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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    Uncertainty in ecosystem services maps : The case of carbon stocks in the Brazilian Amazon forest using regression analysis
    Clec’h, Solen Le; Dufour, Simon ; Bucheli, Janic ; Grimaldi, Michel ; Huber, Robert ; Souza Miranda, Izildinha de; Mitja, Danielle ; Silva Costa, Luiz Gonzaga ; Oszwald, Johan - \ 2019
    Wadden Sea Ecosystem 4 (2019). - ISSN 0946-896X
    Deforestation - Ecosystem services - Prediction intervals - Reliability - Statistical modelling - Variability

    Ecosystem Service (ES) mapping has become a key tool in scientific assessments of human-nature interactions and is being increasingly used in environmental planning and policy-making. However, the associated epistemic uncertainty underlying these maps often is not systematically considered. This paper proposes a basic procedure to present areas with lower statistical reliability in a map of an ES indicator, the vegetation carbon stock, when extrapolating field data to larger case study regions. To illustrate our approach, we use regression analyses to model the spatial distribution of vegetation carbon stock in the Brazilian Amazon forest in the State of Pará. In our analysis, we used field data measurements for the carbon stock in three study sites as the response variable and various land characteristics derived from remote sensing as explanatory variables for the ES indicator. We performed regression methods to map the carbon stocks and calculated three indicators of reliability: RMSE-Root-mean-square-error, R -coefficient of determination - from an out-of-sample validation and prediction intervals. We obtained a map of carbon stocks and made explicit its associated uncertainty using a general indicator of reliability and a map presenting the areas where our prediction is the most uncertain. Finally, we highlighted the role of environmental factors on the range of uncertainty. The results have two implications. (1) Mapping prediction interval indicates areas where the map’s reliability is the highest. This information increases the usefulness of ES maps in environmental planning and governance. (2) In the case of the studied indicator, the reliability of our prediction is very dependent on land cover type, on the site location and its biophysical, socioeconomic and political characteristics. A better understanding of the relationship between carbon stock and land-use classes would increase the reliability of the maps. Results of our analysis help to direct future research and fieldwork and to prevent decision-making based on unreliable maps.

    Heat resistance of spores of 18 strains of Geobacillus stearothermophilus and impact of culturing conditions
    Wells-Bennik, Marjon H.J. ; Janssen, Patrick W.M. ; Klaus, Verena ; Yang, Chi ; Zwietering, Marcel H. ; Besten, Heidy M.W. Den - \ 2019
    International Journal of Food Microbiology 291 (2019). - ISSN 0168-1605 - p. 161 - 172.
    Enumeration - Germination - Inactivation - Modelling - Sporulation - Variability

    In this study, different methods were evaluated for enumeration of spores of G. stearothermophilus, different sporulation methods were assessed for yields and wet heat resistances of obtained spores, and subsequently, the variation in heat resistances of spores was determined. Overall, tryptone soya agar (TSA) was the most suitable medium for enumeration of spores of this thermophilic bacterium. Sporulation on different media both at 55 and at 61 °C led to considerable variation in spore heat resistance. The heat resistance of spores was highest upon sporulation on medium supplemented with free ions of calcium, potassium, magnesium and manganese (CaKMgMn). For 18 different G. stearothermophilus strains that were isolated from various sources, spores were subsequently produced on nutrient agar supplemented with CaKMgMn at 55 °C. Strain ATCC 12980T, also known as 9A20, which is commonly used in steam sterilization tests was included. The survival of spores of all strains was assessed at 125 °C and 130 °C using two independent spore batches per strain. The mean D125°C for spores of the 18 strains was 1.1 min (95% PI 0.48–2.3 min) and the mean D130°C was 0.37 min (95% PI 0.17–0.82 min). For spore inactivation of these 18 strains, a z-value of 11.1 °C was estimated, resulting in an estimated D-value of 2.4 min (95% PI 1.1–5.2) at the reference temperature 121.1 °C. Based on the data sets obtained in this study, it was found that the variability in spore heat resistance could largely be attributed to strain variability and conditions used during sporulation (especially the sporulation medium); reproduction and experimental variabilities were much smaller. The established variabilities were compared with the overall variability in spore heat resistance of G. stearothermophilus based on a meta-analysis of reported D-values. The data presented indicate that strain variability and history of sporulation each account for approximately half of the overall variability observed with respect to the heat resistance of spores of G. stearothermophilus. The findings presented in this study allow for optimal recovery of G. stearothermophilus spores from foods and a better understanding of factors that determine the heat resistance properties of spores of G. stearothermophilus. Moreover, this study once more underlines the limited effects of heat treatments used in the food industry on inactivation of spores of this bacterium.

    Natural Diversity in Heat Resistance of Bacteria and Bacterial Spores : Impact on Food Safety and Quality
    Besten, Heidy M.W. Den; Wells-Bennik, Marjon H.J. ; Zwietering, Marcel H. - \ 2018
    Annual Review of Food Science and Technology 9 (2018). - ISSN 1941-1413 - p. 383 - 410.
    D-value - Pathogens - Spoilage organisms - Thermotolerance - Variability - z-value
    Heat treatments are widely used in food processing often with the aim of reducing or eliminating spoilage microorganisms and pathogens in food products. The efficacy of applying heat to control microorganisms is challenged by the natural diversity of microorganisms with respect to their heat robustness. This review gives an overview of the variations in heat resistances of various species and strains, describes modeling approaches to quantify heat robustness, and addresses the relevance and impact of the natural diversity of microorganisms when assessing heat inactivation. This comparison of heat resistances of microorganisms facilitates the evaluation of which (groups of) organisms might be troublesome in a production process in which heat treatment is critical to reducing the microbial contaminants, and also allows fine-tuning of the process parameters. Various sources of microbiological variability are discussed and compared for a range of species, including spore-forming and non-spore-forming pathogens and spoilage organisms. This benchmarking of variability factors gives crucial information about the most important factors that should be included in risk assessments to realistically predict heat inactivation of bacteria and spores as part of the measures for controlling shelf life and safety of food products.
    Crop yield gap and stability in organic and conventional farming systems
    Schrama, M. ; Haan, J.J. de; Kroonen, M. ; Verstegen, H. ; Putten, W.H. Van der - \ 2018
    Agriculture, Ecosystems and Environment 256 (2018). - ISSN 0167-8809 - p. 123 - 130.
    Ecological intensification - Nutrient leaching - Soil communities - Spatial stability - Sustainable intensification - Variability
    A key challenge for sustainable intensification of agriculture is to produce increasing amounts of food and feed with minimal biodiversity loss, nutrient leaching, and greenhouse gas emissions. Organic farming is considered more sustainable, however, less productive than conventional farming. We analysed results from an experiment started under identical soil conditions comparing one organic and two conventional farming systems. Initially, yields in the organic farming system were lower, but approached those of both conventional systems after 10–13 years, while requiring lower nitrogen inputs. Unexpectedly, organic farming resulted in lower coefficient of variation, indicating enhanced spatial stability, of pH, nutrient mineralization, nutrient availability, and abundance of soil biota. Organic farming also resulted in improved soil structure with higher organic matter concentrations and higher soil aggregation, a profound reduction in groundwater nitrate concentrations, and fewer plant-parasitic nematodes. Temporal stability between the three farming systems was similar, but when excluding years of Phytophthora outbreaks in potato, temporal stability was higher in the organic farming system. There are two non-mutually exclusive mechanistic explanations for these results. First, the enhanced spatial stability in the organic farming system could result from changes in resource-based (i.e. bottom-up) processes, which coincides with the observed higher nutrient provisioning throughout the season in soils with more organic matter. Second, enhanced resource inputs may also affect stability via increased predator-based (i.e. top-down) control. According to this explanation, predators stabilize population dynamics of soil organisms, which is supported by the observed higher soil food web biomass in the organic farming system.We conclude that closure of the yield gap between organic and conventional farming can be a matter of time and that organic farming may result in greater spatial stability of soil biotic and abiotic properties and soil processes. This is likely due to the time required to fundamentally alter soil properties.
    Phases or regimes? Revisiting NDVI trends as proxies for land degradation
    Easdale, Marcos Horacio ; Bruzzone, Octavio ; Mapfumo, Paul ; Tittonell, Pablo - \ 2018
    Land Degradation and Development 29 (2018)3. - ISSN 1085-3278 - p. 433 - 445.
    Desertification - MODIS - Time series analysis - Variability - Wavelets
    One of the main challenges in land degradation assessment is that a rigorous and systematic approach to addressing its complex dynamics is still missing. The development and application of operative tools at regional and global scales remain a challenge. Land degradation is usually defined as a long-term decline in ecosystem function and productivity. Due to its temporal and spatial resolution as well as data availability, the use of time series of spectral vegetation indexes obtained from satellite sensors has become frequent in recent studies in this field. Slope of linear trends of the normalized difference vegetation index is usually considered an accurate indicator and is widely used as a proxy for land degradation. Yet this method is built on a number of simplifying conceptual and methodological assumptions that prevent capturing more complex dynamics, such as cyclic or periodic behaviors. Our aim was to examine the limitations associated with using linear normalized difference vegetation index trends as proxies for land degradation by comparing outcomes with an alternative methodological procedure based on wavelet autoregressive methods. We explored these issues in 5 case studies from Africa and South America. We observed that trend explained a marginal portion of total temporal variability, whereas monotonic functions, such as linear trends, were unable to capture dynamics that were non-unidirectional, resulting in misinterpretation of actual trends. Wavelet autoregressive method results were encouraging as a step towards the application of more accurate methods to provide sound scientific information of land degradation and restoration.
    Next generation of microbiological risk assessment : Potential of omics data for exposure assessment
    Besten, Heidy M.W. den; Amézquita, Alejandro ; Bover-Cid, Sara ; Dagnas, Stéphane ; Ellouze, Mariem ; Guillou, Sandrine ; Nychas, George ; O'Mahony, Cian ; Pérez-Rodriguez, Fernando ; Membré, Jeanne Marie - \ 2018
    International Journal of Food Microbiology 287 (2018). - ISSN 0168-1605 - p. 18 - 27.
    Food safety - Microbial dynamics - Microbiota - Public health - Variability
    In food safety and public health risk evaluations, microbiological exposure assessment plays a central role as it provides an estimation of both the likelihood and the level of the microbial hazard in a specified consumer portion of food and takes microbial behaviour into account. While until now mostly phenotypic data have been used in exposure assessment, mechanistic cellular information, obtained using omics techniques, will enable the fine tuning of exposure assessments to move towards the next generation of microbiological risk assessment. In particular, metagenomics can help in characterizing the food and factory environment microbiota (endogenous microbiota and potentially pathogens) and the changes over time under the environmental conditions associated with processing, preservation and storage. The difficulty lies in moving up to a quantitative exposure assessment, because the development of models that enable the prediction of dynamics of pathogens in a complex food ecosystem is still in its infancy in the food safety domain. In addition, collecting and storing the environmental data (metadata) required to inform the models has not yet been organised at a large scale. In contrast, progress in biomarker identification and characterization has already opened the possibility of making qualitative or even quantitative connection between process and formulation conditions and microbial responses at the strain level. In term of modelling approaches, without changing radically the usual model structure, changes in model inputs are expected: instead of (or as well as) building models upon phenotypic characteristics such as for example minimal temperature where growth is expected, exposure assessment models could use biomarker response intensity as inputs. These new generations of strain-level models will bring an added value in predicting the variability in pathogen behaviour. Altogether, these insights based upon omics techniques will increase our (quantitative) knowledge on pathogenic strains and consequently will reduce our uncertainty; the exposure assessment of a specific combination of pathogen and food will be then more accurate. This progress will benefit the whole community of safety assessors and research scientists from academia, regulatory agencies and industry.
    The borderline range of toxicological methods : Quantification and implications for evaluating precision
    Leontaridou, Maria ; Urbisch, Daniel ; Kolle, Susanne N. ; Ott, Katharina ; Mulliner, Denis S. ; Gabbert, Silke ; Landsiedel, Robert - \ 2017
    Altex 34 (2017)4. - ISSN 1868-596X - p. 525 - 538.
    Borderline range - Non-animal methods - Skin sensitization - Variability
    Test methods to assess the skin sensitization potential of a substance usually use threshold criteria to dichotomize continuous experimental read-outs into yes/no conclusions. The threshold criteria are prescribed in the respective OECD test guidelines and the conclusion is used for regulatory hazard assessment, i.e., classification and labelling of the substance. We can identify a borderline range (BR) around the classification threshold within which test results are inconclusive due to a test method's biological and technical variability. We quantified BRs in the prediction models of the non-animal test methods DPRA, LuSens and h-CLAT, and of the animal test LLNA, respectively. Depending on the size of the BR, we found that between 6% and 28% of the substances in the sets tested with these methods were considered borderline. When the results of individual non-animal test methods were combined into integrated testing strategies (ITS), borderline test results of individual tests also affected the overall assessment of the skin sensitization potential of the testing strategy. This was analyzed for the 2-out-of-3 ITS: Four out of 40 substances (10%) were considered borderline. Based on our findings we propose expanding the standard binary classification of substances into "positive"/"negative" or "hazardous"/"non-hazardous" by adding a "borderline" or "inconclusive" alert for cases where test results fall within the borderline range.
    Variation that can be expected when using particle tracking models in connectivity studies
    Hufnagl, Marc ; Payne, Mark ; Lacroix, Geneviève ; Bolle, Loes J. ; Daewel, Ute ; Dickey-Collas, Mark ; Gerkema, Theo ; Huret, Martin ; Janssen, Frank ; Kreus, Markus ; Pätsch, Johannes ; Pohlmann, Thomas ; Ruardij, Piet ; Schrum, Corinna ; Skogen, Morten D. ; Tiessen, Meinard C.H. ; Petitgas, Pierre ; Beek, Jan K.L. van; Veer, Henk W. van der; Callies, Ulrich - \ 2017
    Journal of Sea Research 127 (2017). - ISSN 1385-1101 - p. 133 - 149.
    Ensemble - Lagrangian approach - Marine protected areas - Model intercomparison - Ocean circulation - Renewable energy - Variability - Wind park

    Hydrodynamic Ocean Circulation Models and Lagrangian particle tracking models are valuable tools e.g. in coastal ecology to identify the connectivity between offshore spawning and coastal nursery areas of commercially important fish, for risk assessment and more for defining or evaluating marine protected areas. Most studies are based on only one model and do not provide levels of uncertainty. Here this uncertainty was addressed by applying a suite of 11 North Sea models to test what variability can be expected concerning connectivity. Different notional test cases were calculated related to three important and well-studied North Sea fish species: herring (Clupea harengus), and the flatfishes sole (Solea solea) and plaice (Pleuronectes platessa). For sole and plaice we determined which fraction of particles released in the respective spawning areas would reach a coastal marine protected area. For herring we determined the fraction located in a wind park after a predefined time span. As temperature is more and more a focus especially in biological and global change studies, furthermore inter-model variability in temperatures experienced by the virtual particles was determined. The main focus was on the transport variability originating from the physical models and thus biological behavior was not included. Depending on the scenario, median experienced temperatures differed by 3. °C between years. The range between the different models in one year was comparable to this temperature range observed between modelled years. Connectivity between flatfish spawning areas and the coastal protected area was highly dependent on the release location and spawning time. No particles released in the English Channel in the sole scenario reached the protected area while up to 20% of the particles released in the plaice scenario did. Interannual trends in transport directions and connectivity rates were comparable between models but absolute values displayed high variations. Most models showed systematic biases during all years in comparison to the ensemble median, indicating that in general interannual variation was represented but absolute values varied. In conclusion: variability between models is generally high and management decisions or scientific analysis using absolute values from only one single model might be biased and results or conclusions drawn from such studies need to be treated with caution. We further concluded that more true validation data for particle modelling are required.

    Sensitivity analysis of greenhouse gas emissions from a pork production chain
    Groen, E.A. ; Zanten, H.H.E. Van; Heijungs, R. ; Bokkers, E.A.M. ; Boer, I.J.M. De - \ 2016
    Journal of Cleaner Production 129 (2016). - ISSN 0959-6526 - p. 202 - 211.
    Growing pigs - IPCC emission factors - Life cycle assessment - Matrix perturbation method - Sensitivity analysis - Variability

    This study aimed to identify the most essential input parameters in the assessment of greenhouse gas (GHG) emissions along the pork production chain. We identified most essential input parameters by combining two sensitivity-analysis methods: the multiplier method and the method of elementary effects. The former shows how much an input parameter influences assessment of GHG emissions, whereas the latter shows the importance of input parameters on uncertainty in the output. For the method of elementary effects, uncertainty ranges were implemented only for input parameters that were identified as being most influential based on the multiplier method or that had large uncertainty ranges based on the literature. Results showed that the most essential input parameters are the feed-conversion ratio, the amount of manure, CH4 emissions from manure management and crop yields, especially of maize and barley. Combining the results of both methods allowed derivation of mitigation options, either based on innovations (e.g. novel feeding strategies) or on management strategies (e.g. reducing mortality rate), and formulation of options for improving reliability of the results. Mitigation options based on innovations were shown to be most effective when directed at improving the feed-conversion ratio; decreasing the amount of manure produced by pigs; improving maize, barley and wheat yields; decreasing the number of sows or piglets per growing pig needed and improving efficiency of N-fertiliser production. Mitigation options based on management strategies were shown to be most effective when farmers strive to reduce feed intake, reduce application of N fertiliser to maize and barley, and reduce the number of sows per growing pig needed towards best practices. Finally, the method of elementary effects showed that reliability of assessing GHG emissions of pork production could be improved when uncertainty ranges are reduced, for example, around direct and indirect N2O emissions of the main feed crops in the pig diet and the CH4 emissions of manure. Also the reliability could be improved by improving data quality of the most essential parameters. Combining two types of sensitivity-analysis methods identified the most essential input parameters in the pork production chain. With this combined analysis, mitigation options via innovations and management strategies were derived, and parameters were identified that improved reliability of the results.

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