Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Model-Based Geostatistics from a Bayesian Perspective: Investigating Area-to-Point Kriging with Small Data Sets
    Steinbuch, Luc ; Orton, Thomas G. ; Brus, Dick J. - \ 2020
    Mathematical Geosciences 52 (2020). - ISSN 1874-8961 - p. 397 - 423.
    Area-to-point kriging (ATPK) is a geostatistical method for creating high-resolution raster maps using data of the variable of interest with a much lower resolution. The data set of areal means is often considerably smaller (<50 observations) than data sets conventionally dealt with in geostatistical analyses. In contemporary ATPK methods, uncertainty in the variogram parameters is not accounted for in the prediction; this issue can be overcome by applying ATPK in a Bayesian framework. Commonly in Bayesian statistics, posterior distributions of model parameters and posterior predictive distributions are approximated by Markov chain Monte Carlo sampling from the posterior, which can be computationally expensive. Therefore, a partly analytical solution is implemented in this paper, in order to (i) explore the impact of the prior distribution on predictions and prediction variances, (ii) investigate whether certain aspects of uncertainty can be disregarded, simplifying the necessary computations, and (iii) test the impact of various model misspecifications. Several approaches using simulated data, aggregated real-world point data, and a case study on aggregated crop yields in Burkina Faso are compared. The prior distribution is found to have minimal impact on the disaggregated predictions. In most cases with known short-range behaviour, an approach that disregards uncertainty in the variogram distance parameter gives a reasonable assessment of prediction uncertainty. However, some severe effects of model misspecification in terms of overly conservative or optimistic prediction uncertainties are found, highlighting the importance of model choice or integration into ATPK.
    Source code in the R programming language, belonging with: Model based geostatistics from a Bayesian perspective: Investigating area‐to‐point kriging with small datasets
    Steinbuch, L. ; Orton, T.G. ; Brus, D.J. - \ 2019
    Wageningen University & Research
    area-to-point kriging - Bayesian statistics - geostatistics - prediction uncertainty - prediction variance - spatial disaggregation - spatial statistics
    Area-to-point kriging (ATPK) is a geostatistical method for creating maps of high resolution using data of much lower resolution. These R-scripts compare prediction uncertainty using different ATPK methods, using simulations and a real world case concerning crop yields in Burkina Faso.
    Model-based geostatistics from a Bayesian perspective: Investigating area-to-point kriging with small datasets
    Steinbuch, L. ; Orton, Thomas ; Brus, D.J. - \ 2019
    Area-to-point kriging (ATPK) is a geostatistical method for creating raster maps of high resolution using data of the variable ofinterest of much lower resolution. The dataset of areal means is often considerably smaller than the size of dataset conventionallydealt with in geostatistical analyses. In contemporary ATPK methods, uncertainty in the variogram parameters is not accounted forin the prediction; this issue can be overcome by applying ATPK in a Bayesian framework. Commonly in Bayesian statistics,posterior distributions of model parameters and posterior predictive distributions are approximated by Markov chain Monte Carlosampling from the posterior, which can be computationally expensive. We therefore implemented a partly analytical solution. Weused this implementation to (i) explore the impact of the prior distribution on predictions and prediction variances, (ii) investigatewhether certain aspects of uncertainty can be disregarded, simplifying the necessary computations, and (iii) test the impact ofvarious model misspecifications. We compared several approaches using simulated data, real-world point data that we aggregatedourselves, and a case study on aggregated crop yields in Burkina Faso. We found the prior distribution to have minimal impact onthe disaggregated predictions.We found that in most cases with known short-range behaviour, an approach that disregardeduncertainty in the variogram range parameter gave a reasonable assessment of prediction uncertainty. However, we found somesevere effects of model misspecification in terms of overly conservative or optimistic prediction uncertainties, highlighting the importance of model choice or integration in ATPK.
    Bayesian Generalized Linear Geostatistical Modelling for mapping subsoil ripening
    Steinbuch, L. ; Heuvelink, G.B.M. - \ 2018
    An analytical approach for Bayesian area-to-point kriging: a case study with crop yields
    Steinbuch, L. ; Brus, D.J. ; Orton, Thomas - \ 2018
    Bayesian Generalized Linear Geostatistical Modelling for mapping subsoil ripening
    Steinbuch, L. ; Heuvelink, G.B.M. - \ 2018
    An analytical approach for Bayesian area-to-point kriging: a case study with crop yields
    Steinbuch, Luc - \ 2018
    An analytical approach for Bayesian area-to-point kriging: a case study with crop yields
    Steinbuch, Luc - \ 2018
    Bayesian Generalized Linear Geostatistical Modelling for mapping subsoil ripening
    Steinbuch, Luc - \ 2018
    Mapping the probability of ripened subsoils using Bayesian logistic regression with informative priors
    Steinbuch, Luc ; Brus, Dick J. ; Heuvelink, Gerard B.M. - \ 2018
    Geoderma 316 (2018). - ISSN 0016-7061 - p. 56 - 69.
    Bayesian statistics - Binomial logistic regression - Informative priors - Soil mapping - Soil mapping uncertainty - Soil ripening

    One of the first soil forming processes in marine and fluviatile clay soils is ripening, the irreversible change of physical and chemical soil properties, especially consistency, under influence of air. We used Bayesian binomial logistic regression (BBLR) to update the map showing unripened subsoils for a reclamation area in the west of The Netherlands. Similar to conventional binomial logistic regression (BLR), in BBLR the binary target variable (the subsoil is ripened or unripened) is modelled by a Bernoulli distribution. The logit transform of the `probability of success' parameter of the Bernoulli distribution was modelled as a linear combination of the covariates soil type, freeboard (the desired water level in the ditches, compared to surface level) and mean lowest groundwater table. To capture all available information, Bayesian statistics combines legacy data summarized in a ‘prior’ probability distribution for the regression coefficients with actual observations. Our research focused on quantifying the influence of priors with different information levels, in combination with different sample sizes, on the resulting parameters and maps. We combined subsamples of different size (ranging from 5% to 50% of the original dataset of 676 observations) with priors representing different levels of trust in legacy data and investigated the effect of sample size and prior distribution on map accuracy. The resulting posterior parameter distributions, calculated by Markov chain Monte Carlo simulation, vary in centrality as well as in dispersion, especially for the smaller datasets. More informative priors decreased dispersion and pushed posterior central values towards prior central values. Interestingly, the resulting probability maps were almost similar. However, the associated uncertainty maps were different: a more informative prior decreased prediction uncertainty. When using the ‘overall accuracy’ validation metric, we found an optimal value for the prior information level, indicating that the standard deviation of the legacy data regression parameters should be multiplied by 10. This effect is only detectable for smaller datasets. The Area Under Curve validation statistic did not provide a meaningful optimal multiplier for the standard deviation. Bayesian binomial logistic regression proved to be a flexible mapping tool but the accuracy gain compared to conventional logistic regression was marginal and may not outweigh the extra modelling and computing effort.

    Mapping subsoil ripening using Bayesian Generalized Linear Modelling
    Steinbuch, L. ; Brus, D.J. ; Heuvelink, G.B.M. - \ 2017
    In: Abstract Book Pedometrics 2017. - Wageningen : - p. 228 - 228.
    Geostatistical interpolation and aggregation of crop growth model outputs
    Steinbuch, L. - \ 2016
    Geostatistical interpolation and aggregation of crop growth model outputs
    Steinbuch, L. - \ 2016
    Many mechanistic crop growth models require daily meteorological data; consequently, model simulations can only be obtained for locations close to weather stations with long-term records. Those simulations deliver potential yields as point data.
    A widely used approach for aggregating (estimating total production per country from the simulated yields at points) is based on agro-ecological Climate Zones (CZ), e.g. the Global Yield Gap Atlas (www.yieldgap.org). A geostatistical approach that exploits the spatial correlation of simulated yields at points and its correlation with external environmental factors offers additional features to the CZ approach: yield predictions adjusted to conditions on every single location, quantification of the uncertainty of the predictions, and quantification of uncertainty of aggregated country production. As a case study, we interpolate and aggregate potential yields of millet in West Africa. We compare the results of the geostatistical approach with those of the CZ approach.
    Column : Energiegevers gezocht
    Vries, J.W. de - \ 2016
    Bloemenkrant 2016 (2016)27 april. - 1 p.
    We gaan op het gebied van robotica nog heel wat meemaken. Op de jaarbijeenkomst van Greenport Westland Oostland op 15 april hield Maarten Steinbuch een presentatie over welke technieken en wat voor robots een rol gaan spelen in onze sector.
    Geostatistical interpolation and aggregation of crop growth model outputs
    Steinbuch, Luc ; Brus, Dick J. ; Bussel, Lenny G.J. van; Heuvelink, Gerard B.M. - \ 2016
    European Journal of Agronomy 77 (2016). - ISSN 1161-0301 - p. 111 - 121.
    Geostatistics - Spatial aggregation - Spatial prediction - Uncertainty - Yield gap - Yield potential

    Many crop growth models require daily meteorological data. Consequently, model simulations can be obtained only at a limited number of locations, i.e. at weather stations with long-term records of daily data. To estimate the potential crop production at country level, we present in this study a geostatistical approach for spatial interpolation and aggregation of crop growth model outputs. As case study, we interpolated, simulated and aggregated crop growth model outputs of sorghum and millet in West-Africa. We used crop growth model outputs to calibrate a linear regression model using environmental covariates as predictors. The spatial regression residuals were investigated for spatial correlation. The linear regression model and the spatial correlation of residuals together were used to predict theoretical crop yield at all locations using kriging with external drift. A spatial standard deviation comes along with this prediction, indicating the uncertainty of the prediction. In combination with land use data and country borders, we summed the crop yield predictions to determine an area total. With spatial stochastic simulation, we estimated the uncertainty of that total production potential as well as the spatial cumulative distribution function. We compared our results with the prevailing agro-ecological Climate Zones approach used for spatial aggregation. Linear regression could explain up to 70% of the spatial variation of the yield. In three out of four cases the regression residuals showed spatial correlation. The potential crop production per country according to the Climate Zones approach was in all countries and cases except one within the 95% prediction interval as obtained after yield aggregation. We concluded that the geostatistical approach can estimate a country's crop production, including a quantification of uncertainty. In addition, we stress the importance of the use of geostatistics to create tools for crop modelling scientists to explore relationships between yields and spatial environmental variables and to assist policy makers with tangible results on yield gaps at multiple levels of spatial aggregation.

    From bounded-noise data to robust PI-controller design
    Steinbuch, Luc ; Keesman, K.J. - \ 2015
    In: European Control Conference, ECC 1999 - Conference Proceedings. - Institute of Electrical and Electronics Engineers Inc. - ISBN 9783952417355 - p. 1988 - 1993.
    bounded-noise - PI-control - robustness - set-membership identification

    An approach is presented to design a robust PI-controller from bounded noise measurement data of a first order process with and without time delay. This controller guarantees a known robust performance. It is shown that in the case without time delay, the conservatism of the robust approach can be reduced by consciously choosing the nominal plant. The theory is illustrated to a first-order process with time delay but it can be extended to second order plants with more general PID-controllers.

    www.gewasbeschermingsmaatregelen.nl
    Steinbuch, Frits - \ 2008
    De website GEWASBESCHERMINGSMAATREGELEN is vanaf 2014 helaas niet meer actief. Voor informatie kunt u zich wenden tot: Erik Toussaint Hoofd Communicatie plant sciences van Wageningen UR E-mail: erik.toussaint@wur.nl
    Evaluation of the 'Fertigation Model', a decision support system for water and nutrient supply for soil grown greenhouse crops
    Voogt, W. ; Winkel, A. van; Steinbuch, F. - \ 2006
    In: III International Symposium on Models for Plant Growth, Environmental Control and Farm Management in Protected Cultivation (HortiModel 2006), Wageningen, the Netherlands, 29-10-2006 / Marcelis, L.F.M., van Straten, G., ISHS - ISBN 9789066056091 - p. 531 - 538.
    Soil grown greenhouse crops require high fertilisation rates. Combined with the common practice of over-irrigation, leaching of nutrients is a serious problem. In order to reduce the environmental impact, a `fertigation¿ model was developed as a decision support system for irrigation and fertiliser supply. The applicability in growers practice was evaluated during two years on commercial nurseries, growing chrysanthemum (Dendranthema grandiflorum). The evaluation was performed by comparison of the actual water and irrigation strategy of the growers with the strategy recommended by the model in a specific section within the same greenhouse. At one chrysanthemum grower a lysimeter was installed to measure water and nutrient leaching. The model performed well in general, without any yield or quality decline by using the model. The irrigation and the nitrogen surplus were decreased significantly compared to the growers standard and consequently reduced the environmental impact. The results indicate also that application of this model depends highly on the growers¿ attitude towards the environmental impact of irrigation and fertilisation at one hand and the avoidance of risks at the other
    From bounded-noise data to robust PI-controller design
    Steinbuch, L. ; Keesman, K.J. - \ 1999
    In: Proceedings European Control Conference ECC'99, Paper F672 (CD-Rom), 31 Aug. - 3 Sep. 1999, Karlsruhe, Germany. - Düsseldorf, Germany : VDI/VDE,Society for Measurement and Automatic Control, 1999 - p. 6 - 6.
    Some possibilities of microwave blanching in the canning industry.
    Stolp, W. ; Zuilichem, D.J. van; Spaans, E.J. ; Steinbuch, E. - \ 1991
    Mikrowellen & HF Magazin 17 (1991). - p. 393 - 398.
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