Effects of Surface Ozone and Climate on Historical (1980–2015) Crop Yields in the United States: Implication for Mid-21st Century Projection View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-11-13

AUTHORS

Yabin Da, Yangyang Xu, Bruce McCarl

ABSTRACT

Surface level ozone pollution imposes significant crop yield damages. However, the quantification has mainly involved chamber experiments, which may not be representative of results in farm fields. Additionally, the relative impacts of ozone under future climate change and their possible interactions remain poorly understood. Here we attempt to empirically fill this gap using historical county-level crop yield, ozone, and climate data in the United States. We explore ozone impacts on corn, soybeans, spring wheat, winter wheat, barley, cotton, peanuts, rice, sorghum, and sunflowers. We also incorporate a variety of climatic variables to investigate potential ozone-climate interactions. The results shed light on future yield consequences of ozone and climate change individually and jointly under a projected climate scenario. Our findings indicate significant negative impacts of ozone exposure for eight of the ten crops we examined, excepting barley and winter wheat. Meanwhile, corn exhibits to be more sensitive to ozone than soybeans. These results differ from those found under chamber experiments. We also find rising temperatures tend to worsen ozone damages while water supplies mitigate that. We find that the average annual historical damages from ozone reached $6.03 billion (in 2015 U.S. dollar) from 1980 to 2015. Finally, our results suggest that the damages caused by climate change-induced ozone elevation are much smaller than the damages caused by the direct effects of climate change itself. More... »

PAGES

355-378

References to SciGraph publications

  • 2012-11-18. Adaptation of US maize to temperature variations in NATURE CLIMATE CHANGE
  • 2019-11-25. Increasing impacts of extreme droughts on vegetation productivity under climate change in NATURE CLIMATE CHANGE
  • 2021-02-16. Climate change as a driver of food insecurity in the 2007 Lesotho-South Africa drought in SCIENTIFIC REPORTS
  • 2004-09. Yield Variability as Influenced by Climate: A Statistical Investigation in CLIMATIC CHANGE
  • 2003-07. Yield Responses of Wheat to Ozone Exposure as Modified by Drought-Induced Differences in Ozone Uptake in WATER, AIR, & SOIL POLLUTION
  • 2017-03-13. Stage-specific, Nonlinear Surface Ozone Damage to Rice Production in China in SCIENTIFIC REPORTS
  • 2018-07-23. Higher temperatures increase suicide rates in the United States and Mexico in NATURE CLIMATE CHANGE
  • 2014-02-28. Estimated crop yield losses due to surface ozone exposure and economic damage in India in ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
  • 2019-03-15. Modified version for SPEI to evaluate and modeling the agricultural drought severity in INTERNATIONAL JOURNAL OF BIOMETEOROLOGY
  • 2018-05-03. U.S. Agro-Climate in 20th Century: Growing Degree Days, First and Last Frost, Growing Season Length, and Impacts on Crop Yields in SCIENTIFIC REPORTS
  • 2017-11-02. The Impact of Climate Change on Agriculture: Findings from Households in Vietnam in ENVIRONMENTAL AND RESOURCE ECONOMICS
  • 2013-11-29. Effect of Ozone on the Relative Yield of Rice Crop in Japan Evaluated Based on Monitored Concentrations in WATER, AIR, & SOIL POLLUTION
  • 2018-07-16. Heat in the Heartland: Crop Yield and Coverage Response to Climate Change Along the Mississippi River in ENVIRONMENTAL AND RESOURCE ECONOMICS
  • 2017-07-19. Loss of crop yields in India due to surface ozone: an estimation based on a network of observations in ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
  • 2015-06-29. Valuing the Ozone-Related Health Benefits of Methane Emission Controls in ENVIRONMENTAL AND RESOURCE ECONOMICS
  • 2014-07-27. Threat to future global food security from climate change and ozone air pollution in NATURE CLIMATE CHANGE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10640-021-00629-y

    DOI

    http://dx.doi.org/10.1007/s10640-021-00629-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1142555297


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    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/2008/05", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Environmental Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Economics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0502", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Environmental Science and Management", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1402", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Applied Economics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1499", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Other Economics", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Agricultural Economics, Texas A&M University, 77843, College Station, USA", 
              "id": "http://www.grid.ac/institutes/grid.264756.4", 
              "name": [
                "Department of Agricultural Economics, Texas A&M University, 77843, College Station, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Da", 
            "givenName": "Yabin", 
            "id": "sg:person.010471454317.38", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010471454317.38"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Atmospheric Sciences, Texas A&M University, 77843, College Station, USA", 
              "id": "http://www.grid.ac/institutes/grid.264756.4", 
              "name": [
                "Department of Atmospheric Sciences, Texas A&M University, 77843, College Station, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Xu", 
            "givenName": "Yangyang", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Agricultural Economics, Texas A&M University, 77843, College Station, USA", 
              "id": "http://www.grid.ac/institutes/grid.264756.4", 
              "name": [
                "Department of Agricultural Economics, Texas A&M University, 77843, College Station, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "McCarl", 
            "givenName": "Bruce", 
            "id": "sg:person.01337167475.14", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01337167475.14"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/s41558-019-0630-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1122861580", 
              "https://doi.org/10.1038/s41558-019-0630-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11356-014-2657-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005808371", 
              "https://doi.org/10.1007/s11356-014-2657-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-018-25212-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103702859", 
              "https://doi.org/10.1038/s41598-018-25212-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11270-013-1797-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010701753", 
              "https://doi.org/10.1007/s11270-013-1797-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep44224", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084132120", 
              "https://doi.org/10.1038/srep44224"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41558-018-0222-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105743427", 
              "https://doi.org/10.1038/s41558-018-0222-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1024577429129", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005303811", 
              "https://doi.org/10.1023/a:1024577429129"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10640-018-0271-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105608708", 
              "https://doi.org/10.1007/s10640-018-0271-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10640-017-0189-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092500793", 
              "https://doi.org/10.1007/s10640-017-0189-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nclimate2317", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036164846", 
              "https://doi.org/10.1038/nclimate2317"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10640-015-9937-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050988246", 
              "https://doi.org/10.1007/s10640-015-9937-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-021-83375-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1135369584", 
              "https://doi.org/10.1038/s41598-021-83375-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nclimate1585", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022202325", 
              "https://doi.org/10.1038/nclimate1585"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11356-017-9729-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090774476", 
              "https://doi.org/10.1007/s11356-017-9729-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/b:clim.0000043159.33816.e5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048069076", 
              "https://doi.org/10.1023/b:clim.0000043159.33816.e5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00484-019-01704-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1112780747", 
              "https://doi.org/10.1007/s00484-019-01704-2"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2021-11-13", 
        "datePublishedReg": "2021-11-13", 
        "description": "Surface level ozone pollution imposes significant crop yield damages. However, the quantification has mainly involved chamber experiments, which may not be representative of results in farm fields. Additionally, the relative impacts of ozone under future climate change and their possible interactions remain poorly understood. Here we attempt to empirically fill this gap using historical county-level crop yield, ozone, and climate data in the United States. We explore ozone impacts on corn, soybeans, spring wheat, winter wheat, barley, cotton, peanuts, rice, sorghum, and sunflowers. We also incorporate a variety of climatic variables to investigate potential ozone-climate interactions. The results shed light on future yield consequences of ozone and climate change individually and jointly under a projected climate scenario. Our findings indicate significant negative impacts of ozone exposure for eight of the ten crops we examined, excepting barley and winter wheat. Meanwhile, corn exhibits to be more sensitive to ozone than soybeans. These results differ from those found under chamber experiments. We also find rising temperatures tend to worsen ozone damages while water supplies mitigate that. We find that the average annual historical damages from ozone reached $6.03 billion (in 2015 U.S. dollar) from 1980 to 2015. Finally, our results suggest that the damages caused by climate change-induced ozone elevation are much smaller than the damages caused by the direct effects of climate change itself.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s10640-021-00629-y", 
        "inLanguage": "en", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1049212", 
            "issn": [
              "0924-6460", 
              "1573-1502"
            ], 
            "name": "Environmental and Resource Economics", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "81"
          }
        ], 
        "keywords": [
          "crop yield", 
          "winter wheat", 
          "county-level crop yields", 
          "historical crop yields", 
          "chamber experiments", 
          "climate change", 
          "spring wheat", 
          "yield damage", 
          "ozone elevation", 
          "farm fields", 
          "future climate change", 
          "wheat", 
          "climate scenarios", 
          "ozone damage", 
          "climatic variables", 
          "ozone impacts", 
          "climate data", 
          "water supply", 
          "barley", 
          "soybean", 
          "yield", 
          "ozone pollution", 
          "negative impact", 
          "historical damage", 
          "significant negative impact", 
          "United States", 
          "climate", 
          "ozone exposure", 
          "sorghum", 
          "crops", 
          "rice", 
          "sunflower", 
          "corn", 
          "cotton", 
          "relative impact", 
          "peanut", 
          "direct effect", 
          "impact", 
          "damage", 
          "supply", 
          "experiments", 
          "variety", 
          "possible interactions", 
          "pollution", 
          "surface ozone", 
          "ozone", 
          "effect", 
          "century projections", 
          "changes", 
          "mid", 
          "scenarios", 
          "interaction", 
          "field", 
          "results", 
          "quantification", 
          "variables", 
          "consequences", 
          "implications", 
          "state", 
          "gap", 
          "elevation", 
          "projections", 
          "temperature", 
          "data", 
          "exposure", 
          "light", 
          "findings", 
          "exhibit"
        ], 
        "name": "Effects of Surface Ozone and Climate on Historical (1980\u20132015) Crop Yields in the United States: Implication for Mid-21st Century Projection", 
        "pagination": "355-378", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1142555297"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10640-021-00629-y"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10640-021-00629-y", 
          "https://app.dimensions.ai/details/publication/pub.1142555297"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-05-10T10:33", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_910.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s10640-021-00629-y"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    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.10.1007/s10640-021-00629-y'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10640-021-00629-y'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10640-021-00629-y'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10640-021-00629-y'


     

    This table displays all metadata directly associated to this object as RDF triples.

    217 TRIPLES      22 PREDICATES      112 URIs      85 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10640-021-00629-y schema:about anzsrc-for:05
    2 anzsrc-for:0502
    3 anzsrc-for:14
    4 anzsrc-for:1402
    5 anzsrc-for:1499
    6 schema:author N495afaee4ea840b69ebe77425ad2d9db
    7 schema:citation sg:pub.10.1007/s00484-019-01704-2
    8 sg:pub.10.1007/s10640-015-9937-6
    9 sg:pub.10.1007/s10640-017-0189-5
    10 sg:pub.10.1007/s10640-018-0271-7
    11 sg:pub.10.1007/s11270-013-1797-5
    12 sg:pub.10.1007/s11356-014-2657-6
    13 sg:pub.10.1007/s11356-017-9729-3
    14 sg:pub.10.1023/a:1024577429129
    15 sg:pub.10.1023/b:clim.0000043159.33816.e5
    16 sg:pub.10.1038/nclimate1585
    17 sg:pub.10.1038/nclimate2317
    18 sg:pub.10.1038/s41558-018-0222-x
    19 sg:pub.10.1038/s41558-019-0630-6
    20 sg:pub.10.1038/s41598-018-25212-2
    21 sg:pub.10.1038/s41598-021-83375-x
    22 sg:pub.10.1038/srep44224
    23 schema:datePublished 2021-11-13
    24 schema:datePublishedReg 2021-11-13
    25 schema:description Surface level ozone pollution imposes significant crop yield damages. However, the quantification has mainly involved chamber experiments, which may not be representative of results in farm fields. Additionally, the relative impacts of ozone under future climate change and their possible interactions remain poorly understood. Here we attempt to empirically fill this gap using historical county-level crop yield, ozone, and climate data in the United States. We explore ozone impacts on corn, soybeans, spring wheat, winter wheat, barley, cotton, peanuts, rice, sorghum, and sunflowers. We also incorporate a variety of climatic variables to investigate potential ozone-climate interactions. The results shed light on future yield consequences of ozone and climate change individually and jointly under a projected climate scenario. Our findings indicate significant negative impacts of ozone exposure for eight of the ten crops we examined, excepting barley and winter wheat. Meanwhile, corn exhibits to be more sensitive to ozone than soybeans. These results differ from those found under chamber experiments. We also find rising temperatures tend to worsen ozone damages while water supplies mitigate that. We find that the average annual historical damages from ozone reached $6.03 billion (in 2015 U.S. dollar) from 1980 to 2015. Finally, our results suggest that the damages caused by climate change-induced ozone elevation are much smaller than the damages caused by the direct effects of climate change itself.
    26 schema:genre article
    27 schema:inLanguage en
    28 schema:isAccessibleForFree false
    29 schema:isPartOf N7cf5823cb11340ff8239dd82f02459b7
    30 Nb27878e376e543f292a8dfac413dad8b
    31 sg:journal.1049212
    32 schema:keywords United States
    33 barley
    34 century projections
    35 chamber experiments
    36 changes
    37 climate
    38 climate change
    39 climate data
    40 climate scenarios
    41 climatic variables
    42 consequences
    43 corn
    44 cotton
    45 county-level crop yields
    46 crop yield
    47 crops
    48 damage
    49 data
    50 direct effect
    51 effect
    52 elevation
    53 exhibit
    54 experiments
    55 exposure
    56 farm fields
    57 field
    58 findings
    59 future climate change
    60 gap
    61 historical crop yields
    62 historical damage
    63 impact
    64 implications
    65 interaction
    66 light
    67 mid
    68 negative impact
    69 ozone
    70 ozone damage
    71 ozone elevation
    72 ozone exposure
    73 ozone impacts
    74 ozone pollution
    75 peanut
    76 pollution
    77 possible interactions
    78 projections
    79 quantification
    80 relative impact
    81 results
    82 rice
    83 scenarios
    84 significant negative impact
    85 sorghum
    86 soybean
    87 spring wheat
    88 state
    89 sunflower
    90 supply
    91 surface ozone
    92 temperature
    93 variables
    94 variety
    95 water supply
    96 wheat
    97 winter wheat
    98 yield
    99 yield damage
    100 schema:name Effects of Surface Ozone and Climate on Historical (1980–2015) Crop Yields in the United States: Implication for Mid-21st Century Projection
    101 schema:pagination 355-378
    102 schema:productId N4cd0881d1170423ab1c2cbacc4900f78
    103 Nd5908d5f8ec84f499f35b4f7c5bc1bf6
    104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1142555297
    105 https://doi.org/10.1007/s10640-021-00629-y
    106 schema:sdDatePublished 2022-05-10T10:33
    107 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    108 schema:sdPublisher N49569403287f42f391c0699411c7d7d8
    109 schema:url https://doi.org/10.1007/s10640-021-00629-y
    110 sgo:license sg:explorer/license/
    111 sgo:sdDataset articles
    112 rdf:type schema:ScholarlyArticle
    113 N24096a86461e4359a79f98b18d908e2a schema:affiliation grid-institutes:grid.264756.4
    114 schema:familyName Xu
    115 schema:givenName Yangyang
    116 rdf:type schema:Person
    117 N49569403287f42f391c0699411c7d7d8 schema:name Springer Nature - SN SciGraph project
    118 rdf:type schema:Organization
    119 N495afaee4ea840b69ebe77425ad2d9db rdf:first sg:person.010471454317.38
    120 rdf:rest N7b042cf4753f4d67b9727036056869b5
    121 N4cd0881d1170423ab1c2cbacc4900f78 schema:name dimensions_id
    122 schema:value pub.1142555297
    123 rdf:type schema:PropertyValue
    124 N7b042cf4753f4d67b9727036056869b5 rdf:first N24096a86461e4359a79f98b18d908e2a
    125 rdf:rest Nbc927189227a4c388daf8030aaeedd6c
    126 N7cf5823cb11340ff8239dd82f02459b7 schema:issueNumber 2
    127 rdf:type schema:PublicationIssue
    128 Nb27878e376e543f292a8dfac413dad8b schema:volumeNumber 81
    129 rdf:type schema:PublicationVolume
    130 Nbc927189227a4c388daf8030aaeedd6c rdf:first sg:person.01337167475.14
    131 rdf:rest rdf:nil
    132 Nd5908d5f8ec84f499f35b4f7c5bc1bf6 schema:name doi
    133 schema:value 10.1007/s10640-021-00629-y
    134 rdf:type schema:PropertyValue
    135 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
    136 schema:name Environmental Sciences
    137 rdf:type schema:DefinedTerm
    138 anzsrc-for:0502 schema:inDefinedTermSet anzsrc-for:
    139 schema:name Environmental Science and Management
    140 rdf:type schema:DefinedTerm
    141 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
    142 schema:name Economics
    143 rdf:type schema:DefinedTerm
    144 anzsrc-for:1402 schema:inDefinedTermSet anzsrc-for:
    145 schema:name Applied Economics
    146 rdf:type schema:DefinedTerm
    147 anzsrc-for:1499 schema:inDefinedTermSet anzsrc-for:
    148 schema:name Other Economics
    149 rdf:type schema:DefinedTerm
    150 sg:journal.1049212 schema:issn 0924-6460
    151 1573-1502
    152 schema:name Environmental and Resource Economics
    153 schema:publisher Springer Nature
    154 rdf:type schema:Periodical
    155 sg:person.010471454317.38 schema:affiliation grid-institutes:grid.264756.4
    156 schema:familyName Da
    157 schema:givenName Yabin
    158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010471454317.38
    159 rdf:type schema:Person
    160 sg:person.01337167475.14 schema:affiliation grid-institutes:grid.264756.4
    161 schema:familyName McCarl
    162 schema:givenName Bruce
    163 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01337167475.14
    164 rdf:type schema:Person
    165 sg:pub.10.1007/s00484-019-01704-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112780747
    166 https://doi.org/10.1007/s00484-019-01704-2
    167 rdf:type schema:CreativeWork
    168 sg:pub.10.1007/s10640-015-9937-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050988246
    169 https://doi.org/10.1007/s10640-015-9937-6
    170 rdf:type schema:CreativeWork
    171 sg:pub.10.1007/s10640-017-0189-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092500793
    172 https://doi.org/10.1007/s10640-017-0189-5
    173 rdf:type schema:CreativeWork
    174 sg:pub.10.1007/s10640-018-0271-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105608708
    175 https://doi.org/10.1007/s10640-018-0271-7
    176 rdf:type schema:CreativeWork
    177 sg:pub.10.1007/s11270-013-1797-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010701753
    178 https://doi.org/10.1007/s11270-013-1797-5
    179 rdf:type schema:CreativeWork
    180 sg:pub.10.1007/s11356-014-2657-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005808371
    181 https://doi.org/10.1007/s11356-014-2657-6
    182 rdf:type schema:CreativeWork
    183 sg:pub.10.1007/s11356-017-9729-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090774476
    184 https://doi.org/10.1007/s11356-017-9729-3
    185 rdf:type schema:CreativeWork
    186 sg:pub.10.1023/a:1024577429129 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005303811
    187 https://doi.org/10.1023/a:1024577429129
    188 rdf:type schema:CreativeWork
    189 sg:pub.10.1023/b:clim.0000043159.33816.e5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048069076
    190 https://doi.org/10.1023/b:clim.0000043159.33816.e5
    191 rdf:type schema:CreativeWork
    192 sg:pub.10.1038/nclimate1585 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022202325
    193 https://doi.org/10.1038/nclimate1585
    194 rdf:type schema:CreativeWork
    195 sg:pub.10.1038/nclimate2317 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036164846
    196 https://doi.org/10.1038/nclimate2317
    197 rdf:type schema:CreativeWork
    198 sg:pub.10.1038/s41558-018-0222-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1105743427
    199 https://doi.org/10.1038/s41558-018-0222-x
    200 rdf:type schema:CreativeWork
    201 sg:pub.10.1038/s41558-019-0630-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122861580
    202 https://doi.org/10.1038/s41558-019-0630-6
    203 rdf:type schema:CreativeWork
    204 sg:pub.10.1038/s41598-018-25212-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103702859
    205 https://doi.org/10.1038/s41598-018-25212-2
    206 rdf:type schema:CreativeWork
    207 sg:pub.10.1038/s41598-021-83375-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1135369584
    208 https://doi.org/10.1038/s41598-021-83375-x
    209 rdf:type schema:CreativeWork
    210 sg:pub.10.1038/srep44224 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084132120
    211 https://doi.org/10.1038/srep44224
    212 rdf:type schema:CreativeWork
    213 grid-institutes:grid.264756.4 schema:alternateName Department of Agricultural Economics, Texas A&M University, 77843, College Station, USA
    214 Department of Atmospheric Sciences, Texas A&M University, 77843, College Station, USA
    215 schema:name Department of Agricultural Economics, Texas A&M University, 77843, College Station, USA
    216 Department of Atmospheric Sciences, Texas A&M University, 77843, College Station, USA
    217 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...