Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region
Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in G...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tenorio, Fatima A.M. [verfasserIn] Eagle, Alison J. [verfasserIn] McLellan, Eileen L. [verfasserIn] Cassman, Kenneth G. [verfasserIn] Howard, Reka [verfasserIn] Below, Fred E. [verfasserIn] Clay, David E. [verfasserIn] Coulter, Jeffrey A. [verfasserIn] Geyer, Allen B. [verfasserIn] Joos, Darin K. [verfasserIn] Lauer, Joseph G. [verfasserIn] Licht, Mark A. [verfasserIn] Lindsey, Alexander J. [verfasserIn] Maharjan, Bijesh [verfasserIn] Pittelkow, Cameron M. [verfasserIn] Thomison, Peter R. [verfasserIn] Wortmann, Charles S. [verfasserIn] Sadras, Victor O. [verfasserIn] Grassini, Patricio [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Field crops research - Amsterdam : Elsevier, 1978, 240, Seite 185-193 |
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| Übergeordnetes Werk: |
volume:240 ; pages:185-193 |
| DOI / URN: |
10.1016/j.fcr.2018.10.017 |
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| Katalog-ID: |
ELV002577240 |
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| 100 | 1 | |a Tenorio, Fatima A.M. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region |
| 264 | 1 | |c 2018 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
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| 520 | |a Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years. | ||
| 650 | 4 | |a Grain nitrogen concentration | |
| 650 | 4 | |a Grain nitrogen removal | |
| 650 | 4 | |a Nitrogen balance | |
| 650 | 4 | |a Maize | |
| 700 | 1 | |a Eagle, Alison J. |e verfasserin |4 aut | |
| 700 | 1 | |a McLellan, Eileen L. |e verfasserin |4 aut | |
| 700 | 1 | |a Cassman, Kenneth G. |e verfasserin |4 aut | |
| 700 | 1 | |a Howard, Reka |e verfasserin |4 aut | |
| 700 | 1 | |a Below, Fred E. |e verfasserin |0 (orcid)0000-0001-7696-3688 |4 aut | |
| 700 | 1 | |a Clay, David E. |e verfasserin |4 aut | |
| 700 | 1 | |a Coulter, Jeffrey A. |e verfasserin |4 aut | |
| 700 | 1 | |a Geyer, Allen B. |e verfasserin |4 aut | |
| 700 | 1 | |a Joos, Darin K. |e verfasserin |4 aut | |
| 700 | 1 | |a Lauer, Joseph G. |e verfasserin |0 (orcid)0000-0001-8910-9560 |4 aut | |
| 700 | 1 | |a Licht, Mark A. |e verfasserin |0 (orcid)0000-0001-6640-7856 |4 aut | |
| 700 | 1 | |a Lindsey, Alexander J. |e verfasserin |0 (orcid)0000-0002-3216-1496 |4 aut | |
| 700 | 1 | |a Maharjan, Bijesh |e verfasserin |4 aut | |
| 700 | 1 | |a Pittelkow, Cameron M. |e verfasserin |4 aut | |
| 700 | 1 | |a Thomison, Peter R. |e verfasserin |4 aut | |
| 700 | 1 | |a Wortmann, Charles S. |e verfasserin |4 aut | |
| 700 | 1 | |a Sadras, Victor O. |e verfasserin |4 aut | |
| 700 | 1 | |a Grassini, Patricio |e verfasserin |4 aut | |
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10.1016/j.fcr.2018.10.017 doi (DE-627)ELV002577240 (ELSEVIER)S0378-4290(18)31524-7 DE-627 ger DE-627 rda eng 630 640 DE-600 48.00 bkl Tenorio, Fatima A.M. verfasserin aut Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years. Grain nitrogen concentration Grain nitrogen removal Nitrogen balance Maize Eagle, Alison J. verfasserin aut McLellan, Eileen L. verfasserin aut Cassman, Kenneth G. verfasserin aut Howard, Reka verfasserin aut Below, Fred E. verfasserin (orcid)0000-0001-7696-3688 aut Clay, David E. verfasserin aut Coulter, Jeffrey A. verfasserin aut Geyer, Allen B. verfasserin aut Joos, Darin K. verfasserin aut Lauer, Joseph G. verfasserin (orcid)0000-0001-8910-9560 aut Licht, Mark A. verfasserin (orcid)0000-0001-6640-7856 aut Lindsey, Alexander J. verfasserin (orcid)0000-0002-3216-1496 aut Maharjan, Bijesh verfasserin aut Pittelkow, Cameron M. verfasserin aut Thomison, Peter R. verfasserin aut Wortmann, Charles S. verfasserin aut Sadras, Victor O. verfasserin aut Grassini, Patricio verfasserin aut Enthalten in Field crops research Amsterdam : Elsevier, 1978 240, Seite 185-193 Online-Ressource (DE-627)32050316X (DE-600)2012484-3 (DE-576)090954912 1872-6852 nnns volume:240 pages:185-193 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.00 Land- und Forstwirtschaft: Allgemeines AR 240 185-193 |
| spelling |
10.1016/j.fcr.2018.10.017 doi (DE-627)ELV002577240 (ELSEVIER)S0378-4290(18)31524-7 DE-627 ger DE-627 rda eng 630 640 DE-600 48.00 bkl Tenorio, Fatima A.M. verfasserin aut Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years. Grain nitrogen concentration Grain nitrogen removal Nitrogen balance Maize Eagle, Alison J. verfasserin aut McLellan, Eileen L. verfasserin aut Cassman, Kenneth G. verfasserin aut Howard, Reka verfasserin aut Below, Fred E. verfasserin (orcid)0000-0001-7696-3688 aut Clay, David E. verfasserin aut Coulter, Jeffrey A. verfasserin aut Geyer, Allen B. verfasserin aut Joos, Darin K. verfasserin aut Lauer, Joseph G. verfasserin (orcid)0000-0001-8910-9560 aut Licht, Mark A. verfasserin (orcid)0000-0001-6640-7856 aut Lindsey, Alexander J. verfasserin (orcid)0000-0002-3216-1496 aut Maharjan, Bijesh verfasserin aut Pittelkow, Cameron M. verfasserin aut Thomison, Peter R. verfasserin aut Wortmann, Charles S. verfasserin aut Sadras, Victor O. verfasserin aut Grassini, Patricio verfasserin aut Enthalten in Field crops research Amsterdam : Elsevier, 1978 240, Seite 185-193 Online-Ressource (DE-627)32050316X (DE-600)2012484-3 (DE-576)090954912 1872-6852 nnns volume:240 pages:185-193 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.00 Land- und Forstwirtschaft: Allgemeines AR 240 185-193 |
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10.1016/j.fcr.2018.10.017 doi (DE-627)ELV002577240 (ELSEVIER)S0378-4290(18)31524-7 DE-627 ger DE-627 rda eng 630 640 DE-600 48.00 bkl Tenorio, Fatima A.M. verfasserin aut Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years. Grain nitrogen concentration Grain nitrogen removal Nitrogen balance Maize Eagle, Alison J. verfasserin aut McLellan, Eileen L. verfasserin aut Cassman, Kenneth G. verfasserin aut Howard, Reka verfasserin aut Below, Fred E. verfasserin (orcid)0000-0001-7696-3688 aut Clay, David E. verfasserin aut Coulter, Jeffrey A. verfasserin aut Geyer, Allen B. verfasserin aut Joos, Darin K. verfasserin aut Lauer, Joseph G. verfasserin (orcid)0000-0001-8910-9560 aut Licht, Mark A. verfasserin (orcid)0000-0001-6640-7856 aut Lindsey, Alexander J. verfasserin (orcid)0000-0002-3216-1496 aut Maharjan, Bijesh verfasserin aut Pittelkow, Cameron M. verfasserin aut Thomison, Peter R. verfasserin aut Wortmann, Charles S. verfasserin aut Sadras, Victor O. verfasserin aut Grassini, Patricio verfasserin aut Enthalten in Field crops research Amsterdam : Elsevier, 1978 240, Seite 185-193 Online-Ressource (DE-627)32050316X (DE-600)2012484-3 (DE-576)090954912 1872-6852 nnns volume:240 pages:185-193 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.00 Land- und Forstwirtschaft: Allgemeines AR 240 185-193 |
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10.1016/j.fcr.2018.10.017 doi (DE-627)ELV002577240 (ELSEVIER)S0378-4290(18)31524-7 DE-627 ger DE-627 rda eng 630 640 DE-600 48.00 bkl Tenorio, Fatima A.M. verfasserin aut Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years. Grain nitrogen concentration Grain nitrogen removal Nitrogen balance Maize Eagle, Alison J. verfasserin aut McLellan, Eileen L. verfasserin aut Cassman, Kenneth G. verfasserin aut Howard, Reka verfasserin aut Below, Fred E. verfasserin (orcid)0000-0001-7696-3688 aut Clay, David E. verfasserin aut Coulter, Jeffrey A. verfasserin aut Geyer, Allen B. verfasserin aut Joos, Darin K. verfasserin aut Lauer, Joseph G. verfasserin (orcid)0000-0001-8910-9560 aut Licht, Mark A. verfasserin (orcid)0000-0001-6640-7856 aut Lindsey, Alexander J. verfasserin (orcid)0000-0002-3216-1496 aut Maharjan, Bijesh verfasserin aut Pittelkow, Cameron M. verfasserin aut Thomison, Peter R. verfasserin aut Wortmann, Charles S. verfasserin aut Sadras, Victor O. verfasserin aut Grassini, Patricio verfasserin aut Enthalten in Field crops research Amsterdam : Elsevier, 1978 240, Seite 185-193 Online-Ressource (DE-627)32050316X (DE-600)2012484-3 (DE-576)090954912 1872-6852 nnns volume:240 pages:185-193 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.00 Land- und Forstwirtschaft: Allgemeines AR 240 185-193 |
| allfieldsSound |
10.1016/j.fcr.2018.10.017 doi (DE-627)ELV002577240 (ELSEVIER)S0378-4290(18)31524-7 DE-627 ger DE-627 rda eng 630 640 DE-600 48.00 bkl Tenorio, Fatima A.M. verfasserin aut Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years. Grain nitrogen concentration Grain nitrogen removal Nitrogen balance Maize Eagle, Alison J. verfasserin aut McLellan, Eileen L. verfasserin aut Cassman, Kenneth G. verfasserin aut Howard, Reka verfasserin aut Below, Fred E. verfasserin (orcid)0000-0001-7696-3688 aut Clay, David E. verfasserin aut Coulter, Jeffrey A. verfasserin aut Geyer, Allen B. verfasserin aut Joos, Darin K. verfasserin aut Lauer, Joseph G. verfasserin (orcid)0000-0001-8910-9560 aut Licht, Mark A. verfasserin (orcid)0000-0001-6640-7856 aut Lindsey, Alexander J. verfasserin (orcid)0000-0002-3216-1496 aut Maharjan, Bijesh verfasserin aut Pittelkow, Cameron M. verfasserin aut Thomison, Peter R. verfasserin aut Wortmann, Charles S. verfasserin aut Sadras, Victor O. verfasserin aut Grassini, Patricio verfasserin aut Enthalten in Field crops research Amsterdam : Elsevier, 1978 240, Seite 185-193 Online-Ressource (DE-627)32050316X (DE-600)2012484-3 (DE-576)090954912 1872-6852 nnns volume:240 pages:185-193 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.00 Land- und Forstwirtschaft: Allgemeines AR 240 185-193 |
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Tenorio, Fatima A.M. @@aut@@ Eagle, Alison J. @@aut@@ McLellan, Eileen L. @@aut@@ Cassman, Kenneth G. @@aut@@ Howard, Reka @@aut@@ Below, Fred E. @@aut@@ Clay, David E. @@aut@@ Coulter, Jeffrey A. @@aut@@ Geyer, Allen B. @@aut@@ Joos, Darin K. @@aut@@ Lauer, Joseph G. @@aut@@ Licht, Mark A. @@aut@@ Lindsey, Alexander J. @@aut@@ Maharjan, Bijesh @@aut@@ Pittelkow, Cameron M. @@aut@@ Thomison, Peter R. @@aut@@ Wortmann, Charles S. @@aut@@ Sadras, Victor O. @@aut@@ Grassini, Patricio @@aut@@ |
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Tenorio, Fatima A.M. |
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Tenorio, Fatima A.M. ddc 630 bkl 48.00 misc Grain nitrogen concentration misc Grain nitrogen removal misc Nitrogen balance misc Maize Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region |
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Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region |
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Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region |
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Tenorio, Fatima A.M. Eagle, Alison J. McLellan, Eileen L. Cassman, Kenneth G. Howard, Reka Below, Fred E. Clay, David E. Coulter, Jeffrey A. Geyer, Allen B. Joos, Darin K. Lauer, Joseph G. Licht, Mark A. Lindsey, Alexander J. Maharjan, Bijesh Pittelkow, Cameron M. Thomison, Peter R. Wortmann, Charles S. Sadras, Victor O. Grassini, Patricio |
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assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the us north central region |
| title_auth |
Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region |
| abstract |
Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years. |
| abstractGer |
Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years. |
| abstract_unstemmed |
Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years. |
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Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region |
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Eagle, Alison J. McLellan, Eileen L. Cassman, Kenneth G. Howard, Reka Below, Fred E. Clay, David E. Coulter, Jeffrey A. Geyer, Allen B. Joos, Darin K. Lauer, Joseph G. Licht, Mark A. Lindsey, Alexander J. Maharjan, Bijesh Pittelkow, Cameron M. Thomison, Peter R. Wortmann, Charles S. Sadras, Victor O. Grassini, Patricio |
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Eagle, Alison J. McLellan, Eileen L. Cassman, Kenneth G. Howard, Reka Below, Fred E. Clay, David E. Coulter, Jeffrey A. Geyer, Allen B. Joos, Darin K. Lauer, Joseph G. Licht, Mark A. Lindsey, Alexander J. Maharjan, Bijesh Pittelkow, Cameron M. Thomison, Peter R. Wortmann, Charles S. Sadras, Victor O. Grassini, Patricio |
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10.1016/j.fcr.2018.10.017 |
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2024-07-06T16:44:18.351Z |
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