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Registro Completo |
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Biblioteca(s): |
Embrapa Meio-Norte. |
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Data corrente: |
22/12/2020 |
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Data da última atualização: |
28/01/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ALMEIDA, F. da S.; MINGOTTE, F. L. C.; COELHO, A. P.; LEMOS, L. B.; SANTANA, M. J. de; ROCHA, M. de M. |
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Afiliação: |
Fernando da Silva Almeida, Instituto Federal do Triangulo Mineiro, Uberaba, MG.; Fábio Luiz Checchio Mingotte, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, SP.; Anderson Prates Coelho, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, SP.; Leandro Borges Lemos, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, SP.; Márcio José de Santana, Instituto Federal do Triangulo Mineiro, Uberaba, MG.; MAURISRAEL DE MOURA ROCHA, CPAMN. |
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Título: |
Does the sowing period change the grain technological quality of cowpea cultivars? |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Revista Brasileira de Ciências Agrárias, v. 15, n. 4, e8677, 2020. |
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ISSN: |
1981-0997 |
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DOI: |
10.5039/agraria.v15i4a8677 |
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Idioma: |
Inglês |
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Conteúdo: |
The aim of the present study was to evaluate whether the sowing period changes the grain technological quality of cowpea cultivars and to indicate which of these have the highest technological quality when grown in the Center-South region of Brazil. The experiment was conducted in a randomized block design, with a 6 x 3 factorial arrangement, using six cowpea cultivars (BRS Itaim, BRS Guariba, BRS Potengi, BRS Cauamé, BRS Novaera and BRS Tumucumaque) and three sowing seasons, with four replicates. The evaluations held referred to the grain size and hydration, cooking time and crude protein content through sulfuric acid digestion. There was an interaction between cowpea cultivars and sowing dates for all analyzed variables. The sowing season alters the grain technological quality of cowpea when under the conditions of the Center-South region of Brazil, with the genotype-season effect dependent. Sowing dates in December and January generated lower technological quality of the cowpea beans. The BRS Tumucumaque cultivar possesses the best grain technological characteristics, having the largest grains with the highest crude protein content, as well as the shortest cooking time. |
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Palavras-Chave: |
Tempo de cozimento. |
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Thesagro: |
Genótipo; Proteína; Vigna Unguiculata. |
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Thesaurus Nal: |
Cooking; Genotype; Proteins. |
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Categoria do assunto: |
Q Alimentos e Nutrição Humana |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/219575/1/SowingChangeGrainQualityCowpeaCultivars.pdf
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Marc: |
LEADER 02022naa a2200289 a 4500
001 2128598
005 2021-01-28
008 2020 bl uuuu u00u1 u #d
022 $a1981-0997
024 7 $a10.5039/agraria.v15i4a8677$2DOI
100 1 $aALMEIDA, F. da S.
245 $aDoes the sowing period change the grain technological quality of cowpea cultivars?$h[electronic resource]
260 $c2020
520 $aThe aim of the present study was to evaluate whether the sowing period changes the grain technological quality of cowpea cultivars and to indicate which of these have the highest technological quality when grown in the Center-South region of Brazil. The experiment was conducted in a randomized block design, with a 6 x 3 factorial arrangement, using six cowpea cultivars (BRS Itaim, BRS Guariba, BRS Potengi, BRS Cauamé, BRS Novaera and BRS Tumucumaque) and three sowing seasons, with four replicates. The evaluations held referred to the grain size and hydration, cooking time and crude protein content through sulfuric acid digestion. There was an interaction between cowpea cultivars and sowing dates for all analyzed variables. The sowing season alters the grain technological quality of cowpea when under the conditions of the Center-South region of Brazil, with the genotype-season effect dependent. Sowing dates in December and January generated lower technological quality of the cowpea beans. The BRS Tumucumaque cultivar possesses the best grain technological characteristics, having the largest grains with the highest crude protein content, as well as the shortest cooking time.
650 $aCooking
650 $aGenotype
650 $aProteins
650 $aGenótipo
650 $aProteína
650 $aVigna Unguiculata
653 $aTempo de cozimento
700 1 $aMINGOTTE, F. L. C.
700 1 $aCOELHO, A. P.
700 1 $aLEMOS, L. B.
700 1 $aSANTANA, M. J. de
700 1 $aROCHA, M. de M.
773 $tRevista Brasileira de Ciências Agrárias$gv. 15, n. 4, e8677, 2020.
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Registro original: |
Embrapa Meio-Norte (CPAMN) |
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Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Cerrados. |
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Data corrente: |
18/08/2023 |
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Data da última atualização: |
18/08/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
MACARRINGUE, L. S.; BOLFE, E. L.; DUVERGER, S. G.; SANO, E. E.; CALDAS, M. M.; FERREIRA, M. C.; ZULLO JUNIOR, J.; MATIAS, L. F. |
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Afiliação: |
LUCRÊNCIO SILVESTRE MACARRINGUE, UNIVERSIDADE ESTADUAL DE CAMPINAS; EDSON LUIS BOLFE, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS; SOLTAN GALANO DUVERGER, UNIVERSIDADE FEDERAL DA BAHIA; EDSON EYJI SANO, CPAC; MARCELLUS MARQUES CALDAS, KANSAS STATE UNIVERSITY; MARCOS CÉSAR FERREIRA, UNIVERSIDADE ESTADUAL DE CAMPINAS; JURANDIR ZULLO JUNIOR, UNIVERSIDADE ESTADUAL DE CAMPINAS; LINDON FONSECA MATIAS, UNIVERSIDADE ESTADUAL DE CAMPINAS. |
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Título: |
Land use and land cover classification in the northern region of Mozambique based on Landsat time series and machine learning. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
ISPRS International Journal of Geo-Information, v. 12, n. 8, 342, Aug. 2023. |
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ISSN: |
2220-9964 |
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DOI: |
https://doi.org/10.3390/ijgi12080342 |
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Idioma: |
Inglês |
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Conteúdo: |
Accurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies. |
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Palavras-Chave: |
Aprendizado de máquina; Cobertura da terra; Feature selection; Floresta aleatória; Google Earth Engine; Machine learning; Miombo; Random forest; Séries temporais. |
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Thesagro: |
Desmatamento; Uso da Terra. |
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Thesaurus NAL: |
Deforestation; Land cover; Land use. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155979/1/AP-LandUse-2023.pdf
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Marc: |
LEADER 02267naa a2200397 a 4500
001 2155979
005 2023-08-18
008 2023 bl uuuu u00u1 u #d
022 $a2220-9964
024 7 $ahttps://doi.org/10.3390/ijgi12080342$2DOI
100 1 $aMACARRINGUE, L. S.
245 $aLand use and land cover classification in the northern region of Mozambique based on Landsat time series and machine learning.$h[electronic resource]
260 $c2023
520 $aAccurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies.
650 $aDeforestation
650 $aLand cover
650 $aLand use
650 $aDesmatamento
650 $aUso da Terra
653 $aAprendizado de máquina
653 $aCobertura da terra
653 $aFeature selection
653 $aFloresta aleatória
653 $aGoogle Earth Engine
653 $aMachine learning
653 $aMiombo
653 $aRandom forest
653 $aSéries temporais
700 1 $aBOLFE, E. L.
700 1 $aDUVERGER, S. G.
700 1 $aSANO, E. E.
700 1 $aCALDAS, M. M.
700 1 $aFERREIRA, M. C.
700 1 $aZULLO JUNIOR, J.
700 1 $aMATIAS, L. F.
773 $tISPRS International Journal of Geo-Information$gv. 12, n. 8, 342, Aug. 2023.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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