Sources of Nitrate Yields in the Mississippi River Basin
Riverine nitrate N in the Mississippi River leads to hypoxia in the Gulf of Mexico. Several recent modeling studies estimated major N inputs and suggested source areas that could be targeted for conservation programs. We conducted a similar analysis with more recent and extensive data that demonstra...
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description | Riverine nitrate N in the Mississippi River leads to hypoxia in the Gulf of Mexico. Several recent modeling studies estimated major N inputs and suggested source areas that could be targeted for conservation programs. We conducted a similar analysis with more recent and extensive data that demonstrates the importance of hydrology in controlling the percentage of net N inputs (NNI) exported by rivers. The average fraction of annual riverine nitrate N export/NNI ranged from 0.05 for the lower Mississippi subbasin to 0.3 for the upper Mississippi River basin and as high as 1.4 (4.2 in a wet year) for the Embarras River watershed, a mostly tile-drained basin. Intensive corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] watersheds on Mollisols had low NNI values and when combined with riverine N losses suggest a net depletion of soil organic N. We used county-level data to develop a nonlinear model of N inputs and landscape factors that were related to winter–spring riverine nitrate yields for 153 watersheds within the basin. We found that river runoff times fertilizer N input was the major predictive term, explaining 76% of the variation in the model. Fertilizer inputs were highly correlated with fraction of land area in row crops. Tile drainage explained 17% of the spatial variation in winter–spring nitrate yield, whereas human consumption of N (i.e., sewage effluent) accounted for 7%. Net N inputs were not a good predictor of riverine nitrate N yields, nor were other N balances. We used this model to predict the expected nitrate N yield from each county in the Mississippi River basin; the greatest nitrate N yields corresponded to the highly productive, tile-drained cornbelt from southwest Minnesota across Iowa, Illinois, Indiana, and Ohio. This analysis can be used to guide decisions about where efforts to reduce nitrate N losses can be most effectively targeted to improve local water quality and reduce export to the Gulf of Mexico. |
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Several recent modeling studies estimated major N inputs and suggested source areas that could be targeted for conservation programs. We conducted a similar analysis with more recent and extensive data that demonstrates the importance of hydrology in controlling the percentage of net N inputs (NNI) exported by rivers. The average fraction of annual riverine nitrate N export/NNI ranged from 0.05 for the lower Mississippi subbasin to 0.3 for the upper Mississippi River basin and as high as 1.4 (4.2 in a wet year) for the Embarras River watershed, a mostly tile-drained basin. Intensive corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] watersheds on Mollisols had low NNI values and when combined with riverine N losses suggest a net depletion of soil organic N. We used county-level data to develop a nonlinear model of N inputs and landscape factors that were related to winter–spring riverine nitrate yields for 153 watersheds within the basin. We found that river runoff times fertilizer N input was the major predictive term, explaining 76% of the variation in the model. Fertilizer inputs were highly correlated with fraction of land area in row crops. Tile drainage explained 17% of the spatial variation in winter–spring nitrate yield, whereas human consumption of N (i.e., sewage effluent) accounted for 7%. Net N inputs were not a good predictor of riverine nitrate N yields, nor were other N balances. We used this model to predict the expected nitrate N yield from each county in the Mississippi River basin; the greatest nitrate N yields corresponded to the highly productive, tile-drained cornbelt from southwest Minnesota across Iowa, Illinois, Indiana, and Ohio. This analysis can be used to guide decisions about where efforts to reduce nitrate N losses can be most effectively targeted to improve local water quality and reduce export to the Gulf of Mexico.</description><identifier>ISSN: 0047-2425</identifier><identifier>ISSN: 1537-2537</identifier><identifier>EISSN: 1537-2537</identifier><identifier>DOI: 10.2134/jeq2010.0115</identifier><identifier>PMID: 21043271</identifier><identifier>CODEN: JEVQAA</identifier><language>eng</language><publisher>Madison: American Society of Agronomy, Crop Science Society of America, Soil Science Society</publisher><subject>Agricultural production ; Basins ; corn ; Creeks & streams ; Exports ; Fertilizers ; Fertilizing ; Fresh Water - chemistry ; Freshwater ; Glycine max ; Hydrology ; Hypoxia ; International trade ; Load ; losses from soil ; Mathematical models ; Methods ; Mississippi ; net nitrogen inputs ; nitrate nitrogen export ; Nitrates ; Nitrates - analysis ; Nitrogen ; organic nitrogen compounds ; prediction ; River basins ; River flow ; riverine habitat ; Rivers ; Runoff ; seasonal variation ; Sewage ; sewage effluent ; soil nutrient balance ; Soils ; Soybeans ; Spring ; Tile drainage ; Water quality ; Watersheds ; Winter ; yields ; Zea mays</subject><ispartof>Journal of environmental quality, 2010-09, Vol.39 (5), p.1657-1667</ispartof><rights>American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America</rights><rights>Copyright American Society of Agronomy Sep/Oct 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5225-e4c2bebc629c30ce5e72a27f9614ef82a13282619da879fcee4d9671b2718e753</citedby><cites>FETCH-LOGICAL-c5225-e4c2bebc629c30ce5e72a27f9614ef82a13282619da879fcee4d9671b2718e753</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.2134%2Fjeq2010.0115$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.2134%2Fjeq2010.0115$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21043271$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>David, Mark B</creatorcontrib><creatorcontrib>Drinkwater, Laurie E</creatorcontrib><creatorcontrib>McIsaac, Gregory F</creatorcontrib><title>Sources of Nitrate Yields in the Mississippi River Basin</title><title>Journal of environmental quality</title><addtitle>J Environ Qual</addtitle><description>Riverine nitrate N in the Mississippi River leads to hypoxia in the Gulf of Mexico. Several recent modeling studies estimated major N inputs and suggested source areas that could be targeted for conservation programs. We conducted a similar analysis with more recent and extensive data that demonstrates the importance of hydrology in controlling the percentage of net N inputs (NNI) exported by rivers. The average fraction of annual riverine nitrate N export/NNI ranged from 0.05 for the lower Mississippi subbasin to 0.3 for the upper Mississippi River basin and as high as 1.4 (4.2 in a wet year) for the Embarras River watershed, a mostly tile-drained basin. Intensive corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] watersheds on Mollisols had low NNI values and when combined with riverine N losses suggest a net depletion of soil organic N. We used county-level data to develop a nonlinear model of N inputs and landscape factors that were related to winter–spring riverine nitrate yields for 153 watersheds within the basin. We found that river runoff times fertilizer N input was the major predictive term, explaining 76% of the variation in the model. Fertilizer inputs were highly correlated with fraction of land area in row crops. Tile drainage explained 17% of the spatial variation in winter–spring nitrate yield, whereas human consumption of N (i.e., sewage effluent) accounted for 7%. Net N inputs were not a good predictor of riverine nitrate N yields, nor were other N balances. We used this model to predict the expected nitrate N yield from each county in the Mississippi River basin; the greatest nitrate N yields corresponded to the highly productive, tile-drained cornbelt from southwest Minnesota across Iowa, Illinois, Indiana, and Ohio. This analysis can be used to guide decisions about where efforts to reduce nitrate N losses can be most effectively targeted to improve local water quality and reduce export to the Gulf of Mexico.</description><subject>Agricultural production</subject><subject>Basins</subject><subject>corn</subject><subject>Creeks & streams</subject><subject>Exports</subject><subject>Fertilizers</subject><subject>Fertilizing</subject><subject>Fresh Water - chemistry</subject><subject>Freshwater</subject><subject>Glycine max</subject><subject>Hydrology</subject><subject>Hypoxia</subject><subject>International trade</subject><subject>Load</subject><subject>losses from soil</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Mississippi</subject><subject>net nitrogen inputs</subject><subject>nitrate nitrogen export</subject><subject>Nitrates</subject><subject>Nitrates - analysis</subject><subject>Nitrogen</subject><subject>organic nitrogen compounds</subject><subject>prediction</subject><subject>River basins</subject><subject>River flow</subject><subject>riverine habitat</subject><subject>Rivers</subject><subject>Runoff</subject><subject>seasonal variation</subject><subject>Sewage</subject><subject>sewage effluent</subject><subject>soil nutrient balance</subject><subject>Soils</subject><subject>Soybeans</subject><subject>Spring</subject><subject>Tile drainage</subject><subject>Water quality</subject><subject>Watersheds</subject><subject>Winter</subject><subject>yields</subject><subject>Zea mays</subject><issn>0047-2425</issn><issn>1537-2537</issn><issn>1537-2537</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkU1PGzEQhi1EBSnlxhlWXNoDKZ6xvfYeAaWliIL4OvRkOc4sONpkg50U5d_Xq4QeOIDkbz9-ZzwvY3vAvyMIeTymZ-R5wwHUBuuBErqPedhkPc5lXktU2-xzSmPOAbkut9g2ApcCNfSYuWsX0VMq2rq4CvPo5lT8CdSMUhGmxfyJit8hpa7NZqG4DX8pFqcuhekX9ql2TaLd9bzDHn4M7s_O-5fXP3-dnVz2vUJUfZIehzT0JVZecE-KNDrUdVWCpNqgA4EGS6hGzuiq9kRyVJUahjk7Q1qJHfZ1pTuL7fOC0txOQvLUNG5K7SJZwzVWWHL-IalLlNoIJTP57V0ScgJKAmAX_vANOs4Fm-YfW62qXE1RdpGPVpCPbUqRajuLYeLi0gK3nUl2bZLtTMr4_lpzMZzQ6D_86koGqhXwEhpavitmLwY32PV8sBY_WL2tXWvdYwzJPtzlW8HBGCM0in8Vh6NN</recordid><startdate>201009</startdate><enddate>201009</enddate><creator>David, Mark B</creator><creator>Drinkwater, Laurie E</creator><creator>McIsaac, Gregory F</creator><general>American Society of Agronomy, Crop Science Society of America, Soil Science Society</general><general>American Society of Agronomy</general><scope>FBQ</scope><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>3V.</scope><scope>7ST</scope><scope>7T7</scope><scope>7TG</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8AF</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>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KL.</scope><scope>L6V</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P64</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope><scope>SOI</scope><scope>7SU</scope><scope>KR7</scope><scope>7X8</scope><scope>7QH</scope><scope>7U6</scope><scope>7UA</scope><scope>F1W</scope><scope>H96</scope><scope>H97</scope><scope>L.G</scope></search><sort><creationdate>201009</creationdate><title>Sources of Nitrate Yields in the Mississippi River Basin</title><author>David, Mark B ; Drinkwater, Laurie E ; McIsaac, Gregory F</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5225-e4c2bebc629c30ce5e72a27f9614ef82a13282619da879fcee4d9671b2718e753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Agricultural production</topic><topic>Basins</topic><topic>corn</topic><topic>Creeks & streams</topic><topic>Exports</topic><topic>Fertilizers</topic><topic>Fertilizing</topic><topic>Fresh Water - chemistry</topic><topic>Freshwater</topic><topic>Glycine max</topic><topic>Hydrology</topic><topic>Hypoxia</topic><topic>International trade</topic><topic>Load</topic><topic>losses from soil</topic><topic>Mathematical models</topic><topic>Methods</topic><topic>Mississippi</topic><topic>net nitrogen inputs</topic><topic>nitrate nitrogen export</topic><topic>Nitrates</topic><topic>Nitrates - analysis</topic><topic>Nitrogen</topic><topic>organic nitrogen compounds</topic><topic>prediction</topic><topic>River basins</topic><topic>River flow</topic><topic>riverine habitat</topic><topic>Rivers</topic><topic>Runoff</topic><topic>seasonal variation</topic><topic>Sewage</topic><topic>sewage effluent</topic><topic>soil nutrient balance</topic><topic>Soils</topic><topic>Soybeans</topic><topic>Spring</topic><topic>Tile drainage</topic><topic>Water quality</topic><topic>Watersheds</topic><topic>Winter</topic><topic>yields</topic><topic>Zea mays</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>David, Mark B</creatorcontrib><creatorcontrib>Drinkwater, Laurie E</creatorcontrib><creatorcontrib>McIsaac, Gregory F</creatorcontrib><collection>AGRIS</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical 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>Science Database (Alumni Edition)</collection><collection>STEM Database</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>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>eLibrary</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</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>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Several recent modeling studies estimated major N inputs and suggested source areas that could be targeted for conservation programs. We conducted a similar analysis with more recent and extensive data that demonstrates the importance of hydrology in controlling the percentage of net N inputs (NNI) exported by rivers. The average fraction of annual riverine nitrate N export/NNI ranged from 0.05 for the lower Mississippi subbasin to 0.3 for the upper Mississippi River basin and as high as 1.4 (4.2 in a wet year) for the Embarras River watershed, a mostly tile-drained basin. Intensive corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] watersheds on Mollisols had low NNI values and when combined with riverine N losses suggest a net depletion of soil organic N. We used county-level data to develop a nonlinear model of N inputs and landscape factors that were related to winter–spring riverine nitrate yields for 153 watersheds within the basin. We found that river runoff times fertilizer N input was the major predictive term, explaining 76% of the variation in the model. Fertilizer inputs were highly correlated with fraction of land area in row crops. Tile drainage explained 17% of the spatial variation in winter–spring nitrate yield, whereas human consumption of N (i.e., sewage effluent) accounted for 7%. Net N inputs were not a good predictor of riverine nitrate N yields, nor were other N balances. We used this model to predict the expected nitrate N yield from each county in the Mississippi River basin; the greatest nitrate N yields corresponded to the highly productive, tile-drained cornbelt from southwest Minnesota across Iowa, Illinois, Indiana, and Ohio. This analysis can be used to guide decisions about where efforts to reduce nitrate N losses can be most effectively targeted to improve local water quality and reduce export to the Gulf of Mexico.</abstract><cop>Madison</cop><pub>American Society of Agronomy, Crop Science Society of America, Soil Science Society</pub><pmid>21043271</pmid><doi>10.2134/jeq2010.0115</doi><tpages>11</tpages></addata></record> |
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subjects | Agricultural production Basins corn Creeks & streams Exports Fertilizers Fertilizing Fresh Water - chemistry Freshwater Glycine max Hydrology Hypoxia International trade Load losses from soil Mathematical models Methods Mississippi net nitrogen inputs nitrate nitrogen export Nitrates Nitrates - analysis Nitrogen organic nitrogen compounds prediction River basins River flow riverine habitat Rivers Runoff seasonal variation Sewage sewage effluent soil nutrient balance Soils Soybeans Spring Tile drainage Water quality Watersheds Winter yields Zea mays |
title | Sources of Nitrate Yields in the Mississippi River Basin |
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