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| Registros recuperados : 29 | |
| 5. |  | BOOTE, K. J.; JONES, J. W.; HOOGENBOOM, G. Simulating growth and yield response of soybean to temperature and photoperiod. In: CONFERENCIA MUNDIAL DE INVESTIGACION EN SOJA, 4., 1989, Actas... Buenos Aires, A A soja, 1989, v. , p. 272-278, 1989.| Biblioteca(s): Embrapa Trigo. |
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| 8. | | SENTELHAS, P. C.; FARIA, R. T. de; CHAVES, M. O.; HOOGENBOOM, G. Avaliação dos geradores de dados meteorológicos WGEN e SIMMETEO, nas condições tropicais e subtropicais brasileiras, usando modelos de simulação de culturas. Revista Brasileira de Agrometeorologia, Santa Maria, v. 9, n. 2, p. 357-376, 2001.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 10. | | RESENES, J. de A.; PAVAN, W.; HÖLBIG, C. A.; FERNANDES, J. M. C.; SHELIA, V.; PORTER, C.; HOOGENBOOM, G. jDSSAT: A JavaScript Module for DSSAT-CSM integration. SoftwareX, n. 10, (e-100271), 2019.| Biblioteca(s): Embrapa Trigo. |
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| 11. | | FERNANDES, J. M. C.; PAVAN, W.; PEQUENO, D.; WIEST, R.; HOLBIG, C. A.; OLIVEIRA, F.; HOOGENBOOM, G. Improving crop pest/disease modeling. In: BOOTE, K. (Ed.). Advances in crop modelling for a sustainable agriculture. Cambridge, UK: Burleigh Dodds Science Publishing, 2019.| Biblioteca(s): Embrapa Trigo. |
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| 12. | | HEINEMANN, A. B.; DOURADO NETO, D.; HOOGENBOOM, G.; MAIA, A. de H. N.; OHSE, S.; MANFRON, P. A. Resposta da soja (Glycine max) ao aumento da concentração de CO2 e temperatura. Insula, Florianópolis, n. 32, p.119-136 , 2003.| Biblioteca(s): Embrapa Florestas; Embrapa Meio Ambiente. |
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| 14. | | AMARAL, T. A.; ANDRADE, C. L. T.; HOOGENBOOM, G.; SILVA, D. F.; GARCIA Y GARCIA, A.; NOCE, M. A. Nitrogen management strategies for maize production systems: experimental data and crop modeling. International Journal of Plant Production, v. 9, n. 1, p. 51-74, 2015.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 16. | | SANTOS, M. L. dos; SANTOS, P. M.; BOOTE, K. J.; PEQUENO, D. N. L.; BARIONI, L. G.; CUADRA, S. V.; HOOGENBOOM, G. Applying the CROPGRO Perennial Forage Model for long-term estimates of Marandu palisadegrass production in livestock management scenarios in Brazil. Field Crops Research, v. 286, 108629, Oct. 2022. 16 p.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Pecuária Sudeste. |
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| 17. | | DIAS, H. B.; CUADRA, S. V.; BOOTE, K. J.; LAMPARELLI, R. A. C.; FIGUEIREDO, G. K. D. A.; SUYKER, A. E.; MAGALHÃES, P. S. G.; HOOGENBOOM, G. Coupling the CSM-CROPGRO-Soybean crop model with the ECOSMOS Ecosystem Model: an evaluation with data from an AmeriFlux site. Agricultural and Forest Meteorology, v. 342, 109697, 2023.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 18. | | BRUNETTI, H. B.; BOOTE, K. J.; SANTOS, P. M.; PEZZOPANE, J. R. M.; PEDREIRA, C. G. S.; LARA, M. A. S.; MORENO, L. S. de B.; HOOGENBOOM, G. Improving the CROPGRO Perennial Forage Model for simulating growth and biomass partitioning of guineagrass. Agronomy Journal, v. 113, n. 4, p. 3299-3314, July/Aug. 2021.| Biblioteca(s): Embrapa Pecuária Sudeste; Embrapa Pesca e Aquicultura. |
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| 19. | | AMARAL, T. A.; ANDRADE, C. L. T.; DUARTE, J. O.; GARCIA, J. C.; GARCIA Y GARCIA, A.; SILVA, D. F.; ALBERNAZ, W. M.; HOOGENBOOM, G. Nitrogen management strategies for smallholder maize production systems: yield and profitability variability. International Journal of Plant Production, v. 9, n. 1, p. 75-98, 2015.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| Registros recuperados : 29 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pecuária Sudeste. Para informações adicionais entre em contato com cppse.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Pecuária Sudeste; Embrapa Pesca e Aquicultura. |
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Data corrente: |
12/08/2021 |
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Data da última atualização: |
10/12/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
BRUNETTI, H. B.; BOOTE, K. J.; SANTOS, P. M.; PEZZOPANE, J. R. M.; PEDREIRA, C. G. S.; LARA, M. A. S.; MORENO, L. S. de B.; HOOGENBOOM, G. |
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Afiliação: |
HENRIQUE B. BRUNETTI, USP-ESALQ; KENNETH J. BOOTE, UNIVERSITY OF FLORIDA; PATRICIA MENEZES SANTOS, CPPSE; JOSE RICARDO MACEDO PEZZOPANE, CPPSE; CARLOS G. S. PEDREIRA, USP-ESALQ; MÁRCIO A. S. LARA, UFLA; LEONARDO SIMOES DE BARROS MORENO, CNPASA; GERRIT HOOGENBOOM, UNIVERSITY OF FLORIDA. |
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Título: |
Improving the CROPGRO Perennial Forage Model for simulating growth and biomass partitioning of guineagrass. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Agronomy Journal, v. 113, n. 4, p. 3299-3314, July/Aug. 2021. |
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ISSN: |
1435-0645 |
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DOI: |
https://doi.org/10.1002/agj2.20766 |
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Idioma: |
Inglês |
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Conteúdo: |
Tropical forage grasses are used for several applications including grazing, silage, and biofuels; with harvesting at varying phenological stages. Mechanistic simulation models can be powerful tools to assist with planning and decision making of pasture utilization strategies. The objective of this study was to improve and evaluate the ability of the Cropping System Model-CROPGRO-Perennial Forage model (CSMCROPGRO-PFM) to simulate growth and biomass partitioning of two guineagrass [Panicum maximum Jacq. syn. Megathyrsus maximus (Jacq.) BK Simon & SWL Jacobs] cultivars, Tanzânia and Mombaça. Data from two experiments with contrasting harvest management and field conditions were used. Model parameters were modified, targeting improvement in d-statistic and root mean square error (RMSE) for aboveground, leaf, stem biomass, leaf area index (LAI), and leaf proportion of aboveground biomass. Major improvement in model performance was achieved by modifying the vegetative partitioning parameters between leaf and stem through increasing partitioning to leaf during early regrowth while increasing it to stem during late regrowth. Modifications were made to parameters affecting leaf and stem senescence, leaf photosynthesis, and leaf area expansion sensitivity to cool weather. The RMSE values decreased from 2,261 to 1,768 kg ha-1 for aboveground biomass, from 1,620 to 874 kg ha-1 for stem biomass, from 11.41 to 7.27% for leaf percentage, from 1.91 to 1.68 for LAI, but increased slightly for leaf biomass. The d-statistic computed over all these variables increased from .86 to .93. The improved model performance for both short and long harvest cycles will facilitate further applications for diverse forage crops utilization strategies. MenosTropical forage grasses are used for several applications including grazing, silage, and biofuels; with harvesting at varying phenological stages. Mechanistic simulation models can be powerful tools to assist with planning and decision making of pasture utilization strategies. The objective of this study was to improve and evaluate the ability of the Cropping System Model-CROPGRO-Perennial Forage model (CSMCROPGRO-PFM) to simulate growth and biomass partitioning of two guineagrass [Panicum maximum Jacq. syn. Megathyrsus maximus (Jacq.) BK Simon & SWL Jacobs] cultivars, Tanzânia and Mombaça. Data from two experiments with contrasting harvest management and field conditions were used. Model parameters were modified, targeting improvement in d-statistic and root mean square error (RMSE) for aboveground, leaf, stem biomass, leaf area index (LAI), and leaf proportion of aboveground biomass. Major improvement in model performance was achieved by modifying the vegetative partitioning parameters between leaf and stem through increasing partitioning to leaf during early regrowth while increasing it to stem during late regrowth. Modifications were made to parameters affecting leaf and stem senescence, leaf photosynthesis, and leaf area expansion sensitivity to cool weather. The RMSE values decreased from 2,261 to 1,768 kg ha-1 for aboveground biomass, from 1,620 to 874 kg ha-1 for stem biomass, from 11.41 to 7.27% for leaf percentage, from 1.91 to 1.68 for LAI, but increased slightly... Mostrar Tudo |
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Palavras-Chave: |
Growth and biomass partitioning; Mombaça; Stem biomass. |
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Thesagro: |
Biomassa; Modelo de Simulação; Panicum Maximum; Pastagem. |
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Thesaurus NAL: |
Forage crops; Megathyrsus maximus; Tanzania. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 02791naa a2200349 a 4500
001 2133598
005 2021-12-10
008 2021 bl uuuu u00u1 u #d
022 $a1435-0645
024 7 $ahttps://doi.org/10.1002/agj2.20766$2DOI
100 1 $aBRUNETTI, H. B.
245 $aImproving the CROPGRO Perennial Forage Model for simulating growth and biomass partitioning of guineagrass.$h[electronic resource]
260 $c2021
520 $aTropical forage grasses are used for several applications including grazing, silage, and biofuels; with harvesting at varying phenological stages. Mechanistic simulation models can be powerful tools to assist with planning and decision making of pasture utilization strategies. The objective of this study was to improve and evaluate the ability of the Cropping System Model-CROPGRO-Perennial Forage model (CSMCROPGRO-PFM) to simulate growth and biomass partitioning of two guineagrass [Panicum maximum Jacq. syn. Megathyrsus maximus (Jacq.) BK Simon & SWL Jacobs] cultivars, Tanzânia and Mombaça. Data from two experiments with contrasting harvest management and field conditions were used. Model parameters were modified, targeting improvement in d-statistic and root mean square error (RMSE) for aboveground, leaf, stem biomass, leaf area index (LAI), and leaf proportion of aboveground biomass. Major improvement in model performance was achieved by modifying the vegetative partitioning parameters between leaf and stem through increasing partitioning to leaf during early regrowth while increasing it to stem during late regrowth. Modifications were made to parameters affecting leaf and stem senescence, leaf photosynthesis, and leaf area expansion sensitivity to cool weather. The RMSE values decreased from 2,261 to 1,768 kg ha-1 for aboveground biomass, from 1,620 to 874 kg ha-1 for stem biomass, from 11.41 to 7.27% for leaf percentage, from 1.91 to 1.68 for LAI, but increased slightly for leaf biomass. The d-statistic computed over all these variables increased from .86 to .93. The improved model performance for both short and long harvest cycles will facilitate further applications for diverse forage crops utilization strategies.
650 $aForage crops
650 $aMegathyrsus maximus
650 $aTanzania
650 $aBiomassa
650 $aModelo de Simulação
650 $aPanicum Maximum
650 $aPastagem
653 $aGrowth and biomass partitioning
653 $aMombaça
653 $aStem biomass
700 1 $aBOOTE, K. J.
700 1 $aSANTOS, P. M.
700 1 $aPEZZOPANE, J. R. M.
700 1 $aPEDREIRA, C. G. S.
700 1 $aLARA, M. A. S.
700 1 $aMORENO, L. S. de B.
700 1 $aHOOGENBOOM, G.
773 $tAgronomy Journal$gv. 113, n. 4, p. 3299-3314, July/Aug. 2021.
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