Ontology type: schema:ScholarlyArticle
2016-12-12
AUTHORSMichael Glotter, Joshua Elliott
ABSTRACTDrought-induced agricultural loss is one of the most costly impacts of extreme weather1–3, and without mitigation, climate change is likely to increase the severity and frequency of future droughts4,5. The Dust Bowl of the 1930s was the driest and hottest for agriculture in modern US history. Improvements in farming practices have increased productivity, but yields today are still tightly linked to climate variation6 and the impacts of a 1930s-type drought on current and future agricultural systems remain unclear. Simulations of biophysical process and empirical models suggest that Dust-Bowl-type droughts today would have unprecedented consequences, with yield losses ∼50% larger than the severe drought of 2012. Damages at these extremes are highly sensitive to temperature, worsening by ∼25% with each degree centigrade of warming. We find that high temperatures can be more damaging than rainfall deficit, and, without adaptation, warmer mid-century temperatures with even average precipitation could lead to maize losses equivalent to the Dust Bowl drought. Warmer temperatures alongside consecutive droughts could make up to 85% of rain-fed maize at risk of changes that may persist for decades. Understanding the interactions of weather extremes and a changing agricultural system is therefore critical to effectively respond to, and minimize, the impacts of the next extreme drought event. More... »
PAGES16193
http://scigraph.springernature.com/pub.10.1038/nplants.2016.193
DOIhttp://dx.doi.org/10.1038/nplants.2016.193
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1022227465
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/27941818
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Biological Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0607",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Plant Biology",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Agriculture",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Climate Change",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Computer Simulation",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Droughts",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Models, Theoretical",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Seasons",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Socioeconomic Factors",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Soybeans",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Triticum",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "United States",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Zea mays",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Department of the Geophysical Sciences, University of Chicago, 5734 S Ellis Avenue, 60637, Chicago, Illinois, USA",
"id": "http://www.grid.ac/institutes/grid.170205.1",
"name": [
"Department of the Geophysical Sciences, University of Chicago, 5734 S Ellis Avenue, 60637, Chicago, Illinois, USA"
],
"type": "Organization"
},
"familyName": "Glotter",
"givenName": "Michael",
"id": "sg:person.01234550006.10",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01234550006.10"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Computation Institute, University of Chicago, 5735 S Ellis Avenue, 60637, Chicago, Illinois, USA",
"id": "http://www.grid.ac/institutes/grid.170205.1",
"name": [
"NASA Goddard Institute for Space Studies, 2880 Broadway, 10025, New York, New York, USA",
"Computation Institute, University of Chicago, 5735 S Ellis Avenue, 60637, Chicago, Illinois, USA"
],
"type": "Organization"
},
"familyName": "Elliott",
"givenName": "Joshua",
"id": "sg:person.01052206206.60",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052206206.60"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s00382-010-0807-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025817787",
"https://doi.org/10.1007/s00382-010-0807-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ncomms6989",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010392333",
"https://doi.org/10.1038/ncomms6989"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00382-007-0340-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031386741",
"https://doi.org/10.1007/s00382-007-0340-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nplants.2014.26",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014092870",
"https://doi.org/10.1038/nplants.2014.26"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature16467",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017978279",
"https://doi.org/10.1038/nature16467"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10584-012-0428-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002370482",
"https://doi.org/10.1007/s10584-012-0428-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11111-013-0190-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051220646",
"https://doi.org/10.1007/s11111-013-0190-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf01092678",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018068003",
"https://doi.org/10.1007/bf01092678"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11069-013-0566-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014637390",
"https://doi.org/10.1007/s11069-013-0566-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00141665",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051195829",
"https://doi.org/10.1007/bf00141665"
],
"type": "CreativeWork"
}
],
"datePublished": "2016-12-12",
"datePublishedReg": "2016-12-12",
"description": "Drought-induced agricultural loss is one of the most costly impacts of extreme weather1\u20133, and without mitigation, climate change is likely to increase the severity and frequency of future droughts4,5. The Dust Bowl of the 1930s was the driest and hottest for agriculture in modern US history. Improvements in farming practices have increased productivity, but yields today are still tightly linked to climate variation6 and the impacts of a 1930s-type drought on current and future agricultural systems remain unclear. Simulations of biophysical process and empirical models suggest that Dust-Bowl-type droughts today would have unprecedented consequences, with yield losses \u223c50% larger than the severe drought of 2012. Damages at these extremes are highly sensitive to temperature, worsening by \u223c25% with each degree centigrade of warming. We find that high temperatures can be more damaging than rainfall deficit, and, without adaptation, warmer mid-century temperatures with even average precipitation could lead to maize losses equivalent to the Dust Bowl drought. Warmer temperatures alongside consecutive droughts could make up to 85% of rain-fed maize at risk of changes that may persist for decades. Understanding the interactions of weather extremes and a changing agricultural system is therefore critical to effectively respond to, and minimize, the impacts of the next extreme drought event.",
"genre": "article",
"id": "sg:pub.10.1038/nplants.2016.193",
"inLanguage": "en",
"isAccessibleForFree": false,
"isFundedItemOf": [
{
"id": "sg:grant.3108952",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3131562",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3132604",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1051401",
"issn": [
"2055-026X",
"2055-0278"
],
"name": "Nature Plants",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "3"
}
],
"keywords": [
"Dust Bowl drought",
"Dust Bowl",
"extreme drought events",
"rain-fed maize",
"agricultural systems",
"drought today",
"rainfall deficit",
"average precipitation",
"drought events",
"weather extremes",
"severe drought",
"climate change",
"consecutive droughts",
"future agricultural systems",
"Warmer temperatures",
"drought",
"biophysical processes",
"maize losses",
"farming practices",
"agricultural losses",
"extremes",
"US agriculture",
"unprecedented consequences",
"risk of changes",
"costly impact",
"empirical model",
"agriculture",
"driest",
"precipitation",
"temperature",
"yield loss",
"impact",
"US history",
"events",
"high temperature",
"changes",
"productivity",
"mitigation",
"maize",
"loss",
"today",
"history",
"decades",
"simulations",
"adaptation",
"bowl",
"consequences",
"model",
"future",
"process",
"system",
"practice",
"risk",
"degree",
"interaction",
"frequency",
"damage",
"deficits",
"improvement",
"severity",
"modern US history"
],
"name": "Simulating US agriculture in a modern Dust Bowl drought",
"pagination": "16193",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1022227465"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1038/nplants.2016.193"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"27941818"
]
}
],
"sameAs": [
"https://doi.org/10.1038/nplants.2016.193",
"https://app.dimensions.ai/details/publication/pub.1022227465"
],
"sdDataset": "articles",
"sdDatePublished": "2022-06-01T22:15",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_695.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1038/nplants.2016.193"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/nplants.2016.193'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/nplants.2016.193'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/nplants.2016.193'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/nplants.2016.193'
This table displays all metadata directly associated to this object as RDF triples.
223 TRIPLES
22 PREDICATES
108 URIs
90 LITERALS
18 BLANK NODES