Growing degree days and cover crop type explain weed biomass in winter cover crops
Abstract Cover crops are increasingly being adopted to provide multiple ecosystem services, including weed suppression. Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Baraibar, Barbara [verfasserIn] Mortensen, David A. [verfasserIn] Hunter, Mitchell C. [verfasserIn] Barbercheck, Mary E. [verfasserIn] Kaye, Jason P. [verfasserIn] Finney, Denise M. [verfasserIn] Curran, William S. [verfasserIn] Bunchek, Jess [verfasserIn] White, Charles M. [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: Agronomy for sustainable development - Berlin : Springer, 1981, 38(2018), 6 vom: 08. Nov. |
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| Übergeordnetes Werk: |
volume:38 ; year:2018 ; number:6 ; day:08 ; month:11 |
| Links: |
|---|
| DOI / URN: |
10.1007/s13593-018-0543-1 |
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| Katalog-ID: |
SPR031888208 |
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| 520 | |a Abstract Cover crops are increasingly being adopted to provide multiple ecosystem services, including weed suppression. Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique dataset of 1764 measurements from seven cover crop research experiments in Pennsylvania (USA) to predict, for the first time, weed biomass in winter cover crops in the fall and spring. We assessed the following predictors: cover crop biomass in the fall and spring, fall and spring growing degree days between planting and cover crop termination, cover crop type (grass, brassica, legume monocultures, and mixtures), system management (organic, conventional), and tillage before cover crop seeding (no-till, tillage). We used random forests to develop the predictive models and identify the most important variables explaining weed biomass in cover crops. Growing degree days, cover crop type, and cover crop biomass were the most important predictor variables in both the fall (r2 = 0.65) and spring (r2 = 0.47). In the fall, weed biomass increased as accumulated growing degree days increased, which was mainly related to early planting dates. Fall weed biomass was greater in legume and brassica monocultures compared to grass monocultures and mixtures. Cover crop and weed biomass were positively correlated in the fall, as early planting of cover crops led to high cover crop biomass but also to high weed biomass. In contrast, high spring cover crop biomass suppressed weeds, especially as spring growing degree days increased. Grass and brassica monocultures and mixtures were more weed-suppressive than legumes. This study is the first to be able to predict weed biomass in winter cover crops using a random forest approach. Results show that weed suppression by winter cover crops can be enhanced with optimal cover crop species selection and seeding time. | ||
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| 700 | 1 | |a Bunchek, Jess |e verfasserin |4 aut | |
| 700 | 1 | |a White, Charles M. |e verfasserin |4 aut | |
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10.1007/s13593-018-0543-1 doi (DE-627)SPR031888208 (SPR)s13593-018-0543-1-e DE-627 ger DE-627 rakwb eng 580 630 640 ASE 48.16 bkl Baraibar, Barbara verfasserin aut Growing degree days and cover crop type explain weed biomass in winter cover crops 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cover crops are increasingly being adopted to provide multiple ecosystem services, including weed suppression. Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique dataset of 1764 measurements from seven cover crop research experiments in Pennsylvania (USA) to predict, for the first time, weed biomass in winter cover crops in the fall and spring. We assessed the following predictors: cover crop biomass in the fall and spring, fall and spring growing degree days between planting and cover crop termination, cover crop type (grass, brassica, legume monocultures, and mixtures), system management (organic, conventional), and tillage before cover crop seeding (no-till, tillage). We used random forests to develop the predictive models and identify the most important variables explaining weed biomass in cover crops. Growing degree days, cover crop type, and cover crop biomass were the most important predictor variables in both the fall (r2 = 0.65) and spring (r2 = 0.47). In the fall, weed biomass increased as accumulated growing degree days increased, which was mainly related to early planting dates. Fall weed biomass was greater in legume and brassica monocultures compared to grass monocultures and mixtures. Cover crop and weed biomass were positively correlated in the fall, as early planting of cover crops led to high cover crop biomass but also to high weed biomass. In contrast, high spring cover crop biomass suppressed weeds, especially as spring growing degree days increased. Grass and brassica monocultures and mixtures were more weed-suppressive than legumes. This study is the first to be able to predict weed biomass in winter cover crops using a random forest approach. Results show that weed suppression by winter cover crops can be enhanced with optimal cover crop species selection and seeding time. Cover crop mixtures (dpeaa)DE-He213 Grass (dpeaa)DE-He213 Legume (dpeaa)DE-He213 Brassica (dpeaa)DE-He213 Weed suppression (dpeaa)DE-He213 Cover crop biomass (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Mortensen, David A. verfasserin aut Hunter, Mitchell C. verfasserin aut Barbercheck, Mary E. verfasserin aut Kaye, Jason P. verfasserin aut Finney, Denise M. verfasserin aut Curran, William S. verfasserin aut Bunchek, Jess verfasserin aut White, Charles M. verfasserin aut Enthalten in Agronomy for sustainable development Berlin : Springer, 1981 38(2018), 6 vom: 08. Nov. (DE-627)312838921 (DE-600)2012314-0 1773-0155 nnns volume:38 year:2018 number:6 day:08 month:11 https://dx.doi.org/10.1007/s13593-018-0543-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_602 GBV_ILN_636 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_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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.16 ASE AR 38 2018 6 08 11 |
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10.1007/s13593-018-0543-1 doi (DE-627)SPR031888208 (SPR)s13593-018-0543-1-e DE-627 ger DE-627 rakwb eng 580 630 640 ASE 48.16 bkl Baraibar, Barbara verfasserin aut Growing degree days and cover crop type explain weed biomass in winter cover crops 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cover crops are increasingly being adopted to provide multiple ecosystem services, including weed suppression. Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique dataset of 1764 measurements from seven cover crop research experiments in Pennsylvania (USA) to predict, for the first time, weed biomass in winter cover crops in the fall and spring. We assessed the following predictors: cover crop biomass in the fall and spring, fall and spring growing degree days between planting and cover crop termination, cover crop type (grass, brassica, legume monocultures, and mixtures), system management (organic, conventional), and tillage before cover crop seeding (no-till, tillage). We used random forests to develop the predictive models and identify the most important variables explaining weed biomass in cover crops. Growing degree days, cover crop type, and cover crop biomass were the most important predictor variables in both the fall (r2 = 0.65) and spring (r2 = 0.47). In the fall, weed biomass increased as accumulated growing degree days increased, which was mainly related to early planting dates. Fall weed biomass was greater in legume and brassica monocultures compared to grass monocultures and mixtures. Cover crop and weed biomass were positively correlated in the fall, as early planting of cover crops led to high cover crop biomass but also to high weed biomass. In contrast, high spring cover crop biomass suppressed weeds, especially as spring growing degree days increased. Grass and brassica monocultures and mixtures were more weed-suppressive than legumes. This study is the first to be able to predict weed biomass in winter cover crops using a random forest approach. Results show that weed suppression by winter cover crops can be enhanced with optimal cover crop species selection and seeding time. Cover crop mixtures (dpeaa)DE-He213 Grass (dpeaa)DE-He213 Legume (dpeaa)DE-He213 Brassica (dpeaa)DE-He213 Weed suppression (dpeaa)DE-He213 Cover crop biomass (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Mortensen, David A. verfasserin aut Hunter, Mitchell C. verfasserin aut Barbercheck, Mary E. verfasserin aut Kaye, Jason P. verfasserin aut Finney, Denise M. verfasserin aut Curran, William S. verfasserin aut Bunchek, Jess verfasserin aut White, Charles M. verfasserin aut Enthalten in Agronomy for sustainable development Berlin : Springer, 1981 38(2018), 6 vom: 08. Nov. (DE-627)312838921 (DE-600)2012314-0 1773-0155 nnns volume:38 year:2018 number:6 day:08 month:11 https://dx.doi.org/10.1007/s13593-018-0543-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_602 GBV_ILN_636 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_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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.16 ASE AR 38 2018 6 08 11 |
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10.1007/s13593-018-0543-1 doi (DE-627)SPR031888208 (SPR)s13593-018-0543-1-e DE-627 ger DE-627 rakwb eng 580 630 640 ASE 48.16 bkl Baraibar, Barbara verfasserin aut Growing degree days and cover crop type explain weed biomass in winter cover crops 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cover crops are increasingly being adopted to provide multiple ecosystem services, including weed suppression. Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique dataset of 1764 measurements from seven cover crop research experiments in Pennsylvania (USA) to predict, for the first time, weed biomass in winter cover crops in the fall and spring. We assessed the following predictors: cover crop biomass in the fall and spring, fall and spring growing degree days between planting and cover crop termination, cover crop type (grass, brassica, legume monocultures, and mixtures), system management (organic, conventional), and tillage before cover crop seeding (no-till, tillage). We used random forests to develop the predictive models and identify the most important variables explaining weed biomass in cover crops. Growing degree days, cover crop type, and cover crop biomass were the most important predictor variables in both the fall (r2 = 0.65) and spring (r2 = 0.47). In the fall, weed biomass increased as accumulated growing degree days increased, which was mainly related to early planting dates. Fall weed biomass was greater in legume and brassica monocultures compared to grass monocultures and mixtures. Cover crop and weed biomass were positively correlated in the fall, as early planting of cover crops led to high cover crop biomass but also to high weed biomass. In contrast, high spring cover crop biomass suppressed weeds, especially as spring growing degree days increased. Grass and brassica monocultures and mixtures were more weed-suppressive than legumes. This study is the first to be able to predict weed biomass in winter cover crops using a random forest approach. Results show that weed suppression by winter cover crops can be enhanced with optimal cover crop species selection and seeding time. Cover crop mixtures (dpeaa)DE-He213 Grass (dpeaa)DE-He213 Legume (dpeaa)DE-He213 Brassica (dpeaa)DE-He213 Weed suppression (dpeaa)DE-He213 Cover crop biomass (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Mortensen, David A. verfasserin aut Hunter, Mitchell C. verfasserin aut Barbercheck, Mary E. verfasserin aut Kaye, Jason P. verfasserin aut Finney, Denise M. verfasserin aut Curran, William S. verfasserin aut Bunchek, Jess verfasserin aut White, Charles M. verfasserin aut Enthalten in Agronomy for sustainable development Berlin : Springer, 1981 38(2018), 6 vom: 08. Nov. (DE-627)312838921 (DE-600)2012314-0 1773-0155 nnns volume:38 year:2018 number:6 day:08 month:11 https://dx.doi.org/10.1007/s13593-018-0543-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_602 GBV_ILN_636 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_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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.16 ASE AR 38 2018 6 08 11 |
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10.1007/s13593-018-0543-1 doi (DE-627)SPR031888208 (SPR)s13593-018-0543-1-e DE-627 ger DE-627 rakwb eng 580 630 640 ASE 48.16 bkl Baraibar, Barbara verfasserin aut Growing degree days and cover crop type explain weed biomass in winter cover crops 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cover crops are increasingly being adopted to provide multiple ecosystem services, including weed suppression. Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique dataset of 1764 measurements from seven cover crop research experiments in Pennsylvania (USA) to predict, for the first time, weed biomass in winter cover crops in the fall and spring. We assessed the following predictors: cover crop biomass in the fall and spring, fall and spring growing degree days between planting and cover crop termination, cover crop type (grass, brassica, legume monocultures, and mixtures), system management (organic, conventional), and tillage before cover crop seeding (no-till, tillage). We used random forests to develop the predictive models and identify the most important variables explaining weed biomass in cover crops. Growing degree days, cover crop type, and cover crop biomass were the most important predictor variables in both the fall (r2 = 0.65) and spring (r2 = 0.47). In the fall, weed biomass increased as accumulated growing degree days increased, which was mainly related to early planting dates. Fall weed biomass was greater in legume and brassica monocultures compared to grass monocultures and mixtures. Cover crop and weed biomass were positively correlated in the fall, as early planting of cover crops led to high cover crop biomass but also to high weed biomass. In contrast, high spring cover crop biomass suppressed weeds, especially as spring growing degree days increased. Grass and brassica monocultures and mixtures were more weed-suppressive than legumes. This study is the first to be able to predict weed biomass in winter cover crops using a random forest approach. Results show that weed suppression by winter cover crops can be enhanced with optimal cover crop species selection and seeding time. Cover crop mixtures (dpeaa)DE-He213 Grass (dpeaa)DE-He213 Legume (dpeaa)DE-He213 Brassica (dpeaa)DE-He213 Weed suppression (dpeaa)DE-He213 Cover crop biomass (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Mortensen, David A. verfasserin aut Hunter, Mitchell C. verfasserin aut Barbercheck, Mary E. verfasserin aut Kaye, Jason P. verfasserin aut Finney, Denise M. verfasserin aut Curran, William S. verfasserin aut Bunchek, Jess verfasserin aut White, Charles M. verfasserin aut Enthalten in Agronomy for sustainable development Berlin : Springer, 1981 38(2018), 6 vom: 08. Nov. (DE-627)312838921 (DE-600)2012314-0 1773-0155 nnns volume:38 year:2018 number:6 day:08 month:11 https://dx.doi.org/10.1007/s13593-018-0543-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_602 GBV_ILN_636 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_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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.16 ASE AR 38 2018 6 08 11 |
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10.1007/s13593-018-0543-1 doi (DE-627)SPR031888208 (SPR)s13593-018-0543-1-e DE-627 ger DE-627 rakwb eng 580 630 640 ASE 48.16 bkl Baraibar, Barbara verfasserin aut Growing degree days and cover crop type explain weed biomass in winter cover crops 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cover crops are increasingly being adopted to provide multiple ecosystem services, including weed suppression. Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique dataset of 1764 measurements from seven cover crop research experiments in Pennsylvania (USA) to predict, for the first time, weed biomass in winter cover crops in the fall and spring. We assessed the following predictors: cover crop biomass in the fall and spring, fall and spring growing degree days between planting and cover crop termination, cover crop type (grass, brassica, legume monocultures, and mixtures), system management (organic, conventional), and tillage before cover crop seeding (no-till, tillage). We used random forests to develop the predictive models and identify the most important variables explaining weed biomass in cover crops. Growing degree days, cover crop type, and cover crop biomass were the most important predictor variables in both the fall (r2 = 0.65) and spring (r2 = 0.47). In the fall, weed biomass increased as accumulated growing degree days increased, which was mainly related to early planting dates. Fall weed biomass was greater in legume and brassica monocultures compared to grass monocultures and mixtures. Cover crop and weed biomass were positively correlated in the fall, as early planting of cover crops led to high cover crop biomass but also to high weed biomass. In contrast, high spring cover crop biomass suppressed weeds, especially as spring growing degree days increased. Grass and brassica monocultures and mixtures were more weed-suppressive than legumes. This study is the first to be able to predict weed biomass in winter cover crops using a random forest approach. Results show that weed suppression by winter cover crops can be enhanced with optimal cover crop species selection and seeding time. Cover crop mixtures (dpeaa)DE-He213 Grass (dpeaa)DE-He213 Legume (dpeaa)DE-He213 Brassica (dpeaa)DE-He213 Weed suppression (dpeaa)DE-He213 Cover crop biomass (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Mortensen, David A. verfasserin aut Hunter, Mitchell C. verfasserin aut Barbercheck, Mary E. verfasserin aut Kaye, Jason P. verfasserin aut Finney, Denise M. verfasserin aut Curran, William S. verfasserin aut Bunchek, Jess verfasserin aut White, Charles M. verfasserin aut Enthalten in Agronomy for sustainable development Berlin : Springer, 1981 38(2018), 6 vom: 08. Nov. (DE-627)312838921 (DE-600)2012314-0 1773-0155 nnns volume:38 year:2018 number:6 day:08 month:11 https://dx.doi.org/10.1007/s13593-018-0543-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_602 GBV_ILN_636 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_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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.16 ASE AR 38 2018 6 08 11 |
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English |
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Enthalten in Agronomy for sustainable development 38(2018), 6 vom: 08. Nov. volume:38 year:2018 number:6 day:08 month:11 |
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Cover crop mixtures Grass Legume Brassica Weed suppression Cover crop biomass Random forest |
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Agronomy for sustainable development |
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Baraibar, Barbara @@aut@@ Mortensen, David A. @@aut@@ Hunter, Mitchell C. @@aut@@ Barbercheck, Mary E. @@aut@@ Kaye, Jason P. @@aut@@ Finney, Denise M. @@aut@@ Curran, William S. @@aut@@ Bunchek, Jess @@aut@@ White, Charles M. @@aut@@ |
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Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique dataset of 1764 measurements from seven cover crop research experiments in Pennsylvania (USA) to predict, for the first time, weed biomass in winter cover crops in the fall and spring. We assessed the following predictors: cover crop biomass in the fall and spring, fall and spring growing degree days between planting and cover crop termination, cover crop type (grass, brassica, legume monocultures, and mixtures), system management (organic, conventional), and tillage before cover crop seeding (no-till, tillage). We used random forests to develop the predictive models and identify the most important variables explaining weed biomass in cover crops. Growing degree days, cover crop type, and cover crop biomass were the most important predictor variables in both the fall (r2 = 0.65) and spring (r2 = 0.47). In the fall, weed biomass increased as accumulated growing degree days increased, which was mainly related to early planting dates. Fall weed biomass was greater in legume and brassica monocultures compared to grass monocultures and mixtures. Cover crop and weed biomass were positively correlated in the fall, as early planting of cover crops led to high cover crop biomass but also to high weed biomass. In contrast, high spring cover crop biomass suppressed weeds, especially as spring growing degree days increased. Grass and brassica monocultures and mixtures were more weed-suppressive than legumes. This study is the first to be able to predict weed biomass in winter cover crops using a random forest approach. 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Baraibar, Barbara |
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Baraibar, Barbara ddc 580 bkl 48.16 misc Cover crop mixtures misc Grass misc Legume misc Brassica misc Weed suppression misc Cover crop biomass misc Random forest Growing degree days and cover crop type explain weed biomass in winter cover crops |
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580 630 640 ASE 48.16 bkl Growing degree days and cover crop type explain weed biomass in winter cover crops Cover crop mixtures (dpeaa)DE-He213 Grass (dpeaa)DE-He213 Legume (dpeaa)DE-He213 Brassica (dpeaa)DE-He213 Weed suppression (dpeaa)DE-He213 Cover crop biomass (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 |
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ddc 580 bkl 48.16 misc Cover crop mixtures misc Grass misc Legume misc Brassica misc Weed suppression misc Cover crop biomass misc Random forest |
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ddc 580 bkl 48.16 misc Cover crop mixtures misc Grass misc Legume misc Brassica misc Weed suppression misc Cover crop biomass misc Random forest |
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Growing degree days and cover crop type explain weed biomass in winter cover crops |
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Growing degree days and cover crop type explain weed biomass in winter cover crops |
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Agronomy for sustainable development |
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Baraibar, Barbara Mortensen, David A. Hunter, Mitchell C. Barbercheck, Mary E. Kaye, Jason P. Finney, Denise M. Curran, William S. Bunchek, Jess White, Charles M. |
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Baraibar, Barbara |
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growing degree days and cover crop type explain weed biomass in winter cover crops |
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Growing degree days and cover crop type explain weed biomass in winter cover crops |
| abstract |
Abstract Cover crops are increasingly being adopted to provide multiple ecosystem services, including weed suppression. Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique dataset of 1764 measurements from seven cover crop research experiments in Pennsylvania (USA) to predict, for the first time, weed biomass in winter cover crops in the fall and spring. We assessed the following predictors: cover crop biomass in the fall and spring, fall and spring growing degree days between planting and cover crop termination, cover crop type (grass, brassica, legume monocultures, and mixtures), system management (organic, conventional), and tillage before cover crop seeding (no-till, tillage). We used random forests to develop the predictive models and identify the most important variables explaining weed biomass in cover crops. Growing degree days, cover crop type, and cover crop biomass were the most important predictor variables in both the fall (r2 = 0.65) and spring (r2 = 0.47). In the fall, weed biomass increased as accumulated growing degree days increased, which was mainly related to early planting dates. Fall weed biomass was greater in legume and brassica monocultures compared to grass monocultures and mixtures. Cover crop and weed biomass were positively correlated in the fall, as early planting of cover crops led to high cover crop biomass but also to high weed biomass. In contrast, high spring cover crop biomass suppressed weeds, especially as spring growing degree days increased. Grass and brassica monocultures and mixtures were more weed-suppressive than legumes. This study is the first to be able to predict weed biomass in winter cover crops using a random forest approach. Results show that weed suppression by winter cover crops can be enhanced with optimal cover crop species selection and seeding time. |
| abstractGer |
Abstract Cover crops are increasingly being adopted to provide multiple ecosystem services, including weed suppression. Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique dataset of 1764 measurements from seven cover crop research experiments in Pennsylvania (USA) to predict, for the first time, weed biomass in winter cover crops in the fall and spring. We assessed the following predictors: cover crop biomass in the fall and spring, fall and spring growing degree days between planting and cover crop termination, cover crop type (grass, brassica, legume monocultures, and mixtures), system management (organic, conventional), and tillage before cover crop seeding (no-till, tillage). We used random forests to develop the predictive models and identify the most important variables explaining weed biomass in cover crops. Growing degree days, cover crop type, and cover crop biomass were the most important predictor variables in both the fall (r2 = 0.65) and spring (r2 = 0.47). In the fall, weed biomass increased as accumulated growing degree days increased, which was mainly related to early planting dates. Fall weed biomass was greater in legume and brassica monocultures compared to grass monocultures and mixtures. Cover crop and weed biomass were positively correlated in the fall, as early planting of cover crops led to high cover crop biomass but also to high weed biomass. In contrast, high spring cover crop biomass suppressed weeds, especially as spring growing degree days increased. Grass and brassica monocultures and mixtures were more weed-suppressive than legumes. This study is the first to be able to predict weed biomass in winter cover crops using a random forest approach. Results show that weed suppression by winter cover crops can be enhanced with optimal cover crop species selection and seeding time. |
| abstract_unstemmed |
Abstract Cover crops are increasingly being adopted to provide multiple ecosystem services, including weed suppression. Understanding what drives weed biomass in cover crops can help growers make the appropriate management decisions to effectively limit weed pressure. In this paper, we use a unique dataset of 1764 measurements from seven cover crop research experiments in Pennsylvania (USA) to predict, for the first time, weed biomass in winter cover crops in the fall and spring. We assessed the following predictors: cover crop biomass in the fall and spring, fall and spring growing degree days between planting and cover crop termination, cover crop type (grass, brassica, legume monocultures, and mixtures), system management (organic, conventional), and tillage before cover crop seeding (no-till, tillage). We used random forests to develop the predictive models and identify the most important variables explaining weed biomass in cover crops. Growing degree days, cover crop type, and cover crop biomass were the most important predictor variables in both the fall (r2 = 0.65) and spring (r2 = 0.47). In the fall, weed biomass increased as accumulated growing degree days increased, which was mainly related to early planting dates. Fall weed biomass was greater in legume and brassica monocultures compared to grass monocultures and mixtures. Cover crop and weed biomass were positively correlated in the fall, as early planting of cover crops led to high cover crop biomass but also to high weed biomass. In contrast, high spring cover crop biomass suppressed weeds, especially as spring growing degree days increased. Grass and brassica monocultures and mixtures were more weed-suppressive than legumes. This study is the first to be able to predict weed biomass in winter cover crops using a random forest approach. Results show that weed suppression by winter cover crops can be enhanced with optimal cover crop species selection and seeding time. |
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Growing degree days and cover crop type explain weed biomass in winter cover crops |
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| score |
7.397979 |