Identifying agricultural disaster risk zones for future climate actions
Identifying agricultural disaster risk regions before the occurrence of climate-related disasters is critical for early mitigation planning. This paper aims to identify these regions based on data from the Food and Agriculture Organization of the United Nations (FAO), the bilateral and multilateral...
Gespeichert in:
Veröffentlicht in: | PloS one 2021-12, Vol.16 (12), p.e0260430 |
---|---|
1. Verfasser: | |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 12 |
container_start_page | e0260430 |
container_title | PloS one |
container_volume | 16 |
creator | Arreyndip, Nkongho Ayuketang |
description | Identifying agricultural disaster risk regions before the occurrence of climate-related disasters is critical for early mitigation planning. This paper aims to identify these regions based on data from the Food and Agriculture Organization of the United Nations (FAO), the bilateral and multilateral trade network data of the World Integrated Trade Solution(WITS) and the agent-based economic model Acclimate. By applying a uniform forcing across agricultural sectors of some breadbasket regions (US, EU and China), when single and simultaneous extreme weather events occur, such as the 2018 European heatwave, production and consumption value losses and gains are calculated at regional and global levels. Comparing the FAO data sets, WITS, and Acclimate's production value losses, the results show a strong dependence of agricultural production losses on a region's output and connectivity level in the global supply and trade network. While India, Brazil, Russia, Canada, Australia, and Iran are highly vulnerable, the imposition of export restrictions to compensate for demand shortfalls makes Sub-Saharan Africa the most vulnerable region, as it is heavily dependent on agricultural imports. In addition, simultaneous extreme weather events can exacerbate the loss of value of agricultural production relative to single extreme weather events. Agricultural practices to increase production such as smart farming, increased investment in plantation agriculture, and diversification of trading partners can help mitigate future food security risks in Sub-Saharan Africa and other agricultural import-dependent regions. |
doi_str_mv | 10.1371/journal.pone.0260430 |
format | Article |
fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2605596749</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A684621198</galeid><doaj_id>oai_doaj_org_article_9e167a67a8da48588ff36c2189e76494</doaj_id><sourcerecordid>A684621198</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-eba210829d24843e8e9e4851b58cedc872fd1d72885471a90689b3b6452989e83</originalsourceid><addsrcrecordid>eNqNkl2L1DAUhoso7rr6D0QLgujFjM1H0-RGWBZdBxYW_LoNaXrayZhpxiQV119vOtNdprIXkkJK8pxz3vPmZNlzVCwRqdC7jRt8r-xy53pYFpgVlBQPslMkCF4wXJCHR_8n2ZMQNkVREs7Y4-yEUF6WHFen2eWqgT6a9sb0Xa46b_Rg4-CVzRsTVIjgc2_Cj_xPqhLy1vm8HdI95NqarYqQKx2N68PT7FGrbIBn036Wffv44evFp8XV9eXq4vxqoZnAcQG1wqjgWDSYckqAg4CkBdUl19BoXuG2QU2FOS9phZQoGBc1qRktseACODnLXh7y7qwLcvIgyNR-WQpWUZGI1YFonNrInU8y_Y10ysj9gfOdVD4abUEKQKxS6eONSio4b1vCNEapUsWooCnX-6naUG-TvmRVsmaWdH7Tm7Xs3C_JGeF8L-bNlMC7nwOEKLcmaLBW9eCGve7kCy3pWOvVP-j93U1Up1IDpm9dqqvHpPKcccowQmJ0aXkPlVYDW6PTU7Ymnc8C3s4CEhPhd-zUEIJcffn8_-z19zn7-ohdg7JxHZwd9jMzB-kB1N6F4KG9MxkVcpz3WzfkOO9ymvcU9uL4ge6Cbgec_AU6pvl7</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2605596749</pqid></control><display><type>article</type><title>Identifying agricultural disaster risk zones for future climate actions</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>Free E-Journal (出版社公開部分のみ)</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Arreyndip, Nkongho Ayuketang</creator><contributor>Ali, Ghaffar</contributor><creatorcontrib>Arreyndip, Nkongho Ayuketang ; Ali, Ghaffar</creatorcontrib><description>Identifying agricultural disaster risk regions before the occurrence of climate-related disasters is critical for early mitigation planning. This paper aims to identify these regions based on data from the Food and Agriculture Organization of the United Nations (FAO), the bilateral and multilateral trade network data of the World Integrated Trade Solution(WITS) and the agent-based economic model Acclimate. By applying a uniform forcing across agricultural sectors of some breadbasket regions (US, EU and China), when single and simultaneous extreme weather events occur, such as the 2018 European heatwave, production and consumption value losses and gains are calculated at regional and global levels. Comparing the FAO data sets, WITS, and Acclimate's production value losses, the results show a strong dependence of agricultural production losses on a region's output and connectivity level in the global supply and trade network. While India, Brazil, Russia, Canada, Australia, and Iran are highly vulnerable, the imposition of export restrictions to compensate for demand shortfalls makes Sub-Saharan Africa the most vulnerable region, as it is heavily dependent on agricultural imports. In addition, simultaneous extreme weather events can exacerbate the loss of value of agricultural production relative to single extreme weather events. Agricultural practices to increase production such as smart farming, increased investment in plantation agriculture, and diversification of trading partners can help mitigate future food security risks in Sub-Saharan Africa and other agricultural import-dependent regions.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0260430</identifier><identifier>PMID: 34855827</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adaptation ; Agricultural industry ; Agricultural practices ; Agricultural production ; Agriculture ; Biology and Life Sciences ; Brazil ; China ; Climate Change ; Connectivity ; Crops, Agricultural - growth & development ; Dairy cattle ; Digital agriculture ; Disaster management ; Disaster risk ; Disasters ; Disasters - prevention & control ; Economic analysis ; Economic impact ; Economic models ; Environmental aspects ; Extreme weather ; Food ; Food security ; Food Supply ; Future climates ; Heat ; Heat waves ; Humans ; Imports ; India ; Influence ; International trade ; Iran ; Management ; Milk ; Mitigation ; Models, Economic ; Natural disasters ; People and Places ; Prices ; Rain ; Regions ; Rice ; Russia ; Social Sciences ; Trade ; Weather</subject><ispartof>PloS one, 2021-12, Vol.16 (12), p.e0260430</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Nkongho Ayuketang Arreyndip. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Nkongho Ayuketang Arreyndip 2021 Nkongho Ayuketang Arreyndip</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-eba210829d24843e8e9e4851b58cedc872fd1d72885471a90689b3b6452989e83</citedby><cites>FETCH-LOGICAL-c692t-eba210829d24843e8e9e4851b58cedc872fd1d72885471a90689b3b6452989e83</cites><orcidid>0000-0002-5316-4063</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8638849/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8638849/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34855827$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ali, Ghaffar</contributor><creatorcontrib>Arreyndip, Nkongho Ayuketang</creatorcontrib><title>Identifying agricultural disaster risk zones for future climate actions</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Identifying agricultural disaster risk regions before the occurrence of climate-related disasters is critical for early mitigation planning. This paper aims to identify these regions based on data from the Food and Agriculture Organization of the United Nations (FAO), the bilateral and multilateral trade network data of the World Integrated Trade Solution(WITS) and the agent-based economic model Acclimate. By applying a uniform forcing across agricultural sectors of some breadbasket regions (US, EU and China), when single and simultaneous extreme weather events occur, such as the 2018 European heatwave, production and consumption value losses and gains are calculated at regional and global levels. Comparing the FAO data sets, WITS, and Acclimate's production value losses, the results show a strong dependence of agricultural production losses on a region's output and connectivity level in the global supply and trade network. While India, Brazil, Russia, Canada, Australia, and Iran are highly vulnerable, the imposition of export restrictions to compensate for demand shortfalls makes Sub-Saharan Africa the most vulnerable region, as it is heavily dependent on agricultural imports. In addition, simultaneous extreme weather events can exacerbate the loss of value of agricultural production relative to single extreme weather events. Agricultural practices to increase production such as smart farming, increased investment in plantation agriculture, and diversification of trading partners can help mitigate future food security risks in Sub-Saharan Africa and other agricultural import-dependent regions.</description><subject>Adaptation</subject><subject>Agricultural industry</subject><subject>Agricultural practices</subject><subject>Agricultural production</subject><subject>Agriculture</subject><subject>Biology and Life Sciences</subject><subject>Brazil</subject><subject>China</subject><subject>Climate Change</subject><subject>Connectivity</subject><subject>Crops, Agricultural - growth & development</subject><subject>Dairy cattle</subject><subject>Digital agriculture</subject><subject>Disaster management</subject><subject>Disaster risk</subject><subject>Disasters</subject><subject>Disasters - prevention & control</subject><subject>Economic analysis</subject><subject>Economic impact</subject><subject>Economic models</subject><subject>Environmental aspects</subject><subject>Extreme weather</subject><subject>Food</subject><subject>Food security</subject><subject>Food Supply</subject><subject>Future climates</subject><subject>Heat</subject><subject>Heat waves</subject><subject>Humans</subject><subject>Imports</subject><subject>India</subject><subject>Influence</subject><subject>International trade</subject><subject>Iran</subject><subject>Management</subject><subject>Milk</subject><subject>Mitigation</subject><subject>Models, Economic</subject><subject>Natural disasters</subject><subject>People and Places</subject><subject>Prices</subject><subject>Rain</subject><subject>Regions</subject><subject>Rice</subject><subject>Russia</subject><subject>Social Sciences</subject><subject>Trade</subject><subject>Weather</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl2L1DAUhoso7rr6D0QLgujFjM1H0-RGWBZdBxYW_LoNaXrayZhpxiQV119vOtNdprIXkkJK8pxz3vPmZNlzVCwRqdC7jRt8r-xy53pYFpgVlBQPslMkCF4wXJCHR_8n2ZMQNkVREs7Y4-yEUF6WHFen2eWqgT6a9sb0Xa46b_Rg4-CVzRsTVIjgc2_Cj_xPqhLy1vm8HdI95NqarYqQKx2N68PT7FGrbIBn036Wffv44evFp8XV9eXq4vxqoZnAcQG1wqjgWDSYckqAg4CkBdUl19BoXuG2QU2FOS9phZQoGBc1qRktseACODnLXh7y7qwLcvIgyNR-WQpWUZGI1YFonNrInU8y_Y10ysj9gfOdVD4abUEKQKxS6eONSio4b1vCNEapUsWooCnX-6naUG-TvmRVsmaWdH7Tm7Xs3C_JGeF8L-bNlMC7nwOEKLcmaLBW9eCGve7kCy3pWOvVP-j93U1Up1IDpm9dqqvHpPKcccowQmJ0aXkPlVYDW6PTU7Ymnc8C3s4CEhPhd-zUEIJcffn8_-z19zn7-ohdg7JxHZwd9jMzB-kB1N6F4KG9MxkVcpz3WzfkOO9ymvcU9uL4ge6Cbgec_AU6pvl7</recordid><startdate>20211202</startdate><enddate>20211202</enddate><creator>Arreyndip, Nkongho Ayuketang</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-5316-4063</orcidid></search><sort><creationdate>20211202</creationdate><title>Identifying agricultural disaster risk zones for future climate actions</title><author>Arreyndip, Nkongho Ayuketang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-eba210829d24843e8e9e4851b58cedc872fd1d72885471a90689b3b6452989e83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptation</topic><topic>Agricultural industry</topic><topic>Agricultural practices</topic><topic>Agricultural production</topic><topic>Agriculture</topic><topic>Biology and Life Sciences</topic><topic>Brazil</topic><topic>China</topic><topic>Climate Change</topic><topic>Connectivity</topic><topic>Crops, Agricultural - growth & development</topic><topic>Dairy cattle</topic><topic>Digital agriculture</topic><topic>Disaster management</topic><topic>Disaster risk</topic><topic>Disasters</topic><topic>Disasters - prevention & control</topic><topic>Economic analysis</topic><topic>Economic impact</topic><topic>Economic models</topic><topic>Environmental aspects</topic><topic>Extreme weather</topic><topic>Food</topic><topic>Food security</topic><topic>Food Supply</topic><topic>Future climates</topic><topic>Heat</topic><topic>Heat waves</topic><topic>Humans</topic><topic>Imports</topic><topic>India</topic><topic>Influence</topic><topic>International trade</topic><topic>Iran</topic><topic>Management</topic><topic>Milk</topic><topic>Mitigation</topic><topic>Models, Economic</topic><topic>Natural disasters</topic><topic>People and Places</topic><topic>Prices</topic><topic>Rain</topic><topic>Regions</topic><topic>Rice</topic><topic>Russia</topic><topic>Social Sciences</topic><topic>Trade</topic><topic>Weather</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Arreyndip, Nkongho Ayuketang</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Science in Context</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>Biological Sciences</collection><collection>Agriculture Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Arreyndip, Nkongho Ayuketang</au><au>Ali, Ghaffar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying agricultural disaster risk zones for future climate actions</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-12-02</date><risdate>2021</risdate><volume>16</volume><issue>12</issue><spage>e0260430</spage><pages>e0260430-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Identifying agricultural disaster risk regions before the occurrence of climate-related disasters is critical for early mitigation planning. This paper aims to identify these regions based on data from the Food and Agriculture Organization of the United Nations (FAO), the bilateral and multilateral trade network data of the World Integrated Trade Solution(WITS) and the agent-based economic model Acclimate. By applying a uniform forcing across agricultural sectors of some breadbasket regions (US, EU and China), when single and simultaneous extreme weather events occur, such as the 2018 European heatwave, production and consumption value losses and gains are calculated at regional and global levels. Comparing the FAO data sets, WITS, and Acclimate's production value losses, the results show a strong dependence of agricultural production losses on a region's output and connectivity level in the global supply and trade network. While India, Brazil, Russia, Canada, Australia, and Iran are highly vulnerable, the imposition of export restrictions to compensate for demand shortfalls makes Sub-Saharan Africa the most vulnerable region, as it is heavily dependent on agricultural imports. In addition, simultaneous extreme weather events can exacerbate the loss of value of agricultural production relative to single extreme weather events. Agricultural practices to increase production such as smart farming, increased investment in plantation agriculture, and diversification of trading partners can help mitigate future food security risks in Sub-Saharan Africa and other agricultural import-dependent regions.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34855827</pmid><doi>10.1371/journal.pone.0260430</doi><tpages>e0260430</tpages><orcidid>https://orcid.org/0000-0002-5316-4063</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2021-12, Vol.16 (12), p.e0260430 |
issn | 1932-6203 1932-6203 |
language | eng |
recordid | cdi_plos_journals_2605596749 |
source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; Free E-Journal (出版社公開部分のみ); PubMed Central; Free Full-Text Journals in Chemistry |
subjects | Adaptation Agricultural industry Agricultural practices Agricultural production Agriculture Biology and Life Sciences Brazil China Climate Change Connectivity Crops, Agricultural - growth & development Dairy cattle Digital agriculture Disaster management Disaster risk Disasters Disasters - prevention & control Economic analysis Economic impact Economic models Environmental aspects Extreme weather Food Food security Food Supply Future climates Heat Heat waves Humans Imports India Influence International trade Iran Management Milk Mitigation Models, Economic Natural disasters People and Places Prices Rain Regions Rice Russia Social Sciences Trade Weather |
title | Identifying agricultural disaster risk zones for future climate actions |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T16%3A20%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Identifying%20agricultural%20disaster%20risk%20zones%20for%20future%20climate%20actions&rft.jtitle=PloS%20one&rft.au=Arreyndip,%20Nkongho%20Ayuketang&rft.date=2021-12-02&rft.volume=16&rft.issue=12&rft.spage=e0260430&rft.pages=e0260430-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0260430&rft_dat=%3Cgale_plos_%3EA684621198%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2605596749&rft_id=info:pmid/34855827&rft_galeid=A684621198&rft_doaj_id=oai_doaj_org_article_9e167a67a8da48588ff36c2189e76494&rfr_iscdi=true |