Projecting future crop productivity for global economic modeling
Assessments of climate change impacts on agricultural markets and land‐use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two bio...
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Veröffentlicht in: | Agricultural economics 2014-01, Vol.45 (1), p.37-50 |
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description | Assessments of climate change impacts on agricultural markets and land‐use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land‐use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10–38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change. |
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We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land‐use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10–38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change.</description><subject>Agricultural land</subject><subject>Agricultural practices</subject><subject>Agricultural productivity</subject><subject>Carbon dioxide</subject><subject>climate</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Crop modeling</subject><subject>Crop production</subject><subject>crops</subject><subject>econometric models</subject><subject>economic analysis</subject><subject>Economic models</subject><subject>Environmental assessment</subject><subject>Environmental impact</subject><subject>Environmental protection</subject><subject>Global economy</subject><subject>Growth models</subject><subject>land productivity</subject><subject>Land use</subject><subject>markets</subject><subject>Productivity</subject><subject>Q100</subject><subject>Q190</subject><subject>space and time</subject><subject>uncertainty</subject><subject>Z000</subject><issn>0169-5150</issn><issn>1574-0862</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kU9LwzAYh4MoOKcXv4AFLyJ0Jmnz7-YccwpuCm7sGNIsHZ3dMpNW3bc3terBg7kEwvP78b5PADhFsIfCuVJLo3sIQ873QAcRlsaQU7wPOhBRERNE4CE48n4FIUohTjrg-snZldFVsVlGeV3VzkTa2W20dXZRh-e3otpFuXXRsrSZKiOj7cauCx2t7cKUIXUMDnJVenPyfXfB7HY4HdzFD4-j-0H_IdaEYh6TXGPCEpYqgwTinGAlsixPGaOEs9ToBGWYYgPhQmiRiSzRlFGOIcohx0QkXXDR9obJXmvjK7kuvDZlqTbG1l6G1UiCGU9QQM__oCtbu02YTqJUNHUUN4WXLRX29d6ZXG5dsVZuJxGUjUzZyJRfMgOMWvi9KM3uH1L2R8PBTyZuM4WvzMdvRrkXSYMJIueTkZzQ-c0YTkdyHPizls-VDY2u8HL2jJuPgij4Eiz5BI-IjRQ</recordid><startdate>201401</startdate><enddate>201401</enddate><creator>Müller, Christoph</creator><creator>Robertson, Richard D</creator><general>Elsevier Science BV</general><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>FBQ</scope><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>7U6</scope></search><sort><creationdate>201401</creationdate><title>Projecting future crop productivity for global economic modeling</title><author>Müller, Christoph ; Robertson, Richard D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5628-5fc257374ae1918852a9bbf47765874ec31b262e00d9c9b9b3c6768201f082593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Agricultural land</topic><topic>Agricultural practices</topic><topic>Agricultural productivity</topic><topic>Carbon dioxide</topic><topic>climate</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Crop modeling</topic><topic>Crop production</topic><topic>crops</topic><topic>econometric models</topic><topic>economic analysis</topic><topic>Economic models</topic><topic>Environmental assessment</topic><topic>Environmental impact</topic><topic>Environmental protection</topic><topic>Global economy</topic><topic>Growth models</topic><topic>land productivity</topic><topic>Land use</topic><topic>markets</topic><topic>Productivity</topic><topic>Q100</topic><topic>Q190</topic><topic>space and time</topic><topic>uncertainty</topic><topic>Z000</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Müller, Christoph</creatorcontrib><creatorcontrib>Robertson, Richard D</creatorcontrib><collection>AGRIS</collection><collection>Istex</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><jtitle>Agricultural economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Müller, Christoph</au><au>Robertson, Richard D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Projecting future crop productivity for global economic modeling</atitle><jtitle>Agricultural economics</jtitle><addtitle>Agricultural Economics</addtitle><date>2014-01</date><risdate>2014</risdate><volume>45</volume><issue>1</issue><spage>37</spage><epage>50</epage><pages>37-50</pages><issn>0169-5150</issn><eissn>1574-0862</eissn><abstract>Assessments of climate change impacts on agricultural markets and land‐use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land‐use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10–38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change.</abstract><cop>Malden</cop><pub>Elsevier Science BV</pub><doi>10.1111/agec.12088</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Agricultural land Agricultural practices Agricultural productivity Carbon dioxide climate Climate change Climate models Crop modeling Crop production crops econometric models economic analysis Economic models Environmental assessment Environmental impact Environmental protection Global economy Growth models land productivity Land use markets Productivity Q100 Q190 space and time uncertainty Z000 |
title | Projecting future crop productivity for global economic modeling |
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