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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 Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
01/10/2015 |
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Data da última atualização: |
30/05/2022 |
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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: |
ARAYAA, A.; HOOGENBOOM, G.; LUEDELING, E.; HADGU, K. M.; KISEKKA, I.; MARTORANO, L. G. |
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Afiliação: |
A. Arayaa, Mekelle University; G. Hoogenboom, Washington State University; E. Luedeling, World Agroforestry Centre / University of Bonn; Kiros M. Hadgu, World Agroforestry Centre; Isaya Kisekka, Kansas State University; LUCIETA GUERREIRO MARTORANO, CPATU. |
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Título: |
Assessment of maize growth and yield using crop models under present and future climate in southwestern Ethiopia. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Agricultural and Forest Meteorology, v. 214/215, p. 252-265, Dec. 2015. |
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DOI: |
http://dx.doi.org/10.1016/j.agrformet.2015.08.259 |
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Idioma: |
Inglês |
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Conteúdo: |
Maize yield productivity in Ethiopia has been below the genetic potential?constrained, among other factors, by frequent moisture stress due to local weather variability. Changes in climate may exacerbate these limitations to productivity, but current research on projecting responses of maize yields to climate change in Ethiopia is inadequate. The research objectives of this project were to (1) calibrate and evaluate the performance of the APSIM-maize and DSSAT CSM-CERES-Maize models, and (2) assess the impact of climate change on future maize yield. The climate periods considered were near future (2010-2039), middle (2040-2069) and end of the 21st century (2070-2099). Climate simulations were conducted using 20 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs; RCP4.5 and RCP8.5). Both crop models reasonably reproduced observations for time to anthesis, time to physiological maturity and crop yields, with values for the index of agreement of 0.86, 0.80 and 0.77 for DSSAT, and 0.50, 0.89 and 0.60 for APSIM. Similarly root mean square errors were moderate for days to anthesis (1.3 and 3.7 days, for DSSAT and APSIM, respectively), maturity (4.5 and 3.1 days), and yield (1.1 and 1.2 tons). Deviations of simulated from observed values were low for days to anthesis (DSSAT: −2.4?2.3%; APSIM: 0?6%) and days to maturity (DSSAT: −0.6?4.4%; APSIM: −1.9?3.3%) but relatively high for yield (DSSAT: −18.5?21.2%; APSIM: −19.1?37.1%). Overall the goodness-of-fit measures indicated that models were useful for assessing maize yield at the study site. Simulations for future climate scenarios projected slight increases in the median yield for the near future (1.7%?2.9% across models and RCPs), with uncertainty increasing toward mid-century (0.6?4.2%). By the end of the 21st century, projections ranged between yield decreases by 6.3% and increases by 4%. Differences between the RCPs were small, probably due to factor interactions, such as higher temperatures reducing the CO2-induced yield gains for the higher RCP. Uncertainties in studies on the impact of climate change on maize might arise mostly from the choice of crop model and GCM. Therefore, the use of multiple crop models along with multiple GCMs would be advisable in order to adequately consider uncertainties about future climate and crop responses and to provide comprehensive information to policy makers and planners. Overall, results of this study (based on two different crop simulation models across 20 GCMs, and two RCPs under similar crop management) consistently indicated a slight increase in yield. MenosMaize yield productivity in Ethiopia has been below the genetic potential?constrained, among other factors, by frequent moisture stress due to local weather variability. Changes in climate may exacerbate these limitations to productivity, but current research on projecting responses of maize yields to climate change in Ethiopia is inadequate. The research objectives of this project were to (1) calibrate and evaluate the performance of the APSIM-maize and DSSAT CSM-CERES-Maize models, and (2) assess the impact of climate change on future maize yield. The climate periods considered were near future (2010-2039), middle (2040-2069) and end of the 21st century (2070-2099). Climate simulations were conducted using 20 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs; RCP4.5 and RCP8.5). Both crop models reasonably reproduced observations for time to anthesis, time to physiological maturity and crop yields, with values for the index of agreement of 0.86, 0.80 and 0.77 for DSSAT, and 0.50, 0.89 and 0.60 for APSIM. Similarly root mean square errors were moderate for days to anthesis (1.3 and 3.7 days, for DSSAT and APSIM, respectively), maturity (4.5 and 3.1 days), and yield (1.1 and 1.2 tons). Deviations of simulated from observed values were low for days to anthesis (DSSAT: −2.4?2.3%; APSIM: 0?6%) and days to maturity (DSSAT: −0.6?4.4%; APSIM: −1.9?3.3%) but relatively high for yield (DSSAT: −18.5?21.2%; APSIM: −19.1... Mostrar Tudo |
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Palavras-Chave: |
Etiópia. |
|
Thesagro: |
Milho; Mudança Climática. |
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Categoria do assunto: |
-- |
|
Marc: |
LEADER 03384naa a2200229 a 4500
001 2025627
005 2022-05-30
008 2015 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1016/j.agrformet.2015.08.259$2DOI
100 1 $aARAYAA, A.
245 $aAssessment of maize growth and yield using crop models under present and future climate in southwestern Ethiopia.$h[electronic resource]
260 $c2015
520 $aMaize yield productivity in Ethiopia has been below the genetic potential?constrained, among other factors, by frequent moisture stress due to local weather variability. Changes in climate may exacerbate these limitations to productivity, but current research on projecting responses of maize yields to climate change in Ethiopia is inadequate. The research objectives of this project were to (1) calibrate and evaluate the performance of the APSIM-maize and DSSAT CSM-CERES-Maize models, and (2) assess the impact of climate change on future maize yield. The climate periods considered were near future (2010-2039), middle (2040-2069) and end of the 21st century (2070-2099). Climate simulations were conducted using 20 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs; RCP4.5 and RCP8.5). Both crop models reasonably reproduced observations for time to anthesis, time to physiological maturity and crop yields, with values for the index of agreement of 0.86, 0.80 and 0.77 for DSSAT, and 0.50, 0.89 and 0.60 for APSIM. Similarly root mean square errors were moderate for days to anthesis (1.3 and 3.7 days, for DSSAT and APSIM, respectively), maturity (4.5 and 3.1 days), and yield (1.1 and 1.2 tons). Deviations of simulated from observed values were low for days to anthesis (DSSAT: −2.4?2.3%; APSIM: 0?6%) and days to maturity (DSSAT: −0.6?4.4%; APSIM: −1.9?3.3%) but relatively high for yield (DSSAT: −18.5?21.2%; APSIM: −19.1?37.1%). Overall the goodness-of-fit measures indicated that models were useful for assessing maize yield at the study site. Simulations for future climate scenarios projected slight increases in the median yield for the near future (1.7%?2.9% across models and RCPs), with uncertainty increasing toward mid-century (0.6?4.2%). By the end of the 21st century, projections ranged between yield decreases by 6.3% and increases by 4%. Differences between the RCPs were small, probably due to factor interactions, such as higher temperatures reducing the CO2-induced yield gains for the higher RCP. Uncertainties in studies on the impact of climate change on maize might arise mostly from the choice of crop model and GCM. Therefore, the use of multiple crop models along with multiple GCMs would be advisable in order to adequately consider uncertainties about future climate and crop responses and to provide comprehensive information to policy makers and planners. Overall, results of this study (based on two different crop simulation models across 20 GCMs, and two RCPs under similar crop management) consistently indicated a slight increase in yield.
650 $aMilho
650 $aMudança Climática
653 $aEtiópia
700 1 $aHOOGENBOOM, G.
700 1 $aLUEDELING, E.
700 1 $aHADGU, K. M.
700 1 $aKISEKKA, I.
700 1 $aMARTORANO, L. G.
773 $tAgricultural and Forest Meteorology$gv. 214/215, p. 252-265, Dec. 2015.
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