Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada
Abstract Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jégo, Guillaume [verfasserIn] Pattey, Elizabeth [verfasserIn] Bourgeois, Gaétan [verfasserIn] Drury, Craig F. [verfasserIn] Tremblay, Nicolas [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Agronomy for sustainable development - Berlin : Springer, 1981, 31(2011), 3 vom: 11. März, Seite 557-570 |
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| Übergeordnetes Werk: |
volume:31 ; year:2011 ; number:3 ; day:11 ; month:03 ; pages:557-570 |
| Links: |
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| DOI / URN: |
10.1007/s13593-011-0014-4 |
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SPR031882315 |
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| 520 | |a Abstract Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500–2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900–3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6–7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. Moreover, these new grain maize cultivars, adapted to a shorter growing season, open new opportunities for using STICS in northern countries. | ||
| 650 | 4 | |a STICS |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Crop model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Maize |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Eastern Canada |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a LAI |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Biomass |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Yield prediction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Earth observation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Pattey, Elizabeth |e verfasserin |4 aut | |
| 700 | 1 | |a Bourgeois, Gaétan |e verfasserin |4 aut | |
| 700 | 1 | |a Drury, Craig F. |e verfasserin |4 aut | |
| 700 | 1 | |a Tremblay, Nicolas |e verfasserin |4 aut | |
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10.1007/s13593-011-0014-4 doi (DE-627)SPR031882315 (SPR)s13593-011-0014-4-e DE-627 ger DE-627 rakwb eng 580 630 640 ASE 48.16 bkl Jégo, Guillaume verfasserin aut Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500–2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900–3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6–7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. Moreover, these new grain maize cultivars, adapted to a shorter growing season, open new opportunities for using STICS in northern countries. STICS (dpeaa)DE-He213 Crop model (dpeaa)DE-He213 Maize (dpeaa)DE-He213 Eastern Canada (dpeaa)DE-He213 LAI (dpeaa)DE-He213 Biomass (dpeaa)DE-He213 Yield prediction (dpeaa)DE-He213 Earth observation (dpeaa)DE-He213 Pattey, Elizabeth verfasserin aut Bourgeois, Gaétan verfasserin aut Drury, Craig F. verfasserin aut Tremblay, Nicolas verfasserin aut Enthalten in Agronomy for sustainable development Berlin : Springer, 1981 31(2011), 3 vom: 11. März, Seite 557-570 (DE-627)312838921 (DE-600)2012314-0 1773-0155 nnns volume:31 year:2011 number:3 day:11 month:03 pages:557-570 https://dx.doi.org/10.1007/s13593-011-0014-4 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_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_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 31 2011 3 11 03 557-570 |
| spelling |
10.1007/s13593-011-0014-4 doi (DE-627)SPR031882315 (SPR)s13593-011-0014-4-e DE-627 ger DE-627 rakwb eng 580 630 640 ASE 48.16 bkl Jégo, Guillaume verfasserin aut Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500–2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900–3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6–7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. Moreover, these new grain maize cultivars, adapted to a shorter growing season, open new opportunities for using STICS in northern countries. STICS (dpeaa)DE-He213 Crop model (dpeaa)DE-He213 Maize (dpeaa)DE-He213 Eastern Canada (dpeaa)DE-He213 LAI (dpeaa)DE-He213 Biomass (dpeaa)DE-He213 Yield prediction (dpeaa)DE-He213 Earth observation (dpeaa)DE-He213 Pattey, Elizabeth verfasserin aut Bourgeois, Gaétan verfasserin aut Drury, Craig F. verfasserin aut Tremblay, Nicolas verfasserin aut Enthalten in Agronomy for sustainable development Berlin : Springer, 1981 31(2011), 3 vom: 11. März, Seite 557-570 (DE-627)312838921 (DE-600)2012314-0 1773-0155 nnns volume:31 year:2011 number:3 day:11 month:03 pages:557-570 https://dx.doi.org/10.1007/s13593-011-0014-4 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_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_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 31 2011 3 11 03 557-570 |
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10.1007/s13593-011-0014-4 doi (DE-627)SPR031882315 (SPR)s13593-011-0014-4-e DE-627 ger DE-627 rakwb eng 580 630 640 ASE 48.16 bkl Jégo, Guillaume verfasserin aut Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500–2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900–3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6–7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. Moreover, these new grain maize cultivars, adapted to a shorter growing season, open new opportunities for using STICS in northern countries. STICS (dpeaa)DE-He213 Crop model (dpeaa)DE-He213 Maize (dpeaa)DE-He213 Eastern Canada (dpeaa)DE-He213 LAI (dpeaa)DE-He213 Biomass (dpeaa)DE-He213 Yield prediction (dpeaa)DE-He213 Earth observation (dpeaa)DE-He213 Pattey, Elizabeth verfasserin aut Bourgeois, Gaétan verfasserin aut Drury, Craig F. verfasserin aut Tremblay, Nicolas verfasserin aut Enthalten in Agronomy for sustainable development Berlin : Springer, 1981 31(2011), 3 vom: 11. März, Seite 557-570 (DE-627)312838921 (DE-600)2012314-0 1773-0155 nnns volume:31 year:2011 number:3 day:11 month:03 pages:557-570 https://dx.doi.org/10.1007/s13593-011-0014-4 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_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_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 31 2011 3 11 03 557-570 |
| allfieldsGer |
10.1007/s13593-011-0014-4 doi (DE-627)SPR031882315 (SPR)s13593-011-0014-4-e DE-627 ger DE-627 rakwb eng 580 630 640 ASE 48.16 bkl Jégo, Guillaume verfasserin aut Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500–2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900–3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6–7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. Moreover, these new grain maize cultivars, adapted to a shorter growing season, open new opportunities for using STICS in northern countries. STICS (dpeaa)DE-He213 Crop model (dpeaa)DE-He213 Maize (dpeaa)DE-He213 Eastern Canada (dpeaa)DE-He213 LAI (dpeaa)DE-He213 Biomass (dpeaa)DE-He213 Yield prediction (dpeaa)DE-He213 Earth observation (dpeaa)DE-He213 Pattey, Elizabeth verfasserin aut Bourgeois, Gaétan verfasserin aut Drury, Craig F. verfasserin aut Tremblay, Nicolas verfasserin aut Enthalten in Agronomy for sustainable development Berlin : Springer, 1981 31(2011), 3 vom: 11. März, Seite 557-570 (DE-627)312838921 (DE-600)2012314-0 1773-0155 nnns volume:31 year:2011 number:3 day:11 month:03 pages:557-570 https://dx.doi.org/10.1007/s13593-011-0014-4 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_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_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 31 2011 3 11 03 557-570 |
| allfieldsSound |
10.1007/s13593-011-0014-4 doi (DE-627)SPR031882315 (SPR)s13593-011-0014-4-e DE-627 ger DE-627 rakwb eng 580 630 640 ASE 48.16 bkl Jégo, Guillaume verfasserin aut Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500–2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900–3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6–7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. Moreover, these new grain maize cultivars, adapted to a shorter growing season, open new opportunities for using STICS in northern countries. STICS (dpeaa)DE-He213 Crop model (dpeaa)DE-He213 Maize (dpeaa)DE-He213 Eastern Canada (dpeaa)DE-He213 LAI (dpeaa)DE-He213 Biomass (dpeaa)DE-He213 Yield prediction (dpeaa)DE-He213 Earth observation (dpeaa)DE-He213 Pattey, Elizabeth verfasserin aut Bourgeois, Gaétan verfasserin aut Drury, Craig F. verfasserin aut Tremblay, Nicolas verfasserin aut Enthalten in Agronomy for sustainable development Berlin : Springer, 1981 31(2011), 3 vom: 11. März, Seite 557-570 (DE-627)312838921 (DE-600)2012314-0 1773-0155 nnns volume:31 year:2011 number:3 day:11 month:03 pages:557-570 https://dx.doi.org/10.1007/s13593-011-0014-4 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_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_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 31 2011 3 11 03 557-570 |
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English |
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Enthalten in Agronomy for sustainable development 31(2011), 3 vom: 11. März, Seite 557-570 volume:31 year:2011 number:3 day:11 month:03 pages:557-570 |
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Enthalten in Agronomy for sustainable development 31(2011), 3 vom: 11. März, Seite 557-570 volume:31 year:2011 number:3 day:11 month:03 pages:557-570 |
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STICS Crop model Maize Eastern Canada LAI Biomass Yield prediction Earth observation |
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Agronomy for sustainable development |
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Jégo, Guillaume @@aut@@ Pattey, Elizabeth @@aut@@ Bourgeois, Gaétan @@aut@@ Drury, Craig F. @@aut@@ Tremblay, Nicolas @@aut@@ |
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2011-03-11T00:00:00Z |
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Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500–2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900–3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6–7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. 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Jégo, Guillaume |
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Jégo, Guillaume ddc 580 bkl 48.16 misc STICS misc Crop model misc Maize misc Eastern Canada misc LAI misc Biomass misc Yield prediction misc Earth observation Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada |
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580 630 640 ASE 48.16 bkl Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada STICS (dpeaa)DE-He213 Crop model (dpeaa)DE-He213 Maize (dpeaa)DE-He213 Eastern Canada (dpeaa)DE-He213 LAI (dpeaa)DE-He213 Biomass (dpeaa)DE-He213 Yield prediction (dpeaa)DE-He213 Earth observation (dpeaa)DE-He213 |
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ddc 580 bkl 48.16 misc STICS misc Crop model misc Maize misc Eastern Canada misc LAI misc Biomass misc Yield prediction misc Earth observation |
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Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada |
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Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada |
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Jégo, Guillaume |
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Jégo, Guillaume Pattey, Elizabeth Bourgeois, Gaétan Drury, Craig F. Tremblay, Nicolas |
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evaluation of the stics crop growth model with maize cultivar parameters calibrated for eastern canada |
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Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada |
| abstract |
Abstract Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500–2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900–3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6–7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. Moreover, these new grain maize cultivars, adapted to a shorter growing season, open new opportunities for using STICS in northern countries. |
| abstractGer |
Abstract Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500–2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900–3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6–7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. Moreover, these new grain maize cultivars, adapted to a shorter growing season, open new opportunities for using STICS in northern countries. |
| abstract_unstemmed |
Abstract Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500–2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900–3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6–7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. Moreover, these new grain maize cultivars, adapted to a shorter growing season, open new opportunities for using STICS in northern countries. |
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Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada |
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| score |
7.400696 |