Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change
Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four...
Ausführliche Beschreibung
Autor*in: |
Timothy S. Thomas [verfasserIn] Richard D. Robertson [verfasserIn] Kenneth Strzepek [verfasserIn] Channing Arndt [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Frontiers in Climate - Frontiers Media S.A., 2020, 4(2022) |
---|---|
Übergeordnetes Werk: |
volume:4 ; year:2022 |
Links: |
---|
DOI / URN: |
10.3389/fclim.2022.787582 |
---|
Katalog-ID: |
DOAJ031033059 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ031033059 | ||
003 | DE-627 | ||
005 | 20230307154429.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230226s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3389/fclim.2022.787582 |2 doi | |
035 | |a (DE-627)DOAJ031033059 | ||
035 | |a (DE-599)DOAJfbf275294b74478d92e5426d2ada576f | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a GE1-350 | |
100 | 0 | |a Timothy S. Thomas |e verfasserin |4 aut | |
245 | 1 | 0 | |a Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change |
264 | 1 | |c 2022 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks. | ||
650 | 4 | |a climate change | |
650 | 4 | |a yield shocks | |
650 | 4 | |a climate uncertainty | |
650 | 4 | |a yield emulator | |
650 | 4 | |a crop models | |
650 | 4 | |a Southern Africa | |
653 | 0 | |a Environmental sciences | |
700 | 0 | |a Richard D. Robertson |e verfasserin |4 aut | |
700 | 0 | |a Kenneth Strzepek |e verfasserin |4 aut | |
700 | 0 | |a Channing Arndt |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Frontiers in Climate |d Frontiers Media S.A., 2020 |g 4(2022) |w (DE-627)1678815624 |w (DE-600)2986708-3 |x 26249553 |7 nnns |
773 | 1 | 8 | |g volume:4 |g year:2022 |
856 | 4 | 0 | |u https://doi.org/10.3389/fclim.2022.787582 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/fbf275294b74478d92e5426d2ada576f |z kostenfrei |
856 | 4 | 0 | |u https://www.frontiersin.org/articles/10.3389/fclim.2022.787582/full |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2624-9553 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 4 |j 2022 |
author_variant |
t s t tst r d r rdr k s ks c a ca |
---|---|
matchkey_str |
article:26249553:2022----::xrmeetadrdcinhcsokyrpisuhrar |
hierarchy_sort_str |
2022 |
callnumber-subject-code |
GE |
publishDate |
2022 |
allfields |
10.3389/fclim.2022.787582 doi (DE-627)DOAJ031033059 (DE-599)DOAJfbf275294b74478d92e5426d2ada576f DE-627 ger DE-627 rakwb eng GE1-350 Timothy S. Thomas verfasserin aut Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks. climate change yield shocks climate uncertainty yield emulator crop models Southern Africa Environmental sciences Richard D. Robertson verfasserin aut Kenneth Strzepek verfasserin aut Channing Arndt verfasserin aut In Frontiers in Climate Frontiers Media S.A., 2020 4(2022) (DE-627)1678815624 (DE-600)2986708-3 26249553 nnns volume:4 year:2022 https://doi.org/10.3389/fclim.2022.787582 kostenfrei https://doaj.org/article/fbf275294b74478d92e5426d2ada576f kostenfrei https://www.frontiersin.org/articles/10.3389/fclim.2022.787582/full kostenfrei https://doaj.org/toc/2624-9553 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 |
spelling |
10.3389/fclim.2022.787582 doi (DE-627)DOAJ031033059 (DE-599)DOAJfbf275294b74478d92e5426d2ada576f DE-627 ger DE-627 rakwb eng GE1-350 Timothy S. Thomas verfasserin aut Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks. climate change yield shocks climate uncertainty yield emulator crop models Southern Africa Environmental sciences Richard D. Robertson verfasserin aut Kenneth Strzepek verfasserin aut Channing Arndt verfasserin aut In Frontiers in Climate Frontiers Media S.A., 2020 4(2022) (DE-627)1678815624 (DE-600)2986708-3 26249553 nnns volume:4 year:2022 https://doi.org/10.3389/fclim.2022.787582 kostenfrei https://doaj.org/article/fbf275294b74478d92e5426d2ada576f kostenfrei https://www.frontiersin.org/articles/10.3389/fclim.2022.787582/full kostenfrei https://doaj.org/toc/2624-9553 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 |
allfields_unstemmed |
10.3389/fclim.2022.787582 doi (DE-627)DOAJ031033059 (DE-599)DOAJfbf275294b74478d92e5426d2ada576f DE-627 ger DE-627 rakwb eng GE1-350 Timothy S. Thomas verfasserin aut Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks. climate change yield shocks climate uncertainty yield emulator crop models Southern Africa Environmental sciences Richard D. Robertson verfasserin aut Kenneth Strzepek verfasserin aut Channing Arndt verfasserin aut In Frontiers in Climate Frontiers Media S.A., 2020 4(2022) (DE-627)1678815624 (DE-600)2986708-3 26249553 nnns volume:4 year:2022 https://doi.org/10.3389/fclim.2022.787582 kostenfrei https://doaj.org/article/fbf275294b74478d92e5426d2ada576f kostenfrei https://www.frontiersin.org/articles/10.3389/fclim.2022.787582/full kostenfrei https://doaj.org/toc/2624-9553 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 |
allfieldsGer |
10.3389/fclim.2022.787582 doi (DE-627)DOAJ031033059 (DE-599)DOAJfbf275294b74478d92e5426d2ada576f DE-627 ger DE-627 rakwb eng GE1-350 Timothy S. Thomas verfasserin aut Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks. climate change yield shocks climate uncertainty yield emulator crop models Southern Africa Environmental sciences Richard D. Robertson verfasserin aut Kenneth Strzepek verfasserin aut Channing Arndt verfasserin aut In Frontiers in Climate Frontiers Media S.A., 2020 4(2022) (DE-627)1678815624 (DE-600)2986708-3 26249553 nnns volume:4 year:2022 https://doi.org/10.3389/fclim.2022.787582 kostenfrei https://doaj.org/article/fbf275294b74478d92e5426d2ada576f kostenfrei https://www.frontiersin.org/articles/10.3389/fclim.2022.787582/full kostenfrei https://doaj.org/toc/2624-9553 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 |
allfieldsSound |
10.3389/fclim.2022.787582 doi (DE-627)DOAJ031033059 (DE-599)DOAJfbf275294b74478d92e5426d2ada576f DE-627 ger DE-627 rakwb eng GE1-350 Timothy S. Thomas verfasserin aut Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks. climate change yield shocks climate uncertainty yield emulator crop models Southern Africa Environmental sciences Richard D. Robertson verfasserin aut Kenneth Strzepek verfasserin aut Channing Arndt verfasserin aut In Frontiers in Climate Frontiers Media S.A., 2020 4(2022) (DE-627)1678815624 (DE-600)2986708-3 26249553 nnns volume:4 year:2022 https://doi.org/10.3389/fclim.2022.787582 kostenfrei https://doaj.org/article/fbf275294b74478d92e5426d2ada576f kostenfrei https://www.frontiersin.org/articles/10.3389/fclim.2022.787582/full kostenfrei https://doaj.org/toc/2624-9553 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 |
language |
English |
source |
In Frontiers in Climate 4(2022) volume:4 year:2022 |
sourceStr |
In Frontiers in Climate 4(2022) volume:4 year:2022 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
climate change yield shocks climate uncertainty yield emulator crop models Southern Africa Environmental sciences |
isfreeaccess_bool |
true |
container_title |
Frontiers in Climate |
authorswithroles_txt_mv |
Timothy S. Thomas @@aut@@ Richard D. Robertson @@aut@@ Kenneth Strzepek @@aut@@ Channing Arndt @@aut@@ |
publishDateDaySort_date |
2022-01-01T00:00:00Z |
hierarchy_top_id |
1678815624 |
id |
DOAJ031033059 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ031033059</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230307154429.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fclim.2022.787582</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ031033059</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJfbf275294b74478d92e5426d2ada576f</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Timothy S. Thomas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">climate change</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">yield shocks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">climate uncertainty</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">yield emulator</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">crop models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Southern Africa</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Richard D. Robertson</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kenneth Strzepek</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Channing Arndt</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Climate</subfield><subfield code="d">Frontiers Media S.A., 2020</subfield><subfield code="g">4(2022)</subfield><subfield code="w">(DE-627)1678815624</subfield><subfield code="w">(DE-600)2986708-3</subfield><subfield code="x">26249553</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2022</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fclim.2022.787582</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/fbf275294b74478d92e5426d2ada576f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fclim.2022.787582/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2624-9553</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2022</subfield></datafield></record></collection>
|
callnumber-first |
G - Geography, Anthropology, Recreation |
author |
Timothy S. Thomas |
spellingShingle |
Timothy S. Thomas misc GE1-350 misc climate change misc yield shocks misc climate uncertainty misc yield emulator misc crop models misc Southern Africa misc Environmental sciences Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change |
authorStr |
Timothy S. Thomas |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)1678815624 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
GE1-350 |
illustrated |
Not Illustrated |
issn |
26249553 |
topic_title |
GE1-350 Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change climate change yield shocks climate uncertainty yield emulator crop models Southern Africa |
topic |
misc GE1-350 misc climate change misc yield shocks misc climate uncertainty misc yield emulator misc crop models misc Southern Africa misc Environmental sciences |
topic_unstemmed |
misc GE1-350 misc climate change misc yield shocks misc climate uncertainty misc yield emulator misc crop models misc Southern Africa misc Environmental sciences |
topic_browse |
misc GE1-350 misc climate change misc yield shocks misc climate uncertainty misc yield emulator misc crop models misc Southern Africa misc Environmental sciences |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Frontiers in Climate |
hierarchy_parent_id |
1678815624 |
hierarchy_top_title |
Frontiers in Climate |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)1678815624 (DE-600)2986708-3 |
title |
Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change |
ctrlnum |
(DE-627)DOAJ031033059 (DE-599)DOAJfbf275294b74478d92e5426d2ada576f |
title_full |
Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change |
author_sort |
Timothy S. Thomas |
journal |
Frontiers in Climate |
journalStr |
Frontiers in Climate |
callnumber-first-code |
G |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
txt |
author_browse |
Timothy S. Thomas Richard D. Robertson Kenneth Strzepek Channing Arndt |
container_volume |
4 |
class |
GE1-350 |
format_se |
Elektronische Aufsätze |
author-letter |
Timothy S. Thomas |
doi_str_mv |
10.3389/fclim.2022.787582 |
author2-role |
verfasserin |
title_sort |
extreme events and production shocks for key crops in southern africa under climate change |
callnumber |
GE1-350 |
title_auth |
Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change |
abstract |
Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks. |
abstractGer |
Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks. |
abstract_unstemmed |
Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 |
title_short |
Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change |
url |
https://doi.org/10.3389/fclim.2022.787582 https://doaj.org/article/fbf275294b74478d92e5426d2ada576f https://www.frontiersin.org/articles/10.3389/fclim.2022.787582/full https://doaj.org/toc/2624-9553 |
remote_bool |
true |
author2 |
Richard D. Robertson Kenneth Strzepek Channing Arndt |
author2Str |
Richard D. Robertson Kenneth Strzepek Channing Arndt |
ppnlink |
1678815624 |
callnumber-subject |
GE - Environmental Sciences |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3389/fclim.2022.787582 |
callnumber-a |
GE1-350 |
up_date |
2024-07-03T18:23:23.875Z |
_version_ |
1803583229920280576 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ031033059</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230307154429.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fclim.2022.787582</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ031033059</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJfbf275294b74478d92e5426d2ada576f</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Timothy S. Thomas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">climate change</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">yield shocks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">climate uncertainty</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">yield emulator</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">crop models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Southern Africa</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Richard D. Robertson</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kenneth Strzepek</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Channing Arndt</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Climate</subfield><subfield code="d">Frontiers Media S.A., 2020</subfield><subfield code="g">4(2022)</subfield><subfield code="w">(DE-627)1678815624</subfield><subfield code="w">(DE-600)2986708-3</subfield><subfield code="x">26249553</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2022</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fclim.2022.787582</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/fbf275294b74478d92e5426d2ada576f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fclim.2022.787582/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2624-9553</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2022</subfield></datafield></record></collection>
|
score |
7.3997 |