Stratification sampling methods such as cLHS and stratified random sampling use existing ancillary information to provide an effective means for stratifying sampling locations within existing feature space and can offer improved spatial predictions of landscape-associated soil C contents. (cLHS) method, proposed by Minasny and McBratney (2006), provides soil scientists with a robust sample allocation. Mapped soil C contents from the three sampling methods using regression kriging resulted in root mean square error (В±SE) values of 11.1 В± 0.5, 13.4 В± 0.3, and 10.1 В± 0.2 Mg ha в€’1, respectively, for random, stratified, and cLHS sampling. The conditioned Latin hypercube sampling. This method proposes to stratify sampling in presence of ancillary data. Large advantages of cLHS relative to stratified random sampling were not apparent at this site, although cLHS consistently sampled the tails of the soil C content population distribution. clhs Conditioned Latin Hypercube Sampling Description Implementation of the conditioned Latin hypercube sampling, as published by Minasny and McBrat-ney (2006).

Random stratified and cLHS methods best approximated mean and variance of the population at sample sizes of 5 and 12%. These methods propose to stratify sampling in presence of ancillary data. A population of 903 gridded samples was collected and then subsampled based on stratification of curvature, slope, land cover, and soil type using 1, 5, 12, and 35% data set proportions. clhs Conditioned Latin Hypercube Sampling Description Implementation of the conditioned Latin hypercube sampling, as published by Minasny and McBrat-ney (2006) and the DLHS variant method (Minasny and McBratney, 2010). This study compared simple random, stratified random, and cLHS predictions of soil C content and their associated variability. Although the theoretical basis for cLHS has been demonstrated, few empirical evaluations have been performed. The potential advantage of cLHS is that it uses existing ancillary landscape data in geographic information systems to select stratified random samples. Various methods to minimize error have been posed, including conditioned Latin hypercube sampling (cLHS).

An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, ). Accurate estimates of soil carbon (C) contents over large spatial scales require extensive sampling and are susceptible to error associated with landscape variability. Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) .

Conditioned latin hypercube sampling