On spatial prediction of soil properties in the presence of a spatial trend: the empirical best linear unbiased predictor (E‐BLUP) with REML

By R. M. Lark, Brian R. Cullis, Sue J. Welham. 2006

268 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/9f84c6c8d375b13ea69daa9fec61e936877d762e

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Citations in the corpus

Kriging a soil variable with a simple nonstationary variance model (2009, background): “Lark, Cullis, and Welham (2006) discussed this in more detail.”
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (2018, methodology): “…highly anisotropic so optimizing variogram modeling through379 likfit is important (by default, geoR implements the Restricted Maximum Likelihood approach for380 estimation of variogram parameters, which is often considered the most reliable estimate of variogram381 parameters Lark et al. (2006)).”
A tutorial guide to geostatistics: Computing and modelling variograms and kriging (2014, methodology): “Lark et al. (2006) described and illustrated the method, and more recently Lark (2012) has summarized it.”
An error budget for different sources of error in digital soil mapping (2011, methodology): “where G is the correlation matrix where the correlation depends only on the relative locations of the observations, I is the identity matrix, σ 2 is the variance of the independent error and ξ is the ratio of the variance of u to σ 2 (Lark et al., 2006).”
Spatial interpolation methods applied in the environmental sciences: A review (2014, methodology): “RK-D performed better than SK in terms of the level of detail and accuracy, and RK-D (with 222 samples) even performed better than SK (with 2251 samples), leading to a suggestion that future studies should focus more on the quality of sampling and on quality of auxiliary environmental predictors, ra”
Establishing the precision and robustness of farmers’ crop experiments (2019, methodology): “We favour the maximum likelihood estimator (Diggle and Ribeiro, 2007; Lark et al., 2006) which Table 1”