ray

doi:10.1016/j.geoderma.2014.03.002

Cited by 43 publications

(31 citation statements)

References 22 publications

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“…Following Lark et al . (), the map legend presents the probabilities (as percentages) together with the verbal phrases, to avoid some of the problems that have been shown to occur with purely verbal expressions (Budescu et al ., ).…”

Section: Resultsmentioning

confidence: 99%

“…The range of probabilities larger than 0.66 is divided into 'likely' and 'very likely' at 0.9. Following Lark et al (2014), the map legend presents the probabilities (as percentages) together with the verbal phrases, to avoid some of the problems that have been shown to occur with purely verbal expressions (Budescu et al, 2009). The distribution of available Mg shown in Figure 10 is the most detailed map of that variable published to date, because it uses all the RSSS data and NSI data (by cokriging).…”

Section: Spatial Variation Of Available Mg As Mapped By Cokrigingmentioning

confidence: 99%

“…E-mail: murray.lark@nottingham.ac.uk Received 13 April 2018;revised version accepted 10 September 2018 generalized information that shows, for example, where particular problems such as nutrient deficiencies or acidification might be expected to occur (e.g. Lark et al, 2014). In this paper we report a study to map available magnesium (Mg) in the topsoil of agricultural land across England and Wales.…”

Section: Introductionmentioning

confidence: 99%

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Combining two national‐scale datasets to map soil properties, the case of available magnesium in England and Wales

Lark

Ander

Broadley

2018

European J Soil Science

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Summary Given the costs of soil survey it is necessary to make the best use of available datasets, but data that differ with respect to some aspect of the sampling or analytical protocol cannot be combined simply. In this paper we consider a case where two datasets were available on the concentration of plant‐available magnesium in the topsoil. The datasets were the Representative Soil Sampling Scheme (RSSS) and the National Soil Inventory (NSI) of England and Wales. The variable was measured over the same depth interval and with the same laboratory method, but the sample supports were different and so the datasets differ in their variance. We used a multivariate geostatistical model, the linear model of coregionalization (LMCR), to model the joint spatial distribution of the two datasets. The model allowed us to elucidate the effects of the sample support on the two datasets, and to show that there was a strong correlation between the underlying variables. The LMCR allowed us to make spatial predictions of the variable on the RSSS support by cokriging the RSSS data with the NSI data. We used cross‐validation to test the validity of the LMCR and showed how incorporating the NSI data restricted the range of prediction error variances relative to univariate ordinary kriging predictions from the RSSS data alone. The standardized squared prediction errors were computed and the coverage of prediction intervals (i.e. the proportion of sites at which the prediction interval included the observed value of the variable). Both these statistics suggested that the prediction error variances were consistent for the cokriging predictions but not for the ordinary kriging predictions from the simple combination of the RSSS and NSI data, which might be proposed on the basis of their very similar mean values. The LMCR is therefore proposed as a general tool for the combined analysis of different datasets on soil properties. Highlights Differences in sample support mean that two datasets on a soil property cannot be combined simply. We showed how a multivariate geostatistical model can be used to elucidate the relationships between two such datasets. The same model allows soil properties to be mapped jointly from such data. This offers a general basis for combining soil datasets from diverse sources

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Section: Resultsmentioning

confidence: 99%

Section: Spatial Variation Of Available Mg As Mapped By Cokrigingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combining two national‐scale datasets to map soil properties, the case of available magnesium in England and Wales

Lark

Ander

Broadley

2018

European J Soil Science

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“…The approach used by Lark et al . () adopted the likelihood scale of the Intergovernmental Panel on Climate Change (IPCC) to describe quantified uncertainty using verbal scales (Mastrandrea et al ., ). These probabilities were then converted to verbal scales and mapped to support users where there was a potential need for intervention on land‐use and management.…”

Section: Methodsmentioning

confidence: 99%

Improving the information content in soil pH maps: a case study

Robinson

Benke

Norng³

et al. 2017

European J Soil Science

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Summary Uncertainties associated with legacy data contribute to the spatial uncertainty of predictions for soil properties such as pH. Examples of potential sources of error in maps of soil pH include temporal variation and changes in land use over time. Prediction of soil pH can be improved with a linear mixed model (LMM) to analyse factors that contribute to uncertainty. Probabilities from conditional simulations in combination with agronomic critical thresholds for acid‐sensitive species can be used to identify areas that are likely, or very likely, to be below these critical thresholds for plant production. Because of rapid changes in farming systems and management practices, there is a need to be vigilant in monitoring changes in soil acidification. This is because soil acidification is an important factor in primary production and soil sustainability. In this research, legacy data from south‐western Victoria (Australia) were used with model‐based geostatistics to produce a map of soil pH that accommodates a variety of error sources, such as the time of sampling, seasonal variation, differences in analytical method, effects of changes in land use and variable soil sample depth in legacy data. Spatial covariates that are representative of soil‐forming factors were used to improve predictions. To transform spatial prediction and estimates of error in soil pH into more informative and usable maps with more information content, simulations from the conditional distribution were used to compute the probability of a soil's pH being less than critical agronomic production thresholds at each of the prediction locations. These probabilities were mapped to reveal areas of potential risk. Highlights Can maps of soil pH be improved by accounting for temporal variation and change in land use? First example of taking account of temporal variability in sampling for pH in spatial models. Key factors for uncertainty in spatial prediction include time of sampling and sample depth. Accuracy improved by accounting for additional sources of error combined with conditional simulations.

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“…Kriging method uses a weight model that ensures more response to nearby points as in the method of weighted mean in conventional statistics theorem (McGrath et al, 2004). In the recent years, researchers have studies many kriging methods as; Simple kriging (Li et al, 2009), Ordinary kriging (Alvarez-Gallego et al, 2015;Elbasiouny et al, 2014;Sanusi et al, 2014), universal kriging (Cafaro et al, 2014;Hiemstra et al, 2009), cokriging (Lark et al, 2014;Warnery et al, 2015) and indicator kriging (Chica-Olmo et al, 2014). The most common two methods; ordinary and universal kriging were used for this study.…”

mentioning

confidence: 99%

Dose rate estimates and spatial interpolation maps of outdoor gamma dose rate with geostatistical methods; A case study from Artvin, Turkey

Yeşilkanat

Kobya

Taşkın³

et al. 2015

Journal of Environmental Radioactivity

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Mapping trace element deficiency by cokriging from regional geochemical soil data: A case study on cobalt for grazing sheep in Ireland

Cited by 43 publications

References 22 publications

Combining two national‐scale datasets to map soil properties, the case of available magnesium in England and Wales

Combining two national‐scale datasets to map soil properties, the case of available magnesium in England and Wales

Improving the information content in soil pH maps: a case study

Dose rate estimates and spatial interpolation maps of outdoor gamma dose rate with geostatistical methods; A case study from Artvin, Turkey

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