Biomedical subjects

D J Brus

Publications and source records attributed to D J Brus.

4 recordsLinked to original sources

PubMed2006

Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?

Seventy-two squares of 100 ha were selected by stratified random sampling with probabilities proportional to size (pps) to survey landscape changes in the period 1996-2003. The area of the plots times the urbanization pressure was used as a size measure. The central question of this study is whether the sampling with probabilities proportional to size leads to gain in precision compared to equal probability sampling. On average 1.03 isolated buildings per 100 ha have been built, while 0.90 buildings per 100 ha have been removed, leading to a net change of 0.13 building per 100 ha. The area with unspoiled natural relief has been reduced by 2.3 ha per 100 ha, and the length of linear relicts by 137 m per 100 ha. On average 74 m of linear green elements have been planted per 100 ha, while 106 m have been removed, leading to a net change of -31 m per 100 ha. For the state variables 'unspoiled natural relief', 'linear relicts', 'removed linear green elements', and 'new-removed linear green elements' there is a gain in precision due to the pps-sampling. For the remaining state variables there is no gain or even a loss of precision ('new buildings', 'removed buildings', 'new-removed buildings', 'new linear green elements'). Therefore, if many state variables must be monitored or when interest is not only in the change but also in the current totals, we recommend to keep things simple, and to select plots with equal probability.

Environmental Monitoring↗

PubMed2003

A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables.

In estimating spatial means of environmental variables of a region from data collected by convenience or purposive sampling, validity of the results can be ensured by collecting additional data through probability sampling. The precision of the pi estimator that uses the probability sample can be increased by interpolating the values at the nonprobability sample points to the probability sample points, and using these interpolated values as an auxiliary variable in the difference or regression estimator. These estimators are (approximately) unbiased, even when the nonprobability sample is severely biased such as in preferential samples. The gain in precision compared to the pi estimator in combination with Simple Random Sampling is controlled by the correlation between the target variable and interpolated variable. This correlation is determined by the size (density) and spatial coverage of the nonprobability sample, and the spatial continuity of the target variable. In a case study the average ratio of the variances of the simple regression estimator and pi estimator was 0.68 for preferential samples of size 150 with moderate spatial clustering, and 0.80 for preferential samples of similar size with strong spatial clustering. In the latter case the simple regression estimator was substantially more precise than the simple difference estimator.

Data Collection↗

PubMedDate not supplied

Mapping the probability of exceeding critical thresholds for cadmium concentrations in soils in The Netherlands.

The probability of exceeding critical thresholds of Cd concentrations in the soil was mapped at a national scale. The critical thresholds in soil were based on food quality criteria for Cd in crops or in organs of cattle (Bos taurus), and were calculated by inverting a regression model for the Cd concentration in the crop, with the Cd concentration in soil, soil organic matter (SOM) content, clay content, and pH as predictors. The probability of exceeding the critical threshold for Cd in soil per node of a 500- x 500-m grid was approximated by Monte Carlo simulation, using the estimated cumulative distribution functions (cdf) of SOM, clay, pH, and Cd as input. The cdfs were estimated by simple indicator kriging with local prior means. For SOM, clay, and pH, detailed maps of soil type and land use were used to define subregions with assumed constant local means of the indicators (a priori distributions). The cdfs were sampled by Latin hypercube sampling. We accounted for correlation between the actual and critical Cd concentrations in soil by drawing Cd values from cdfs conditional on SOM and clay. The estimated probability for grassland is negligible, even in areas with high Cd concentrations in soil, and for maize (Zea mays L.) land the probability is almost everywhere smaller than 5%. For arable soils, however, these probabilities commonly are larger than 5% when sugar beet (Beta vulgaris L.) or wheat (Triticum aestivum L.) is taken as a reference crop, and locally exceed 50%.

Animals↗

PubMedDate not supplied

Uncertainty and sensitivity analysis of spatial predictions of heavy metals in wheat.

Heavy metals seriously threaten the health of human beings when they enter the food chain. Therefore, policymakers require precise predictions of heavy metal concentrations in agricultural crops. In this paper we quantify the uncertainty of regression predictions of Cd and Pb in wheat (Triticum aestivum L.) and the contributions to the uncertainties in these predictions associated with inputs to the regression model. For each node of the 500- x 500-m grid covering the arable soils in The Netherlands, a latin hypercube sample size of 1000 is constructed from the uncertainty distributions of the explanatory variables (pH, soil organic matter [SOM], and heavy metal concentration in soil), the regression coefficients, and the random term of the regression model. This sample is used as input for the regression model to obtain 1000 values from the uncertainty distributions of the log(Cd) and log(Pb) concentration in wheat. There were no nodes where the recent EU quality standards for Cd and Pb (0.2 mg kg(-1) fresh wt.) in wheat were almost certain to be exceeded. For most nodes with clay soils, the quality standard for Cd in wheat almost certainly will not be exceeded; for Pb this is much less certain. The uncertainty in the Cd concentration in soil contributes most to the uncertainty in the predicted Cd concentrations in wheat (36% on the average), followed by the random term of the regression model (23%). For Pb the contribution of the random term is by far the largest (52%).

Agriculture↗