Preparing suitable climate scenario data to assess impacts on local food safety (online first)
Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and ill...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hofstra, N [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © Wageningen UR |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Food research international - Barking : Elsevier Science Publ., 1992, 68(2015) |
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| Übergeordnetes Werk: |
volume:68 ; year:2015 |
| Links: |
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| DOI / URN: |
10.1016/j.foodres.2014.08.017 |
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| 520 | |a Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and illustrates how this tool can be used with impact models, such as bacterial and mycotoxin growth and pesticide models. As an example, coarse gridded data from two global climate models (GCMs), HadGEM2-ES and CCSM4, are selected and downscaled using the “Delta method” with quantile-quantile correction for Ukkel, Belgium. Observational daily temperature and precipitation data from 1981 to 2000 are used as a reference for this downscaling. Data are provided for four future representative concentration pathways (RCPs) for the periods 2031–2050 and 2081–2100. These RCPs are radiative forcing scenarios for which future climate conditions are projected. The climate projections for these RCPs show that both temperature and precipitation will increase towards the end of the century in Ukkel. The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. The actual algorithm of the downscaling method and its detailed manual is available in the supplementary material. | ||
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10.1016/j.foodres.2014.08.017 doi PQ20160617 (DE-627)OLC1967522103 (DE-599)GBVOLC1967522103 (PRQ)c2095-1ec88542de854522f1cf3e98751105a03723d0fbdae21cafd09ffbff98a59620 (KEY)0075213420150000068000000000preparingsuitableclimatescenariodatatoassessimpact DE-627 ger DE-627 rakwb eng 630 640 540 660 DNB Hofstra, N verfasserin aut Preparing suitable climate scenario data to assess impacts on local food safety (online first) 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and illustrates how this tool can be used with impact models, such as bacterial and mycotoxin growth and pesticide models. As an example, coarse gridded data from two global climate models (GCMs), HadGEM2-ES and CCSM4, are selected and downscaled using the “Delta method” with quantile-quantile correction for Ukkel, Belgium. Observational daily temperature and precipitation data from 1981 to 2000 are used as a reference for this downscaling. Data are provided for four future representative concentration pathways (RCPs) for the periods 2031–2050 and 2081–2100. These RCPs are radiative forcing scenarios for which future climate conditions are projected. The climate projections for these RCPs show that both temperature and precipitation will increase towards the end of the century in Ukkel. The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. The actual algorithm of the downscaling method and its detailed manual is available in the supplementary material. Nutzungsrecht: © Wageningen UR Leerstoelgroep Milieusysteemanalyse Environmental Systems Analysis Group Leemans, R oth Liu, C oth Enthalten in Food research international Barking : Elsevier Science Publ., 1992 68(2015) (DE-627)131076213 (DE-600)1111695-X (DE-576)029165865 0963-9969 nnns volume:68 year:2015 http://dx.doi.org/10.1016/j.foodres.2014.08.017 Volltext http://library.wur.nl/WebQuery/wurpubs/479542 http://www.narcis.nl/publication/RecordID/oai:library.wur.nl:wurpubs%2F479542 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 AR 68 2015 |
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10.1016/j.foodres.2014.08.017 doi PQ20160617 (DE-627)OLC1967522103 (DE-599)GBVOLC1967522103 (PRQ)c2095-1ec88542de854522f1cf3e98751105a03723d0fbdae21cafd09ffbff98a59620 (KEY)0075213420150000068000000000preparingsuitableclimatescenariodatatoassessimpact DE-627 ger DE-627 rakwb eng 630 640 540 660 DNB Hofstra, N verfasserin aut Preparing suitable climate scenario data to assess impacts on local food safety (online first) 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and illustrates how this tool can be used with impact models, such as bacterial and mycotoxin growth and pesticide models. As an example, coarse gridded data from two global climate models (GCMs), HadGEM2-ES and CCSM4, are selected and downscaled using the “Delta method” with quantile-quantile correction for Ukkel, Belgium. Observational daily temperature and precipitation data from 1981 to 2000 are used as a reference for this downscaling. Data are provided for four future representative concentration pathways (RCPs) for the periods 2031–2050 and 2081–2100. These RCPs are radiative forcing scenarios for which future climate conditions are projected. The climate projections for these RCPs show that both temperature and precipitation will increase towards the end of the century in Ukkel. The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. The actual algorithm of the downscaling method and its detailed manual is available in the supplementary material. Nutzungsrecht: © Wageningen UR Leerstoelgroep Milieusysteemanalyse Environmental Systems Analysis Group Leemans, R oth Liu, C oth Enthalten in Food research international Barking : Elsevier Science Publ., 1992 68(2015) (DE-627)131076213 (DE-600)1111695-X (DE-576)029165865 0963-9969 nnns volume:68 year:2015 http://dx.doi.org/10.1016/j.foodres.2014.08.017 Volltext http://library.wur.nl/WebQuery/wurpubs/479542 http://www.narcis.nl/publication/RecordID/oai:library.wur.nl:wurpubs%2F479542 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 AR 68 2015 |
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10.1016/j.foodres.2014.08.017 doi PQ20160617 (DE-627)OLC1967522103 (DE-599)GBVOLC1967522103 (PRQ)c2095-1ec88542de854522f1cf3e98751105a03723d0fbdae21cafd09ffbff98a59620 (KEY)0075213420150000068000000000preparingsuitableclimatescenariodatatoassessimpact DE-627 ger DE-627 rakwb eng 630 640 540 660 DNB Hofstra, N verfasserin aut Preparing suitable climate scenario data to assess impacts on local food safety (online first) 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and illustrates how this tool can be used with impact models, such as bacterial and mycotoxin growth and pesticide models. As an example, coarse gridded data from two global climate models (GCMs), HadGEM2-ES and CCSM4, are selected and downscaled using the “Delta method” with quantile-quantile correction for Ukkel, Belgium. Observational daily temperature and precipitation data from 1981 to 2000 are used as a reference for this downscaling. Data are provided for four future representative concentration pathways (RCPs) for the periods 2031–2050 and 2081–2100. These RCPs are radiative forcing scenarios for which future climate conditions are projected. The climate projections for these RCPs show that both temperature and precipitation will increase towards the end of the century in Ukkel. The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. The actual algorithm of the downscaling method and its detailed manual is available in the supplementary material. Nutzungsrecht: © Wageningen UR Leerstoelgroep Milieusysteemanalyse Environmental Systems Analysis Group Leemans, R oth Liu, C oth Enthalten in Food research international Barking : Elsevier Science Publ., 1992 68(2015) (DE-627)131076213 (DE-600)1111695-X (DE-576)029165865 0963-9969 nnns volume:68 year:2015 http://dx.doi.org/10.1016/j.foodres.2014.08.017 Volltext http://library.wur.nl/WebQuery/wurpubs/479542 http://www.narcis.nl/publication/RecordID/oai:library.wur.nl:wurpubs%2F479542 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 AR 68 2015 |
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10.1016/j.foodres.2014.08.017 doi PQ20160617 (DE-627)OLC1967522103 (DE-599)GBVOLC1967522103 (PRQ)c2095-1ec88542de854522f1cf3e98751105a03723d0fbdae21cafd09ffbff98a59620 (KEY)0075213420150000068000000000preparingsuitableclimatescenariodatatoassessimpact DE-627 ger DE-627 rakwb eng 630 640 540 660 DNB Hofstra, N verfasserin aut Preparing suitable climate scenario data to assess impacts on local food safety (online first) 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and illustrates how this tool can be used with impact models, such as bacterial and mycotoxin growth and pesticide models. As an example, coarse gridded data from two global climate models (GCMs), HadGEM2-ES and CCSM4, are selected and downscaled using the “Delta method” with quantile-quantile correction for Ukkel, Belgium. Observational daily temperature and precipitation data from 1981 to 2000 are used as a reference for this downscaling. Data are provided for four future representative concentration pathways (RCPs) for the periods 2031–2050 and 2081–2100. These RCPs are radiative forcing scenarios for which future climate conditions are projected. The climate projections for these RCPs show that both temperature and precipitation will increase towards the end of the century in Ukkel. The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. The actual algorithm of the downscaling method and its detailed manual is available in the supplementary material. Nutzungsrecht: © Wageningen UR Leerstoelgroep Milieusysteemanalyse Environmental Systems Analysis Group Leemans, R oth Liu, C oth Enthalten in Food research international Barking : Elsevier Science Publ., 1992 68(2015) (DE-627)131076213 (DE-600)1111695-X (DE-576)029165865 0963-9969 nnns volume:68 year:2015 http://dx.doi.org/10.1016/j.foodres.2014.08.017 Volltext http://library.wur.nl/WebQuery/wurpubs/479542 http://www.narcis.nl/publication/RecordID/oai:library.wur.nl:wurpubs%2F479542 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 AR 68 2015 |
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10.1016/j.foodres.2014.08.017 doi PQ20160617 (DE-627)OLC1967522103 (DE-599)GBVOLC1967522103 (PRQ)c2095-1ec88542de854522f1cf3e98751105a03723d0fbdae21cafd09ffbff98a59620 (KEY)0075213420150000068000000000preparingsuitableclimatescenariodatatoassessimpact DE-627 ger DE-627 rakwb eng 630 640 540 660 DNB Hofstra, N verfasserin aut Preparing suitable climate scenario data to assess impacts on local food safety (online first) 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and illustrates how this tool can be used with impact models, such as bacterial and mycotoxin growth and pesticide models. As an example, coarse gridded data from two global climate models (GCMs), HadGEM2-ES and CCSM4, are selected and downscaled using the “Delta method” with quantile-quantile correction for Ukkel, Belgium. Observational daily temperature and precipitation data from 1981 to 2000 are used as a reference for this downscaling. Data are provided for four future representative concentration pathways (RCPs) for the periods 2031–2050 and 2081–2100. These RCPs are radiative forcing scenarios for which future climate conditions are projected. The climate projections for these RCPs show that both temperature and precipitation will increase towards the end of the century in Ukkel. The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. The actual algorithm of the downscaling method and its detailed manual is available in the supplementary material. Nutzungsrecht: © Wageningen UR Leerstoelgroep Milieusysteemanalyse Environmental Systems Analysis Group Leemans, R oth Liu, C oth Enthalten in Food research international Barking : Elsevier Science Publ., 1992 68(2015) (DE-627)131076213 (DE-600)1111695-X (DE-576)029165865 0963-9969 nnns volume:68 year:2015 http://dx.doi.org/10.1016/j.foodres.2014.08.017 Volltext http://library.wur.nl/WebQuery/wurpubs/479542 http://www.narcis.nl/publication/RecordID/oai:library.wur.nl:wurpubs%2F479542 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 AR 68 2015 |
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The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. 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preparing suitable climate scenario data to assess impacts on local food safety (online first) |
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Preparing suitable climate scenario data to assess impacts on local food safety (online first) |
| abstract |
Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and illustrates how this tool can be used with impact models, such as bacterial and mycotoxin growth and pesticide models. As an example, coarse gridded data from two global climate models (GCMs), HadGEM2-ES and CCSM4, are selected and downscaled using the “Delta method” with quantile-quantile correction for Ukkel, Belgium. Observational daily temperature and precipitation data from 1981 to 2000 are used as a reference for this downscaling. Data are provided for four future representative concentration pathways (RCPs) for the periods 2031–2050 and 2081–2100. These RCPs are radiative forcing scenarios for which future climate conditions are projected. The climate projections for these RCPs show that both temperature and precipitation will increase towards the end of the century in Ukkel. The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. The actual algorithm of the downscaling method and its detailed manual is available in the supplementary material. |
| abstractGer |
Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and illustrates how this tool can be used with impact models, such as bacterial and mycotoxin growth and pesticide models. As an example, coarse gridded data from two global climate models (GCMs), HadGEM2-ES and CCSM4, are selected and downscaled using the “Delta method” with quantile-quantile correction for Ukkel, Belgium. Observational daily temperature and precipitation data from 1981 to 2000 are used as a reference for this downscaling. Data are provided for four future representative concentration pathways (RCPs) for the periods 2031–2050 and 2081–2100. These RCPs are radiative forcing scenarios for which future climate conditions are projected. The climate projections for these RCPs show that both temperature and precipitation will increase towards the end of the century in Ukkel. The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. The actual algorithm of the downscaling method and its detailed manual is available in the supplementary material. |
| abstract_unstemmed |
Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and illustrates how this tool can be used with impact models, such as bacterial and mycotoxin growth and pesticide models. As an example, coarse gridded data from two global climate models (GCMs), HadGEM2-ES and CCSM4, are selected and downscaled using the “Delta method” with quantile-quantile correction for Ukkel, Belgium. Observational daily temperature and precipitation data from 1981 to 2000 are used as a reference for this downscaling. Data are provided for four future representative concentration pathways (RCPs) for the periods 2031–2050 and 2081–2100. These RCPs are radiative forcing scenarios for which future climate conditions are projected. The climate projections for these RCPs show that both temperature and precipitation will increase towards the end of the century in Ukkel. The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. The actual algorithm of the downscaling method and its detailed manual is available in the supplementary material. |
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