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Registro Completo |
Biblioteca(s): |
Embrapa Agroindústria de Alimentos; Embrapa Hortaliças; Embrapa Semiárido. |
Data corrente: |
02/02/2022 |
Data da última atualização: |
02/02/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
MELLO, A. F. S.; SILVA, G. O. da; SILVA, J. da; SAMBORSKI, T.; FERREIRA, J. C.; CARVALHO, J. L. V. de; NUTTI, M. R.; SIQUIEROLI, A. C. S.; BRAGA, M. B.; TRUJILO, F. C. D.; GRÜNEBERG, W. |
Afiliação: |
ALEXANDRE FURTADO SILVEIRA MELLO, CNPH; GIOVANI OLEGARIO DA SILVA, CNPH; JUSCIMAR DA SILVA, CNPH; TARCÍSIO SAMBORSKI, INSTITUTO FEDERAL FARROUPILHA; JOSE CARLOS FERREIRA, CPATSA; JOSE LUIZ VIANA DE CARVALHO, CTAA; MARILIA REGINI NUTTI, CTAA; ANA CAROLINA SILVA SIQUIEROLI, UNIVERSIDADE FEDERAL DE UBERLÂNDIA; MARCOS BRANDAO BRAGA, CNPH; FEDERICO CELEDONIO DIAZ TRUJILO, INTERNATIONAL POTATO CENTER; WOLFGANG GRÜNEBERG, INTERNATIONAL POTATO CENTER. |
Título: |
'CIP BRS Nuti': a new orange flesh sweetpotato cultivar. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
HortScience, v. 57, n. 3, p. 376-378, Mar. 2022. |
ISSN: |
2327-9834 |
DOI: |
https://doi.org/10.21273/HORTSCI16292-21 |
Idioma: |
Inglês |
Notas: |
Na publicação: Marília Regini Nuti. |
Conteúdo: |
?CIP BRS Nuti? (CIP 106902.1) sweetpotato (Ipomoea batatas) was developed by the International Potato Center (CIP) in Peru by controlled crossing between the elite orange flesh sweetpotato (OFSP) clone (CIP1010-48.1) and the advanced OFSP clone (CIP194-583.2). This clone will be released by Embrapa as a cultivar for Brazil, after being selected from among 80 clones imported from CIP. ?CIP BRS Nuti? is an OFSP with a mean commercial root yield of 35 t-ha-1 across 12 Brazilian site locations. |
Palavras-Chave: |
Biofortificação; Cultivar Beauregard; Cultivar CIP BRS Nuti. |
Thesagro: |
Batata Doce; Ipomoea Batatas; Melhoramento Genético Vegetal; Meloidogyne Incognita; Meloidogyne Javanica; Propagação Vegetativa; Variedade Resistente. |
Thesaurus Nal: |
Biofortification; Sweet potatoes. |
Categoria do assunto: |
-- A Sistemas de Cultivo Q Alimentos e Nutrição Humana |
Marc: |
LEADER 01743naa a2200421 a 4500 001 2139546 005 2022-02-02 008 2022 bl uuuu u00u1 u #d 022 $a2327-9834 024 7 $ahttps://doi.org/10.21273/HORTSCI16292-21$2DOI 100 1 $aMELLO, A. F. S. 245 $a'CIP BRS Nuti'$ba new orange flesh sweetpotato cultivar.$h[electronic resource] 260 $c2022 500 $aNa publicação: Marília Regini Nuti. 520 $a?CIP BRS Nuti? (CIP 106902.1) sweetpotato (Ipomoea batatas) was developed by the International Potato Center (CIP) in Peru by controlled crossing between the elite orange flesh sweetpotato (OFSP) clone (CIP1010-48.1) and the advanced OFSP clone (CIP194-583.2). This clone will be released by Embrapa as a cultivar for Brazil, after being selected from among 80 clones imported from CIP. ?CIP BRS Nuti? is an OFSP with a mean commercial root yield of 35 t-ha-1 across 12 Brazilian site locations. 650 $aBiofortification 650 $aSweet potatoes 650 $aBatata Doce 650 $aIpomoea Batatas 650 $aMelhoramento Genético Vegetal 650 $aMeloidogyne Incognita 650 $aMeloidogyne Javanica 650 $aPropagação Vegetativa 650 $aVariedade Resistente 653 $aBiofortificação 653 $aCultivar Beauregard 653 $aCultivar CIP BRS Nuti 700 1 $aSILVA, G. O. da 700 1 $aSILVA, J. da 700 1 $aSAMBORSKI, T. 700 1 $aFERREIRA, J. C. 700 1 $aCARVALHO, J. L. V. de 700 1 $aNUTTI, M. R. 700 1 $aSIQUIEROLI, A. C. S. 700 1 $aBRAGA, M. B. 700 1 $aTRUJILO, F. C. D. 700 1 $aGRÜNEBERG, W. 773 $tHortScience$gv. 57, n. 3, p. 376-378, Mar. 2022.
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Embrapa Hortaliças (CNPH) |
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Registro Completo
Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
11/12/2018 |
Data da última atualização: |
08/07/2019 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
SAMUEL-ROSA, A.; DALMOLIN, R. S. D.; GUBIANI, P. I.; OLIVEIRA, S. R. de M.; TEIXEIRA, W. G.; VIANA, J. H. M.; RIBEIRO, E.; TORNQUIST, C. G.; ANJOS, L. H. C. dos; SOUZA, J. J. E. L. de; OTTONI, M. V.; MEDEIROS, P. S. C. de; GRIS, D. J.; ROSIN, N. A.; BUENO, J. M. M.; SANTOS, H. G. dos; WEBER, E. J.; FLORES, C. A.; COSTA, E. M.; OLIVEIRA, R. P. de; FILIPPINI ALBA, J. M.; PEDROSO NETO, J. C.; PEDRON, F. de A.; CAVIGLIONE, J. H.; VALLADARES, G. S.; MIRANDA, C. S. S.; DEMATTÊ, J. A. M.; MARQUES JÚNIOR, J.; SIQUEIRA, D. S.; AQUINO, R. E. de; SILVERO, N. E. Q.; GENÚ, A. M.; BROETTO, T.; CANCIAN, L. C.; MIGUEL, P.; ZALAMENA, J.; DOTTO, A. C.; ALMEIDA, J. A. de; REICHERT.; CURCIO, G. R.; COLLIER, L. S.; CARVALHO JUNIOR, W. de; FONTANA, A.; OLIVEIRA, A. P. de; VOGELMANN, E. S.; MALLMANN, F. J. K.; VASQUES, G. de M.; LEPSCH, I. F.; FINK, J. R.; KER, J. C.; SILVA, L. S. da; FREITAS, P. L. de; BIELUCZYK, B.; TIECHER, T. |
Afiliação: |
ALESSANDRO SAMUEL-ROSA, UFSM; RICARDO SIMÃO DINIZ DALMOLIN, UFSM; PAULO IVONIR GUBIANI, UFSM; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; WENCESLAU GERALDES TEIXEIRA, CNPS; JOAO HERBERT MOREIRA VIANA, CNPMS; ELOI RIBEIRO, ISRIC World Soil Information; CARLOS GUSTAVO TORNQUIST, UFRGS; LÚCIA HELENA CUNHA DOS ANJOS, UFRRJ; JOSÉ JOÃO LELIS LEAL DE SOUZA, UFRGN; MARTA VASCONCELOS OTTONI, Serviço Geológico do Brasil; PAULA SUÉLEN CORRÊA DE MEDEIROS, IBGE; DIEGO JOSÉ GRIS, UFSM; NÍCOLAS AUGUSTO ROSIN, UFSM; JEAN MICHEL MOURA BUENO, UFSM; HUMBERTO GONCALVES DOS SANTOS, CNPS; ELISEU JOSÉ WEBER, UFRGS; CARLOS ALBERTO FLORES, CPACT; ELIAS MENDES COSTA, UFRRJ; RONALDO PEREIRA DE OLIVEIRA, CNPS; JOSE MARIA FILIPPINI ALBA, CPACT; JOÃO CHRISÓSTOMO PEDROSO NETO, Epamig; FABRÍCIO DE ARAÚJO PEDRON, UFSM; JOÃO HENRIQUE CAVIGLIONE, Iapar; GUSTAVO SOUZA VALLADARES, UFPI; CARMEM SUEZE SILVA MIRANDA, Univasf; JOSÉ ALEXANDRE MELO DEMATTÊ, USP; JOSÉ MARQUES JÚNIOR, Unesp; DIEGO SILVA SIQUEIRA, Unesp; RENATO ELEOTERIO DE AQUINO, Unesp; NELIDA ELIZABET QUIÑONEZ SILVERO, Unesp; ALINE MARQUES GENÚ, UNICENTRO; TIAGO BROETTO, Catena Planejamento Territorial; LUCIANO CAMPOS CANCIAN, UFSM; PABLO MIGUEL, UFPel; JOVANI ZALAMENA, UFSC; ANDRÉ CARNIELETTO DOTTO, USP; JAIME ANTONIO DE ALMEIDA, Udesc; JOSÉ MIGUEL REICHERT, UFSM; GUSTAVO RIBAS CURCIO, CNPF; LEONARDO SANTOS COLLIER, UFG; WALDIR DE CARVALHO JUNIOR, CNPS; ADEMIR FONTANA, CNPS; ALINE PACOBAHYBA DE OLIVEIRA, CNPS; EDUARDO SALDANHA VOGELMANN, FURG; FÁBIO JOEL KOCHEM MALLMANN, Universidade Regional Integrada do Alto Uruguai e das Missões; GUSTAVO DE MATTOS VASQUES, CNPS; IGO FERNANDO LEPSCH, USP; JESSÉ RODRIGO FINK, IFPR; JOÃO CARLOS KER, UFV; LEANDRO SOUZA DA SILVA, UFSM; PEDRO LUIZ DE FREITAS, CNPS; WANDERLEI BIELUCZYK, USP; TALES TIECHER, UFRGS. |
Título: |
Bringing together Brazilian soil scientists to share soil data. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: proceedings... Viçosa, MG: SBCS, 2018. v. 1, p. 63-64. WCSS 2018. |
Idioma: |
Inglês |
Conteúdo: |
In the Amazon region, types of soil known as Archaeological Black Lands (ABL) present anthropic horizon A and are associated with prolonged human occupation by indigenous societies from the pre-Columbian period, where chemical and physical attributes have better quality than other types of soil in the Amazon, setting a large organic carbon reservoir. However, the conversion of these natural ecosystems into cultivated environments make emerge changes in soil carbon dynamics, often leading to a decline in soil organic carbon content. Therefore, our aim was to use data mining techniques to generate predictive models for the effect of soil use on carbon stock in natural and transformed areas of Archaeological Black Lands. We carried out our experiment in Manicoré and Apuí, Amazonas State, Brazil. After field data collection and laboratory analysis, we obtained a set of data consisting of 21 attributes, 20 predictive attributes consisting of 13 soil physical attributes, 6 soil chemical attributes, 1 soil use related attribute, and 1 response variable, referring to soil carbon stock (SCS), which is the classification target. Due to the large number of attributes, we performed a selection procedure to eliminate attributes of low correlation to the response variable. For data classification, we used the binary induction technique of the decision tree through software Weka 3.8. The results obtained showed that for the depth of 0.00-0.05 and 0.05-0.10 m, the best selected subset was determined using the Wrapper method for attribute selection. In the depth of 0.00-0.05 m, we generated a model of 79% accuracy containing only six rules, including, as the most important classification attribute was soil use. For the depth of 0.05-0.10 m, we generated an eight-rule decision tree of 74% accuracy including sand as the most important attribute. In this context, we highlight the Wrapper method efficiency to select subsets of predictive attributes, capable to generate more understandable decision trees, using a smaller number of attributes in the classification process, making it faster and with a lower computational cost. In addition, data mining techniques were efficient at providing predictive models capable to assist the decision-making process on possible management practices with the potential to conserve or increase soil carbon stock in archeological black lands. MenosIn the Amazon region, types of soil known as Archaeological Black Lands (ABL) present anthropic horizon A and are associated with prolonged human occupation by indigenous societies from the pre-Columbian period, where chemical and physical attributes have better quality than other types of soil in the Amazon, setting a large organic carbon reservoir. However, the conversion of these natural ecosystems into cultivated environments make emerge changes in soil carbon dynamics, often leading to a decline in soil organic carbon content. Therefore, our aim was to use data mining techniques to generate predictive models for the effect of soil use on carbon stock in natural and transformed areas of Archaeological Black Lands. We carried out our experiment in Manicoré and Apuí, Amazonas State, Brazil. After field data collection and laboratory analysis, we obtained a set of data consisting of 21 attributes, 20 predictive attributes consisting of 13 soil physical attributes, 6 soil chemical attributes, 1 soil use related attribute, and 1 response variable, referring to soil carbon stock (SCS), which is the classification target. Due to the large number of attributes, we performed a selection procedure to eliminate attributes of low correlation to the response variable. For data classification, we used the binary induction technique of the decision tree through software Weka 3.8. The results obtained showed that for the depth of 0.00-0.05 and 0.05-0.10 m, the best selected subset was d... Mostrar Tudo |
Palavras-Chave: |
Anthropic soils; Árvore de decisão; Data mining techniques; Decision tree; Estoque de carbono; Mineração de dados; Soil management system. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 04807nam a2200829 a 4500 001 2101195 005 2019-07-08 008 2018 bl uuuu u01u1 u #d 100 1 $aSAMUEL-ROSA, A. 245 $aBringing together Brazilian soil scientists to share soil data.$h[electronic resource] 260 $aIn: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: proceedings... Viçosa, MG: SBCS, 2018. v. 1, p. 63-64. WCSS 2018.$c2018 520 $aIn the Amazon region, types of soil known as Archaeological Black Lands (ABL) present anthropic horizon A and are associated with prolonged human occupation by indigenous societies from the pre-Columbian period, where chemical and physical attributes have better quality than other types of soil in the Amazon, setting a large organic carbon reservoir. However, the conversion of these natural ecosystems into cultivated environments make emerge changes in soil carbon dynamics, often leading to a decline in soil organic carbon content. Therefore, our aim was to use data mining techniques to generate predictive models for the effect of soil use on carbon stock in natural and transformed areas of Archaeological Black Lands. We carried out our experiment in Manicoré and Apuí, Amazonas State, Brazil. After field data collection and laboratory analysis, we obtained a set of data consisting of 21 attributes, 20 predictive attributes consisting of 13 soil physical attributes, 6 soil chemical attributes, 1 soil use related attribute, and 1 response variable, referring to soil carbon stock (SCS), which is the classification target. Due to the large number of attributes, we performed a selection procedure to eliminate attributes of low correlation to the response variable. For data classification, we used the binary induction technique of the decision tree through software Weka 3.8. The results obtained showed that for the depth of 0.00-0.05 and 0.05-0.10 m, the best selected subset was determined using the Wrapper method for attribute selection. In the depth of 0.00-0.05 m, we generated a model of 79% accuracy containing only six rules, including, as the most important classification attribute was soil use. For the depth of 0.05-0.10 m, we generated an eight-rule decision tree of 74% accuracy including sand as the most important attribute. In this context, we highlight the Wrapper method efficiency to select subsets of predictive attributes, capable to generate more understandable decision trees, using a smaller number of attributes in the classification process, making it faster and with a lower computational cost. In addition, data mining techniques were efficient at providing predictive models capable to assist the decision-making process on possible management practices with the potential to conserve or increase soil carbon stock in archeological black lands. 653 $aAnthropic soils 653 $aÁrvore de decisão 653 $aData mining techniques 653 $aDecision tree 653 $aEstoque de carbono 653 $aMineração de dados 653 $aSoil management system 700 1 $aDALMOLIN, R. S. D. 700 1 $aGUBIANI, P. I. 700 1 $aOLIVEIRA, S. R. de M. 700 1 $aTEIXEIRA, W. G. 700 1 $aVIANA, J. H. M. 700 1 $aRIBEIRO, E. 700 1 $aTORNQUIST, C. G. 700 1 $aANJOS, L. H. C. dos 700 1 $aSOUZA, J. J. E. L. de 700 1 $aOTTONI, M. V. 700 1 $aMEDEIROS, P. S. C. de 700 1 $aGRIS, D. J. 700 1 $aROSIN, N. A. 700 1 $aBUENO, J. M. M. 700 1 $aSANTOS, H. G. dos 700 1 $aWEBER, E. J. 700 1 $aFLORES, C. A. 700 1 $aCOSTA, E. M. 700 1 $aOLIVEIRA, R. P. de 700 1 $aFILIPPINI ALBA, J. M. 700 1 $aPEDROSO NETO, J. C. 700 1 $aPEDRON, F. de A. 700 1 $aCAVIGLIONE, J. H. 700 1 $aVALLADARES, G. S. 700 1 $aMIRANDA, C. S. S. 700 1 $aDEMATTÊ, J. A. M. 700 1 $aMARQUES JÚNIOR, J. 700 1 $aSIQUEIRA, D. S. 700 1 $aAQUINO, R. E. de 700 1 $aSILVERO, N. E. Q. 700 1 $aGENÚ, A. M. 700 1 $aBROETTO, T. 700 1 $aCANCIAN, L. C. 700 1 $aMIGUEL, P. 700 1 $aZALAMENA, J. 700 1 $aDOTTO, A. C. 700 1 $aALMEIDA, J. A. de 700 1 $aREICHERT. 700 1 $aCURCIO, G. R. 700 1 $aCOLLIER, L. S. 700 1 $aCARVALHO JUNIOR, W. de 700 1 $aFONTANA, A. 700 1 $aOLIVEIRA, A. P. de 700 1 $aVOGELMANN, E. S. 700 1 $aMALLMANN, F. J. K. 700 1 $aVASQUES, G. de M. 700 1 $aLEPSCH, I. F. 700 1 $aFINK, J. R. 700 1 $aKER, J. C. 700 1 $aSILVA, L. S. da 700 1 $aFREITAS, P. L. de 700 1 $aBIELUCZYK, B. 700 1 $aTIECHER, T.
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