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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    Everything is relative and nothing is certain. Toward a theory and practice of comparative probabilistic LCA
    Heijungs, Reinout ; Guinée, Jeroen B. ; Mendoza Beltrán, Angelica ; Henriksson, Patrik J.G. ; Groen, Evelyne - \ 2019
    The International Journal of Life Cycle Assessment 24 (2019)9. - ISSN 0948-3349 - p. 1573 - 1579.
    Comparative analysis - Correlations - Life cycle assessment - Uncertainty

    Introduction: It is widely recognized that LCA is in most cases relative and contains uncertainties due to choices and data. This paper analyses the combination of the two comparative uncertainties. Basic concepts: We carefully define the idea of relativity and uncertainty within LCA. We finish off by giving an example of case where inappropriate handling of comparative uncertainties will lead to a misleading result for a decision-maker. Correlations: We develop a generic framework for probabilistic comparative LCA and analyse at which places correlations may be present. We also discuss the most convenient approaches for handling such correlated uncertainties. Conclusion: We put the elements discussed in a structure that provides a research agenda for dealing with comparative uncertainties in LCA.

    Assessing broad life cycle impacts of daily onboard decision-making, annual strategic planning, and fisheries management in a northeast Atlantic trawl fishery
    Ziegler, F. ; Groen, E.A. ; Hornborg, S. ; Bokkers, E.A.M. ; Karlsen, K.M. ; Boer, I.J.M. de - \ 2018
    The International Journal of Life Cycle Assessment 23 (2018)7. - ISSN 0948-3349 - p. 1357 - 1367.
    Purpose: Capture fisheries are the only industrial-scale harvesting of a wild resource for food. Temporal variability in environmental performance of fisheries has only recently begun to be explored, but only between years, not within a year. Our aim was to better understand the causes of temporal variablility withing and between years and to identify improvement options through management at a company level and in fisheries management. Methods We analyzed the variability in broad environmental impacts of a demersal freeze trawler targeting cod, haddock, saithe, and shrimp, mainly in the Norwegian Sea and in the Barents Sea. The analysis was based on daily data for fishing activities between 2011 and 2014 and the functional unit was a kilo of landing from one fishing trip. We used biological indicators in a novel hierarchic approach, depending on data availability, to quantify biotic impacts. Landings were categorized as target (having defined target reference points) or bycatch species (classified as threatened or as data-limited). Indicators for target and bycatch impacts were quantified for each fishing trip, as was the seafloor area swept. Results and discussion No significant difference in fuel use was found between years, but variability was considerable within a year, i.e., between fishing trips. Trips targeting shrimp were more fuel intensive than those targeting fish, due to a lower catch rate. Steaming to and from port was less important for fuel efficiency than steaming between fishing locations. A tradeoff was identified between biotic and abiotic impacts. Landings classified as main target species generally followed the maximum sustainable yield (MSY) framework, and proportions of threatened species were low, while proportions of data-limited bycatch were larger. This improved considerably when reference points were defined for saithe in 2014. Conclusions The variability between fishing trips shows that there is room for improvement through management. Fuel use per landing was strongly influenced by target species, fishing pattern, and fisheries management. Increased awareness about the importance of onboard decision-making can lead to improved performance. This approach could serve to document performance over time helping fishing companies to better understand the effect of their daily and more long-term decision-making on the environmental performance of their products. Recommendations Fishing companies should document their resource use and production on a detailed level. Fuel use should be monitored as part of the management system. Managing authorities should ensure that sufficient data is available to evaluate the sustainability of exploitation levels of all harvested species
    Selective improvement of global datasets for the computation of locally relevant environmental indicators : A method based on global sensitivity analysis
    Uwizeye, U.A. ; Gerber, Pierre J. ; Groen, Evelyne A. ; Dolman, Mark A. ; Schulte, Rogier P.O. ; Boer, Imke J.M. de - \ 2017
    Environmental Modelling & Software 96 (2017). - ISSN 1364-8152 - p. 58 - 67.
    Decision-making - Environmental modelling - Global datasets - Global sensitivity analysis

    Several global datasets are available for environmental modelling, but information provided is hardly used for decision-making at a country-level. Here we propose a method, which relies on global sensitivity analysis, to improve local relevance of environmental indicators from global datasets. This method is tested on nitrogen use framework for two contrasted case studies: mixed dairy supply chains in Rwanda and the Netherlands. To achieve this, we evaluate how indicators computed from a global dataset diverge from same indicators computed from survey data. Second, we identify important input parameters that explain the variance of indicators. Subsequently, we fix non-important ones to their average values and substitute important ones with field data. Finally, we evaluate the effect of this substitution. This method improved relevance of nitrogen use indicators; therefore, it can be applied to any environmental modelling using global datasets to improve their relevance by prioritizing important parameters for additional data collection.

    Benchmarking nutrient use efficiency of dairy farms : The effect of epistemic uncertainty
    Mu, W. ; Groen, E.A. ; Middelaar, C.E. van; Bokkers, E.A.M. ; Hennart, S. ; Stilmant, D. ; Boer, I.J.M. de - \ 2017
    Agricultural Systems 156 (2017). - ISSN 0308-521X - p. 25 - 33.
    Clustering farms - Environmental performance - Farm comparison - Global sensitivity analysis - Monte Carlo simulation - Nutrient use efficiency

    The nutrient use efficiency (NUE) of a system, generally computed as the amount of nutrients in valuable outputs over the amount of nutrients in all inputs, is commonly used to benchmark the environmental performance of dairy farms. Benchmarking the NUE of farms, however, may lead to biased conclusions because of differences in major decisive characteristics between farms, such as soil type and production intensity, and because of epistemic uncertainty of input parameters caused by errors in measurement devices or observations. This study aimed to benchmark the nitrogen use efficiency (NUEN; calculated as N output per unit of N input) of farm clusters with similar characteristics while including epistemic uncertainty, using Monte Carlo simulation. Subsequently, the uncertainty of the parameters explaining most of the output variance was reduced to examine if this would improve benchmarking results. Farms in cluster 1 (n = 15) were located on sandy soils and farms in cluster 2 (n = 17) on loamy soils. Cluster 1 farms were more intensive in terms of milk production per hectare and per cow, had less grazing hours, and fed more concentrates compared to farms in cluster 2. The mean NUEN of farm in cluster 1 was 43%, while in cluster 2 it was 26%. Input parameters that explained most of the output variance differed between clusters. For cluster 1, input of feed and output of roughage were most important, whereas for cluster 2, the input of mineral fertilizer (or fixation) was most important. For both clusters, the output of milk was relatively important. Including the epistemic uncertainty of input parameters showed that only 37% of the farms in cluster 1 (out of 105 mutual comparisons) differed significantly in terms of their NUEN, whereas in cluster 2 this was 82% (out of 120 comparisons). Therefore, benchmarking NUEN of farms in cluster 1 was no longer possible, whereas farms in cluster 2 could still be ranked when uncertainty was included. After reducing the uncertainties of the most important parameters, 72% of the farms in cluster 1 differed significantly in terms of their NUEN, and in cluster 2 this was 87%. Results indicate that reducing epistemic uncertainty of input parameters can significantly improve benchmarking results. The method presented in this study, therefore, can be used to draw more reliable conclusions regarding benchmarking the NUE of farms, and to identify the parameters that require more precision to do so.

    Methods for global sensitivity analysis in life cycle assessment
    Groen, Evelyne A. ; Bokkers, Eddy ; Heijungs, Reinout ; Boer, Imke J.M. de - \ 2017
    The International Journal of Life Cycle Assessment 22 (2017)7. - ISSN 0948-3349 - p. 1125 - 1137.
    Correlation - Key issue analysis - Random balance design - Regression - Sensitivity analysis - Sobol’ sensitivity index - Variance decomposition
    Purpose: Input parameters required to quantify environmental impact in life cycle assessment (LCA) can be uncertain due to e.g. temporal variability or unknowns about the true value of emission factors. Uncertainty of environmental impact can be analysed by means of a global sensitivity analysis to gain more insight into output variance. This study aimed to (1) give insight into and (2) compare methods for global sensitivity analysis in life cycle assessment, with a focus on the inventory stage. Methods: Five methods that quantify the contribution to output variance were evaluated: squared standardized regression coefficient, squared Spearman correlation coefficient, key issue analysis, Sobol’ indices and random balance design. To be able to compare the performance of global sensitivity methods, two case studies were constructed: one small hypothetical case study describing electricity production that is sensitive to a small change in the input parameters and a large case study describing a production system of a northeast Atlantic fishery. Input parameters with relative small and large input uncertainties were constructed. The comparison of the sensitivity methods was based on four aspects: (I) sampling design, (II) output variance, (III) explained variance and (IV) contribution to output variance of individual input parameters. Results and discussion: The evaluation of the sampling design (I) relates to the computational effort of a sensitivity method. Key issue analysis does not make use of sampling and was fastest, whereas the Sobol’ method had to generate two sampling matrices and, therefore, was slowest. The total output variance (II) resulted in approximately the same output variance for each method, except for key issue analysis, which underestimated the variance especially for high input uncertainties. The explained variance (III) and contribution to variance (IV) for small input uncertainties were optimally quantified by the squared standardized regression coefficients and the main Sobol’ index. For large input uncertainties, Spearman correlation coefficients and the Sobol’ indices performed best. The comparison, however, was based on two case studies only. Conclusions: Most methods for global sensitivity analysis performed equally well, especially for relatively small input uncertainties. When restricted to the assumptions that quantification of environmental impact in LCAs behaves linearly, squared standardized regression coefficients, squared Spearman correlation coefficients, Sobol’ indices or key issue analysis can be used for global sensitivity analysis. The choice for one of the methods depends on the available data, the magnitude of the uncertainties of data and the aim of the study.
    Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment : what is the risk?
    Groen, E.A. ; Heijungs, R. - \ 2017
    Environmental Impact Assessment Review 62 (2017). - ISSN 0195-9255 - p. 98 - 109.
    correlation - covariance matrix - global sensitivity analysis - Uncertainty propagation

    Life cycle assessment (LCA) is an established tool to quantify the environmental impact of a product. A good assessment of uncertainty is important for making well-informed decisions in comparative LCA, as well as for correctly prioritising data collection efforts. Under- or overestimation of output uncertainty (e.g. output variance) will lead to incorrect decisions in such matters. The presence of correlations between input parameters during uncertainty propagation, can increase or decrease the the output variance. However, most LCA studies that include uncertainty analysis, ignore correlations between input parameters during uncertainty propagation, which may lead to incorrect conclusions. Two approaches to include correlations between input parameters during uncertainty propagation and global sensitivity analysis were studied: an analytical approach and a sampling approach. The use of both approaches is illustrated for an artificial case study of electricity production. Results demonstrate that both approaches yield approximately the same output variance and sensitivity indices for this specific case study. Furthermore, we demonstrate that the analytical approach can be used to quantify the risk of ignoring correlations between input parameters during uncertainty propagation in LCA. We demonstrate that: (1) we can predict if including correlations among input parameters in uncertainty propagation will increase or decrease output variance; (2) we can quantify the risk of ignoring correlations on the output variance and the global sensitivity indices. Moreover, this procedure requires only little data.

    Assessing greenhouse gas emissions of milk production : which parameters are essential?
    Wolf, Patricia ; Groen, Evelyne A. ; Berg, Werner ; Prochnow, Annette ; Bokkers, E.A.M. ; Heijungs, Reinout ; Boer, Imke J.M. de - \ 2017
    The International Journal of Life Cycle Assessment 22 (2017)3. - ISSN 0948-3349 - p. 441 - 455.
    Correlation - Dairy - Life cycle assessment - Monte Carlo simulation - Sensitivity analysis
    Purpose: Life cycle assessment (LCA) studies of food products, such as dairy, require many input parameters that are affected by variability and uncertainty. Moreover, correlations may be present between input parameters, e.g. between feed intake and milk yield. The purpose of this study was to identify which input parameters are essential to assess the greenhouse gas (GHG) emissions of milk production, while accounting for correlations between input parameters, and using a systematic approach. Methods: Three diets corresponding to three grazing systems (zero-, restricted and unrestricted grazing) were selected, which were defined to aim for a milk yield of 10,000 kg energy corrected milk (ECM) cow−1 year−1. First, a local sensitivity analysis was used to identify which parameters influence GHG emissions most. Second, a global sensitivity analysis was used to identify which parameters are most important to the output variance. The global analysis included correlations between feed intake and milk yield and between N fertilizer rates and crop yields. The local and global sensitivity analyses were combined to determine which parameters are essential. Finally, we analysed the effect of changing the most important correlation coefficient (between feed intake and milk yield) on the output variance and global sensitivity analysis. Results and discussion: The total GHG emissions for 1 kg ECM ranged from 1.08 to 1.12 kg CO2 e, depending on the grazing system. The local sensitivity analysis identified milk yield, feed intake, and the CH4 emission factor of enteric fermentation of the cows as most influential parameters in the LCA model. The global sensitivity analysis identified the CH4 emission factor of enteric fermentation, milk yield, feed intake and the direct N2O emission factor of crop cultivation as most important parameters. For both grazing systems, N2O emission factor for grazing also turned out to be important. In addition, the correlation coefficient between feed intake and milk yield turned out to be important. The systematic approach resulted in more parameters than previously found. Conclusions: By combining a local and a global sensitivity analysis, parameters were determined which are essential to assess GHG emissions of milk production. These parameters are the CH4 emission factor of enteric fermentation, milk yield, feed intake, the direct N2O emission factor of crop cultivation and the N2O emission factor for grazing. Future research should focus on reducing uncertainty and improving data quality of these essential parameters.
    Reducing uncertainty at minimal cost: a method to identify important input parameters and prioritize data collection
    Uwizeye, U.A. ; Groen, E.A. ; Gerber, P.J. ; Schulte, Rogier P.O. ; Boer, I.J.M. de - \ 2016
    In: Book of Abstracts of the 10th international conference on Life Cycle Assessment of Food. - - 5 p.
    Sensitivity analysis, uncertainty analysis, minimum data, data quality
    The study aims to illustrate a method to identify important input parameters that explain most of the output variance ofenvironmental assessment models. The method is tested for the computation of life-cycle nitrogen (N) use efficiencyindicators among mixed dairy production systems in Rwanda. We performed a global sensitivity analysis, and ranked theimportance of parameters based on the squared standardized regression coefficients (SRC). First the probability distributionsof 126 input parameters were defined, based on primary and secondary data, which were collected from feed processors,dairy farms, dairy processing plants and slaughterhouses, and literature. Second, squared SRCs were calculated to explainthe output variance of the life-cycle nitrogen use efficiency, life-cycle net nitrogen balance, and nitrogen hotspot indexindicators. Results show that input parameters considered can be classified into three categories. The first category (I)includes 115 input parameters with low squared SRCs (<0.01), which are less important and can be established with defaultor regional averages. The second category (II) includes 5 important input parameters, with squared SRCs between 0.01 and0.1; that can be established with country specific data. The third category (III) includes 6 input parameters with squaredSRCs >0.1; that contribute most to the output variance of at least one of the life-cycle nitrogen use efficiency indicators.These most important parameters need to be established with accuracy thus require high data quality. The input parametersof category II and III include emission factors and coefficients that are specific for a region as well as activity data that arespecific to the livestock production system. By carrying out such analysis during the scoping analysis, any LCA study infood sector can cut on the cost of data collection phase by focusing on input parameters that can be fixed through goodpractices in data collection. Further work on global life-cycle nutrient use performance will benefit from these results togenerate analysis at lesser data collection cost.
    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.

    An uncertain climate : the value of uncertainty and sensitivity analysis in environmental impact assessment of food
    Groen, E.A. - \ 2016
    Wageningen University. Promotor(en): Imke de Boer, co-promotor(en): Eddy Bokkers; R. Heijungs. - Wageningen : Wageningen University - ISBN 9789462577558 - 239
    environment - environmental impact - climatic change - uncertainty analysis - screening - sensitivity analysis - modeling - greenhouse gases - farms - dairy farms - food production - correlation analysis - milieu - milieueffect - klimaatverandering - onzekerheidsanalyse - screenen - gevoeligheidsanalyse - modelleren - broeikasgassen - landbouwbedrijven - melkveebedrijven - voedselproductie - correlatieanalyse

    ABSTRACT

    Production of food contributes to climate change and other forms of environmental impact. Input data used in environmental impact assessment models, such as life cycle assessment (LCA) and nutrient balance (NB) analysis, may vary due to seasonal changes, geographical conditions or socio-economic factors (i.e. natural variability). Moreover, input data may be uncertain, due to measurement errors and observational errors that exist around modelling of emissions and technical parameters (i.e. epistemic uncertainty). Although agricultural activities required for food production are prone to natural variability and epistemic uncertainty, very few case studies in LCA and NB analysis made a thorough examination of the effects of variability and uncertainty. This thesis aimed to enhance understanding the effects of variability and uncertainty on the results, by means of uncertainty and sensitivity analysis. Uncertainty analysis refers to the estimation of the uncertainty attribute of a model output using the uncertainty attributes of the model in- puts. There are three types of sensitivity analyses: (I) a local sensitivity analysis addresses what happens to the output when input parameters are changed, i.e. the intrinsic model behaviour of a parameter; (II) a screening analysis addresses what happens to the output based on the un- certainty range of the different input parameters; and (III) a global sensitivity analysis addresses how much the uncertainty around each input parameter contributes to the output variance. Both the screening analysis and the global sensitivity analysis combine the intrinsic model behaviour with the information of uncertainty around input parameters. Applying uncertainty analysis and sensitivity analysis can help to reduce the efforts for data collection, support the development of mitigation strategies and improve overall reliability, leading to more informed decision making in environmental impact assessment models. Including uncertainty in environmental impact assessment models showed that: (1) the type of uncertainty analysis or sensitivity analysis applied depends on the question to be addressed and the available information; (2) in some cases it is no longer possible to benchmark environmental performance if epistemic uncertainty is included; (3) including correlations between input parameters during uncertainty propagation will either increase or decrease output variance, which can be predicted beforehand; (4) under specific characteristics of the input parameters, ignoring correlation has a minimal effect on the model outcome. Systematically combining a local and global sensitivity analysis in environmental impact assessment models: (1) resulted in more parameters than found previously in similar studies (for the case studies discussed in this thesis); (2) allowed finding mitigation options, either based on innovations (derived from the local sensitivity analysis) or on management strategies (derived from the global sensitivity analysis); (3) showed for which parameters reliability should be improved by increasing data quality; (4) showed that reducing the (epistemic) uncertainty of the most important parameters can affect the comparison of the environmental performance.

    Methods for sensitivity analysis in life cycle assessment of animal production systems
    Groen, E.A. ; Bokkers, E.A.M. ; Heijungs, R. ; Boer, I.J.M. de - \ 2015
    - p. 21 - 21.
    Environmental impact of the agri-food industry has been of increasing concern, and in particular international awareness about the impact of animal production systems has been rising. Life cycle assessment (LCA) is a commonly applied framework that quantifies the environmental impact of a product over its entire production chain. Input parameters required to describe animal production systems, however, can be uncertain due to e.g. temporal variability or unknowns about the true value of emission factors. Analysing the effect of uncertainty in input parameters can be done by means of a sensitivity analysis (SA). Although many LCA studies have been performed over the last decade, few applied a systematic and consistent SA to address the effect of input uncertainties on the output. Two main types of SA exist. A local SA determines the effect of a (small) change in one of the input parameters at a time, whereas a global sensitivity analysis determines how much each input parameter contributes to the output uncertainty. This study aims to give insight into state-of-the-art methods for global SA in LCA and to compare several methods based on their ability to explain the output variance. This is done by applying all methods to two case studies with different sizes and input uncertainties. The first case study is a hypothetical test system, while the second case study described the production of whitefish in the northeast Atlantic.
    From the limited LCA literature on global SA available, three methods were found that quantify the contribution to output variance: (standardized) regression coefficients, (Spearman) correlation coefficients and a Taylor approximation. Outside the LCA domain we found other methods, such as random balance design and the Sobol’ method, that quantify the contribution to output variance. For most methods, it is not known under which conditions they perform optimally, or if there is one method that outperforms other methods in LCA. Results showed that for case studies without any outliers (irrespective of the size of the case studies), Key issue analysis, standardized regression coefficients, and Spearman correlation coefficient were able to explain most of the output variance. For case studies with outliers (irrespective of their size) we found that the Sobol’ method was able to explain most of the output variance followed by SCC. We conclude that using sophisticated methods for global SA might only be useful for high quality data, so that the quantification of the uncertainty is as accurate as the provided input data. For LCAs in animal production systems this means that standardized regression coefficients, and Spearman correlation coefficient might be preferred because it is easier to understand and apply. This theoretical exercise shows that recommendation towards environmental improvement options in animal production systems can be made taking into account input uncertainties. For example, when comparing scenarios, the regression or correlation coefficients indicate which parameters that should be known with high confidence, before presenting results. Also, parameters with the highest regression (or correlation) coefficients indicate parameters that could contain opportunities for improvement options.
    Sensitivity analysis of greenhouse gas emissions of a pork production system
    Groen, E.A. ; Zanten, H.H.E. van; Bokkers, E.A.M. ; Boer, I.J.M. de - \ 2015
    In: Abstracts SETAC Europe 21st LCA Case Study Symposium. - SETAC - p. 8 - 8.
    Environmental impact of the agrifood industry has been of increasing concern, and in particular international awareness about the impact of animal production systems has been rising. Pork production, for example, is one of the largest producers of meat in Europe, and contributes considerably to the total N2O emissions. Life cycle assessment (LCA) is a commonly applied framework that quantifies the environmental impact of a product over the entire chain. Emission factors that quantify e.g. volatilization of nitrogen and enteric fermentation, however, contain large ranges according to the IPCC framework. Analysing the effect of uncertainty in emission factors, can be done by means of a sensitivity analysis. Although many LCA studies have been performed, few applied a systematic and consistent sensitivity analysis to address the effect of uncertainty in emission factors on the total greenhouse gas emissions. In this study, we applied two types of sensitivity analyses: (1) based on the point values, a local sensitivity analysis (multiplier method) determines SETAC Europe 21st LCA Case Study Symposium 4 the effect of a small change in the emission factors; (2) based on the error ranges of the IPCC emission factors, a screening analysis (method of elementary effects) determines the effect of change within the actual
    ranges. The two methods were applied to a case study of a Dutch pork production system. Preliminary results show that based on the local sensitivity analysis, the CH4 emissions of pig manure, followed by the N2O emissions of the production of N-fertilizer were most sensitive. Based on the screening analysis, direct and indirect N2O emissions during production of corn influenced the total greenhouse gas emissions most, followed by CH4 and N2O emissions of pig manure. The results show that based on the assumption that all emission factors can vary with the same amount (e.g. plus or minus 10%), as is assumed during a local sensitivity analysis, a different set of sensitivity parameters is found than when considering the actual ranges of the emission factors. The local sensitivity analysis can be used to develop mitigation strategies. The screening analysis indicates which emission factors have the most influence on the total greenhouse gas emissions within existing ranges, which is useful when comparing production system or assessing the reliability of the LCA results.
    Sensitivity analysis of greenhouse gas emissions of a pork production system
    Groen, Evelyne - \ 2015
    Methods for sensitivity analysis in life cycle assessment of animal production systems
    Groen, Evelyne - \ 2015
    D4.3 - BSCI final run impact assessment from all cases
    Boer, I.J.M. de; Bokkers, E.A.M. ; Berentsen, P.B.M. ; Ziegler, F. ; Krewer, C. ; Sund, V. ; Groen, E.A. ; Veldhuizen, L.J.L. ; Olsen, P. - \ 2014
    Wageningen : Animal Sciences Group (EU deliverable (WhiteFish project on automated and differentiated calculation of sustainability for cod and haddock products) ) - 33 p.
    D2.3 - Third version of BCSI
    Boer, I.J.M. de; Bokkers, E.A.M. ; Berentsen, P.B.M. ; Ziegler, F. ; Krewer, C. ; Sund, V. ; Groen, E.A. ; Veldhuizen, L.J.L. ; Olsen, P. - \ 2014
    Wageningen : Animal Sciences Group (EU deliverable (WhiteFish project on automated and differentiated calculation of sustainability for cod and haddock products) ) - 10 p.
    Environmental impacts of a demersal freeze trawler on a fishing trip basis
    Ziegler, F. ; Groen, E.A. ; Hornborg, S. ; Bokkers, E.A.M. ; Karlsen, K.M. ; Boer, I.J.M. de - \ 2014
    - p. G:23 - G:23.
    Life Cycle Assessment (LCA) quantifies the environmental impact of products, often using annual average data. Fisheries often show high spatial and temporal variability within a year and annual values may be too coarse to identify causes and improvement options on an appropriate level. Using LCA methodology, we analysed two years of data of a demersal freeze trawler targeting cod, haddock, saithe and shrimp mainly in the Norwegian and Barents Seas. The product was a kg of landed fish or shrimp from one fishing trip, frozen at sea. We quantified standard LCA impacts and biotic indicators (e.g. impacts on target and bycatch stocks) showing large variation between fishing trips. Fuel use was the main driver of emission-based impacts. Shrimp trawling was more fuel intensive than fish trawling per kg landed, due to lower catch rates. Bycatch (defined as catch of species other than the main target species) was low due to use of a species-selective grid in shrimp trawling. Fish trawling required less fuel, but landed varying amounts of bycatch. Quantifying environmental impacts of seafood products on a fine scale could help fishing companies, managers and certifiers to better understand the effect of decision-making on the environmental performance of seafood products.
    Sensitivity analysis in life cycle assessment
    Groen, E.A. ; Heijungs, R. ; Bokkers, E.A.M. ; Boer, I.J.M. de - \ 2014
    In: Proceedings of the Life Cycle Assessment Food Conference (LCA Food 2014). - - p. 482 - 488.
    Life cycle assessments require many input parameters and many of these parameters are uncertain; therefore, a sensitivity analysis is an essential part of the final interpretation. The aim of this study is to compare seven sensitivity methods applied to three types of case stud-ies. Two (hypothetical) case studies describing electricity production: one shows linear and another shows non-linear behavior. The third case study describes a large (existing) case study of seafood production containing high input uncertainties. The methods are compared based on their results, i.e. variance decomposition and ranking of the input parameters. Results show that Sobol’ sensitivity indices per-form the best for all three case studies. The Sobol’ method can be a useful method in case of non-linear LCA models or LCA models that include outliers.
    Sensitivity analysis in life cycle assessment
    Groen, E.A. ; Heijungs, R. ; Bokkers, E.A.M. ; Boer, I.J.M. de - \ 2014
    Sensitivity analysis in life cycle assessment
    Groen, Evelyne - \ 2014
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