Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects
Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventio...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Black, Christopher K. [verfasserIn] Masters, Michael D. [verfasserIn] LeBauer, David S. [verfasserIn] Anderson-Teixeira, Kristina J. [verfasserIn] DeLucia, Evan H. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Plant and soil - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948, 419(2017), 1-2 vom: 27. Juli, Seite 391-404 |
|---|---|
| Übergeordnetes Werk: |
volume:419 ; year:2017 ; number:1-2 ; day:27 ; month:07 ; pages:391-404 |
| Links: |
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| DOI / URN: |
10.1007/s11104-017-3333-7 |
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| Katalog-ID: |
SPR01674764X |
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| 245 | 1 | 0 | |a Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects |
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| 520 | |a Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool. | ||
| 650 | 4 | |a Minirhizotron |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Stan |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bayesian modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Root volume |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Root allocation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Masters, Michael D. |e verfasserin |4 aut | |
| 700 | 1 | |a LeBauer, David S. |e verfasserin |4 aut | |
| 700 | 1 | |a Anderson-Teixeira, Kristina J. |e verfasserin |4 aut | |
| 700 | 1 | |a DeLucia, Evan H. |e verfasserin |4 aut | |
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10.1007/s11104-017-3333-7 doi (DE-627)SPR01674764X (SPR)s11104-017-3333-7-e DE-627 ger DE-627 rakwb eng 570 580 ASE 48.32 bkl 48.52 bkl Black, Christopher K. verfasserin aut Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool. Minirhizotron (dpeaa)DE-He213 Stan (dpeaa)DE-He213 Bayesian modeling (dpeaa)DE-He213 Root volume (dpeaa)DE-He213 Root allocation (dpeaa)DE-He213 Masters, Michael D. verfasserin aut LeBauer, David S. verfasserin aut Anderson-Teixeira, Kristina J. verfasserin aut DeLucia, Evan H. verfasserin aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 419(2017), 1-2 vom: 27. Juli, Seite 391-404 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:419 year:2017 number:1-2 day:27 month:07 pages:391-404 https://dx.doi.org/10.1007/s11104-017-3333-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 48.32 ASE 48.52 ASE AR 419 2017 1-2 27 07 391-404 |
| spelling |
10.1007/s11104-017-3333-7 doi (DE-627)SPR01674764X (SPR)s11104-017-3333-7-e DE-627 ger DE-627 rakwb eng 570 580 ASE 48.32 bkl 48.52 bkl Black, Christopher K. verfasserin aut Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool. Minirhizotron (dpeaa)DE-He213 Stan (dpeaa)DE-He213 Bayesian modeling (dpeaa)DE-He213 Root volume (dpeaa)DE-He213 Root allocation (dpeaa)DE-He213 Masters, Michael D. verfasserin aut LeBauer, David S. verfasserin aut Anderson-Teixeira, Kristina J. verfasserin aut DeLucia, Evan H. verfasserin aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 419(2017), 1-2 vom: 27. Juli, Seite 391-404 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:419 year:2017 number:1-2 day:27 month:07 pages:391-404 https://dx.doi.org/10.1007/s11104-017-3333-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 48.32 ASE 48.52 ASE AR 419 2017 1-2 27 07 391-404 |
| allfields_unstemmed |
10.1007/s11104-017-3333-7 doi (DE-627)SPR01674764X (SPR)s11104-017-3333-7-e DE-627 ger DE-627 rakwb eng 570 580 ASE 48.32 bkl 48.52 bkl Black, Christopher K. verfasserin aut Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool. Minirhizotron (dpeaa)DE-He213 Stan (dpeaa)DE-He213 Bayesian modeling (dpeaa)DE-He213 Root volume (dpeaa)DE-He213 Root allocation (dpeaa)DE-He213 Masters, Michael D. verfasserin aut LeBauer, David S. verfasserin aut Anderson-Teixeira, Kristina J. verfasserin aut DeLucia, Evan H. verfasserin aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 419(2017), 1-2 vom: 27. Juli, Seite 391-404 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:419 year:2017 number:1-2 day:27 month:07 pages:391-404 https://dx.doi.org/10.1007/s11104-017-3333-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 48.32 ASE 48.52 ASE AR 419 2017 1-2 27 07 391-404 |
| allfieldsGer |
10.1007/s11104-017-3333-7 doi (DE-627)SPR01674764X (SPR)s11104-017-3333-7-e DE-627 ger DE-627 rakwb eng 570 580 ASE 48.32 bkl 48.52 bkl Black, Christopher K. verfasserin aut Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool. Minirhizotron (dpeaa)DE-He213 Stan (dpeaa)DE-He213 Bayesian modeling (dpeaa)DE-He213 Root volume (dpeaa)DE-He213 Root allocation (dpeaa)DE-He213 Masters, Michael D. verfasserin aut LeBauer, David S. verfasserin aut Anderson-Teixeira, Kristina J. verfasserin aut DeLucia, Evan H. verfasserin aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 419(2017), 1-2 vom: 27. Juli, Seite 391-404 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:419 year:2017 number:1-2 day:27 month:07 pages:391-404 https://dx.doi.org/10.1007/s11104-017-3333-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 48.32 ASE 48.52 ASE AR 419 2017 1-2 27 07 391-404 |
| allfieldsSound |
10.1007/s11104-017-3333-7 doi (DE-627)SPR01674764X (SPR)s11104-017-3333-7-e DE-627 ger DE-627 rakwb eng 570 580 ASE 48.32 bkl 48.52 bkl Black, Christopher K. verfasserin aut Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool. Minirhizotron (dpeaa)DE-He213 Stan (dpeaa)DE-He213 Bayesian modeling (dpeaa)DE-He213 Root volume (dpeaa)DE-He213 Root allocation (dpeaa)DE-He213 Masters, Michael D. verfasserin aut LeBauer, David S. verfasserin aut Anderson-Teixeira, Kristina J. verfasserin aut DeLucia, Evan H. verfasserin aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 419(2017), 1-2 vom: 27. Juli, Seite 391-404 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:419 year:2017 number:1-2 day:27 month:07 pages:391-404 https://dx.doi.org/10.1007/s11104-017-3333-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 48.32 ASE 48.52 ASE AR 419 2017 1-2 27 07 391-404 |
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English |
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Enthalten in Plant and soil 419(2017), 1-2 vom: 27. Juli, Seite 391-404 volume:419 year:2017 number:1-2 day:27 month:07 pages:391-404 |
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Enthalten in Plant and soil 419(2017), 1-2 vom: 27. Juli, Seite 391-404 volume:419 year:2017 number:1-2 day:27 month:07 pages:391-404 |
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findex.gbv.de |
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Minirhizotron Stan Bayesian modeling Root volume Root allocation |
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Plant and soil |
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Black, Christopher K. @@aut@@ Masters, Michael D. @@aut@@ LeBauer, David S. @@aut@@ Anderson-Teixeira, Kristina J. @@aut@@ DeLucia, Evan H. @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR01674764X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519202145.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11104-017-3333-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR01674764X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11104-017-3333-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="a">580</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">48.32</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">48.52</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Black, Christopher K.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions Perennial root systems are large, deep, and persistent. 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|
| author |
Black, Christopher K. |
| spellingShingle |
Black, Christopher K. ddc 570 bkl 48.32 bkl 48.52 misc Minirhizotron misc Stan misc Bayesian modeling misc Root volume misc Root allocation Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects |
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Black, Christopher K. |
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electronic Article |
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570 - Life sciences; biology 580 - Plants (Botany) |
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570 580 ASE 48.32 bkl 48.52 bkl Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects Minirhizotron (dpeaa)DE-He213 Stan (dpeaa)DE-He213 Bayesian modeling (dpeaa)DE-He213 Root volume (dpeaa)DE-He213 Root allocation (dpeaa)DE-He213 |
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ddc 570 bkl 48.32 bkl 48.52 misc Minirhizotron misc Stan misc Bayesian modeling misc Root volume misc Root allocation |
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ddc 570 bkl 48.32 bkl 48.52 misc Minirhizotron misc Stan misc Bayesian modeling misc Root volume misc Root allocation |
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ddc 570 bkl 48.32 bkl 48.52 misc Minirhizotron misc Stan misc Bayesian modeling misc Root volume misc Root allocation |
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Elektronische Aufsätze Aufsätze Elektronische Ressource |
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Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects |
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Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects |
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Black, Christopher K. |
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Black, Christopher K. Masters, Michael D. LeBauer, David S. Anderson-Teixeira, Kristina J. DeLucia, Evan H. |
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root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects |
| title_auth |
Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects |
| abstract |
Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool. |
| abstractGer |
Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool. |
| abstract_unstemmed |
Aims Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials’ relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool. |
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Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects |
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| score |
7.400201 |