Application of probability analysis to assess nitrogen supply to grain crops in northern Australia
Grain yield and protein of cereal crops in northern Australia provide a useful indicator of the supply of available nitrogen (N) to the crop. Our intention was to utilize this principle on a site-specific basis through an associated probabilistic framework to identify the likelihood that grain yield...
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Veröffentlicht in: | Precision agriculture 2004-04, Vol.5 (2), p.95-110 |
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description | Grain yield and protein of cereal crops in northern Australia provide a useful indicator of the supply of available nitrogen (N) to the crop. Our intention was to utilize this principle on a site-specific basis through an associated probabilistic framework to identify the likelihood that grain yield was limited by N supply. Yield and protein data were taken at harvest from sorghum, wheat and barley crops near Dalby, southern Queensland, in 1999. Considerable variation was found in grain yield for the three crops, but less so for grain protein. Frequency-response relationships, derived from historical multiple N field experiments, were applied to identify areas where grain yield was limited by N supply. This approach indicated that there was a 60% or higher likelihood that plant-available N was yield-limiting for 17%, 23%, and 26% of the area sown to sorghum, wheat and barley, respectively. These areas were not necessarily those where crop yield was relatively low. Calculation of N removal and N supply, using N transfer efficiency relationships, verified that those areas with a high likelihood of response to N had considerably lower supplies of N compared to other areas. The application of probability analysis offers a unique strategy to identify within-field areas where N supply could be yield-limiting, and provides a rationale for predicting the spatial variation and likely range of N supplies for successive seasons. |
doi_str_mv | 10.1023/B:PRAG.0000022356.01537.67 |
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Our intention was to utilize this principle on a site-specific basis through an associated probabilistic framework to identify the likelihood that grain yield was limited by N supply. Yield and protein data were taken at harvest from sorghum, wheat and barley crops near Dalby, southern Queensland, in 1999. Considerable variation was found in grain yield for the three crops, but less so for grain protein. Frequency-response relationships, derived from historical multiple N field experiments, were applied to identify areas where grain yield was limited by N supply. This approach indicated that there was a 60% or higher likelihood that plant-available N was yield-limiting for 17%, 23%, and 26% of the area sown to sorghum, wheat and barley, respectively. These areas were not necessarily those where crop yield was relatively low. Calculation of N removal and N supply, using N transfer efficiency relationships, verified that those areas with a high likelihood of response to N had considerably lower supplies of N compared to other areas. The application of probability analysis offers a unique strategy to identify within-field areas where N supply could be yield-limiting, and provides a rationale for predicting the spatial variation and likely range of N supplies for successive seasons.</description><identifier>ISSN: 1385-2256</identifier><identifier>EISSN: 1573-1618</identifier><identifier>DOI: 10.1023/B:PRAG.0000022356.01537.67</identifier><language>eng</language><publisher>Dordrecht: Springer Nature B.V</publisher><subject>Agricultural production ; Barley ; Cereal crops ; crop production ; Crop yield ; Crops ; equations ; fertilizer requirements ; Field tests ; Grain ; Grain crops ; grain sorghum ; Hordeum vulgare ; mathematical models ; measurement ; Nitrogen ; nitrogen fertilizers ; nutrient availability ; nutrient use efficiency ; precision agriculture ; Proteins ; remote sensing ; seeds ; Sorghum ; Sorghum bicolor ; spatial distribution ; spectral analysis ; Triticum aestivum ; Wheat</subject><ispartof>Precision agriculture, 2004-04, Vol.5 (2), p.95-110</ispartof><rights>Kluwer Academic Publishers 2004</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c227t-6f5b1968c8d0e925d027cea1bca7c614814f8081f22d645f7e1c3d5f21a6f3f13</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Kelly, R.M</creatorcontrib><creatorcontrib>Strong, W.M</creatorcontrib><creatorcontrib>Jensen, T.A</creatorcontrib><creatorcontrib>Butler, D</creatorcontrib><title>Application of probability analysis to assess nitrogen supply to grain crops in northern Australia</title><title>Precision agriculture</title><description>Grain yield and protein of cereal crops in northern Australia provide a useful indicator of the supply of available nitrogen (N) to the crop. Our intention was to utilize this principle on a site-specific basis through an associated probabilistic framework to identify the likelihood that grain yield was limited by N supply. Yield and protein data were taken at harvest from sorghum, wheat and barley crops near Dalby, southern Queensland, in 1999. Considerable variation was found in grain yield for the three crops, but less so for grain protein. Frequency-response relationships, derived from historical multiple N field experiments, were applied to identify areas where grain yield was limited by N supply. This approach indicated that there was a 60% or higher likelihood that plant-available N was yield-limiting for 17%, 23%, and 26% of the area sown to sorghum, wheat and barley, respectively. These areas were not necessarily those where crop yield was relatively low. Calculation of N removal and N supply, using N transfer efficiency relationships, verified that those areas with a high likelihood of response to N had considerably lower supplies of N compared to other areas. The application of probability analysis offers a unique strategy to identify within-field areas where N supply could be yield-limiting, and provides a rationale for predicting the spatial variation and likely range of N supplies for successive seasons.</description><subject>Agricultural production</subject><subject>Barley</subject><subject>Cereal crops</subject><subject>crop production</subject><subject>Crop yield</subject><subject>Crops</subject><subject>equations</subject><subject>fertilizer requirements</subject><subject>Field tests</subject><subject>Grain</subject><subject>Grain crops</subject><subject>grain sorghum</subject><subject>Hordeum vulgare</subject><subject>mathematical models</subject><subject>measurement</subject><subject>Nitrogen</subject><subject>nitrogen fertilizers</subject><subject>nutrient availability</subject><subject>nutrient use efficiency</subject><subject>precision agriculture</subject><subject>Proteins</subject><subject>remote sensing</subject><subject>seeds</subject><subject>Sorghum</subject><subject>Sorghum bicolor</subject><subject>spatial distribution</subject><subject>spectral analysis</subject><subject>Triticum aestivum</subject><subject>Wheat</subject><issn>1385-2256</issn><issn>1573-1618</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpFkFtPwzAMhSsEEtffQLT3DjtZLtvbQDCQJoGAPUdpl4yg0pS4e9i_p2NI-MWWfI7l8xXFCGGMwMXN7ezldb4Yw744F1KNAaXQY6WPijOUWpSo0BwPszCy5Fyq0-Kc6BMAASb8rKjmXdfE2vUxtSwF1uVUuSo2sd8x17pmR5FYn5gj8kSsjX1OG98y2g6-3X6zyS62rM6pIzYMbcr9h88tm2-pz66J7rI4Ca4hf_XXL4rVw_373WO5fF483c2XZc257ksVZIVTZWqzBj_lcg1c195hVTtdK5wYnAQDBgPnazWRQXusxVoGjk4FEVBcFKPD3SHD99ZTbz_TNg8ZyGqppgqlMoNodhANDxNlH2yX45fLO4tg90jtrd0jtf9I7S9Sq_Rgvj6Yg0vWbXIku3rjgAJgqo0EED91c3Uw</recordid><startdate>200404</startdate><enddate>200404</enddate><creator>Kelly, R.M</creator><creator>Strong, W.M</creator><creator>Jensen, T.A</creator><creator>Butler, D</creator><general>Springer Nature B.V</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X2</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>M0C</scope><scope>M0K</scope><scope>M2P</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope></search><sort><creationdate>200404</creationdate><title>Application of probability analysis to assess nitrogen supply to grain crops in northern Australia</title><author>Kelly, R.M ; Strong, W.M ; Jensen, T.A ; Butler, D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c227t-6f5b1968c8d0e925d027cea1bca7c614814f8081f22d645f7e1c3d5f21a6f3f13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Agricultural production</topic><topic>Barley</topic><topic>Cereal crops</topic><topic>crop production</topic><topic>Crop yield</topic><topic>Crops</topic><topic>equations</topic><topic>fertilizer requirements</topic><topic>Field tests</topic><topic>Grain</topic><topic>Grain crops</topic><topic>grain sorghum</topic><topic>Hordeum vulgare</topic><topic>mathematical models</topic><topic>measurement</topic><topic>Nitrogen</topic><topic>nitrogen fertilizers</topic><topic>nutrient availability</topic><topic>nutrient use efficiency</topic><topic>precision agriculture</topic><topic>Proteins</topic><topic>remote sensing</topic><topic>seeds</topic><topic>Sorghum</topic><topic>Sorghum bicolor</topic><topic>spatial distribution</topic><topic>spectral analysis</topic><topic>Triticum aestivum</topic><topic>Wheat</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kelly, R.M</creatorcontrib><creatorcontrib>Strong, W.M</creatorcontrib><creatorcontrib>Jensen, T.A</creatorcontrib><creatorcontrib>Butler, D</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Collection</collection><collection>Agriculture Science Database</collection><collection>ProQuest Science Journals</collection><collection>Environmental Science Database</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Precision agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kelly, R.M</au><au>Strong, W.M</au><au>Jensen, T.A</au><au>Butler, D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of probability analysis to assess nitrogen supply to grain crops in northern Australia</atitle><jtitle>Precision agriculture</jtitle><date>2004-04</date><risdate>2004</risdate><volume>5</volume><issue>2</issue><spage>95</spage><epage>110</epage><pages>95-110</pages><issn>1385-2256</issn><eissn>1573-1618</eissn><abstract>Grain yield and protein of cereal crops in northern Australia provide a useful indicator of the supply of available nitrogen (N) to the crop. Our intention was to utilize this principle on a site-specific basis through an associated probabilistic framework to identify the likelihood that grain yield was limited by N supply. Yield and protein data were taken at harvest from sorghum, wheat and barley crops near Dalby, southern Queensland, in 1999. Considerable variation was found in grain yield for the three crops, but less so for grain protein. Frequency-response relationships, derived from historical multiple N field experiments, were applied to identify areas where grain yield was limited by N supply. This approach indicated that there was a 60% or higher likelihood that plant-available N was yield-limiting for 17%, 23%, and 26% of the area sown to sorghum, wheat and barley, respectively. These areas were not necessarily those where crop yield was relatively low. Calculation of N removal and N supply, using N transfer efficiency relationships, verified that those areas with a high likelihood of response to N had considerably lower supplies of N compared to other areas. The application of probability analysis offers a unique strategy to identify within-field areas where N supply could be yield-limiting, and provides a rationale for predicting the spatial variation and likely range of N supplies for successive seasons.</abstract><cop>Dordrecht</cop><pub>Springer Nature B.V</pub><doi>10.1023/B:PRAG.0000022356.01537.67</doi><tpages>16</tpages></addata></record> |
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subjects | Agricultural production Barley Cereal crops crop production Crop yield Crops equations fertilizer requirements Field tests Grain Grain crops grain sorghum Hordeum vulgare mathematical models measurement Nitrogen nitrogen fertilizers nutrient availability nutrient use efficiency precision agriculture Proteins remote sensing seeds Sorghum Sorghum bicolor spatial distribution spectral analysis Triticum aestivum Wheat |
title | Application of probability analysis to assess nitrogen supply to grain crops in northern Australia |
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