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| 20. |  | ARVOR, D.; MEIRELLES, M. S. P.; DUBREUIL, V.; BEGUÈ, A.; SHIMABUKURO, Y. E. Analyzing the agricultural transition in Mato Grosso, Brazil, using satellite-derived indices. Applied Geography, v. 32, p. 702-713, 2011.| Biblioteca(s): Embrapa Solos. |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
02/08/2013 |
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Data da última atualização: |
05/08/2013 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
BERNARDES, T.; MOREIRA, M. A.; VERONA, J. D.; SHIMABUKURO, Y. E.; LUIZ, A. J. B. |
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Afiliação: |
TIAGO BERNARDES, CEMADEN; MAURÍCIO ALVES MOREIRA, INPE; JANE DELANE VERONA, INPE; YOSIO EDEMIR SHIMABUKURO, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA. |
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Título: |
Variáveis e modelos para estimativa da produtividade do cafeeiro a partir de índices de vegetação derivados de imagens Landsat. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 16., 2013, Foz do Iguaçu. Anais... Foz do Iguaçu: INPE, 2013. p. 0720-0727. |
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Idioma: |
Português |
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Conteúdo: |
Abstract. Coffee fields present a specific pattern of productivity resulting in high and low production in alternated years. Branches grown the first phenological year will produce coffee beans the second phenological year. In high-production years a plant works mostly to grain-filling to the detriment of new branches which will be responsible for production the following year. In low-production years the plant works rather to grow new branches which will produce beans the subsequent year. This feature can be related to the foliar biomass, which can be estimated through remote sensing derived vegetation indices. Several studies report this feature must be incorporated in modeling coffee yield coupled with agrometeorogical models. In this paper we derived Landsat vegetation indices related to coffee plots in order to obtain relationships to yield of the same coffee plots. Biophisical variables and yield data were colected in interviews with farmers from four locations in the whole largest Brazilian coffee-exporting province. Vegetation indices and biophysical variables were selected through stepwise regression in order to obtain the best regression models to estimate coffee yield. Outcomes showed that general models and specific models obtained for Mundo Novo variety presented Pearson's correlation coeficients (r) from 0,64 to 0,71 while models for Catuaí variety showed better results (r = 0,85). Although coffee yield cannot be estimated exclusively from these models, they can be usefull coupled with agrometeorogical models for estimating coffee yield. MenosAbstract. Coffee fields present a specific pattern of productivity resulting in high and low production in alternated years. Branches grown the first phenological year will produce coffee beans the second phenological year. In high-production years a plant works mostly to grain-filling to the detriment of new branches which will be responsible for production the following year. In low-production years the plant works rather to grow new branches which will produce beans the subsequent year. This feature can be related to the foliar biomass, which can be estimated through remote sensing derived vegetation indices. Several studies report this feature must be incorporated in modeling coffee yield coupled with agrometeorogical models. In this paper we derived Landsat vegetation indices related to coffee plots in order to obtain relationships to yield of the same coffee plots. Biophisical variables and yield data were colected in interviews with farmers from four locations in the whole largest Brazilian coffee-exporting province. Vegetation indices and biophysical variables were selected through stepwise regression in order to obtain the best regression models to estimate coffee yield. Outcomes showed that general models and specific models obtained for Mundo Novo variety presented Pearson's correlation coeficients (r) from 0,64 to 0,71 while models for Catuaí variety showed better results (r = 0,85). Although coffee yield cannot be estimated exclusively from these models, they ca... Mostrar Tudo |
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Palavras-Chave: |
Biophysics variables; Coffee; Coffee yield; Radiometric correction; Stepwise regression; Vegetation indices. |
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Thesagro: |
Café; Produtividade; Sensoriamento remoto. |
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Thesaurus NAL: |
Agricultural management models; Grain yield; Statistical models. |
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Categoria do assunto: |
A Sistemas de Cultivo |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/86911/1/2013AA03.pdf
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Marc: |
LEADER 02593nam a2200301 a 4500
001 1963264
005 2013-08-05
008 2013 bl uuuu u00u1 u #d
100 1 $aBERNARDES, T.
245 $aVariáveis e modelos para estimativa da produtividade do cafeeiro a partir de índices de vegetação derivados de imagens Landsat.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 16., 2013, Foz do Iguaçu. Anais... Foz do Iguaçu: INPE, 2013. p. 0720-0727.$c2013
520 $aAbstract. Coffee fields present a specific pattern of productivity resulting in high and low production in alternated years. Branches grown the first phenological year will produce coffee beans the second phenological year. In high-production years a plant works mostly to grain-filling to the detriment of new branches which will be responsible for production the following year. In low-production years the plant works rather to grow new branches which will produce beans the subsequent year. This feature can be related to the foliar biomass, which can be estimated through remote sensing derived vegetation indices. Several studies report this feature must be incorporated in modeling coffee yield coupled with agrometeorogical models. In this paper we derived Landsat vegetation indices related to coffee plots in order to obtain relationships to yield of the same coffee plots. Biophisical variables and yield data were colected in interviews with farmers from four locations in the whole largest Brazilian coffee-exporting province. Vegetation indices and biophysical variables were selected through stepwise regression in order to obtain the best regression models to estimate coffee yield. Outcomes showed that general models and specific models obtained for Mundo Novo variety presented Pearson's correlation coeficients (r) from 0,64 to 0,71 while models for Catuaí variety showed better results (r = 0,85). Although coffee yield cannot be estimated exclusively from these models, they can be usefull coupled with agrometeorogical models for estimating coffee yield.
650 $aAgricultural management models
650 $aGrain yield
650 $aStatistical models
650 $aCafé
650 $aProdutividade
650 $aSensoriamento remoto
653 $aBiophysics variables
653 $aCoffee
653 $aCoffee yield
653 $aRadiometric correction
653 $aStepwise regression
653 $aVegetation indices
700 1 $aMOREIRA, M. A.
700 1 $aVERONA, J. D.
700 1 $aSHIMABUKURO, Y. E.
700 1 $aLUIZ, A. J. B.
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Embrapa Meio Ambiente (CNPMA) |
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