| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Epagri-Sede. Para informações adicionais entre em contato com biblio@epagri.sc.gov.br. |
|
Registro Completo |
|
Biblioteca(s): |
Epagri-Sede. |
|
Data corrente: |
21/01/2015 |
|
Data da última atualização: |
21/01/2015 |
|
Autoria: |
COSTA, T. V. da; OSHIRO, L. M. Y.; SILVA, E. C. S. e. |
|
Título: |
O potencial do mapará Hypophthalmus spp. (Osteichthyes, Siluriformes) como uma espécie alternativa para a piscicultura na Amazônia. |
|
Ano de publicação: |
2010 |
|
Fonte/Imprenta: |
Boletim do Instituto de Pesca, São Paulo, SP, v. 36, n. 3, p. 165-174, jul./set. 2010. |
|
Idioma: |
Português |
|
Palavras-Chave: |
Amazona; Composição centesimal; Criação; Pesca. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 00610naa a2200181 a 4500
001 1122772
005 2015-01-21
008 2010 bl uuuu u00u1 u #d
100 1 $aCOSTA, T. V. da
245 $aO potencial do mapará Hypophthalmus spp. (Osteichthyes, Siluriformes) como uma espécie alternativa para a piscicultura na Amazônia.
260 $c2010
653 $aAmazona
653 $aComposição centesimal
653 $aCriação
653 $aPesca
700 1 $aOSHIRO, L. M. Y.
700 1 $aSILVA, E. C. S. e.
773 $tBoletim do Instituto de Pesca, São Paulo, SP$gv. 36, n. 3, p. 165-174, jul./set. 2010.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Epagri-Sede (Epagri-Sede) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
|
Voltar
|
|
|
Registro Completo
|
Biblioteca(s): |
Epagri-Sede. |
|
Data corrente: |
09/11/2022 |
|
Data da última atualização: |
09/11/2022 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
HAHN, L.; PARENT, L.; FELTRIM, A. L.; ROZANE, D. E.; ENDER, M. M.; TASSINARI, A.; KRUG, A. V.; BERGHETTI, Á. L. P.; BRUNETTO, G. |
|
Título: |
Local Factors Impact Accuracy of Garlic Tissue Test Diagnosis. |
|
Ano de publicação: |
2022 |
|
Fonte/Imprenta: |
Agronomy, Basel, Switzerland, v. 12, n. 11, p. 1-15, 2022. |
|
Idioma: |
Inglês |
|
Conteúdo: |
The low productivity of garlic in Brazil requires more efficient nutritional management. For this, environmental and fertilization-related factors must be adjusted to a set of local conditions. Our objective was to provide an accurate diagnosis of the nutrient status of garlic crops in southern Brazil. The dataset comprised 1024 observations, 962 as field tests conducted during the 2015?2017 period to train the model, and 61 field observations collected during the 2018?2019 period to validate the model. Machine learning models (MLM) related garlic yield to managerial, edaphic, plant, and climatic features. Compositional data analysis (CoDa) methods allowed classification of nutrients in the order of limitation to yield where MLM detected nutrient imbalance. Tissue analysis alone returned an accuracy of 0.750 in regression and 0.891 in classification about the yield cutoff of 11 ton ha−1. Adding all features documented in the dataset, accuracy reached 0.855 in regression and 0.912 in classification. Local diagnosis based on MLM and CoDa and accounting for local features differed from regional diagnosis across features. Local nutrient diagnosis may differ from regional diagnosis because several yield-impacting factors are taken into account and benchmark compositions are representative of local conditions.' |
|
Thesagro: |
Adaboost; Allium sativum; análise composicional; fatores limitantes do crescimento; machine learning; random forest. |
|
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
|
|
|
|
Marc: |
LEADER 02143naa a2200289 a 4500
001 1132707
005 2022-11-09
008 2022 bl uuuu u00u1 u #d
100 1 $aHAHN, L.
245 $aLocal Factors Impact Accuracy of Garlic Tissue Test Diagnosis.$h[electronic resource]
260 $c2022
520 $aThe low productivity of garlic in Brazil requires more efficient nutritional management. For this, environmental and fertilization-related factors must be adjusted to a set of local conditions. Our objective was to provide an accurate diagnosis of the nutrient status of garlic crops in southern Brazil. The dataset comprised 1024 observations, 962 as field tests conducted during the 2015?2017 period to train the model, and 61 field observations collected during the 2018?2019 period to validate the model. Machine learning models (MLM) related garlic yield to managerial, edaphic, plant, and climatic features. Compositional data analysis (CoDa) methods allowed classification of nutrients in the order of limitation to yield where MLM detected nutrient imbalance. Tissue analysis alone returned an accuracy of 0.750 in regression and 0.891 in classification about the yield cutoff of 11 ton ha−1. Adding all features documented in the dataset, accuracy reached 0.855 in regression and 0.912 in classification. Local diagnosis based on MLM and CoDa and accounting for local features differed from regional diagnosis across features. Local nutrient diagnosis may differ from regional diagnosis because several yield-impacting factors are taken into account and benchmark compositions are representative of local conditions.'
650 $aAdaboost
650 $aAllium sativum
650 $aanálise composicional
650 $afatores limitantes do crescimento
650 $amachine learning
650 $arandom forest
700 1 $aPARENT, L.
700 1 $aFELTRIM, A. L.
700 1 $aROZANE, D. E.
700 1 $aENDER, M. M.
700 1 $aTASSINARI, A.
700 1 $aKRUG, A. V.
700 1 $aBERGHETTI, Á. L. P.
700 1 $aBRUNETTO, G.
773 $tAgronomy, Basel, Switzerland$gv. 12, n. 11, p. 1-15, 2022.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Epagri-Sede (Epagri-Sede) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
